EARA – European Animal Research Association – Animal Testing – Very Important Statistics and Facts – Researches – Time – History @ The top five animals used for biomedical research, in 2017, have just been published by the European Commission. @ Article: Age-related disease burden as a measure of population ageing – The Lancet Public Health – VOLUME 4, ISSUE 3, PE123-E124, MARCH 01, 2019 @ Article: Health and Aging: Unifying Concepts, Scores, Biomarkers and Pathways. @ Article: Emerging Anti-Aging Strategies – Scientific Basis and Efficacy. @ ´´In pharmacology, a drug is a chemical substance, typically of known structure, which, when administered to a living organism, produces a biological effect.[5] A pharmaceutical drug, also called a medication or medicine, is a chemical substance used to treat, cure, prevent, or diagnose a disease or to promote well-being.[3]´´ @ https://en.wikipedia.org/wiki/Drug#cite_note-5 @ VERY IMPORTANT INFORMATION LIKE VIDEOS, WEBSITES, LINKS AND IMAGES

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´´The world people need to have very efficient researches and projects resulting in very innovative drugs, vaccines, therapeutical substances, medical devices and other technologies according to the age, the genetics and medical records of the person. So, the treatment, disgnosis and prognosis will be very efficient and better, of course´´. Rodrigo Nunes Cal

https://science1984.wordpress.com/2021/08/14/do-the-downloads-of-very-important-detailed-and-innovative-data-of-the-world-about-my-dissertation-like-the-graphics-i-did-about-the-variations-of-weights-of-all-mice-control/

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CARCINÓGENO DMBA EM MODELOS EXPERIMENTAIS

Avaliação da influência da atividade física aeróbia e anaeróbia na progressão do câncer de pulmão experimental – Summary – Resumo

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https://www.ncbi.nlm.nih.gov/pubmed/30574426

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6675520/

https://www.ncbi.nlm.nih.gov/pubmed/31440392

https://www.thelancet.com/action/showPdf?pii=S2468-2667%2819%2930026-X

https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(19)30026-X/fulltext

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http://www.facebook.com/scientificblog http://www.nobelprize.org http://www.instagram.edu http://www.michigan.edu

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https://www.ema.europa.eu/en/human-regulatory/research-development/ethical-use-animals-medicine-testing?fbclid=IwAR3DAaXZIxHAzTkl0ArXc7g7sKITwzkWatB03uU4Wsi1_-fYH9Rk6VoZKxM

https://www.eara.eu/animal-research-law?fbclid=IwAR3qCB2AkSwbNWLXQGdWls1H8O12JT4YvgAR93nYoNHX9Bx6G4ZlrKdJRGs

https://www.mdc-berlin.de/news/press/neuromuscular-organoid-its-contracting?fbclid=IwAR19dQ5AZ3mnuaXubDSgjm10bW7sfUg0gu6oNrl5QrlfDfjxZ-otfuMZ6go

https://onlinelibrary.wiley.com/doi/full/10.1002/advs.201902295?fbclid=IwAR01_BMa3XATe9eVbTrpf_EWTsDdpJb-mntxllMdRgd6kVALfktWHxnmdnY

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http://www.google.com https://senseaboutscience.org/activities/maddox-prize-2019/?fbclid=IwAR3yGZW6dDmKtW7gm-r8YBZ2wcfbFeDDgKNen2MrloFUrKQo76fYzaVJu1I

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Por motivos técnicos a versão portuguesa do site encontra-se temporariamente indisponível.Contacts

DYSBRAIND: DYSMETABOLISM IN BRAIN DISEASES

hugo v miranda 2
Hugo Vicente Miranda

Principal Investigator
[2014-2018] Post-Doc – Cell and Molecular Neurosciences in CEDOC, NOVA Medical School | Faculdade de Ciências Médicas, Universidade NOVA de Lisboa
[2010-2017] Post-Doc – Cell and Molecular Neurosciences in Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa
[2006-2010] PhD in Biochemistry – Metabolic Regulation in Faculdade de Ciências da Universidade de Lisboa
[2000-2005] Pre-Bologna Licenciate Degree – Biochemistry in Faculdade de Ciências da Universidade de Lisboa

CV

Location:

CEDOC
Campus Sant’Ana
Pólo de Investigação, NMS, UNL
Rua Câmara Pestana, nº 6
Lab 3.8
1150-082 Lisboa, Portugal

Phone: (+351) 218 803 101 (Ext. 26071)
Lab ext: 26040
Fax: (+351) 218 851 920
E-mail: hmvmiranda(at)nms.unl.pt

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Main Interests:

Our research relies in the hypothesis that brain dysmetabolism is an underlying cause of neurodegenerative diseases such as Parkinson’s or Alzheimer’s.
Epidemiological findings suggest that dysmetabolism such as in Diabetes is an important risk factors for neurodegenerative diseases. Importantly, we identified that protein glycation, a major consequence of hyperglycemia, is a molecular link between these disorders. Therefore, we are focused in understanding how sugar stress potentiates neurodegeneration. This will ultimately allow to identify novel therapeutic targets for brain diseases.

esquema glycation
Research Areas:

Our primary questions are:
1. What is the impact of diabetes (hyperglycemia) in brain diseases?
2. What are the effects of dysbiosis in Parkinson’s disease?
3. Are glycation defenses putative therapeutic agents for Parkinson’s disease?
4. What are the major proteome response mechanisms to glycation in models of brain diseases?

Experimental Systems:

1. in vitro systems – cell and molecular biology techniques
2. mouse models of the diseases – effects of normo/hypercaloric diets in mice
3. Proteomics and genetic approaches

hvm_aims2
Discover more about the DysBrainD: Dysmetabolism in Brain Diseases Lab:

– Doença de Parkinson deixa marcas numa proteína no sangue
– Biomarcadores para a doença de Parkinson | Biomarkers for Parkinson’s DiseaseWP Post Tabs

Team members

PhD Students

ana monteiro
Ana Chegão, MScana.chegao(at)nms.unl.pt

MSc Students

Foto Ana Rita Marçal
Ana Rita Marçal, BScana.marcal(at)nms.unl.pt

Mariana
Mariana P.Guarda, BScmariana.guarda(at)nms.unl.pt

Research Fellow

Foto Luís Sousa
Luís Sousa, MScluis.sousa(at)nms.unl.pt

Former Lab Members

barbara f gomes
Bárbara F. Gomes, MSc

Foto Diana Chamico
Diana Chamiço, Secondary School

Yanni Schneider
Yanni Schneider

Open positions

Motivated students (Undergraduate, Master and PhD) are encouraged to apply. Interested candidates, please send your CV, a letter explaining your interests and the name/contact information for 1-3 references to Hugo Vicente Miranda (hmvmiranda(at)nms.unl.pt).CEDOC CHRONIC DISEASES FCM NOVA © 2014

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Keyword search  GoNOV. 14, 2019

Simulation-based method to target Epilepsy goes into clinical trial

FEATURED

In what represents a major milestone on the path to clinical application, a novel method to improve outcomes of Epilepsy surgery has now received approval for clinical testing in 13 French hospitals.


• Human Brain Project research provides basis for a possible personalized medicine advance in Epilepsy care

• New approach could bring first breakthrough in decades for drug-resistant patients undergoing brain surgery
 

08 11 2019 – In what represents a major milestone on the path to clinical application, a novel method to improve outcomes of Epilepsy surgery has now received approval for clinical testing in 13 French hospitals. The approach could provide a better therapeutic perspective against the drug-resistant form of the disease, which constitutes one third of all cases, and is a development by Human Brain Project scientist Viktor Jirsa and an interdisciplinary team of collaborators. To help clinicians plan surgery strategies, the scientists create personalized brain models of patients and simulate the spread of abnormal activity during epileptic seizures. The method represents the first example of a personalized brain modeling approach entering the clinic and will now be assessed over four years in a cohort of 356 patients under strict requirements. 

Epilepsy is a wide-spread neurological disorder that affects around 50 million people worldwide. In many cases, the seizures that mark the disease can be controlled by drugs, but close to a third of all patients are drug resistant. For them, the only remaining option is surgical removal of the epileptogenic zone, the area from which the seizure activity first emerges and then spreads. During surgery preparation it is critical to localize this area as precisely as possible in the brain, but very challenging with current methods. As a result, surgery outcomes are difficult to predict, with success rates of only around 60%.

“This low success rate has largely stayed the same for the last 30 years. We hope our approach can finally improve the odds for patients”, says Prof. Viktor Jirsa. The scientist is Director of Inserm’s Institut de Neurosciences des Systèmes (INS) at Aix-Marseille University and Director of Research at CNRS. In the Human Brain Project he is the Deputy Leader of the research area of Theoretical Neuroscience.

Over the last five years and in large part within the framework of the Human Brain Project, Jirsa and his team worked on an approach that could bring a change. The team has adapted the open network simulator The Virtual Brain towards applications in Epilepsy. This work has laid foundations for the project EPINOV, short for “Improving EPilepsy surgery management and progNOsis using Virtual brain technology”, a consortium coordinated by Prof. Fabrice Bartolomei (Hôpital de la Timone) that brings together theorists like Jirsa, clinical neuroscientists, in particular from Marseille and Lyon, and the industry partner Dassault Systèmes.

After two pilot studies showed promising results for the approach, the EPINOV-consortium has received approval from the French regulatory authority to put their approach to the test in a full-scale multi-centric trial with almost 400 prospective patients.

“It represents the world’s first clinical trial ongoing using full brain network modeling,” says Jirsa. “When the authorization came in, it was like a huge pressure was relieved from me after all this hard work. Then followed last preparations to assure that all steps in the workflow of virtualization and evaluation of the patient brains are in order during the four year period of the trial.”

Fabrice Bartolomei explains “This type of epilepsy affects millions of patients worldwide. The personalized modeling of epilepsy networks in drug-resistant patients is an innovative and scientifically validated approach, which proposes to enrich the interpretation of neurophysiological and neuroimaging tests, and thus to improve the surgical prognosis of epilepsy in an individualized way”.

Viktor Jirsa and his close collaborators Profs. Randy McIntosh at Baycrest Center Toronto and Petra Ritter at Charité Berlin started building The Virtual Brain as an open source brain network simulation engine from 2010 on, using neuronal population models and structural information from neuroimaging. 

“In the Human Brain Project environment the conditions were perfect to go the decisive steps further towards applying it in a clinical context. The science underlying this trial is almost entirely a result of our work in the HBP”, the scientist says.

First a personalized brain model is created from data on the individually measured anatomy, structural connectivity and brain dynamics for each patient. Through a series of steps it is turned into a dynamic model, on which the seizure propagation can be simulated. High Performance Computing enables the personalisation of the brain network models through the application of machine learning. The resulting “brain avatar” is customized to the individual patient and allows testing and estimating during surgery preparation. “In a small cohort of retrospective surgery patients we were able to demonstrate that the predictions of the patient’s brain model correlate well with positive surgery outcome”, Jirsa explains, “and other labs have confirmed our results independently.” A detailed account on this work can be read here.

In half of the cases, the surgeons will have information from the Epilepsy-model in their staff meetings, where therapeutic interventions are planned. “It´s a blind random design, half of these patients will be operated taking our model predictions into account, the other half will not. After four years, the statistics will show us hopefully to what degree the model predictions changed the surgery practice, results, and outcome”, Jirsa says.

Within the Epinov consortium the industrial partner Dassault Systèmes will develop a virtual brain-based simulation software prototype that could subsequently be provided to clinics worldwide. Headquartered in France, Dassault Systèmes is a multinational software company focused on 11 industries including life sciences and the development of patient-centric modeling and simulation experiences.

“There is a very big responsibility”, Jirsa emphasizes “and at the same time it’s very exciting that we have this chance to improve clinical practice and ultimately patients’ lives. And if the approach succeeds, it would also be the first modelling-based example of personalized medicine that makes the jump from research to clinical practice – so the outcome will certainly be a signal to the field”.

Prof. Katrin Amunts, Scientific Research Director of the Human Brain Project, highlights the significance of the move into the clinic: “This breakthrough by Viktor Jirsa and his colleagues is a fantastic example of a new type of technology-enabled computational neuro-medicine. It is one of our central aims to catalyze developments like this that make concrete contributions in the fight against brain diseases to benefit patients.” 

Prof. Philippe Ryvlin, who leads the Medical Informatics Platform in HBP and the epilepsy surgery section of the European Reference Network EpiCARE emphasises that “this clinical trial, which will be the largest randomised study ever performed in epilepsy surgery, demonstrates that simulation of the human brain, as developed in HBP, has now reached a stage where it can be readily applied to address unmet medical needs.”

Virtual Brain Researchers are also continuing their activities on clinical modeling for stroke and Alzheimer’s in collaboration with experts that the HBP brings together. For Jirsa seeing his work as a theoretical scientist gain this potential impact has been the result of a unique convergence: “That we have come to this point was made possible by clinical expertise and our activities on The Virtual Brain and the Human Brain Project all coming together – right place, right time, right people.”

Viktor Jirsa
Viktor Jirsa is Director of the Inserm Institut de Neurosciences des Systèmes at Aix-Marseille-Université. Dr. Jirsa is a theoretical neuroscientist working in the field of brain connectivity and brain network modeling. He leads the multi-scale brain connectome efforts in the Human Brain Project and is Curator of the neuroinformatics platform The Virtual Brain (http:www.thevirtualbrain.org). (Photo: private)

Contact
Prof. Viktor Jirsa
Email: viktor.jirsa@univ-amu.fr
tel : ++33 (0)4 91 32 42 51

Media Contact:
Peter Zekert
Human Brain Project
Public Relations Officer
Tel.: +49 (0) 2461 61-96860
Email: press@humanbrainproject.eu

Further Material:

Photos (© INS UMR 1106, high-res material available on request):

The theorist and the clinician: Viktor Jirsa (left) has developed the modelling underlying the clinical trial. Neurologist Fabrice Bartolomei (right) is the clinical coordinator.

Figures:

Figure 1: Based on brain network simulations using TVB, patient-specific seizure propagation characteristics can be reproduced. The figure shows a simulation result for a particular patient, in which seizure activity is generated from epileptogenic zones (nodes 61 and 64), and sequential recruitment is induced through the individual brain connectome. ©Ahn et al (2019) 

Figure 2: Combining non-invasive structural brain imaging data (anatomy and connectivity data from Magnetic Resonance Imaging (MRI) and Diffusion Tensor weighted Imaging (DTI)) with computational models of neuronal populations, patient-specific brain models can be created  and used as personalized in-silico platforms for clinical hypothesis testing. The virtual brain models are capable of simulating human brain imaging routinely measured in hospitals, inlcuding electroencephalography (EEG), magnetoencephalography (MEG), stereotactic electroencephalography (SEEG), and functional Magnetic Resonance Imaging (fMRI measuring the blood oxygenation level dependent (BOLD) signal)). © Petkoski et al (2019)

Original Publications:

Olmi S, Petkoski S, Guye M, Bartolomei F, Jirsa V: Controlling seizure propagation in large-scale brain networks. PLOS Computational Biology, Vol. 15, No. 2 2019-02-25. https://doi.org/10.1371/journal.pcbi.1006805

Proix T, Jirsa VK, Bartolomei F, Guye M, Truccolo W.: Predicting the spatiotemporal diversity of seizure propagation and termination in human focal epilepsy. Nat Commun. 2018 Mar 14;9(1):1088. doi: 10.1038/s41467-018-02973-y.

Proix T, Bartolomei F, Guye M, Jirsa VK (2017) Individual brain structure and modeling predict seizure propagation. Brain 140 (3): 641-654 https://www.ncbi.nlm.nih.gov/pubmed/28364550

Jirsa VK, Proix T, Perdikis D, Woodman MM, Wang H, Gonzalez-Martinez J, Bernard C, Bénar C, Chauvel P, Bartolomei F (2016) The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread. Neuroimage doi :10.1016/j.neuroimage.2016.04.049 

Further Information:


EPINOV
Improving EPilepsy surgery management and progNOsis using Virtual brain technology
http://www.epinov.com/ 

This work is supported by a public grant overseen by the French National Research Agency (ANR) as part of the second “Investissements d’Avenir” program (reference: ANR-17-RHUS-0004).

NOVEMBER 08 Gazettelabo.fr, : EPINOV lance le premier essai clinique d’une chirurgie cérébrale assistée par la technologie de cerveau virtuel: EPINOV TRIAL, une première mondiale


Related News
 

JULY 1, 2019

The Human Brain Project – synergy between neuroscience, computing, informatics and brain inspired technologies


In a new PLOS Biology community page, leading scientists from the Human Brain Project give an overview of the project’s science and infrastructure approach, as well as opportunities for the scientific community to make use of novel computational ressources for neuroscience. https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000344 


JUNE 3, 2019
The scientific case for brain simulations
HBP scientists argue for brain simulators as “mathematical observatories” for neuroscience
Simulations of large-scale networks of neurons are a key element in the European Human Brain Project (HBP). In a new perspective article scientists from the HBP argue why such simulations are indispensable for bridging the scales between the neuron and system levels in the brain. The authors describe the need for open general-purpose simulation engines that can run a multitude of different candidate models of the brain at different levels of biological detail. Comparing predictions derived from such simulations with experimental data will allow systematic testing and refinement of models in a loop between computational and experimental neuroscience. The article has been published as a featured perspective in the leading journal Neuron.
https://www.humanbrainproject.eu/en/follow-hbp/news/the-scientific-case-for-brain-simulations/ 


NOV. 29, 2017
Epilepsy: Building Personalised Models of the Brain
Human Brain Project scientist Viktor Jirsa is the head of a team creating personalised brain models for patients with intractable epilepsy. He explains the process and how the HBP’s brain models and cross-displinary collaborations are central to a new way of targeting epilepsy surgery.
https://www.humanbrainproject.eu/en/follow-hbp/news/the-power-of-a-personalised-model-of-the-patients-brain/

Simulation engines in HBP

“The simulation aspect in HBP gives us the opportunity to bring together our macroscale-approach to modelling and simulation with a range of different simulation-based approaches. Scientifically, that lets us better understand how phenomena on the micro-level might translate to higher order phenomena on the meso- and macro-scale, and can open possibilities of a “consistency test” between the models.” Viktor Jirsa 

NEURON and Arbor can run biophysically detailed, multicompartmental neuron models to help understand how dendritic structures affect the integration of synaptic inputs and, consequently, the network dynamics.

More abstracted simulators like NEST can simulate spiking networks of billions of simplified point neurons which model the basic integration of signals and firing of the neurons. This is close to the level of description widely used in AI today, allowing for connections of this type of brain simulations to technology development in this area as well as in robotics. In contrast to most current AI algorithms, NEST and HBP’s neuromorphic hardware systems already capture the fact that real neurons communicate using sparse but robust pulses, and thus operate in a very energy efficient way.

Whole brain-level simulation with the engine The Virtual Brain uses population firing-rate models that summarize dynamics of larger populations of neurons. Such coarse-grained simulation can already help understand and predict large-scale dynamics. Since alterations of such dynamics can be a feature of brain pathologies, like the seizure propagation in Epilepsy, this approach has led to first clinical developments based on patient-specific brain modeling.

About the Human Brain Project

Understanding the organisation of the human brain at all relevant levels is a big challenge, but necessary to improve treatment of brain disorders, create new computing technologies and provide insight into our humanity. Modern ICT brings this within reach. The HBP’s unique strategy uses it to gather, integrate and analyse brain data, understand the healthy and diseased brain, and emulate its computational capabilities. By sharing our tools with researchers worldwide, we aim to catalyse global collaboration.
Unlocking the brain’s secrets promises major scientific, social and economic benefits. One is improved diagnosis and treatment of brain-related diseases; a growing health burden in our ageing population. A second is neuroscience’s potential to contribute to approaches for future ICT, including extreme-scale and neuromorphic computing. The HBP will also contribute to a brain-inspired approach to Artificial Intelligence and robotics.

The HBP studies the brain at different levels, from genomics to higher-level brain functions. To help achieve this goal, the HBP is building an ICT-based research infrastructure to facilitate research collaboration, via the sharing of software tools, data and models. Incorporating inputs from the scientific community, the HBP’s scientists and engineers ensure that our infrastructure meets real research needs. Another aim is to accelerate medical research, by facilitating researchers’ secure access to broader data sets of patient data, as well as HBP tools and models. The HBP also educates young scientists to work across disciplinary boundaries and addresses the ethical implications of its work. Finally, it helps to integrate global brain research efforts and leads Europe’s contribution in this field.


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©2017 Human Brain Project.
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Science Advice for Policy by European AcademiesHome > Topics > Transforming the future of ageing

Transforming the future of ageing

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Jean-Pierre Michel
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Diana Kuh
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Rose Anne Kenny
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Richard Reilly
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Liat Ayalon

Axel Boersch-Supan

Jacques Bringer

Alfonso Cruz Jentoft

Giovanni Gambassi

Alan Gow

Tomasz Grodzicki

Lenka Lhotska

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Carlo Patrono

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Eline Slagboom

Anne Pieter van der Mei

Leo van Wissen

Jose Vina

In Europe and around the world, people are living longer than ever before. This is one of the greatest achievements of the past century, but it also brings challenges for European societies and the EU as a whole.

We must adjust to an ageing and shrinking workforce, and find financially viable ways to deliver high-quality health and social care for all.

What the report says

SAPEA’s evidence review report shows that the ageing process needs to be transformed. Europe must tackle the challenges presented by ageing in every generation.

  • When it comes to predicting how people age, evidence indicates that genetic factors are relatively minor compared to lifestyle behaviours such as a healthy diet and physical activity. Policies to promote these behaviours from early childhood, and even in unborn children, contribute directly to a healthy ageing process across people’s whole lives.
  • Ageing in the future will take place in a very different context from the past and will be profoundly affected by phenomena such as climate changeair pollution and antibiotic resistance, as well as ongoing social changes. Policies will only be successful if they accommodate these changes.
  • Technology is already changing the experience of ageing, including wearable and assistive devices and the advent of AI. But barriers of acceptance and practicality must be overcome.
  • Education improvements at a young age are vital not only to improve individual health, but also to equip our future workforce with the skills it needs to support an ageing population in a rapidly changing society.

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HomeActivitiesMaddox Prize 2019

Maddox Prize 2019

Two courageous scientists, forest fire expert Bambang Hero Saharjo and pharmacist Olivier Bernard, awarded the 2019 John Maddox Prize for Standing up for Science

Bambang Hero Saharjo, who is the Indonesian lead expert witness on environmentally catastrophic peatland fires has been awarded the 2019 John Maddox Prize for his courage and integrity in standing up for sound science in the face of harassment, intimidation, and law suits. The judges awarded a further prize, for exceptional communication of evidence by someone early in their career, to Olivier Bernard, a Canadian pharmacist who challenged high dose vitamin C injections for cancer patients.

Prof. Dr. Ir. H. Bambang Hero Saharjo, Bogor Agricultural University, is the foremost expert on illegal and destructive forest and land fires in Indonesia, and the winner of the 2019 Maddox Prize. Peatland forest fires, which are often started by companies who want to clear land cheaply and quickly, including palm oil companies, cause enormous environmental damage; up to 5 September, global fires this year have released more carbon dioxide than annual emissions from the EU and Japan combined. They are also incredibly dangerous, recent Indonesian fires are said by Unicef to be putting 10 million children at risk. Bambang’s expertise allows him to trace the route and source of fires and he has testified in 500 court cases investigating fires. He has also helped local groups to understand the evidence about health and environmental damage. In 2015 Bambang’s testimony was instrumental in palm oil company JJP’s guilty verdict; in 2018 they filed a $33.5 million lawsuit (SLAPP) against him on a technicality. He continues to testify and stand up for the Indonesian people’s constitutional right to a healthy environment, one of the very few scientists in his field who are prepared to do so.

Olivier Bernard, a pharmacist from Quebec, has been awarded the John Maddox Prize for an early career researcher for standing up to alternative health proponents who lobbied for the government to “approve and reimburse” high dose vitamin C injections for cancer patients, which have no basis in evidence. Olivier spoke out repeatedly, describing the scientific evidence and speaking directly to politicians and affected groups. He endured a campaign of harassment, including complaints to his employer and professional body, revealing the address of the pharmacy where he works, a smear campaign, calls for a boycott of his wife’s books, as well as death threats to him and his family. He stood up to this barrage of harassment, and his strength in speaking out has resulted in the creation of a government taskforce to protect scientists who speak on sensitive topics, and an inter-professional advisory committee to support healthcare professionals who speak publicly.

The John Maddox Prize, now in its eighth year, is a joint initiative of the charity Sense about Science, which promotes the public interest in sound science and evidence, and the leading international scientific journal Nature, and is awarded to one or two people a year. This year there were 206 nominations from 38 countries.


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There were nominations relating to research conduct, publishing and integrity. While judges felt that other nominations more strongly satisfied the public, rather than professional, communication criteria, they wanted to draw attention to the extraordinary contribution made over the past year by Elisabeth Bik of Science Integrity Digest, finding about two-thirds of duplicated images in biomedical papers appear to have been duplicated on purpose; and by Ivan Oransky, Retraction Watch, over many years, in highlighting the retraction of published papers. They also commended the work of James Heathers, of Northeastern University USA, an early career researcher who was nominated for his work on the GRIM test – a simple statistical test of whether results are accurately reported – and his publicity about the limitations of reported research through @justsaysinmice.

The judges said that a number of nominees, including the winners, had notably persevered in communicating about science and evidence at a point when things became incredibly difficult and silence would have been the easiest option – or was an option taken by others in the field. For this, they wanted to recognise:

  • Malegapuru William Makgoba, acclaimed for his challenge to AIDS denial in South Africa, went on to expose and improve the treatment of mentally ill patients in South Africa.
  • Marc Edwards for his work in the 2001 – 2004 lead water crisis in Washington DC and, more recently, his initial reports exposing the Flint water crisis in Michigan, USA.
  • Jane Hutton, who advocated for the transparency of statistical evidence behind claims about the deficit faced by the UK’s USS pension scheme, which has drawn attention to the wider issue that many statistical models on which the public depends are obscured from public scrutiny.
  • While the conduct of research is not directly within the remit of the prize, the judges wished to record the nomination of Niloufar Bayani, Taher Ghadirian, Houman Jokar, Sepideh Kashani, Amirhossein Khaleghi, Abdolreza Kouhpayeh, Sam Rajabi, and Morad Tahbaz who have been in prison in Iran on espionage charges related to the setting of camera traps for tracking the critically endangered Asiatic cheetah and encourage international support for their cause.

WINNERS’ COMMENTS

Bambang Hero Saharjo: “I still do not believe that I am receiving the prestigious John Maddox Prize. Only last year I was criminalised for presenting evidence and being forced to pay nearly rp.510 billion by the palm oil companies, who had been found guilty of preparing to plant palms by burning 1000 hectares of peatland. Finally the lawsuit was rejected and I am free. Using fire for land preparation is so destructive to the environment and it is destroying the health of local people. This is what the evidence shows. The prize will give me more power to say it and to fight the misrepresentation by companies who continue use of fire.”

Olivier Bernard: “I was extremely surprised and extremely grateful to be a recipient of the John Maddox Prize! Some of my professional role models have won it in the past, so it’s an immense honour. Throughout the controversy surrounding vitamin C injections in Quebec, I have learned that scientific decisions made by political entities can be easily swayed by interest groups. I’ve also learned that fighting for science can be stressful and scary, and may even come at a personal price. But defenders of science cannot afford to stay in the background. I vow to use this award as an opportunity to engage more people and scientists in defending science publicly, and to show them that even if it can be difficult to do so, the positive outcomes far outweigh the negatives ones.”


JUDGES’ COMMENTS

Tracey Brown OBE, director, Sense about Science and judge: “Our winners exemplify the spirit of the Maddox prize. In Bambang and Olivier we see people standing up for the rights of their fellow citizens and championing the value of scientific reasoning for us all. They saw the easier path of silence or complicity and rejected it to take responsibility for communicating evidence. Our winners are an example of what can be achieved by one person, standing up against misinformation and corruption. We have seen a rapid increase in the global nominations for the Maddox prize. That tells you something about the need to recognize people who take such responsibility.”

Magdalena Skipper, editor-in-chief, Nature, and judge: “We received many excellent nominations this year from such an inspiring group of candidates, all of whom are making great strides in their area. This year’s winners were chosen for their exceptional efforts in raising awareness of issues they are passionate about and for which they have faced criticism and adversity in striving for evidence-based policy and practices. The John Maddox Prize recognises and rewards those who stand up for scientific rigour and we are delighted to be awarding it this year to two notable and dedicated campaigners.”

Lord (Martin) Rees of Ludlow OM FRS, University of Cambridge and judge: “Rain forests are under threat, but their preservation matters to all of us who care about climate and biodiversity. So it’s right that we should acclaim a man who seized the chance to really make a difference – by persistent and effective campaigning against powerful interests.”

Natasha Loder, The Economist and judge: “This year we were particularly impressed with the wide range of nominations we received, which came from 38 countries and an array of disciplines. There are some powerful stories here, our winners show what one individual, armed with a scientific approach, can achieve.”

Professor Sir Colin Blakemore FRS, professor of neuroscience & philosophy, School of Advanced Study, University of London and judge“For more senior scientists or journalists, the fight against misrepresentation and prejudice takes courage and conviction. But for a younger person, standing up for the truth can mean risking a career. Olivier Bernard’s crusade against the cruel deception of those who peddle vitamin C injections to vulnerable cancer sufferers is a remarkable example of personal commitment to the fight for honesty and integrity in the application of science.”

Anin Luo, early career biophysicist, Yale University, and judge: “I think that the early-career category is critical because we should speak up about evidence not just when tenured or secure in professional life—it should be encouraged from the very beginning. Encouraging those at an early stage of their career to stand up for science is critical in bringing about a culture change in communicating scientific evidence.”


Sir Patrick Vallance, UK  Government Chief Scientific Adviser (GCSA) and Head of the Government Science and Engineering (GSE) profession: “The two recipients of the Maddox prize have shown incredible bravery by standing up for the science, even in very testing circumstances. We can only have confidence in policy when it is informed by the very best science which it is why it is vital that scientists have the courage to speak out.  These two are inspirational.”


PARTNERS

The prize is run and funded by Sense about Science, where Sir John Maddox was a founding trustee, and Nature, where he was editor for over 20 years, with support from Clare and Andrew Lyddon.

Published: 11 November 2019

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Home > News > Transplanting human nerve cells into a mouse brain reveals how they wire into brain circuits

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Transplanting human nerve cells into a mouse brain reveals how they wire into brain circuits

21 November 2019A team of researchers led by Pierre Vanderhaeghen and Vincent Bonin (VIB-KU Leuven, ULB and NERF) showed how human nerve cells can develop at their own pace, and form highly precise connections with the surrounding mouse brain cells. These findings shed new light on the unique features of the human brain and open new perspectives for brain repair and the study of brain diseases. 
The brain cortex, the outside layer of our brain often referred to as grey matter, is one of the most complex structures found in living organisms. It gives us the advanced cognitive abilities that distinguish us from other animals. 
Neuroscientist Prof. Pierre Vanderhaeghen (VIB-KU Leuven, ULB) explains what makes the human brain so unique: “One remarkable feature of human neurons is their unusually long development. Neural circuits take years to reach full maturity in humans, but only a few weeks in mice or some months in monkeys.” 
“This long period of maturation allows much more time for the modulation of brain cells and circuits, which allows us to learn efficiently for an extended period up until late adolescence. It’s a very important and unique feature for our species, but what lies at its origin remains a mystery.”
Understanding the mechanisms underlying brain circuit formation is important, for example if we want to treat brain disease, adds Prof. Vincent Bonin of Neuro-Electronics Research Flanders (NERF, empowered by imec, KU Leuven and VIB): “Disturbances of circuit development have been linked to intellectual disability, for example, and to psychiatric diseases such as schizophrenia. However, it has remained impossible to study human neural circuits in action in great detail – until now!”
Human brain cells in a mouse brainIn a joint research effort, the teams of Vanderhaegen and Bonin developed a novel strategy to transplant human neurons as individual cells into the mouse brain and to follow their development over time. 
Dr. Daniele Linaro: “We differentiated human embryonic stem cells into neurons and injected them into the brains of young mouse pups. This allows us to investigate human neurons in a living brain over many months. We can also apply a whole range of biological tools in these cells to study human neural circuit formation and human brain diseases.” 
The researchers discovered that the transplanted human cells follow the same developmental plan as they would in a human brain, with a months-long period of maturation typical for human neurons. This means that our nerve cells may follow an ‘internal clock’ of development that is surprisingly independent of the surrounding environment. 
Moreover, the human cells were able to function in the mouse neural circuits. “After months of maturation, the human neurons began to process information, for example responding to visual inputs from the environment,” says Dr. Ben Vermaercke, who conducted the experiments together with Linaro. “The human cells even showed different responses depending on the type of stimulus, indicating a surprisingly high degree of precision in the connections between the transplanted cells and the host mouse’s brain circuits.” 
A milestone with a lot of potentialThis study constitutes the first demonstration of genuine circuit integration of neurons derived from human pluripotent stem cells. According to Bonin, “it’s a technological milestone that opens up exciting possibilities to study how genetic information, environmental cues and behavior together shape how the brain wires itself up”. On the one hand, this model could be applied to study a whole range of diseases that are thought to impact the development of human neurons into neural circuits. The researchers plan to use neurons with genetic mutations linked to diseases such as intellectual disability to try and understand what goes wrong during maturation and circuit formation.
“Our findings also imply that nerve cells retain their ‘juvenile’ properties even in an adult (mouse) brain. This could have potentially important implications for neural repair,” adds Vanderhaeghen. “The fact that transplanted young human neurons can integrate into adult circuits is promising news in terms of treatment development for neurodegeneration or stroke, where lost neurons could potentially be replaced by transplanting new neurons.”
PublicationXenotransplanted human cortical neurons reveal species-specific development and functional integration into mouse visual circuits, Linaro, Vermaercke et al. 2019 Neuron​
Questions from patientsA breakthrough in research is not the same as a breakthrough in medicine. The realizations of VIB researchers can form the basis of new therapies, but the development path still takes years. This can raise a lot of questions. That is why we ask you to please refer questions in your report or article to the email address that VIB makes available for this purpose: patienteninfo@vib.be. Everyone can submit questions concerning this and other medically-oriented research directly to VIB via this address.

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Pierre Vanderhaeghen (VIB-KU Leuven, ULB) 


Vincent Bonin of Neuro-Electronics Research Flanders​

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IMI2 – Call 20 webinars

Start Date 22/01/2020 End Date 31/01/2020  

IMI held webinars on IMI2 – Call 20 from Wednesday 22 January to Tuesday 31 January.

All webinars on the Call topics featured a presentation by the EFPIA topic coordinator and time for questions and answers. The webinars represent an excellent opportunity to learn more about the Call topics, interact directly with the topic coordinators, and get in touch with potential project partners.

The webinar on IMI’s rules and procedures included presentations of IMI’s intellectual property policy and tips on the preparation of proposal submissions. IMI also held a dedicated webinar for small and medium-sized enterprises (SMEs). This covered elements of the different Call topics that may be of particular relevance for SMEs, as well as a presentation of IMI’s rules and procedures with a focus on aspects that are most important for SMEs.

The slides presented are published below, along with recordings of all webinars and lists of participants who agreed for their details to be published.

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The topic texts and details of how to apply can be found on the IMI2 – Call 20 page.
 

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Topic-specific webinars for IMI2 – Call 20

Early diagnosis, prediction of radiographic outcomes and development of rational, personalised treatment strategies to improve long-term outcomes in psoriatic arthritis
Wednesday 22 January | 11:00
Download the presentation | Watch the recording | Download the list of participants

Academia and industry united innovation and treatment for tuberculosis (UNITE4TB)
Thursday 23 January | 11:00
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Tumour plasticity
Friday 24 January | 11:00
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Innovations to accelerate vaccine development and manufacture
Monday 27 January | 11:00
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Proton versus photon therapy for oesophageal cancer – a trimodality strategy
Tuesday 28 January | 15:00
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Handling of protein drug products and stability concerns
Friday 31 January | 11:00
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Developmentally Engineered Callus Organoid Bioassemblies Exhibit Predictive In Vivo Long Bone Healing

Gabriella Nilsson HallLuís Freitas MendesCharikleia GklavaLiesbet GerisFrank P. LuytenIoannis PapantoniouFirst published:10 December 2019 https://doi.org/10.1002/advs.201902295SECTIONSPDFTOOLSSHARE

Abstract

Clinical translation of cell‐based products is hampered by their limited predictive in vivo performance. To overcome this hurdle, engineering strategies advocate to fabricate tissue products through processes that mimic development and regeneration, a strategy applicable for the healing of large bone defects, an unmet medical need. Natural fracture healing occurs through the formation of a cartilage intermediate, termed “soft callus,” which is transformed into bone following a process that recapitulates developmental events. The main contributors to the soft callus are cells derived from the periosteum, containing potent skeletal stem cells. Herein, cells derived from human periosteum are used for the scalable production of microspheroids that are differentiated into callus organoids. The organoids attain autonomy and exhibit the capacity to form ectopic bone microorgans in vivo. This potency is linked to specific gene signatures mimicking those found in developing and healing long bones. Furthermore, callus organoids spontaneously bioassemble in vitro into large engineered tissues able to heal murine critical‐sized long bone defects. The regenerated bone exhibits similar morphological properties to those of native tibia. These callus organoids can be viewed as a living “bio‐ink” allowing bottom‐up manufacturing of multimodular tissues with complex geometric features and inbuilt quality attributes.

1 Introduction

Tissue‐engineered advanced therapy medicinal products (TE‐ATMPs) are poised to revolutionize health care by replacing or restoring the function of damaged organs. Although major advances in the field of cell therapy manufacturing have been witnessed, only a small fraction of TE‐ATMPs exhibit quality attributes that could guarantee predictive performance in vivo and hence support clinical translation.13 To tackle these hurdles, a conceptual and technical merging of developmental biology and engineering principles is taking place within regenerative medicine. These “developmental engineering” strategies strive to mimic developmental events while guaranteeing robustness and predictive outcomes in a clinical setting.47 According to this strategy, cellular self‐assemblies and condensations of the appropriate length scale are key initiators for the formation of transient tissue structures capable of executing developmental programs with a high level of independence leading to organogenesis processes.89 These processes are regulated through the activation of tissue‐specific genes and pathways characterized by a high degree of autonomy resulting in tissues that are able to undergo a similar cascade of processes even ex vivo.10 This type of recapitulation of developmental events has previously been demonstrated with human adult stem cells, for example, for the formation of epithelial1 and liver11 organoids.

In the context of bone tissue engineering, fracture healing of long bones includes the formation of a cartilaginous “soft callus” that subsequently is transformed into bone,12 a process that resembles the well‐described and tightly synchronized process of endochondral ossification in the growth plate during development.1315 The autonomy of the growth plate cartilage in embryonic cartilage anlagen was previously reported, and even when the cartilage anlage was decomposed into single cells and re‐implanted subcutaneously, re‐organization occurred and a growth plate‐like structure was formed.410 Furthermore, investigations inspired by “developmental engineering” demonstrated recapitulation of endochondral ossification in ectopic environments using embryonic stem cells16 or bone marrow mesenchymal stromal cells (BM‐MSCs)1718 and orthotopically using rat19 or human20 BM‐MSCs. However, only partially successful results have been demonstrated due to scalability challenges and uncontrolled complexity in 3D cell culture formats currently used for inducing chondrogenic differentiation.1821 The use of scaffold‐free microspheroid cultures could provide a more homogeneous 3D culture format to precisely engineer soft callus‐like microenvironments.2225 The ability to produce populations of small functional modules will constitute a major step toward the incorporation of design principles in skeletal living implant manufacturing.2

The formation of high‐throughput cell microspheroid populations of defined size and their use as building modules for bottom‐up tissue formation strategies is gaining momentum for various TE applications.222628 However, the construction of complex engineered tissues possessing multicomponent tissue architecture is still elusive. Although bottom‐up approaches have been suggested in recent years in order to build larger tissue structures from micromodules, the majority of these studies used cell microspheroids with minimal cell‐secreted extracellular matrix (ECM).26 Regarding long bone defect regeneration, there is scarce literature on the potential of modular bioengineering strategies to generate larger implants while there is no understanding yet of how this architecture dictates whole tissue function after implantation. Ideally, modules for regeneration of long bone defects should possess an autonomy that would guarantee that the repeated functional units can synergistically contribute to the regenerative process, resulting in a predictive clinical outcome.

In this work, we present a developmental bioengineering strategy based on self‐assembly of human‐periosteum‐derived cells (hPDCs). hPDCs have great promise for regeneration of long bone defects, since the majority of cells forming the “soft callus” during fracture healing are derived from the periosteum.141529 In addition, recently published studies demonstrated the presence of skeletal stem cells within the periosteum with improved capacity to regenerate bone as compared to BM‐MSCs.1530 Herein, self‐assembly of hPDCs allowed scalable production of semiautonomous callus organoids that formed bone microorgans upon implantation. The in vitro maturation toward callus organoids was linked to gene expression patterns encountered in the embryonic growth plate and during fracture healing. Furthermore, an assembly of multiple callus organoids resulted in multimodular constructs that formed large bone organs ectopically and healed critical‐sized long bone defects in mice. In both cases, bone organs were formed in the absence of contaminating fibrotic tissue and exhibited a well‐developed bone marrow compartment, thus demonstrating the potential of this modular approach (Figure 1a) for future clinical applications.

image
Figure 1Open in figure viewerPowerPointLong‐term culture of periosteal microspheroids. a) Schematic overview of the bioengineering process starting with cellular aggregation, condensation, and differentiation followed by callus organoid assembly and implantation in ectopic and orthotopic environment. b) Projection area of microspheroids over time (87–400 microspheroids, 10–90 percentiles). c) Representative bright‐field images of microspheroids over time. d) Representative 3D renderings of confocal images of stained with DAPI (nucleus) and Phalloidin (F‐actin) over time. e) DNA quantification of microspheroids over time, normalized to day 0 (5 h) (n = 6, 10–90 percentiles). f) Representative confocal z‐projection images of LIVE (green)/DEAD (red) staining over time. g) Semiquantification of cell proliferation in microspheroids over time. EdU fluorescent area was normalized to DAPI fluorescent area (10–15 microspheroids per condition, 10–90 percentiles). h) Representative fluorescent images of proliferating cells (EdU, red) in microspheroids over time, blue represents the nucleus. **p < 0.01; ***p < 0.001; one‐way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test. Scale bars: c,d,f,h) 50 µm.

2 Results

2.1 Long‐Term Culture of Microspheroids Follows Early Pattern of Endochondral Ossification

Endochondral ossification is initiated with cell aggregation and condensation, followed by chondrocyte specification, differentiation, and formation of a cartilage tissue intermediate that subsequently is replaced by bone.31 Here, cell aggregation, condensation, and differentiation of hPDC microspheroids were studied over a period of 4 weeks (Figure 1b,c). The self‐aggregation process comprised two steps. Initially, over a course of 5 h (day 0), hPDCs self‐assembled to form a stack of cells until a spheroid shape was attained (Figure 1c,d; Movie S1, Supporting Information). Filamentous‐actin (F‐actin) staining demonstrated changes in the actin cytoskeleton by formation of stress fibers during the first week as well as compaction of microspheroids with a more confined cortical actin network over time and its thinning after 3 weeks (Figure 1d).

3D visualization of cell nuclei showed the presence of nuclear condensation and fragmentation indicating occurrence of apoptosis in some cells starting from day 1432 (Figure S1a, white arrows, Supporting Information). Furthermore, DNA quantification suggested a stable number of cells during 2 weeks followed by a 44% decrease after 3 weeks (Figure 1e). The majority of cells in the microspheroids were viable; however, an increase in dead cells was observed during the last week of the culture period (Figure 1f). Messenger ribonucleic acid (mRNA) transcripts of the marker of proliferation Ki‐67 (MKI67) declined after 21 days (Figure S1d, Supporting Information) and 5‐ethynyl‐2′‐deoxyuridine (EdU) staining confirmed this trend by revealing a high number of proliferating cells (46%) during the first weeks, which subsequently decreased and was almost absent after 4 weeks in culture (Figure 1g,h). This decrease in proliferation is also seen during endochondral ossification31 indicating chondrocyte differentiation and maturation of the microspheroid cells.

To further define the differentiation stages of the microspheroids, gene expression of relevant markers was analyzed (Figure 2a). The early chondrogenic transcription factor sex‐determining region Y box (SOX)9 was upregulated (5‐fold) the first 14 days in culture followed by a downregulation while the cartilage matrix marker collagen type II alpha 1 (COL2A1) was highly upregulated (6100‐fold) after 21 days in culture. The early osteogenic and pre‐hypertrophic marker runt‐related transcription factor 2 (RUNX2) was upregulated after 7 (10‐fold) and 14 days (16‐fold) where after a downregulation was seen. The transcription factor osterix (OSX or SP7), which is directly regulated by RUNX2 and expressed in pre‐hypertrophic chondrocytes and osteoblasts, followed a similar expression trend.3334 Distinct upregulation of the hypertrophic markers collagen type X alpha 1 chain (COL10A1) (1340‐fold) and Indian hedgehog signaling molecule (IHH) (33‐fold) was detected at day 21. In addition, alkaline phosphatase (ALP) gene expression was upregulated (19‐fold) at day 14 and integrin binding sialoprotein (BSP or IBSP), linked to matrix mineralization and osteoblast differentiation,3537 was upregulated 8400‐fold, after 21 days in culture. No significant upregulation of the analyzed genes (SOX9COL2A1RUNX2OSXCOL10A1IHHALP, and BSP) was detected between day 21 and day 28 (Figure S2a, Supporting Information). Therefore, the following analyses were performed until day 21. In summary, the above results demonstrated a proliferation phase that was interchanged with cellular differentiation and maturation defined by genes associated with both hypertrophic chondrocyte and osteogenic differentiation.

image
Figure 2Open in figure viewerPowerPointMicrospheroids follow endochondral ossification patterns toward pre‐hypertrophic callus organoids able to form bone in vivo. a) Quantification of mRNA transcript of chondrogenic and pre‐hypertrophic/hypertrophic gene markers was performed and normalized to D0 (n = 6 mean value ± SEM). *p < 0.05; **p < 0.01; ***p < 0.001; one‐way ANOVA followed by Tukey’s multiple comparison test. b–e) Representative sections of: b) Alcian Blue, c) Safranin O, d) IHH immunostaining, and e) confocal z‐projection image of OSX immunostaining over time. f) Schematic view of individual callus organoid implantation. g) 3D rendering of nano‐CT images after 4 weeks in vivo implantation. h) Safranin O, i) Masson’s Trichrome, and j) TRAP staining after 4 weeks in vivo. k) Bright‐field image of invading blood vessels (black arrow, #: microwell) and l) CD31 immunostaining 4 weeks after implantation (* represents the agarose mold). Scale bars: b–e) 50 µm; g) 100 µm; h–j) the upper row represents 100 µm and the lower row 50 µm; k) 400 µm; and l) 50 µm.

2.2 Microspheroids Mature toward Pre‐Hypertrophic Callus Organoids That Form Bone Microorgans In Vivo

The gene expression analysis indicated chondrogenic differentiation toward hypertrophy in combination with osteogenic differentiation at day 21 (Figure 2a). Furthermore, Alcian Blue staining at low pH, specific for glycosaminoglycan (GAG), confirmed an increased presence of cartilage‐like ECM within the microspheroids, and pre‐hypertrophic like cells were visible after 3 weeks in culture (Figure 2b, black arrows). Safranin O staining demonstrated slight presence of cartilage‐specific sulfated GAGs after 21 days in culture (Figure 2c) and immunostaining confirmed the presence of IHH, OSX, and COL2 protein after 14 days in culture (Figure 2d,e; Figure S1b,d,e, Supporting Information). The gene expression and histological analysis demonstrated that the microspheroids, containing ≈250 aggregated cells (day 0, Figure 2b,c), matured into microtissues with differentiated cells and ECM (day 14, Figure 2b,c).

Based on the upregulation of hypertrophic gene markers (Figure 2a) and the presence of pre‐hypertrophic cells (Figure 2b), day 21 microtissues were chosen to be implanted subcutaneously to evaluate their capacity to mature into bone in vivo. Implantation of whole agarose microwell platforms with a diameter of 5 mm was carried out in immunodeficient mice to ensure that microtissues would remain entrapped in their microwells (Figure 2f). After 4 weeks of ectopic implantation, nano‐computed tomography (nano‐CT) scans demonstrated the formation of distinct mineralized spheres (Figure 2g) with a volume of (5.4 ± 3.54) × 105 µm3 and an average diameter of 209 ± 41 µm (24 spheres quantified from three explants). Histological sections further demonstrated the presence of bone matrix (Figure 2h,i) surrounding a marrow compartment with osteoclast activity (Figure 2j) and blood vessels (Figure 2k,l). These data demonstrated the development of microtissues with proteoglycan‐rich ECM positive for IHH and COL2. Furthermore, these microtissues were able to form bone microorgans in vivo (Figure 2g), confirming that these implants behaved as single semiautonomous bone‐forming modules in vivo acting as callus organoids. This defines a maturation process from microspheroids (day 0) to microtissues (day 14) and finally callus organoids (day 21) (Figure 2e).

2.3 Callus Organoids Fuse into Larger Constructs In Vitro

In order to demonstrate that the above‐mentioned microtissues and callus organoids can be used as building modules to form larger constructs, we initially studied the fusion process of two callus organoids. Despite long‐term culture as microtissues, creating a substantial amount of secreted ECM, the callus organoids spontaneously fused over 24 h (Movie S2, Supporting Information). Subsequently, ≈3000 modules were flushed out of their microwells and assembled in an agarose well (2 mm diameter and depth) for fusion into a multimodule construct (Figure 3a; Figure S2b, Supporting Information). 14 (microtissues) and 21 day (callus organoids) modules were chosen for further analysis based on chondrogenic (SOX9 and COL2A1) and hypertrophic (COL10A1IHH, and ALP) gene markers (Figure 2a,d,e), as well as cell morphology (Figure 2b–d). Both 14 day and 21 day modules fused into larger constructs that could be handled and transported (Figure 3a); however, single callus organoid structures were still visually discernible in 21 day constructs. As a control to these structures, a macropellet formed with the same number of cells and cultured for 3 weeks in the same media formulation was introduced (Figure 3a, Macropellet).

image
Figure 3Open in figure viewerPowerPointAssembly of cartilage intermediate microtissues into larger bone forming constructs. a) Schematic drawing demonstrating module assembly into an agarose macrowell (left) and representative photographs of the day 14, day 21 constructs, and Macropellet (right). b) Alcian Blue staining of fused constructs and Macropellet. c) 3D rendering of nano‐CT images 4 and 8 weeks after implantation. d) Quantification of mineralized tissue 4 and 8 weeks after implantation (mean value ± SEM, n = 3–6). e) Representative images of CD31 immunostaining (black arrows demonstrate blood vessels), and f) quantification 8 weeks after implantation (mean value ± SEM, n = 3–6). ANOVA followed by Tukey’s multiple comparison test. Scale bars: a) 500 µm, b) 500 and 100 µm, c) 500 µm, and d) 100 µm.

Alcian Blue staining demonstrated increased module fusion within the day 14 constructs as compared to day 21 constructs (Figure 3b), albeit both module constructs contained positive staining thoughout their structures. In contrast, the macropellet, not assembled with modules, did only show Alcian Blue staining at the periphery (Figure 3b). Safranin O staining corresponded to the Alcian Blue staining seen in macropellets. In contrast, Safranin O positive areas were found throughout the day 21 constructs (Figure S3a, Supporting Information). None of the constructs demonstrated positive staining for Alizarin red or von Kossa (Figure S3b,c, Supporting Information), indicating that mineralization was not present in the constructs. In conclusion, these results demonstrated the formation of larger constructs through assembly of micromodules resulting in more homogenously distributed GAG‐rich ECM as compared to macropellets (Figure 3b).

2.4 Assembled Callus Organoids Form Single Large Bone Organs In Vivo

Next, day 7, 14, and 21 constructs, as well as the macropellets were implanted ectopically in immunodeficient mice to investigate their capacity to form bone in vivo (4 and 8 weeks). None of the day 7 constructs were retrieved (n = 4). However, mineralization was detected with nano‐CT in the other three conditions after 4 week implantation (Figure 3c). No significant difference in mineralization percentage was seen between the conditions after 4 or 8 weeks. However, a nonmineralized core was detected in the macropellet at both timepoints (Figure 3c, white arrows). Furthermore, after 8 weeks’ ectopic implantation, the day 21 constructs and macropellets contained a mineralized cortex, while the mineralized tissue in the day 14 constructs appeared porous hence less mature. The number of blood vessels was quantified with CD31 immunostaining, and no significant difference between the constructs was detected although a larger number of day 21 constructs (5/6) contained a high amount (>50 blood vessels mm−2) of blood vessels as compared to day 14 constructs (2/4) and macropellets (0/3) (Figure 3e,f; Figure S3d, Supporting Information).

Safranin O and Massons’s Trichrome staining on histology sections after 4 weeks’ implantation revealed that day 21 constructs contained bone, bone marrow, as well as remodeling cartilage indicating the occurrence of endochondral ossification (Figure 4a; Figure S3f, Supporting Information). Although no significant difference was detected, limited bone marrow compartments were seen in the day 14 constructs and macropellets in contrast to the day 21 constructs (Figure 4a,b,e). Strikingly, significant areas of fibrotic tissue were detected in both day 14 constructs and macropellets as compared to day 21 constructs (Figure 4f). Furthermore, tartrate‐resistant acid phosphatase (TRAP) staining (Figure 4c) demonstrated osteoclast activity in all constructs although more prominent in day 21 constructs. Human osteocalcin (hOCN) staining demonstrated that implanted cells contributed to the bone formation in all constructs (day 14 construct: 74 ± 10%, day 21 construct: 58 ± 18%, and macropellet: 72 ± 2%) (Figure 4d,g; Figure S3e, Supporting Information).

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Figure 4Open in figure viewerPowerPointHistological assessment after ectopic implantation. a) Safranin O, b) Masson’s Trichrome, c) TRAP, and d) hOCN staining of day14, day 21 constructs, and Macropellet 4 weeks after in vivo implantation. e–g) Quantification (mean value ± SEM) of: e) bone marrow compartment (n = 5–6), f) fibrous tissue area (n = 5–6), and g) hOCN positive cells (n = 3–4) 4 weeks after implantation. h) Hematoxylin and eosin (H&E), i) Safranin O, j) Masson’s Trichrome (M’s T), and k) hOCN staining after 8 weeks in vivo implantation. *p < 0.05; **p < 0.01; ***p < 0.001; one‐way ANOVA followed by Tukey’s multiple comparison test. Scale bars: a–c,h–j) 500 µm (left) and 100 µm (right) and d) 100 µm.

Furthermore, hematoxylin–eosin (H&E), safranin O, and Masson’s Trichrome staining revealed mature bone in all conditions after 8 weeks’ implantation, but the day 14 constructs and macropellets still contained domains of fibrotic tissue which were absent in the day 21 constructs (Figure 4h–j). In addition, OCN positive cells of human origin were present also after 8 weeks’ implantation (Figure 4k). Although mineralized, some of the day 21 constructs maintained a hypertrophic chondrocyte phenotype after both four (three of six implants) and eight (one of six implants) weeks in vivo implantation (Figure S3f, Supporting Information). Taken together, these results supported that callus organoids fused into larger day 21 constructs in vitro and further developed into bone organs in vivo.

2.5 Temporal Gene Expression Patterns during Callus Organoid Formation Follow the Endochondral Ossification Process toward a Niche for Matrix Remodeling and Bone Organ Formation

To better explain the differentiation pathway of the callus organoids, an RNA sequencing analysis of D0 (5 h), D7, D14, and D21 modules was performed demonstrating a similar trend as the limited gene expression analysis (Figure 2a; Figure S4a,b Supporting Information). Furthermore, the number of significant (p < 0.05 and log2‐fold > 1) differentially expressed genes decreases over time from 3949 (D0–D7) and 847 (D7–D14) to 55 (D14–D21) and 84 (D21–pellet) (Figure 5a) indicating that the most dramatic changes occurred at the early stages of differentiation.

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Figure 5Open in figure viewerPowerPointRNA sequencing analyses of spheroids and Macropellet (n = 3–4). a) Volcano plots of differentially expressed genes from RNA‐seq data between the different spheroids and Macropellet. X‐ and Y‐axes represent log2fold change and log10p‐value, respectively, and green dots represent genes with log2FoldChange > 1 and padj < 0.05. b) Gene Ontology (GO) Biological Processes (2017b) of genes within the five different clusters of the 400 most variable genes (log2FoldChange > 1 and padj < 0.05). c) Heat map of spheroid mRNA transcript levels of genes regulating endochondral ossification (relative expression value for each gene). d) Venn diagram of the number of significant differentially expressed genes (green dots in (a)) for the different spheroid maturations. Green text represents genes associated with endochondral ossification and gray text genes associated with angiogenesis.

Pathway analysis using Enrichr3839 with WikiPathway (2016) grouped upregulated genes (D0–D21) into endochondral ossification (WP474, adj. p‐value 1.0e‐13) and embryonic skeletal system development (Gene Ontology (GO): 00 48706, adj. p‐value 1.379e‐8) from GO Biological Process (BP) (Data File S1, Supporting Information).3840 Next, unsupervised clustering was performed on the 400 most variable genes in order to gain a holistic overview of signaling action during the callus organoid maturation process and the GO enrichment for each cluster was defined (Data Files S2 and S3, Supporting Information). The first cluster with a continuously upgoing trend included genes enriched to skeletal and cartilage development and regulation of mitogen‐activated protein kinases (MAPK) and ERK1/2 signaling (where ERK = extracellular signal‐regulated kinase) involving the wingless‐INT (WNT), bone morphogenetic protein (BMP), and fibroblast growth factor (FGF) signaling (Figure 5b). These are crucial signaling pathways driving endochondral ossification working in a converging manner toward chondrocyte hypertrophy.41 Interestingly, genes related to WNT signaling were also present in the transient downregulated cluster 3. This cluster included genes related to transforming growth factor beta (TGF‐β)/BMP related SMAD and WNT signaling motivating a converging crosstalk during callus organoid maturation. In addition, Notch, which is important for stem cell maintenance, suppression of chondrocyte differentiation, and proliferation,42 was represented in the downregulated cluster 3. These two clusters (clusters 1 and 3) indicate cell signaling regulation analogous to the molecular cascade of events present during endochondral ossification.41

Cluster 2, with constant downregulation, included genes associated with DNA transcriptional activity correlating with the decrease in cell proliferation occurring during the transition from proliferative to hypertrophic chondrocytes43 which was indicative also in our data (Figure 1g,h). Genes associated with ECM disassembly (MMP13) and produced by hypertrophic chondrocytes (COL10A, COL9, and SPP1) were grouped in the constantly upregulated cluster 4 supporting maturation toward hypertrophic callus organoids that exhibited a high turnover and capacity to remodel the surrounding matrix. In addition, genes linked to calcium‐ion regulation were highly represented in the upregulated cluster 5, suggesting a gradual transition to a pre‐hypertrophic niche favoring mineralization, although no in vitro mineralization was detected (Figure S3b,c, Supporting Information).44

The GO enrichment of the unsupervised clusters demonstrated that the callus organoid maturation followed signaling pathways regulating endochondral ossification. This was further supported by analysis of well‐known regulators. During the first phase (D7), important regulators of chondrocyte proliferation, differentiation, and organization were upregulated, including IGF1 and its receptor IGF1R,45 GLI3,4648 PTHrP,49 and the SOX trio (SOX 5/6/9)50 (Figure 5c). From day 14 onward, the PTHrP positive state converted into a IHH positive state, followed by increased expression of chondrocyte hypertrophy activators, such as GLI1FOXA2MEF2COSX, RUNX2, and RUNX34851 (Figure 5c). This pattern was mirrored in the gene expression of matrix proteins and regulators involved in endochondral ossification with a distinct upregulation of collagens (COL2A1, COL9A1, COL10A1, and COL11A1) and signaling factors correlated to pre‐hypertrophic/hypertrophic chondrocytes and osteoblasts (SPP1, IBSP, DMP1, and ALPL).4851

These data demonstrate a regulatory “switch” between D7 and D14 plausibly crucial for bone formation in vivo. Genes significantly upregulated between both D7–D14 and D14–D21 included FOXA2DMP1, and SCIN which are crucial for chondrocyte hypertrophy,52 cartilage–bone transition,53 and bone resorption,54 respectively, indicating their significant role in bone formation and also in the current manufacturing approach (Figure 5d). Subsequently, comparison from D14 to D21 indicated further maturation linking to the in vivo formation of a bone organ with matrix remodeling and the presence of bone marrow (Figure 4). Since only a limited number of genes were significantly changed (55 up/downregulated genes, Figure 5a) during this period, individual analysis was performed for these genes (Figure S4c, Supporting Information). Around 15 of the 43 differentially upregulated genes (Figure S4c, Supporting Information) have been associated with endochondral ossification and pre‐hypertrophic/hypertrophic chondrocytes (Table S1, Supporting Information) whereof IBSP,3637 chondromodulin (CNMD or LECT1),5556 and WNT457 were exclusively significant for D14–D21 (Figure 5d). Interestingly, 15 of the 55 significantly changed genes D14–D21 (Figure S4c, Supporting Information) have been associated with regulation of angiogenesis (Table S2, Supporting Information), a pivotal event during the transition from cartilage to bone in endochondral ossification. Of these angiogenic genes, six were exclusively differentially expressed between D14 and D21 (Figure 5d). Conclusively, the RNA‐seq analysis demonstrated endochondral maturation from initial microspheroids (aggregated cells) to callus organoids (cells and ECM) exhibiting pre‐hypertrophic characteristics and active remodeling of the secreted ECM resulting in bone organ formation in vivo.

2.6 Assembled Callus Organoids Heal Critical‐Sized Long Bone Defects

Based on the ectopic implantation and RNA sequencing results, day 21 modules were defined as “callus organoids” and selected as modules for the formation of larger constructs and orthotopic implantation in a murine, critical‐sized long bone defect.58 An agarose mold based on the dimensions of the critical‐sized defect and with the decrease in size during fusion of callus organoids accounted for was fabricated (Figure 6a). Next, ≈6000 callus organoids were seeded into the agarose mold (Figure 6b) and fused during 24 h resulting in a construct (≈4.5 mm length and 2 mm wide) (Figure 6c) that was fitted into the tibia defects of immunodeficient mice (Figure 6d).

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Figure 6Open in figure viewerPowerPointHealing of murine critical‐sized long bone defect. a) Schematic visualization of implant formation. b,c) Bright‐field image 1 h (b) and 24 h (c) after callus organoid assembly. d) Photograph of a 4 mm tibia defect after healing. e) X‐ray images of tibia defect with a day 21 construct. f) Negative control: X‐ray of empty defect after 8 weeks. g) Quantification of mineralized volume in defects with day 21 construct and empty defects (n = 4 animals for each condition, two‐way ANOVA followed by Tukey’s multiple comparison test). h) Nano‐CT 3D rendering images over time of defect with day 21 construct. i) Cross section of 3D rendering of native tibia and defect 8 weeks after day 21 construct implantation. j–l) In vivo CT quantification of structure: j) thickness, and k) linear density over time visualized with l) in vivo CT 2D images. m–p) Comparison between native tibia and healed defect 8 weeks after construct implantation was demonstrated by ex vivo nano‐CT quantification of: m) mineralized tissue (%), n) medullary cavity (%), o) structure thickness, and p) structure linear density (n = 4, unpaired t‐test). q) H&E, r) Masson’s Trichrome, and s) hOCN immunohistological staining of defect 8 weeks after day 21 construct implantation. *p < 0.05; **p < 0.01; ****p < 0.0001. Scale bars: b,c,e,h,i) 1 mm, l) 500 µm, q–s) overview 1 mm and zoom‐in 100 µm.

X‐ray images and 3D renderings of in vivo CT scans demonstrated occurrence of mineralization after 2 weeks, and bridging of defects was detected after 4 weeks followed by increased corticalization until week 8 (Figure 6e,h; Figure S5a, Supporting Information). No bridging was detected in the empty defects after 8 weeks (Figure 6f) while quantification of in vivo CT images confirmed the increase in mineralized tissue over time in experimental conditions (Figure 6g). Cross section of the nano‐CT 3D rendering from week 8 demonstrated the presence of cortical bone in the defect with a nonmineralized compartment in the center, suggesting a defined bone marrow cavity (Figure 6i). Structure thickness increased significantly from week 2 to 4 correlating to the time of defect bridging (Figure 6j; Figure S5a, Supporting Information). Furthermore, the number of structures decreased from week 4 to 8 indicating remodeling from a trabecular to a more cortical structure, which was also visible on in vivo CT images (Figure 6k,l).

Next, the healed defects at week 8 were compared to native tibia at the same location as the defect, in mice of the same age and gender. No significant differences were found regarding mineralized percentage, volume (Figure 6m; Figure S5b, Supporting Information), structure linear density (Figure 6p), or medullary cavity occupancy (Figure 6n), while the medullary volume in healed defects was significantly larger than in native bone (Figure S5c, Supporting Information). In addition, structure thickness was lower in the healed defects as compared to native bone indicating that longer healing time may be necessary for full regeneration (Figure 6o).

H&E and Masson’s Trichrome staining after 8 weeks confirmed full bridging (3/4) with the presence of mature bone and bone marrow (Figure 6q,r; Figure S5d,e, Supporting Information), and hOCN staining (Figure 6s) revealed the contribution of donor cells to the bone formation process. In conclusion, the assembly of multiple callus organoids into an easy‐to‐handle scaffold‐free implant resulted in full bridging of a critical‐sized long bone defect by the formation of cortical‐like bone tissue with a medullary cavity containing bone marrow with the absence of fibrous tissue. In addition, structural characterization of the regenerated defect showed high similarities to native tibia.

3 Discussion

In this work, we developed a bottom‐up modular strategy for scalable biofabrication of cartilage intermediate tissues that were able to form ossicles without contaminating tissue compartments while exhibiting a unique capacity to heal critical‐sized long bone defects. During native fracture healing, cells from the periosteum are the main contributors of the callus.1429 These cells have recently been shown to possess a higher regenerative capacity than bone marrow mesenchymal cells and contain a skeletal stem cell population with distinct functions during endogenous bone repair.1530 Moreover, it was recently reported that periosteum contains not only renewable skeletal stem cells forming membranous, cortical bone, but also endochondral bone upon damage.59 Hence, this understudied progenitor cell source possesses critical advantages in terms of clinical application for the design of engineered ATMPs aiming to heal large long bone defects.

To date, the formation of cartilage intermediates in vitro was obtained through the use of pellets containing large amount of cells (>2 × 105 cells).17186061 However, the use of such methods has resulted in diffusion‐related challenges such as the formation of undifferentiated tissue compartments in vitro which hinder the concerted progression of tissue maturation to their final phenotype upon implantation.18 This was also detected in our study by the large fibrotic compartments encountered within macropellet explants (Figure 3). In addition, when chondrogenic61 and hypertrophic62 pellets were fused into larger structures, limited remodeling in vivo was shown. Hence, we designed cell microspheroids (comprised of 250 cells) that would not exceed 150 µm in diameter to match with the length scale that diffusible signals can be transported and to mimic the initial developmental event of growth plate formation (condensation) whereby only a few hundred cells are needed.63 During differentiation, we observed that cells underwent a cascade of molecular and cellular events that reflect endochondral ossification allowing them to transform from cellular spheroids to semiautonomous microtissue structures, callus organoids, capable of undergoing organogenesis (Figures 13). In addition, the assembly of callus organoids into larger tissue structures resulted in implants containing active regenerative components throughout their structure. Ultimately, the populations of callus organoids described in our study could be viewed as a living “bio‐ink” that also allows the formation of scaffold‐free tissue structures with intricate geometric features (Figure S6, Supporting Information).64

In order to obtain quality attributes that could be linked to the functionality of the callus organoids, high‐depth transcriptomic profiling was carried out. Sets of genes were determined to provide signatures to identify whether engineered microtissue niches have attained the degree of autonomy required for bone organ formation. These were compared to recent studies focused on the identification of transcription factor panels that control differentiation transitions from one zone to the other in the growth plate.4851 With this comparison, we were able to discern similar temporal gene regulation kinetics and link the phenotypic state and semiautonomous function of our microtissues to that of an “early pre‐hypertrophic” stage for day 14 modules (microtissues) and that of “late pre‐hypertrophic” stage for day 21 modules (callus organoids). In addition, GO term analysis of the 400 most variable genes revealed additional etiologies (Figure 5b) for the striking bone organ formation observed in our study. Upregulated clusters indicated gradual transition to pre‐hypertrophy, favoring mineralization as well as ECM disassembly and organization (Figure 5b). Apart from the relevance of ECM disassembly and organization in the transition from hypertrophic cartilage to bone,65 this property could also be key in regulating the orchestrated transition of the multimodular constructs into a single ossicle by facilitating ECM reorganization and vascular invasion across the implant. This could also explain the rapid vascularization and bone marrow formation of day 21 constructs observed as early as 4 week postimplantation (Figure 4) as well as host integration in the long bone defect (Figure 6). Chan et al.66 have previously demonstrated the importance of endochondral ossification for the formation of hematopoietic stem‐cell (HSC) niches, and here we provide a set of metrics that would allow fine tuning and robust bone organ formation.

Although scaffold‐free constructs are beneficial for mimicking native tissue morphology, a combination of the callus organoids with suitable biomaterials could further enable upscaling into centimeter‐sized implants and even enhance their performance.6769 Functionalized biomaterials possessing molecular signatures relevant to the timescales of the differentiation cascades and the proper length scale could interact and support endochondral ossification events. Petersen et al. recently demonstrated that the architecture of collagen scaffolds can direct endochondral fracture healing in vivo.70 They showed that scaffold pores oriented along the defect resulted in ECM alignment and controlled invasion of progenitor cells and blood vessels leading to the onset of endochondral ossification. In the present work, we observed rapid vascularization and bone formation which was attributed to active ECM remodeling, a dynamic property that could further be supported by properly designed scaffolds through the delivery of relevant enzymes.71

Localized delivery of growth factors through tailored biomaterials could further direct tissue maturation in vivo while avoiding release of supraphysiological levels, which for BMP‐2 has been proven to cause severe side effects including swelling and heterotopic bone formation.72 Herberg et al. demonstrated that a combination of BMP‐2 and TGF‐β1 releasing microparticles in cell‐based constructs resulted in mineralized bridging in tibia defects which was further enhanced by mechanical stimulation of the defect.73 Furthermore, nanoscale fibronectin coatings on polycaprolactone scaffolds were shown to allow incorporation of ultralow dose BMP‐2 (100 ± 8 ng cm−2) resulting in bone formation in vivo.74 However, it is of note that the use of biomaterials could also have adverse effects for tissue regeneration when their properties are not coupled to the precise regenerative context. For example, collagen I scaffolds used in both clinical and research applications for bone regeneration were recently shown to impede osteogenic differentiation and fracture healing.75 This highlights the importance of thorough understanding of the interaction between the scaffold material and the biology for a specific application.

There are still a number of technical challenges that need to be addressed for future biomanufacturing of callus organoids for mass production. The transition of the static process developed in this study to bioreactor systems where thousands of organoids could be generated could aid in its full automation and enhance its capability. In addition, the transfer of this process to stirred bioreactor systems could potentially allow increased flexibility in terms of achievable scale.76 At the same time, already available technologies for isolating single microtissue modules for at‐line quality controls could provide an ideal method for real‐time evaluation of their degree of autonomy77 allowing the implementation of real‐time potency monitoring as envisaged in the quality by design paradigm for cell therapy. Bioprinting technologies with the capacity to manipulate single spheroids have been developed through laser‐induced forward transfer, a high‐resolution method using laser pulses.78 Finally, robotic devices have been shown to possess the capacity to manipulate single spheroids and positioning them in preordered grids allowing them to fuse7980 or depositing them in printed scaffolds.78

Another technical bottleneck that will need to be addressed is the vascularization of multicentimeter‐sized implants. Although chondrocytes possess resistance to stress conditions found at the implantation site such as hypoxia and low nutrient availability, it is expected that vascularization will be a prerequisite for cell survival in large implants.81 Recently, vascularized structures based on the concomitant use of mesenchymal condensations, of similar dimensions to the ones presented here and endothelial cells, exhibited improved in vivo functionality.8 Moreover, sacrificial writing into functional tissue (SWIFT) bioprinting with direct fabrication of vasculature in organoid suspensions could also be employed for introducing vasculature patterns when upscaling to larger callus‐organoid‐based implants.82 In addition, using purified stem cell populations recently described by Chan et al.83 could substantially enhance the potential and efficiency of the strategy described in this work.

4 Conclusion

In conclusion, the described callus organoids provide an engineering approach for predictive design of large‐scale living implants. The callus organoids exhibited a deterministic behavior by reaching autonomy thresholds attributed to synchronized activation of molecular pathways providing robustness and potentially facilitating regulatory approval and safety. Furthermore, this process is scalable both in terms of production of single callus organoids and in terms of tissue implant size and at the same time allowing the design of intricate geometric features. Importantly, the in vivo functional assessment of orthotopic bone formation with bridging of the long bone defect took place within the timelines of natural fracture healing and resulted in a bone structure highly resembling native long bone.12 With these advancements, we believe that future biofabrication of skeletal implants using callus organoids will follow design principles resulting in achieving “bone by design”. This will eventually pave the way for the biomanufacturing of clinically relevant implants possessing robust functionality and causal connection with the clinical outcome. This can revolutionize the mitigation of currently unmet clinical challenges such as healing of critical‐size long bone defects.

5 Experimental Section

Cell Expansion: hPDCs were isolated from periosteal biopsies of nine different donors, and two different cell pools were created (ages of 29 ± 12 and 14 ± 3 years) as previously described.84 The hPDC pools were expanded (5700 cells cm−2) until passage 7 (in vivo, RNA‐seq) and 10 (in vitro) at 37 °C, 5% CO2, and 95% humidity in Dulbecco’s modified Eagle medium (DMEM, Life Technologies, UK) with 10% fetal bovine serum (HyClone FBS, Thermo Scientific, USA), 1% antibiotic–antimycotic (100 units mL−1 penicillin, 100 mg mL−1 streptomycin, and 0.25 mg mL−1 amphotericin B), and 1 × 10−3 m sodium pyruvate (Life Technologies, UK). Medium was changed every 2–3 days, and cells were harvested with TrypLE Express (Life Technologies, UK) at a confluence of 80–90%. TrypLE Express was used for all passaging and harvesting steps during cell handling. The ethical committee for Human Medical Research (Katholieke Universiteit Leuven) approved all procedures, and patients’ informed consent forms were obtained (ML7861).

Formation of Microspheroids: Agarose microwell inserts for formation of a high number of microspheroids with homogeneous size distribution were created as previously described by Leijten et al.85 Briefly, 3 % (w/v) Agarose (Invitrogen, Belgium) was poured onto a polydimethylsiloxaan (PDMS, Dow Corning Sylgard 184 elastomer, MAVOM Chemical Solutions) master mould containing pillars with a diameter of 200 µm. The agarose was let to solidify where after microwell inserts with an area of ≈1.8 cm2 were punched out, placed in 24‐well plates, 1 mL of phosphate‐buffered saline (PBS; Lonza, Verviers, Belgium) was added and the wells were sterilized under UV for 30 min. Each well insert contained ≈2000 microwells. hPDCs were harvested and seeded with a concentration of 500 000 cells per well to obtain ≈250 cells per spheroid after self‐aggregation. Microspheroids were differentiated into microtissues in a serum‐free chemically defined chondrogenic medium (CM) containing LG‐DMEM (Gibco) supplemented with 1% antibiotic–antimycotic (100 units mL−1 penicillin, 100 mg mL−1 streptomycin, and 0.25 mg mL−1 amphotericin B), 1 × 10−3 m ascorbate‐2 phosphate, 100 × 10−9 m dexamethasone, 40 µg mL−1 proline, 20 × 10−6 m of Rho‐kinase inhibitor Y27632 (Axon Medchem), ITS+ Premix Universal Culture Supplement (Corning) (including 6.25 µg mL−1 insulin, 6.25 µg mL−1 transferrin, 6.25 µg mL−1 selenious acid, 1.25 µg mL−1 bovine serum albumin (BSA), and 5.35 µg mL−1 linoleic acid), 100 ng mL−1 BMP‐2 (INDUCTOS), 100 ng mL−1 growth/differentiation factor 5 (GDF5) (PeproTech), 10 ng mL−1 TGF‐β1 (PeproTech), 1 ng mL−1 BMP‐6 (PeproTech), and 0.2 ng mL−1 basic FGF‐2 (R&D systems).86 Half of the media volume was changed every 3–4 days.

Viability Assay: Cell viability in microspheroids was assessed qualitatively with LIVE/DEAD Viability/Cytotoxicity Kit (Invitrogen, USA) for mammalian cells by following the manufacturer’s protocol. Briefly, microspheroids were rinsed with PBS, where after they were incubated in 2 × 10−6 m Calcein AM and 4 × 10−6 m ethidium homodimer‐1 for 30 min at 37 °C, 5% CO2, and 95% humidity. Stained microspheroids were visualized with a confocal microscope ZEISS LSM 510 META (Cell imaging core facility of KU Leuven) with 4 µm thick slices.

Cell Proliferation Assay: Cell proliferation during microspheroid differentiation was visualized with Click‐iT EdU Imaging Kit (Life Technologies, USA) according to the manufacturer’s protocol. Briefly, 10 × 10−6 m EdU was added to the microspheroids during 4 days for each time point. Next, samples were fixed in 4% paraformaldehyde (PFA), EdU was detected with Alexa Fluor azide, stained with Hoechst 33 342 (5 µg mL−1) followed by visualization with a Leica M165 FC microscope (Microsystems, Belgium). The percentage of EdU/Hoechst (proliferating per all cells) stained area was quantified using ImageJ software87 for 10–15 microspheroids per time point.

Cytoskeleton and Nuclei Visualization: Cell nucleus and F‐actin distribution within microspheroids was visualized by staining with 2.5 µg mL−1 4′,6‐diamidino‐2‐phenylindole (DAPI) (Invitrogen) and 0.8 U mL−1 Alexa Fluor 488 phalloidin (Invitrogen) during 1 h at room temperature. Stained spheroids were imaged with an inverted laser scanning fluorescence confocal microscope ZEISS LSM 510 META (Cell imaging core facility of KU Leuven) with 1 µm thick slices using an argon ion 488 nm and MaiTai laser.

DNA Quantification, Total RNA Extraction, and Quantitative Reverse Transcription–Polymerase Chain Reaction Analysis: Quantitative real‐time polymerase chain reaction (qRT‐PCR) was used to quantify mRNA of markers relevant for endochondral ossification. Pooled microspheroids (≈2000 microspheroids represent n = 1) were washed in PBS followed by cell lysis in 350 µL RLT lysis buffer (Qiagen, Germany) and 3.5 µL β‐mercaptoethanol (Sigma Aldrich, Germany), vortexed and stored at −80 °C. DNA assay kit QuantiT dsDNA HS kit (Invitrogen) was used to quantify the DNA content for each condition. Cell lysate was spun down and the DNA assay was performed according to the manufacturer’s protocol. RNeasy Mini Kit (Qiagen) was used to isolate the total amount of RNA from lysed cells. After RNA extraction, the RNA concentration was quantified with NanoDrop 2000 (Thermo Scientific), and sample purity was evaluated at A260/A280 (protein purity; ≈2.0+) and A260/A230 (salt purity; 2.0–2.2). RevertAid H Minus First Strand cDNA Synthesis Kit (Thermo Scientific, USA) was used for reverse transcription; 500 ng of RNA was mixed with 1 µg of oligo(dT18) for each reaction (5 min at 65 °C). The reaction mixture (4 µL 5× reaction buffer, 1 µL ribolock ribonuclease inhibitor, 2 µL dNTPmix (10 × 10−3 m), and 1 µL RevertAid H Minus M‐MuL VRT) was added to the samples and run in Applied Biosystems Veriti 96‐Well Fast Thermal Cycler (60 min at 42 °C followed by 10 min at 70 °C). qRT‐PCR was further performed with the cDNA, SYBR Green (Life Technologies) and primers designed for the specific human markers in cycling: 95 °C, 3 s; 60 °C, 20 s. Glyceraldehyde 3‐phosphate dehydrogenase (GAPDH) was used as house‐keeping gene and relative differences in expression were calculated using the 2−ΔΔCt method.88

RNA Sequencing: RNA isolation from samples (n = 3–4) was performed as described above. The Genomics Core Leuven performed the sequencing and the RNA‐seq expression analysis as follows. Library preparation was performed with the Illumina TruSeq Stranded mRNA Sample Preparation Kit, according to the manufacturer’s protocol. Denaturation of RNA was performed at 65 °C in a thermocycler and cooled down to 4 °C. Samples were indexed to allow for multiplexing. Sequencing libraries were quantified using the Qubit fluorometer (Thermo Fisher Scientific, MA, USA). Library quality and size range were assessed using the Bioanalyzer (Agilent Technologies) with the DNA 1000 kit (Agilent Technologies, CA, USA) according to the manufacturer’s recommendations. Each library was diluted to a final concentration of 2 × 10−9 m and sequenced on Illumina HiSeq4000 according to the manufacturer’s recommendations generating 50 bp single‐end reads. A minimum of 14M reads per sample were produced. Quality control of raw reads was performed with FastQC v0.11.5. Adapters were filtered with ea‐utils v1.2.2.18. Splice‐aware alignment was performed with TopHat v2.0.13 against the human hg19. The number of allowed mismatches was 2. Reads that mapped to more than one site to the reference genome were discarded. The minimal score of alignment quality to be included in count analysis was 10. Resulting sequence alignment map (SAM) and binary alignment map (BAM) alignment files were handled with Samtools v0.1.19.24. Quantification of reads per gene was performed with HT‐Seq count v0.5.3p3. Count‐based differential expression analysis was done with R‐based (The R Foundation for Statistical Computing, Vienna, Austria) Bioconductor package DESeq. Reported p‐values were adjusted for multiple testing with the Benjamini–Hochberg procedure, which controls false discovery rate (FDR). A list of differentially expressed genes was selected at an FDR of 0.05.

Formation of Microtissue Constructs: Macrowells with a diameter and a depth of 2 mm (ectopic implantation) and a length of 5 mm, a width of 3 mm, and a depth of 2 mm (orthotopic implantation) were created with 3% w/v agarose (Invitrogen, Belgium) and sterilized under UV. Microtissues were recuperated from their microwells by gently pipetting up and down several times. The microtissue suspension was concentrated with centrifugation to a volume corresponding to the macrowells. Next, the microtissues were added into the macrowells (≈3000 for ectopic and ≈6000 for large bone defect implantation) and incubated for 1 h to sediment, where after CM was added and constructs were incubated for additional 23 h to fuse into constructs.

In Vivo Implantation of Microtissue Constructs: Subcutaneous implantation was used to validate the construct’s autonomy to form cartilage and bone tissue. Bone and cartilage do not naturally form in this location and chondro‐ and osteo‐inductive signals must therefore arise from the construct itself. After 24 h fusion, the microtissue constructs were implanted subcutaneously in immune compromised mice (Rj:NMRInu/nu). Explants were taken out 4 and 8 weeks after in vivo implantation and fixed in 4% PFA for subsequent nano‐CT and histological analysis. A large bone defect mouse model, described elsewhere,58 was used to assess the impact of the environment and mechanical loading on the bone forming potential of the day 21 microtissue constructs. Briefly, a custom‐made Ilizarov fixator was fixed to the tibia using 27 G steel needles. The tibia was exposed, and a 4 mm mid‐diaphyseal segment was removed with a diamond saw. Custom‐made constructs (≈6000 callus organoids per construct, n = 4) were placed into the defect, and the skin was sutured to close the wound. An empty defect was used as control (n = 4). Defects were monitored with in vivo micro‐CT (SkyScan 1076, Bruker micro‐CT, BE) 1, 2, 4, 6, and 8 weeks after surgery (voxel size of 9 µm). Animals were sacrificed after 8 weeks; the tibia was fixed in 4% PFA and analyzed with ex vivo nano‐CT and processed for histology. All procedures on animal experiments were approved by the local ethical committee for Animal Research, KU Leuven. The animals were housed according to the regulations of the Animalium Leuven (KU Leuven).

Quantification of Mineralized Tissue from In Vivo Micro‐CT and Ex Vivo Nano‐CT: Ex vivo nano‐CT (Pheonix Nanotom M, GE Measurement, and Control Solutions) was used for 3D quantification of mineralized tissue in each explant. Explants were scanned with a diamond target, mode 0, 500 ms exposure time, 1 frame average, 0 image skip, 2400 images, and a 0.2 mm aluminum filter. Subcutaneous explants were scanned at a voltage of 60 kV and a current of 140 µA resulting in a voxel size of 2 µm. Large bone defect explants and native tibia were scanned at a voltage of 60 kV and a current of 390 µA resulting in a voxel size of 5.6 µm. CTAn (Bruker micro‐CT, BE) was used for all image processing and quantification of mineralized tissue based on automatic Otsu segmentation, 3D space closing, and despeckle algorithm. Percentage of mineralized tissue was calculated with respect to the total explant volume. CTvox (Bruker micro‐CT, BE) was used to create 3D visualization.

Histochemistry and Immuno‐Histochemistry: Retrieved subcutaneous explants were fixed in 4% PFA overnight and decalcified in ethylenediaminetetraacetic acid (EDTA)/PBS (pH 7.5) for 10 days at 4 °C followed by paraffin embedding. Tibias were fixed in 2% PFA overnight and decalcified in EDTA/PBS (pH 7.5) for 3 weeks then dehydrated and embedded in paraffin. Ectopic samples were sectioned at 5 µm and tibias at 6 µm. Histology was performed according to previously reported methods of H&E, Alcian Blue, Masson’s Trichrome, and Safranin O staining.10 Immuno‐histochemistry was performed on PFA‐fixed microtissues (Osterix), paraffin‐embedded PFA‐fixed microtissues (Indian Hedgehog), and paraffin‐embedded EDTA‐decalcified explants (human osteocalcin, CD31). Epitope retrieval was performed with Uni‐Trieve (INNOVEX Bioscience, USA) for 30 min at 70 °C. Quenching of endogenous peroxidase activity was performed with 3% H2O2 for 10 min. Next, sections were blocked in serum for 30 min and incubated overnight at 4 °C with the primary antibodies human osterix (R&D Systems, MAB7547: dilution 1:300), human osteocalcin29 (a gift from E. Van Herck, Legendo, KU Leuven, BE; dilution 1:5000), rabbit polyclonal anti‐Ihh antibody–N‐terminal (Abcam, ab80191; dilution 1:50), rabbit anticollagen type II (Merck Millipore, AB761; dilution 1:50), or purified rat antimouse CD31 (BD Biosciences, USA, 550 274; dilution 1:50). Next, slides were blocked and incubated with the secondary antibodies Alexa 488 antimouse (Thermo Fisher Scientific, A11001; dilution 1:500), horseradish peroxidase (HRP) conjugated goat anti‐guineaPig or—rabbit (Jackson ImmunoResearch, UK; dilution 1:500) for 30 min and peroxidase activity was determined using 3,3′‐diaminobenzidine (DAB) (K3468, Dako, USA). For detection of CD31, the secondary antibody Biotin conjugated Goat‐anti‐Rat Ig (BD Biosceinces, USA, 559 286) and a tyramide signal amplification (TSA) Biotin detection system (PerkinElmer, USA) were used. Stained histology sections were visualized with a Leica M165 FC microscope (Microsystems, Belgium) or an inverted laser scanning fluorescence confocal microscope ZEISS LSM 510 META (Cell imaging core facility of KU Leuven). Histomorphometry was performed in ImageJ software using ROI manager87 on three to four nonconsecutive sections per sample, and mean values from these sections were used as data point for one sample.

Transcriptomics Analysis: An unsupervised analysis of the RNA‐seq data and subsequently gene visualization was performed. For this, a [gene × experimental condition] matrix was obtained from the bulk RNA‐seq data. First genes were ranked based on variance, and then the gene expression profile of 400 most variable genes across four time points was selected for downstream analysis. Gene expression values were mean and log2‐normalized. Then, k‐means clustering was used to computationally cluster these genes based on their expression profiles.

In order to select the number of clusters, the elbow method was applied and determined that k = 5 was the optimal parameter for achieving the most robust partition. Clustering results were visualized in order to provide insight into the patterns of correlation between samples and expression levels. A profile plot, also known as parallel coordinate plot was plotted using ggplot2—a package for data visualization within the R‐statistical computing environment (http://www.r‐project.org/) in order to visualize the expression levels of a total of 400 gene transcripts across all four time points including k‐means cluster information. Subsequently, Gene Ontology enrichment of Biological Processes (2017b) for each cluster was performed with Enrichr.3839

Statistical Analysis: All experiments were performed with at least three replicates per condition. Data were represented as mean ± standard error of the mean (SEM) or box‐plot with 10–90 percentiles, if otherwise not stated. Data were compared with one‐way or two‐way ANOVA and Tukey’s Multiple Comparison test or Student’s t test. Results were considered statistically different for p‐values lower than 0.05 (*p < 0.05, **p < 0.01, ***p < 0.001). Statistical analysis was performed with GraphPad Prism 8 (GraphPad Software, Inc., USA) unless otherwise stated.

Acknowledgements

F.P.L. and I.P. contributed equally to this work. Kathleen Bosmans is thanked for performing in vivo experiments, and Melanie Van den Broeck and Inge Van Hoven for their experimental assistance. Research was funded by the Research Foundation Flanders (FWO) G.N.H: 1S05116N, I.P.: 12O7916N, and CARTiPLEX: G0A4718N, the European Research Council under the European Union’s Seventh Framework Program (FP/2007‐2013)/ERC (249191) and Horizon 2020 Framework Program (H2020/2014‐2021)/ERC (772418) and the special research fund of the KU Leuven (GOA/13/016 and C24/17/077). This work was supported by the partners of Regenerative Medicine Crossing Borders (http://www.regmedxb.com). Powered by Health—Holland, and Top Sector Life Sciences & Health. Confocal images were recorded on a Zeiss LSM 510 and 880—Airyscan, Cell and Tissue Imaging Cluster (CIC), Supported by Hercules AKUL/15/37_GOH1816N and FWO G.0929.15 to Pieter Vanden Berghe, KU Leuven. RNA sequencing and a part of the RNA‐seq expression analysis were performed by the Genomics Core Leuven, KU Leuven. The micro (or nano)‐CT images were generated on the X‐ray computed tomography facility of the Department of Development and Regeneration of the KU Leuven, financed by the Hercules Foundation (project AKUL/13/47). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work was part of Prometheus, the KU Leuven R&D division for skeletal tissue engineering (http://www.kuleuven.be/prometheus).

Conflict of Interest

The authors declare no conflict of interest.

Supporting Information

References

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Volume7, Issue2

January 22, 2020

1902295

  • Figures
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  • MetricsDetails© 2019 The Authors. Published by WILEY‐VCH Verlag GmbH & Co. KGaA, WeinheimThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
    •  
    KeywordsFunding Information
    • Research Foundation Flanders. Grant Numbers: G.N.H: 1S05116N, I.P.: 12O7916N, CARTiPLEX: G0A4718N
    • European Research Council. Grant Numbers: FP/2007‐2013, 249191, H2020/2014‐2021, 772418
    • KU Leuven. Grant Numbers: GOA/13/016, C24/17/077
    • Herculesstichting. Grant Numbers: AKUL/15/37_GOH1816N, FWO G.0929.15
    Publication History
    • Issue Online:22 January 2020
    • Version of Record online:10 December 2019
    • Manuscript revised:18 October 2019
    • Manuscript received:27 August 2019

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  5. NEUROMUSCULAR ORGANOID: IT’S CONTRACTING!
Neuromuscular_Organoid
© Jorge Miguel Faustino Martins, MDC

PRESS RELEASE NO. 2 / JANUARY 16, 2020 / BERLIN

Neuromuscular organoid: It’s contracting!

MDC researchers established a new model to study neuromuscular development and disorders. For the first time two distinct human tissues co-developed in the lab from one progenitor cell type and self-organized into a complex, functional neuromuscular organoid.

The Gouti lab from the Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) has developed functional neuromuscular organoids (NMOs) that self-organize into spinal cord neurons and muscle tissue. Together the two cell types form a complex neuronal network that directs muscle tissue to contract. The neuromuscular organoids, described in the journal Cell Stem Cell, represent a breakthrough for the study of human neuromuscular system development and disease.  

The control of body movement is achieved by a complex neuromuscular network that includes the generation of rhythmic patterns of neuronal activity essential for locomotion. Defects in this network are the cause of incurable neuromuscular diseases that result in paralysis and death. Studying the diseases that affect this system has been difficult due to the limited availability of reliable human cell culture models.© Jorge Miguel Faustino Martins, MDC

Live imaging of muscle contraction and neuronal activity in a neuromuscular organoid.


There are several unique features that make these organoids a particularly attractive model for neuromuscular development and disease. A key feature is the development of functional neuromuscular junctions, which transmit signals that are essential for movement. Neuromuscular organoids are the first example where spinal cord neurons, skeletal muscles and Schwann cells co-develop from the same progenitor cells and form functional neuromuscular junctions. Furthermore, the organoids developed a complex circuitry that mimics central pattern generator (CPG) circuits, which produce oscillating, rhythmic signals critical for breathing and walking.
 
“Our initial goal was to develop functional neuromuscular junctions, but the findings exceeded our expectations because the additional development of the CPG-like networks was an exciting but unexpected finding,” said Dr. Mina Gouti, who heads MDC’s Stem Cell Modeling of Development and Disease Lab. “This has not been shown in a human in vitro model before, and offers entirely novel possibilities, including the study of CPG involvement in neurodegenerative diseases.”

The two-in-one challenge

By combining the potential of stem cells with the powerful organoid technology, neuromuscular organoids present an exciting model to study neuromuscular diseases

 Jorge Miguel Faustino Martins

Jorge Miguel Faustino MartinsFirst author of the paper

Organoids are miniature, simplified organ-like structures grown in the lab. While techniques for developing organoids for different tissues have advanced in the past decade, it has remained a significant challenge to simultaneously grow two different tissue types into a single functional organoid. For example, to study human neuromuscular junctions, spinal cord neurons and muscles have initially been grown separately and later combined and allowed to interact. In that approach, junctions indeed formed but showed limited functionality and part of the reason was the absence of the all-important Schwann cells.
 
“It is very limiting if you only have an enriched system for neurons or muscles and then combine them,” said Jorge-Miguel Faustino Martins, first author and a bioengineering PhD student in Gouti’s lab. “It doesn’t really mimic what happens in the embryo where you have both systems developing simultaneously. By combining the potential of stem cells with the powerful organoid technology, neuromuscular organoids present an exciting model to study neuromuscular diseases as well as a robust model for developmental studies where the formation of complex neuromuscular circuitry can be analyzed in real time in 3D microenvironment closer to the one present in the embryo.”

The importance of the right progenitor cell type

To overcome this challenge, Gouti and her colleagues followed up on their earlier findings that allowed the conversion of human pluripotent stem cells into axial stem cells, which are known to give rise to both the spinal cord and skeletal muscle during normal embryonic development. They turned the stem cells into the desired type of axial stem cells, called neuromesodermal progenitor cells.These organoids started contracting after 40 days in culture.

Profile Image · Gouti

Mina GoutiCorresponding author of the paper

Neuromuscular_Organoid
A human neuromuscular organoid cultured in the lab for 100 days with a neural (green) and a muscular part (purple).© Jorge Miguel Faustino Martins, MDC

When the axial stem cells were placed in a 3D cell culture, they differentiated and self-organized into complex structures comprising spinal cord neurons and skeletal muscle tissue. This resulted in the development of functional neuromuscular junctions containing terminal Schwann cells, and the formation of complex spinal neural networks similar to central pattern generators (CPG).
 
“It is striking that the two different tissues can self-organize in 3D and develop advanced functional networks”, Gouti said. “These organoids started contracting after 40 days in culture. This activity was driven by the release of neurotransmitter acetylcholine from the resident motor neurons in the organoid and was not due to spontaneous muscle activity seen in other systems. We were able to show that, because pharmacological blocking of the acetylcholine receptors was sufficient to abolish muscle contraction.”

The organoids grow to 6 millimeters in diameter on average and can be kept alive in the lab without deteriorating for several months.  Currently, the oldest neuromuscular organoids have been kept in culture for 1 ½ years. Importantly, analysis showed neuromuscular organoids can be formed with similar efficiency from different human pluripotent stem cell lines. Thus, this will be a widely applicable approach, particularly suitable for the study of neuromuscular diseases using patient derived induced pluripotent stem cells (iPSCs).

A model for neuromuscular diseases

To assess the potential of NMOs to study neuromuscular diseases, the team modelled an autoimmune disease affecting neuromuscular junctions called myasthenia gravis. Serum from two patients with the disease was applied to several organoids for 72 hours and this resulted in fewer muscle contractions mirroring the muscle weakness experienced by patients.These findings recapitulate key aspects of disease pathology suggesting that the neuromuscular organoids can reliably model neuromuscular disorders.

Simone Spuler

Simone SpulerCo-author of the paper

“These findings recapitulate key aspects of disease pathology suggesting that the neuromuscular organoids can reliably model neuromuscular disorders,” said Dr. Simone Spuler, a paper coauthor and researcher at the Charité Hospital Muscle Research Unit.
The diverse genetic origins and onset of debilitating neuromuscular diseases such as spinal muscular atrophy and amyotrophic lateral sclerosis have hampered the development of patient specific therapeutics. “The derivation of NMOs using patient-derived iPSCs will allow us to investigate their precise origin and progression. These organoids are better suited to study the contributions of specific cell types, such as terminal Schwann cells, at different stages of neuromuscular junction development and maturation that may contribute to neuromuscular diseases,” Gouti clarified. Future studies will apply patient-derived neuromuscular organoids to assess the effectiveness of different drugs and for personalized medicine approaches.

Text: Laura Petersen

Further information

Gouti LabStem Cell Modeling of Development and Disease

Downloads

Video: Live imaging of muscle contraction and neuronal activity in the neuromuscular organoid. Jorge Miguel Faustino Martins, MDC
Picture: Neuromuscualar organoid cultured in the lab for 100 days with a neural (green) and a muscular (purple) part. Jorge Miguel Faustino Martins, MDC

Literature

Faustino Martins JM et al.: “Self-organizing 3D human trunk neuromuscular organoids,” Cell Stem Cell, DOI: 10.1016/j.stem.2019.12.007

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Mina Gouti
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Mina.Gouti@mdc-berlin.de
 
Christina Anders
Editor, Communications Department
Max Delbrück Center for Molecular Medicine (MDC)
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PRESS RELEASE NO. 4 A close-up look at mutated DNA in cancer cellsPCAWG, the largest cancer research consortium in the world, has set itself the task of improving our understanding of genetic mutations in tumors. A new study by the international research group, to which the MDC substantially contributed, is now being published in the journal Nature.PRESS RELEASE NO. 3 BIH, MDC und Charité launch a new research focusSingle cell technologies for Personalized Medicine – The Berlin Institute of Health (BIH ), the Berlin Institute for Medical Systems Biology (BIMSB) of the Max Delbrück Center for Molecular Medicine (MDC) and Charité – Universitätsmedizin Berlin are launching a joint research initiative.PRESS RELEASE NO. 1 PlasmidFactory partnership to continueHarnessing jumping genes to advance gene-therapy cancer treatment: In an effort to optimize the Sleeping Beauty system, the MDCell – Helmholtz Innovation Lab is extending its collaboration with PlasmidFactory.PRESS RELEASE NO. 62 Strong change of course for muscle researchScientists have discovered a new subtype of muscle stem cells. These cells have the ability to build and regenerate new muscles, making them interesting targets for the development of gene therapies.

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Animal research in the European Union (EU) is regulated under Directive 2010/63/EU on the protection of animals used for scientific purposes.

The final aim of the Directive is to replace all animal research with non-animal methods of research, such as organoids or through computer simulations.

EU regulations on animal research

SpeciesProceduresRehomingHusbandry & CareAuthorisation

The Directive harmonises animal research legislation throughout the EU, to ensure high standards of animal welfare and scientific research. It was implemented into national laws in each EU Member State in 2013.  Member States who had stricter measures to protect research animals before the Directive was introduced can maintain them as long as they do not hinder EU-wide scientific cooperation and trade.

Animals can only be used in research in the EU when there is a convincing scientific justification, when the expected benefits of the research outweigh the potential risks in terms of animal suffering and when the scientific objectives cannot be achieved using non-animal alternative methods. Only projects that fulfil these requirements are authorised.

The Directive sets out legal requirements to implement the 3Rs principles of replacement, reduction and refinement: replace animals with non-animal methods where possible; reduce the number of animals used to a minimum while still obtaining scientifically valid results; and refine practices to reduce any possible pain, suffering, distress or lasting harm to the animals.

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Directive 2010/63/EU harmonises animal research legislation throughout the EU to ensure high standards of animal welfare and scientific research. Image: Understanding Animal Research (UAR)

In the EU, animals are used for a limited number of research purposes including basic research, applied research into human and animal diseases and cures, the protection of species and the environment, and education and training. The EU-wide ban on animal testing for cosmetic purposes has been complete since 2013.

Species

The selection of species depends on the type, aim and method of the research. Scientists must use the species least able to experience pain and suffering, with which they can obtain relevant results. The origin of animals also matters:  species including mice, rats, zebrafish, frogs, rabbits, cats, dogs and non-human primates need to be specifically bred for research purposes.

Authorities can grant an exemption for the use of stray animals and animals taken from the wild if the research objective is only achievable in these animals. Some animals can only be used if the research cannot be done in any other species or in exceptional circumstances: non-human primates may only be used for basic or specific medical research, or research aimed at preservation of the species.

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Non-human primates can only be used for a limited number of research purposes and if the research cannot be done in any other species. Image: UAR

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The use of an animal in research, either invasive or non-invasive, is called a ‘procedure’ when it leads to a level of pain, suffering, distress or lasting harm that is equivalent to or higher than an injection with a needle. Procedures can only be carried out in an authorised establishment and as part of an authorised research project. 

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A ‘procedure’ is any use of an animal in research that leads to a level of suffering that is equivalent to or higher than an injection with a needle. Image: UAR

The severity of procedures is divided up into four categories: ‘non-recovery’, ‘mild’, ‘moderate’, or ‘severe’. Suffering should always be minimised, which is why animals are given local or general anaesthesia and analgesia (numbing all feeling, and numbing pain only, respectively) where appropriate. For example, the animal might not be anaesthetised if the injection causes more harm than the procedure itself, or if the painkiller interferes with the goal of the research. 

Such exceptions are never allowed in the case of severe procedures. In non-recovery procedures, the animal is put under full-anaesthesia and stays unconscious throughout the entire procedure, so it does not experience any suffering.

A procedure ends when animals are no longer studied. Animals are then put back into their housing where they continue to be cared for. If their welfare is compromised because the level of suffering is expected to stay moderate or severe, animals need to be humanely killed. Killing is carried out by a qualified person using a humane method appropriate for the species. If the health and wellbeing of an animal has fully restored after use in mild or moderate procedures, it can be reused.

Rehoming

When animals are no longer required for study, they can be rehomed if they are healthy and it would not endanger public health or the environment. Before animals are rehomed, they are first socialised to get used to their new lives. The same is true for any wild animals that are returned to their habitat.

Animal husbandry and care 

Animals used in research are kept in purpose-built animal-facilities (‘establishments’) of breeders, suppliers, and ‘users’ such as universities, pharmaceutical companies and research organisations. All animals in establishments are provided with species-specific housing, environmental enrichment, food, water and care to suit their needs, health requirements and overall wellbeing. These conditions are checked daily, and any restrictions to animals’ needs must be kept to a minimum.

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Animals are kept in species-specific housing with environmental enrichment, food, water and care to suit their needs. Image: UAR

Staff must be adequately trained before they are allowed to care for the animals, carry out or design procedures, or kill animals. Staff works under supervision until they have demonstrated their practical competence, and receive continuous training. Installations and equipment must be suited to the species housed and to the particular procedures carried out in the establishment. The standard of equipment must ensure that procedures are performed efficiently, causing the least amount of harm and obtaining reliable results through the minimum number of animals.

Every breeder, supplier and user has a designated veterinarian or animal welfare expert and an animal-welfare body (AWB). The veterinarian or animal welfare expert advises on animal wellbeing and treatment, while the AWB is a senior committee that advises staff on welfare matters related to the acquisition, care, housing and use of the animals. The AWB also keeps track of how research projects affect the animals, and gives advice on the application and developments in the replacement, reduction and refinement of the use of animals in research.

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Regular inspections are carried out of breeders, suppliers, users and their establishments. Image: Cmdragon

Authorities appointed in each EU Member State carry out regular inspections of breeders, suppliers, users and their establishments. Some inspections are carried out without warning, and inspections take place more or less frequently depending on the number and types of animals and projects, but covering at least one third of the users each year. These ‘competent authorities’ also grant authorisation for animal research projects to take place within their Member State.

Authorisation

Authorisation is required for all establishments. Separate authorisation is required for breeders, suppliers and users of research animals, which lasts for a limited time. Authorisation is only granted or renewed when establishments fulfil the requirements of the Directive. Establishments have to adhere to a wide range of general and species-specific requirements set out in a dedicated annex of the Directive.

Every research project that makes use of animals must be authorised before it can start. Applications for project authorisation have to contain a project proposal with the scheduled procedures, a non-technical (lay) summary, and include a scientific explanation of why animal research is needed in the project. They must show that the 3Rs have been considered and outline the number of animals that will be used and the level of suffering they are expected to experience. A project proposal is evaluated through a harm-benefit analysis by the ‘competent authority’ in each EU Member State, which then decides whether to grant the license.

Every country in the EU has a national committee for the protection of animals used for scientific purposes. This committee advises the competent authority and animal-welfare bodies in the country on matters relating to research animals, including their acquisition, breeding, accommodation, care and use. All national committees share information on EU level about best practices and the functioning of animal-welfare bodies.

5277695536_4e0a812daf_o-300x200.jpg

The European Commission and EU Member States support research into non-animal methods and 3Rs initiatives.
Image: Novartis AG

The European Commission and EU Member States support research into developing and confirming the effectivity of new non-animal methods and 3Rs initiatives. These methods have to produce at least the same amount of information as the animal method, while using fewer or no animals and/or causing the animals less harm. The European Commission’s Joint Research Centre focuses on developing and validating non-animal methods, as well as helping make sure new non-animal methods are internationally recognised and accepted for regulatory purposes.

The Directive has to be reviewed by 10 November 2017. The Commission’s review of the Directive is currently underway; the main focus is to find out if there are any scientific developments in non-animal alternatives, especially to the use of non-human primates, that need to be taken into account.

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This content applies to human and veterinary medicines

The European Medicines Agency (EMA) supports the implementation of the so-called 3Rs principles – replace, reduce and refine – for the ethical use of animals in medicine testing across the European Union (EU). These principles encourage alternatives to the use of animals in the testing of medicines while safeguarding scientific quality and improving animal welfare where the use of animals cannot be avoided.

Directive 2010/63/EU requires marketing authorisation holders to integrate the 3Rs and welfare standards for the treatment of animals in all aspects of the development, manufacture and testing of medicines.

The Directive aims to protect animals in scientific research, with the final aim of replacing all animal research with non-animal methods.

3Rs principles

The 3Rs stand for:

  • replacing the use of animals with non-animal methods where possible;
  • reducing the number of animals used to a minimum while still obtaining scientifically valid results;
  • refining practices to minimise the stress and improve the welfare of study animals used for regulatory purposes.
3Rs principle

EMA role

EMA supports the implementation of Directive 2010/63/EU and the 3Rs principles in the EU, by:

EMA has a dedicated Joint CVMP/CHMP 3Rs Working Group (J3RsWG), which provides advice to its scientific committees on all matters concerning the use of animals in regulatory testing of medicines.

For more information, see:

EMA actions on 3Rs in 2016-17

Biennial report 2016/2017 - Joint CVMP/CHMP Working group on the Application of the 3Rs in Regulatory Testing of Medical ProductsEMA has published a PDF iconreport summarising actions carried out by its committees in 2016 and 2017 to support the implementation of the 3Rs principles.

EMA intends to publish a report on this topic every two years.

Scientific guidelines

EMA develops scientific guidelines to help medicine developers comply with Directive 2010/63/EU in integrating the 3Rs and welfare standards for the treatment of animals in the testing of medicines:

EMA’s Joint CVMP/CHMP 3Rs Working Group also conducted a review of all EMA guidelines to ensure that they do not make reference to animal tests that are no longer considered appropriate. For more information and the guidelines that EMA has or will update as a result, see:

Veterinary medicine testing outside the EU

Wherever the manufacture or batch testing of veterinary medicines to be marketed in the European Economic Area (EEA) takes place, they must conform to EU ethical and animal welfare standards.

For more information, see:

Recommendations on 3Rs in European Pharmacopoeia

In the table below, EMA provides recommendations on 3Rs methods in the European Pharmacopoeia to help marketing authorisation holders comply with new or revised measures.

DocumentApplies to
PDF iconRecommendation highlighting the need to ensure compliance with 3R methods described in the European PharmacopoeiaAll medicinal products
PDF iconRecommendation highlighting recent measures in the human field to promote 3Rs measures described in the European PharmacopoeiaHuman vaccines
PDF iconRecommendation highlighting recent updates for the 3Rs methods described in the European Pharmacopoeia applicable to human vaccines against hepatitis AHuman vaccines against hepatitis A
PDF iconRecommendation highlighting recent measures in the veterinary field to promote 3Rs measures described in the European PharmacopoeiaVeterinary vaccines
PDF iconRecommendation for veterinary vaccines, highlighting the need to update marketing authorisations to remove the target animal batch safety test (TABST) following removal of the requirement from the European Pharmacopoeia monographsVeterinary vaccines

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The Lancet Public Health

 

COMMENT| VOLUME 4, ISSUE 3, PE123-E124, MARCH 01, 2019

Age-related disease burden as a measure of population ageing

Open AccessPublished:March, 2019DOI:https://doi.org/10.1016/S2468-2667(19)30026-XPlumX MetricsPrevious ArticleAdministrative data and long-term trends in child maltreat …Next ArticlePerinatal self-harm: an overlooked public health issue

Register to receive Update alerts from this journalImprovements in health care and social circumstances have resulted in people living longer. Still, not all people thrive in good health as they age: the oldest old might be long lived but many suffer from multiple, interacting health problems that can profoundly affect the management of their numerous and often complex health isssues.1 In population health, common population ageing metrics, such as longevity, do not accurately reflect the state of health in which people who are living longer find themselves.2In The Lancet Public Health, Angela Chang and colleagues3 used the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 to better understand health and longevity. To compare changes in population ageing across 195 countries between 1990 and 2017, they classified 92 diseases as age related, meaning that the incidence rate of each increased quadratically with age. Next, they summed the disability-adjusted life-years (DALYs) from each disease among adults to calculate the age-related disease burden. Remarkably, age-related disease burden, which accounted for 51·3% (95% uncertainty interval [UI] 48·5–53·9) of the total disease burden globally, decreased in all countries between 1990 and 2017. Burden varied between countries, being lowest in Switzerland (104·9 DALYs [95% UI 95·7–115·5] per 1000 adults aged 25 years and older), Singapore (108·3 DALYs [98·6–119·9]), South Korea (110·1 DALYs [100·7–120·4]), Japan, and Italy (110·6 DALYs [101·3–121·7]) in 2017. At the other end of the scale were countries such as Papua New Guinea (506·6 DALYs [452·3–576·1]), the Marshall Islands (396·9 DALYs [358·3–442·7]), and Afghanistan (380·2 DALYs [340·4–423·3]), which notably had a low Socio-demographic Index (SDI; a measure of country income per capita, average years of education, and total fertility rate under 25 years). This combination of low life expectancy and high age-related disease burden challenges the pessimism inherent in viewing poor health as simply the cost of longevity. As Chang and colleagues show, the manifold benefits of improving population health and ageing extend to lower rates of health burden from whichever cause, age related or otherwise.Even so, ill health and longevity are linked. Death in relation to age-related disease was estimated from the cumulative death rates from the 92 diseases. The authors rescaled their presentation of mortality in relation to age-related disease burden by considering the fraction of country-level deaths in each age group against the country-specific cumulative death rate of the age group 80–84 years (the highest observed life expectancy). They showed that the advantage to being one of the countries with the lowest cumulative age-related death rate for each age group in 2017—the frontier countries Switzerland, South Korea, Singapore, and Kuwait—was most evident at younger ages; across all country-specific SDI levels, death rates converged around the age of 75 years. This convergence in death rates, however, should not blur the clear benefit of a lower age-related disease burden with old age.On average, poor health in old age increases the risk of death. What is tricky about health in old age is that it involves more than just disease. Health problems that increase with age go beyond what disease alone can capture, such as impairments in cognition, mood, and physical performance. These remain relevant, even when not disabling. This is why many believe that we have reached “the end of the disease era”.2 If so, we can ask whether DALYs can optimally capture population ageing. By contrast, a broader approach to quantifying how health changes with age motivated the frailty index.4 The frailty index score represents the fraction of health problems of old age—which encompasses diseases, disabilities, clinically observable signs and symptoms, and biomarkers that they have—relative to the number that were measured. This approach recognises that health problems other than diseases are important and that such problems interact on various levels and do not accumulate independently of each other. Although heterogeneous across individuals, over any given time interval, health deficits accumulate characteristically. The accumulation is, on average, gradual and occurs chiefly as a function of the number of baseline deficits. Its characteristic patterns make the frailty index useful in understanding the complexities of ageing,5 including how age-related deficit accumulation modifies both disease risk and expression for common maladies of old age.6People living with the poorest levels of health commonly use high levels of care and resources.7 Despite this heavy use of health care, at least in England and Wales, increases in survival for older people between 1991 and 2011 were not seen for people living with severe frailty, which remained notably lethal.8 By contrast, a Swedish study comparing survivorship of 70-year-olds between 1970 and 2000 found that the lethality of frailty fell in general but most among those with severe frailty.9 As such, it would be useful to expand on Chang and colleagues’ work3 to understand country-level estimates of different degrees of population ageing.It will be crucial to understand how health care and policy can adapt to better serve those with the worst health. Will a focus on prevention offset interventions that can prolong life in old age without improving health? Moving contemporary medical care from the traditional paradigm of seeing diseases individually to a model that appreciates the complexities and heterogeneity of ageing is proving to be incomplete and slow.2 Whether the public health community can lead this reconceptualisation and offer a quantitative approach to address the complexities of health in ageing will be enlightening.I would like to thank Kenneth Rockwood (Division of Geriatric Medicine, Dalhousie University) for providing his expertise in writing this Comment. I declare no competing interests.

References

  1. 1.
    • Zeng Y 
    • Feng Q 
    • Hesketh T 
    • Christensen K 
    • Vaupel JW
    Survival, disabilities in activities of daily living, and physical and cognitive functioning among the oldest-old in China: a cohort study.Lancet. 2017; 389: 1619-1629View in Article 
  2. 2.
    • Cesari M 
    • Marzetti E 
    • Thiem U 
    • et al.
    The geriatric management of frailty as paradigm of “The end of the disease era”.Eur J Intern Med. 2016; 31: 11-14View in Article 
  3. 3.
    • Chang AY 
    • Skirbekk VF 
    • Tyrovoras S 
    • Kassebaum NJ 
    • Dielman JL
    Measuring population ageing: an analysis of the Global Burden of Disease Study 2017.Lancet Public Health. 2019; 4: e159-e167View in Article 
  4. 4.
    • Mitnitski AB 
    • Mogilner AJ 
    • Rockwood K
    Accumulation of deficits as a proxy measure of aging.ScientificWorldJournal. 2001; 1: 323-336View in Article 
  5. 5.
    • Rutenberg AD 
    • Mitnitski AB 
    • Farrell SG 
    • Rockwood K
    Unifying aging and frailty through complex dynamical networks.Exp Gerontol. 2018; 107: 126-129View in Article 
  6. 6.
    • Wallace LMK 
    • Theou O 
    • Godin J 
    • Andrew MK 
    • Bennett DA 
    • Rockwood K
    Investigation of frailty as a moderator of the relationship between neuropathology and dementia in Alzheimer’s disease: a cross-sectional analysis of data from the Rush Memory and Aging Project.Lancet Neurol. 2019; 18: 177-184View in Article 
  7. 7.
    • Gilbert T 
    • Neuburger J 
    • Kraindler J 
    • et al.
    Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study.Lancet. 2018; 391: 1775-1782View in Article 
  8. 8.
    • Mousa A 
    • Savva GM 
    • Mitnitski A 
    • et al.
    Is frailty a stable predictor of mortality across time? Evidence from the Cognitive Function and Ageing Studies.Age Ageing. 2018; 47: 721-727View in Article 
  9. 9.
    • Backman K 
    • Joas E 
    • Falk H 
    • Mitnitski A 
    • Rockwood K 
    • Skoog I
    Changes in the lethality of frailty over 30 years: evidence from two cohorts of 70-year-olds in Gothenburg Sweden.J Gerontol A Biol Sci Med Sci. 2017; 72: 945-950View in Article 

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Comment
http://www.thelancet.com/public-health Vol 4 March 2019 e123
Age-related disease burden as a measure of population ageing
Improvements in health care and social circumstances
have resulted in people living longer. Still, not all
people thrive in good health as they age: the oldest
old might be long lived but many suffer from multiple,
interacting health problems that can profoundly
affect the management of their numerous and often
complex health isssues.1
In population health, common
population ageing metrics, such as longevity, do not
accurately reflect the state of health in which people
who are living longer find themselves.2
In The Lancet Public Health, Angela Chang and
colleagues3
used the Global Burden of Diseases, Injuries,
and Risk Factors Study 2017 to better understand health
and longevity. To compare changes in population
ageing across 195 countries between 1990 and 2017,
they classified 92 diseases as age related, meaning
that the incidence rate of each increased quadratically
with age. Next, they summed the disability-adjusted
life-years (DALYs) from each disease among adults to
calculate the age-related disease burden. Remarkably,
age-related disease burden, which accounted for 51·3%
(95% uncertainty interval [UI] 48·5–53·9) of the total
disease burden globally, decreased in all countries
between 1990 and 2017. Burden varied between
countries, being lowest in Switzerland (104·9 DALYs
[95% UI 95·7–115·5] per 1000 adults aged 25 years
and older), Singapore (108·3 DALYs [98·6–119·9]),
South Korea (110·1 DALYs [100·7–120·4]), Japan,
and Italy (110·6 DALYs [101·3–121·7]) in 2017. At
the other end of the scale were countries such as
Papua New Guinea (506·6 DALYs [452·3–576·1]), the
Marshall Islands (396·9 DALYs [358·3–442·7]), and
Afghanistan (380·2 DALYs [340·4–423·3]), which notably
had a low Socio-demographic Index (SDI; a measure of
country income per capita, average years of education,
and total fertility rate under 25 years). This combination
of low life expectancy and high age-related disease
burden challenges the pessimism inherent in viewing
poor health as simply the cost of longevity. As Chang
and colleagues show, the manifold benefits of improving
population health and ageing extend to lower rates
of health burden from whichever cause, age related or
otherwise.
Even so, ill health and longevity are linked. Death
in relation to age-related disease was estimated from
the cumulative death rates from the 92 diseases. The
authors rescaled their presentation of mortality in
relation to age-related disease burden by considering
the fraction of country-level deaths in each age group
against the country-specific cumulative death rate of
the age group 80–84 years (the highest observed life
expectancy). They showed that the advantage to being
one of the countries with the lowest cumulative agerelated death rate for each age group in 2017—the
frontier countries Switzerland, South Korea, Singapore,
and Kuwait—was most evident at younger ages; across
all country-specific SDI levels, death rates converged
around the age of 75 years. This convergence in death
rates, however, should not blur the clear benefit of a
lower age-related disease burden with old age.
On average, poor health in old age increases the risk
of death. What is tricky about health in old age is that
it involves more than just disease. Health problems
that increase with age go beyond what disease alone
can capture, such as impairments in cognition, mood,
and physical performance. These remain relevant, even
when not disabling. This is why many believe that we
have reached “the end of the disease era”.2
If so, we can
ask whether DALYs can optimally capture population
ageing. By contrast, a broader approach to quantifying
how health changes with age motivated the frailty
index.4
The frailty index score represents the fraction
of health problems of old age—which encompasses
diseases, disabilities, clinically observable signs and
symptoms, and biomarkers that they have—relative
to the number that were measured. This approach
recognises that health problems other than diseases are
important and that such problems interact on various
levels and do not accumulate independently of each
other. Although heterogeneous across individuals,
over any given time interval, health deficits accumulate
characteristically. The accumulation is, on average,
gradual and occurs chiefly as a function of the number
of baseline deficits. Its characteristic patterns make the
frailty index useful in understanding the complexities of
ageing,5
including how age-related deficit accumulation
modifies both disease risk and expression for common
maladies of old age.6
People living with the poorest levels of health
commonly use high levels of care and resources.7
Despite
See Articles page e159
Comment
e124 http://www.thelancet.com/public-health Vol 4 March 2019
this heavy use of health care, at least in England and
Wales, increases in survival for older people between
1991 and 2011 were not seen for people living with
severe frailty, which remained notably lethal.8
By
contrast, a Swedish study comparing survivorship of
70-year-olds between 1970 and 2000 found that the
lethality of frailty fell in general but most among those
with severe frailty.9
As such, it would be useful to expand
on Chang and colleagues’ work3
to understand countrylevel estimates of different degrees of population ageing.
It will be crucial to understand how health care and
policy can adapt to better serve those with the worst
health. Will a focus on prevention offset interventions
that can prolong life in old age without improving
health? Moving contemporary medical care from the
traditional paradigm of seeing diseases individually
to a model that appreciates the complexities and
heterogeneity of ageing is proving to be incomplete
and slow.2
Whether the public health community can
lead this reconceptualisation and offer a quantitative
approach to address the complexities of health in ageing
will be enlightening.
D Scott Kehler
Division of Geriatric Medicine, Dalhousie University, Halifax,
NS B3H 2E1, Canada
scott.kehler@dal.ca
I would like to thank Kenneth Rockwood (Division of Geriatric Medicine,
Dalhousie University) for providing his expertise in writing this Comment.
I declare no competing interests.
Copyright © 2019 The Author(s). Published by Elsevier Ltd. This is an Open
Access article under the CC BY-NC-ND 4.0 license.
1 Zeng Y, Feng Q, Hesketh T, Christensen K, Vaupel JW. Survival, disabilities in
activities of daily living, and physical and cognitive functioning among the
oldest-old in China: a cohort study. Lancet 2017; 389: 1619–29.
2 Cesari M, Marzetti E, Thiem U, et al. The geriatric management of frailty as
paradigm of “The end of the disease era”. Eur J Intern Med 2016; 31: 11–14.
3 Chang AY, Skirbekk VF, Tyrovoras S, Kassebaum NJ, Dielman JL. Measuring
population ageing: an analysis of the Global Burden of Disease Study 2017.
Lancet Public Health 2019; 4: e159–67.
4 Mitnitski AB, Mogilner AJ, Rockwood K. Accumulation of deficits as a proxy
measure of aging. ScientificWorldJournal 2001; 1: 323–36.
5 Rutenberg AD, Mitnitski AB, Farrell SG, Rockwood K. Unifying aging and
frailty through complex dynamical networks. Exp Gerontol 2018;
107: 126–29.
6 Wallace LMK, Theou O, Godin J, Andrew MK, Bennett DA, Rockwood K.
Investigation of frailty as a moderator of the relationship between
neuropathology and dementia in Alzheimer’s disease: a cross-sectional
analysis of data from the Rush Memory and Aging Project. Lancet Neurol
2019; 18: 177–84.
7 Gilbert T, Neuburger J, Kraindler J, et al. Development and validation of a
Hospital Frailty Risk Score focusing on older people in acute care settings
using electronic hospital records: an observational study. Lancet 2018;
391: 1775–82.
8 Mousa A, Savva GM, Mitnitski A, et al. Is frailty a stable predictor of mortality
across time? Evidence from the Cognitive Function and Ageing Studies.
Age Ageing 2018; 47: 721–27.
9 Backman K, Joas E, Falk H, Mitnitski A, Rockwood K, Skoog I. Changes in the
lethality of frailty over 30 years: evidence from two cohorts of 70-year-olds
in Gothenburg Sweden. J Gerontol A Biol Sci Med Sci 2017; 72: 945–50.

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Aging Dis. 2019 Aug 1;10(4):883-900. doi: 10.14336/AD.2018.1030. eCollection 2019 Aug.

Health and Aging: Unifying Concepts, Scores, Biomarkers and Pathways.

Fuellen G1Jansen L2Cohen AA3Luyten W4Gogol M5Simm A6Saul N7Cirulli F8Berry A8Antal P9,10Köhling R11Wouters B12Möller S1.

Author information

11Rostock University Medical Center, Institute for Biostatistics and Informatics in Medicine and Aging Research (IBIMA), Rostock, Germany.22Institute of Philosophy, University of Rostock, Germany.33Department of Family Medicine, University of Sherbrooke, Sherbrooke, Canada.44KU Leuven, Department of Pharmaceutical and Pharmacological Sciences, Leuven, Belgium.55Institute of Gerontology, University Heidelberg, Germany.66Department of Cardiac Surgery, Medical Faculty, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.77Humboldt-University of Berlin, Institute of Biology, Berlin, Germany.88Center for Behavioral Sciences and Mental Health, Istituto Superiore di Sanità, Italy.99Budapest University of Technology and Economics, Budapest, Hungary.1010Abiomics Europe Ltd., Hungary.1111Rostock University Medical Center, Institute for Physiology, Rostock, Germany.1212KU Leuven, Department of Biology, Leuven, Belgium.

Abstract

Despite increasing research efforts, there is a lack of consensus on defining aging or health. To understand the underlying processes, and to foster the development of targeted interventions towards increasing one’s health, there is an urgent need to find a broadly acceptable and useful definition of health, based on a list of (molecular) features; to operationalize features of health so that it can be measured; to identify predictive biomarkers and (molecular) pathways of health; and to suggest interventions, such as nutrition and exercise, targeted at putative causal pathways and processes. Based on a survey of the literature, we propose to define health as a state of an individual characterized by the core features of physiological, cognitive, physical and reproductive function, and a lack of disease. We further define aging as the aggregate of all processes in an individual that reduce its wellbeing, that is, its health or survival or both. We define biomarkers of health by their attribute of predicting future health better than chronological age. We define healthspan pathways as molecular features of health that relate to each other by belonging to the same molecular pathway. Our conceptual framework may integrate diverse operationalizations of health and guide precision prevention efforts.

KEYWORDS:

aging; biological age; biomarker; health; terminology; wellbeingPMID: 31440392 PMCID: PMC6675520 DOI: 10.14336/AD.2018.1030Free PMC Article

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Aging Dis. 2019 Aug; 10(4): 883–900.Published online 2019 Aug 1. doi: 10.14336/AD.2018.1030PMCID: PMC6675520PMID: 31440392

Health and Aging: Unifying Concepts, Scores, Biomarkers and Pathways

Georg Fuellen,1,*Ludger Jansen,2,*Alan A Cohen,3Walter Luyten,4Manfred Gogol,5Andreas Simm,6Nadine Saul,7Francesca Cirulli,8Alessandra Berry,8Peter Antal,9,10Rüdiger Köhling,11Brecht Wouters,12 and Steffen Möller1Author informationArticle notesCopyright and License informationDisclaimerThis article has been cited by other articles in PMC.Go to:

Abstract

Despite increasing research efforts, there is a lack of consensus on defining aging or health. To understand the underlying processes, and to foster the development of targeted interventions towards increasing one’s health, there is an urgent need to find a broadly acceptable and useful definition of health, based on a list of (molecular) features; to operationalize features of health so that it can be measured; to identify predictive biomarkers and (molecular) pathways of health; and to suggest interventions, such as nutrition and exercise, targeted at putative causal pathways and processes. Based on a survey of the literature, we propose to define health as a state of an individual characterized by the core features of physiological, cognitive, physical and reproductive function, and a lack of disease. We further define aging as the aggregate of all processes in an individual that reduce its wellbeing, that is, its health or survival or both. We define biomarkers of health by their attribute of predicting future health better than chronological age. We define healthspan pathways as molecular features of health that relate to each other by belonging to the same molecular pathway. Our conceptual framework may integrate diverse operationalizations of health and guide precision prevention efforts.Keywords: terminology, health, aging, biological age, wellbeing, biomarkerGo to:

Introduction

For some years, the concepts of health and healthspan have been advocated as the primary goal of medical diagnosis and intervention [14]. Given their importance for national and international allocation of resources in research and care, it is important to define these terms as precisely as possible. In this paper, we suggest a set of operational definitions, including definitions of health and related terms such as wellbeing, biological age, and aging, and we place these into a consistent systematic framework. Our aim in presenting these definitions is to support empirical studies, in particular in health and aging research, and to facilitate the comparability of results. For this reason, we aim for a coherent set of definitions that are practical in the sense that they can be used in actual research contexts. This requires that the definitions can be operationalized, that they are based on a sufficient consensus in the research communities and are sufficiently robust to be applied to different experimental and clinical settings covering molecular as well as higher-level phenotypic phenomena common for a variety of biological species – in particular human and model organisms such as C. elegans and mouse.

Specifically, we dissect health into a hierarchical system of its various aspects, allowing to analyze its features in detail, and to identify the biomarkers, molecular pathways and corresponding supportive interventions for the various aspects of health. While beyond the scope of the present paper, the inter-related aspects of health that we describe can in principle be scored and weighted, and thus provide a way for the overall measurement and comparison of the health of different individuals. Defining health based on disease and dysfunction, we follow a consensus approach by means of a literature survey. For disease, we employ the World Health Organization (WHO) International Statistical Classification of Diseases and Related Health Problems, and for dysfunction, we start with the WHO International classification of functioning, disability and health. The latter will then be utilized as background for the review of pertinent papers from health and healthspan research, to systematize our findings. From this consensus, we then derive appropriate definitions of healthspan, healthspan-enhancing processes and biomarkers of health, as well as wellbeing, aging, and biological age. In order to allow the step from prediction to enhancement, we finally distinguish between correlative features on the one hand, and causal features which are potential targets of interventions in order to increase healthspan on the other hand. Our definitions are designed to apply to most animal species, although the literature we surveyed, and thus the operationalization of health we suggest, is specifically targeted to human and the model organisms C. elegans and mouse. Overall, we arrive at a framework of definitions, covering states, time periods, associated processes and predictors of future states, as given in Table 1. We suggest that this generic framework of simple and threshold-free definitions of common terms places these into context while still preserving, to a maximum degree, their intuitive meaning.

Table 1

Framework of definitions.

 StateTime periodUnderlying biological processesPredictor of future state
Single conceptshealthhealthspanhealthspan-enhancing processeshealth biomarkers
survivallifespanlifespan-enhancing processessurvival biomarkers
Integrative concepts…wellbeing“wellspan”wellspan-enhancing processesbiological age
… and their oppositesillbeing“illspan”aging processes
Baseline referencebaseline organismal statechronological timeaverage biological processeschronological age

Frequently used terminology that we can fit into our framework is marked in boldface. The terms in the last row, and specifically the term “average biological processes” refer to a specifically selected reference population.

In this paper, we will first present a framework for the different kinds of terminological categories (states, time periods, processes, predictors). We then define the key term health and closely related terms such as healthspan. We define the term survival, contrast its meaning with health, and propose to integrate both terms under the integrative concept of wellbeing. Often used indicators of health such as quality of life, activities of daily living, lack of frailty, or self-reported health (in case of human), and indices such as the Healthy Aging Index can then be viewed as projections or surrogates of wellbeing. We further define aging as the set of all processes in an individual that reduce its wellbeing, that is, its health or survival or both. Regarding predictors, we define the term biomarker (for features of health, survival, or wellbeing) as generically as possible, as a predictor for these features that is better than chronological age. Such a biomarker is a feature itself, and as any feature, it may be composed of more elementary features. We discuss various classes of biomarkers (of aging), considering, for example, causality of various kinds. We define healthspan pathways as molecular features of health that relate to each other, specifically by belonging to the same molecular pathway. Precise definitions of other standard concepts such as biological age follow naturally.Go to:

How to Define Health with Respect to a Reference

Therapeutic interventions affecting aging and health may have different goals. Often, the emphasis of preventive as well as curative interventions was on the extension of lifespan. But for most people the mere extension of life is not desirable: if it were possible to live for several hundred years in a vigilant coma, hardly anyone would prefer such a long enduring vegetative state to a normal human life with a much shorter lifespan. For this and other reasons, emphasis has shifted to increasing healthspan, i.e., the time period that an individual spends in a state of health. Lifespan is relatively easy to be operationalized. While, from a theoretical perspective, life is both intensionally and extensionally vague at its borders [5], this does not matter much in the context of medical research. For practical purposes, “being alive”, that is, survival, can be modelled as a binary state: any individual, as a whole, is either alive or it is not. (We consider only the survival of an individual as a whole, not the life status of body parts like organs, tissues, or single cells). The time period of an individual spent alive is its lifespan. Death is the irreversible end of biological life.

In contrast, it is much more difficult to operationalize health and healthspan. For one, the definition of health itself is contested: Is it an intrinsic property of an individual, or is it the extrinsic statistical property of instantiating certain features better than the average of a relevant reference group? Is it a subjective value-judgement, or is it ascribed to individuals in a socio-constructive way [67]? We will argue that an operational definition of health needs to incorporate elements from all these approaches. Second, as one may expect, the features of health turn out to be quite different in humans and model organisms like C. elegans or mouse. Third, it is not clear whether there is an irreversible end of healthspan other than death. Often, the healthspan of an individual is assumed to begin with conception or birth and end at some later time. But individuals can have diseases or dysfunctions during their life and then recover; they may even be born with a disease or a dysfunction and then have their health (re-)gained. We thus do not define healthspan as a single coherent time-interval, but allow it to stretch over unconnected intervals, and simply define healthspan as the time an individual spends in a state of health, where “health” is, in turn, operationalized as described below. This allows us to stay uncommitted to the question whether it is possible in principle to (re-)gain the state of health.

These problems of the WHO definition motivate an approach in which the severity of any deviation from health is weighted on an individual basis, taking into account that goals may change with time and circumstances. This also holds for the time period in which health is desired. Possibly, some individuals (such as athletes) may want to trade better physical function in the short term for worse health in the long term. Thus, there may be trade-offs between different features of health, as well as between the intensity and the extension of health, given that both cannot necessarily be optimized at the same time. We will introduce the concept of wellbeing (different from the WHO term “well-being” featuring in the WHO definition of health) in order to integrate health (healthspan) and survival (lifespan) according to the subjective weighting of individuals. As our operationalizations of health and wellbeing make use of more features than one, these features have to be weighted in order to be integrated into a single score. Such weighting is necessarily subjective, and different weights may reflect different preferences of different people. In case of non-human animals, weighting is (implicitly or explicitly) carried out by the researcher; here, the subjective view of the researcher replaces the preferences of the individual.

Our definition of health will thus contain a subjective element with respect to certain weighting factors, but the very features that are weighted comprise objectively measurable aspects. Thus, we will not advertise a subjective definition of health [6]. Subjective theories of health define health in subjective terms: Individuals are healthy, according to these theories, if they feel healthy or report to be healthy. Feeling healthy is usually considered a necessary aspect of health, and self-reports are often used to operationalize health. However, subjective aspects cannot be the whole story: Individuals can feel healthy although they have diseases or dysfunctions still unknown to them or ignored by them. In addition, coping strategies and compensation as well as a change in goals and values may influence the subjective assessment of one’s health. Moreover, subjective theories are not feasible for other species like worm or mouse: even if worms or mice had a subjective self-conception of being healthy, they would not be able to self-report their health status at an interview.

In contrast to a subjective approach to defining health, we will define health as a state of an individual based on specific objective features, namely the absence of disease and dysfunction. As most of these features can be realized in a gradual manner, the question arises where exactly to put the threshold: We need to introduce thresholds in order to distinguish between healthy and unhealthy individuals. In order not to have to introduce arbitrary thresholds, we will refer to the average realization of the features in a certain reference population. Thus, our approach is threshold-free in the sense that we do not set any thresholds a priori; we only provide a recipe for setting these in a generic way.

Furthermore, in view of the controversial discussion found in the literature, we refrain from starting top-down with a new definition of health, for which we then have to find means to operationalize it. Instead, we opt for a bottom-up strategy and first look at how health is de facto operationalized in the research literature, and then systematize the findings. A near-consensus in the health literature is that health is a state of an individual that lacks dysfunction and is free from diseases (while it is a matter of debate what exactly counts as a dysfunction or a disease). However, the following issues arise: Which functions are these, dependent on species? While the health of humans will in part consist of their capability to exercise higher cognitive functions, these will be irrelevant for worms. To which extend does a function need to be exercised, or to which degree does a disease have to lack its manifestation, in order to count an individual as healthy? Finally, which weight should be assigned to each of these criteria? As noted, different human individuals will decide on this in different ways.

In order to address these issues, we start with two well-established codified classification systems provided by the WHO. Using the ICD, the International Statistical Classification of Diseases and Related Health Problems, [9], disease is operationalized by criteria to establish that an individual is affected by disease. Using the ICF, the International classification of functioning, disability and health, [10], dysfunction is operationalized by criteria to establish that an individual is affected by dysfunction. While taking the ICD as a given, we filter the definitions of the ICF by their follow-up in the literature on health and healthspan, arriving at a pragmatic community consensus. Starting from the ICF classification, we reviewed pertinent papers from health and healthspan research with respect to how they operationalize health, systematizing our findings according to the ICF classification. In some cases, our review gave us reason to modify the default presented by the ICF classification.

Once the different features have been selected and measured, we can compare the values measured for these with the reference values that are the average in a reference population. For example, we can compare the grip strength of a 60-year old individual with the average grip strength of 60-year olds in the reference population. Depending on whether the value measured is below or above average, optionally considering the variability of values in the reference population, we can assign a score to this feature, and we can consider this individual to be in bad or good health with respect to this feature. E.g., a very simple (and often inadequate) scoring would assign -1 to values below average and 0 to values above average.

Using some subjective weighting, we can then integrate the scores for all features into one overall health score. This would be done in a standardized way, which reflects the different aspects of health. Such an approach mirrors the use of qualifiers in the ICF. The simplest health score would employ equal weighting; if it were based on binary scores, it would amount to just counting the number of diseases or dysfunctions of an individual, based on a list of measured ones. Indeed, dysfunctions are often listed, scored and summed up in the literature, yielding frailty and health scores (as detailed below). In case of disease, the ICD considers disease severity in some cases, but the idea of scoring diseases by severity can be implemented in principle for most if not all diseases. In fact, such severity scores can be based on a calculation of dysfunction due to disease (more precisely, of disability-related sequelae of disease and injury). On this basis, as part of the worldwide GBD (Global Burden of Disease) studies, YLDs (years lived with disability) and HALEs (healthy life expectancies), equal to the sum of the prevalence multiplied by the general public’s assessment of the severity of health loss, were calculated, establishing country-age-sex-year reference population data for sequelae, where same country may or may not imply similar genetics [11].

Given a health score, it is thus straightforward to compare the score of two individuals, or to compare the score of an individual at different times. We can then talk about health in comparative terms, i.e., we can talk about “better” or “worse” health. However, as noted, to define “good” or “bad” health in absolute terms, we need a reference value as a threshold dividing good health from bad health. Full scores of 100% on all features would be required by the WHO definition of health. As this is too strong a requirement, we would like to say that an individual is in good health, if the health score of the individual is above a specific threshold. However, already in the simple case of grip strength a threshold is not straightforward to define in such a way that a grip strength below threshold must necessarily be considered unhealthy. As we strive for definitions that do not require the setting of (arbitrary) thresholds, we refer to a baseline as the reference value also for the health score itself and take note of any deviations. As noted, our standard baseline for defining the health of an individual is the age-matched population average, as it develops along the time axis. Thus, for a reference population, we will consider that its average health develops as a function of chronological time, driven by “average” biological processes (see also Table 1). We take the reference population to be fixed once and invariant thereafter (though we consider various age groups within the reference population); also, we expect that it matches sufficiently well in terms of the years (or, more generally, time period(s)) during which the samplings and measurements are done. (A reference population from the 19th century would not be considered to be a good match for individuals of the 20th century).

An alternative choice is an age-invariant reference population that does not change as the individual gets older, for example, a “young adult” reference population. This choice would allow us to follow an individual on the same scale over time. If the features of this individual stay constant, this may be interpreted positively as “stability”. If an age-matched reference population were used, the change of features then observed for such a “stable” individual would instead be interpreted positively or negatively (depending on how the measurements in the age-matched reference population changed along the time axis, and on how these measurements are interpreted as features of health). For example, if the grip strength of an individual does not change, this observation would indicate “stability”. If grip strength deteriorates in the age-matched reference, however, the relative change in grip strength would indicate an improvement in relative terms [12]. (A related aspect that is beyond the scope of this paper is the need to consider all biomarker measurements on an individual basis, not just with respect to the average in a reference population. One idea here is to employ factors such as genetics/ethnicity or sex to define specific reference populations that are a better match for certain individuals, but their size and therefore the robustness of the average feature measurements based on these subpopulations necessarily becomes smaller, and missing values become more of a problem. For example, to compare two individuals of different regional origin, two different reference populations may be employed, and the resulting relative measurements be compared. Another idea is the consideration of specific composite features consisting of the feature F1 that was measured to estimate health, and, based on some scientific evidence, another feature F2 that is used to elaborate on the difference between the measurement of F1 and the population average given for F1. For example, a genetic feature reflecting low cardiac risk (F2) may suggest that a blood pressure measurement (F1) higher than average does not contribute to a below-average health score for some specific individual, following [12]).

Our threshold-free definition of health is matched, in a natural way, by our definition of a biomarker of health as a predictor for health (see section 4). Quite simply, a predictor for health has to predict the future state of health of an individual better than chronological age. This threshold-free definition allows flexibility in the same way as the standard definition of a biomarker of aging with respect to predictions that are better than chronological age [13]. A level of (statistical) significance may be required for the improvement in predictive accuracy, by a more restrictive yet compatible definition. As described, the thresholds for the measurements are by default based on a reference population (see also [1415]). Our relative definition of health is compatible with the definition of predictors relative to the baseline of chronological age. Moreover, we do not distinguish linear and progressive aspects of aging; these may be considered in more restrictive definitions (see section 6).

Traditionally, aging researchers were concerned with increasing lifespan; we call the underlying biological processes lifespan-enhancing processes. Instrumental for this goal is the search for features that are correlated with the lifespan of an individual, and can thus be used as predictors of survival, that is, as biomarkers of future (residual) lifespan. Such predictors are usually found based on statistical reasoning: What is the statistical life expectancy of an individual with the biomarkers in question? Similarly, the goal of health researchers is to uncover biological processes that enhance health and, thereby, the healthspan of individuals. We call the (molecular) processes resulting in health healthspan-enhancing processes. Just as there are predictors of survival (residual lifespan), there are predictors of future health (residual healthspan); naturally, there is a lot of overlap. Furthermore, ideal predictors are to be distinguished from the estimates that may be calculated for these. Along these lines, we suggest that the ideal predictor of both residual lifespan and healthspan may simply be called “biological age” (see Section 4). A similar approach was taken by [16], and the resulting predictor was referred to as “biofunctional age”.Go to:

Defining, Operationalizing and Measuring Health

Operationalizing health by dissecting it into a hierarchy of its various aspects is a difficult task. However, as described, in the literature on human health the two main aspects of health are dysfunction and disease; both have been codified by standard classifications, the most visible ones being the ICD and the ICF published by the WHO. We wish to do justice to both aspects – absence of disease and dysfunction – by considering both as contributors to health, using an integrated approach.

Based on the ICF and the ICD as a guide, we surveyed the literature and assembled the various ways to operationalize health in both humans and model organisms. The results of this review, presented in Table 2, is an operational consensus definition of health, which encompasses the aspects of both disease and dysfunction, and includes integrative concepts such as quality of life as well as pathological and prodromal features. Each feature can be operationalized in order to be a useful object of inquiry (see the references in Table 2). Each such operationalization gives rise to a score, possibly a binary one (yes/no). Each feature can be weighted, in order to be integrated with the other features, where the weights may reflect the subjective preferences of the individual or the researcher. The features of Table 2, distilled from our digest of the literature, represent a current, yet limited and biased understanding of health, so they are also subject to change in the light of new scientific findings, and they shall be refined by feedback from the scientific community. Our operational consensus definition of health allows to describe the state of health of an individual, characterized by the features listed in Table 2 and their measurements for that individual.

Table 2

Features contributing to a definition of health.

Featurelimited to speciespathologicalReferences
physiological function   
stress resistance  [1721], cf.
 thermo-tolerance (=heat shock tolerance)  [2223]
 hypoxic stress tolerance  [2425]
 osmotic stress tolerance  [26]
 oxidative stress tolerance  [192327]
 metabolic status / homeostasis xcf.[2], cf. [28 29]
 redox status / homeostasis x[3031]
 immune status / homeostasis xcf. [2032]
 
physical & cognitive function (=strength and cognition)
 motivated/stimulated locomotion(worm) [33]
 (motor) balance, dexterityhuman/mouse [3438]
 muscle/neuronal/intestinal integrity x[3941]
 
 physical function (=strength)
  [unmotivated/unstimulated] locomotion  cf. [18204243]
  grip strengthhuman/mouse cf. [204445]
  pharyngeal pumpingworm [18224647]
  gait speed, chair risinghuman/ (mouse) cf. [204849]
  muscle integrity x[4041]
 
 cognitive function (=cognition)
  sensory perception  cf. [205052]
  (short-term) memory,
processing speed
(human/ mouse) [5356]
  sleep, cardiac rhythm  cf. [2057]
  executive/verbal functionhuman/ mouse [5859]
  neuronal integrity x[60]
 
reproductive function
 number of offspring  [6164]
 offspring health/survival  [6566]
 
lack of frailty, Healthy Aging Index (and similar), allostatic load; lack of physiological dysregulation, self-reported health, quality of life(human) [26774]
 
(prodromal) organ/physiological function (heart/cardiovascular, neurological, etc.)
(prodromal) paralysis, protein aggregation/plaques
human/animal modelxcf. [207576]
 
lack of disease and medications(human) e.g., [7778]

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Synonyms are marked by “=”, given in parentheses. Species-specificity noted in parentheses is debatable. Pathological features are features that are predictive of future health problems, but they are not usually regarded as features of health per se.

When defining health by absence of disease and dysfunction, our pragmatic approach to defining disease is based on the adoption of the ICD, using all codes. This may be deemed problematic, because many items in the ICD do not represent diseases. For example, chapter XV (codes O00-O99) concerns “pregnancy, birth and puerperium”, chapter XIX (S00-T98) deals with “injury, poisoning and certain other consequences of external causes”, and chapter XX (V01-Y84) lists “external causes of morbidity and mortality”. For this reason, the ICD is, despite its name, not so much a classification of diseases, but a classification of diagnoses. Nevertheless, such permissiveness is not problematic for us, since we weight the various features of health. While some ICD codes are indeed irrelevant in the light of most non-operational definitions of health, we do not need to exclude these codes beforehand, as this is already accounted for by the fact that these codes are likely to have zero weight in any specific implementation of our approach.

Our pragmatic approach to defining dysfunction consists of adopting the ICF, but only as the first step. Since we are concerned with dysfunction, we only consider the part of the ICF concerning “body functions”, not the other parts on “body structures”, “activities and participation” or “environmental factors”. In fact, the chapter on “activities and participation” is redundant for our purposes, because its entries are mirrored in the chapter on “body functions” except for some specific human-related aspects (see also [79]). With the ICF list of body functions in mind, we surveyed the literature, and collected the hierarchical framework of features of Table 2 that can be mapped to the ICF codes in a consistent fashion.

Specifically, in Table 2, the notion of physiological function as found in the literature includes many ICF body functions, such as functions of the cardiovascular, hematological, immunological and respiratory systems (ICF, Body functions, chapter 4), functions of the digestive, metabolic and endocrine systems (ICF, Body functions, chapter 5), genitourinary function (ICF, Body functions, part of chapter 6), and functions of the skin and related structures (ICF, Body functions, chapter 8). The notions of physical and cognitive function as found in the literature include neuromusculoskeletal and movement-related functions (ICF, Body functions, chapter 7) and, more specific to cognitive function, mental functions, sensory functions and pain (ICF, Body functions, chapters 1+2) as well as voice and speech functions (ICF, Body functions, chapter 3). Finally, the notion of reproduction from the literature includes, naturally, reproductive functions (ICF, Body functions, part of chapter 6). Notably, the ICF does not list any important body functions that we miss in our framework, which indicats that our list of features is likely to be complete for our purposes.

In summary, our literature survey of health and healthspan shows that health is operationalized in terms of stress resistance and homeostasis (which we summarize as physiological function), strength (physical function), cognition (cognitive function), and reproduction, as well as in disease-related and integrative terms, see Table 2. Reassuringly, this set of higher-level terms matches closely the NIH toolbox approach that distinguishes four major domains of function: cognition, motor, sensation, and emotion [80]. It also resembles closely the five domains constituting intrinsic capacity: locomotion, sensation, cognition, psychological issues and vitality, where vitality unfolds into hormonal and cardio-respiratory function and energy metabolism [79]. Similar to our approach, the latter approach is also making use of the ICF, with a focus on body functions, and it can be extended by extrinsic factors, defining the more general term of “functional ability”.

Recently, the combination of strength and cognition (physical and cognitive function) displayed in Table 2 gained popularity. In C. elegans healthspan research, health is now often operationalized in the form of “stimulated locomotion”, which can be clearly distinguished from locomotion that is just due to the search for food [43]. In human, “cognitive frailty” was proposed to cover both aspects [81]. The corresponding term strength and cognition refers to both, strength and cognition, giving rise to a hierarchy reflected by the table. The other hierarchy-generating properties in Table 2 are the various specializations of physiological, physical, cognitive and reproductive function. We also included histological and molecular features that are called “pathological” and that are predictive of future health problems, although they are not usually regarded as features of (worse) health per se. For example, no individual suffers directly from microscopic lesions in muscle tissue, or from elevated values of cholesterol or prostate-specific antigen, or from some specific variant of the APOE or BRCA gene; instead, “healthy” measurements of these features are biomarkers of health (cf. Section 4). Along the same lines, we consider pathological features that are early indicators of the onset of specific diseases (“prodromal” features, e.g., protein plaques indicative of Alzheimer’s disease).

Table 2 includes dysfunctions, as well as various integrative approaches towards listing and indexing them, such as (lack of) frailty, “healthy aging” indices and the like. Such indices often include features on various levels of abstraction, but a rigorous justification for a specific selection of features is usually lacking. For example, frailty is defined as a state of reduced physiological fitness that includes multimorbidity, functional limitation, and geriatric syndromes, representing a compendium of interacting factors contributing to poorer health outcomes [80]. There are two more widespread definitions of frailty by [6768], but there is still a lack of consensus [82]. Further indices were introduced with an emphasis on “healthy” or “successful” aging, for example, the Healthy Aging Index by [69], the Successful Aging Index by [70], or the Healthy Aging Score by [71]. These indices include features from the sociodemographic domain partly based on self-assessment, disease-related scores such as disease counts, some laboratory markers such as blood pressure, and some examination scores such as the Mini-Mental State Examination test result. As another example of an integrative concept, allostatic load is based on laboratory markers [72]. A lot more indexes were developed, recently reviewed by [83], most recently encompassing multiple blood-based biomarkers [84], clinical and blood-based biomarkers [8586], functional measures and questionnaires [87], multimorbidity [88], or combinations of these [89].

Although the ICD and the ICF are intended to be complementary [90], some overlaps between features of disease and dysfunction may be identified by careful inspection. (For example, the German modification of the ICD comprises several codes for dysfunctions (“Funktionseinschränkungen”, functional limitations), in its chapter XII, codes U50-U52. These codes are not contained in the international WHO version of the ICD, and they are intended to be applied for the initial period of inpatient treatment only). Thus, there is threat of double counting dysfunctions by having codes for them not only in the ICF, but also in ICD. This can be avoided by identifying the respective terms and mapping them to each other. The same holds true for overlaps among single features and integrative ones, and for overlaps among the latter.

In the last row of Table 2, we consider disease and medication. As described, we define diseases pragmatically as anything that is codified by the ICD. Intake of medications is, of course, neither necessary nor sufficient for having a disease. It can, however, be used as a proxy for information about health. Moreover, it is possible to map the feature-based descriptions available for many diseases to animal species if these can also be identified and scored in these species [91]. Similarly, the feature-based descriptions available for human dysfunctions can often be mapped to similar or even identical descriptions of animal dysfunctions.

While health and survival may be contrasted, these two concepts may also be integrated by taking a weighted average, as motivated by [92]. We suggest that the weighted average of an individual’s healthspan and lifespan is the best objective measurement of success of any healthcare intervention. Naturally, the weighting factor for health on the one hand and survival on the other hand will be subjective (as described in Section 1). Our short term for the weighted average of health and survival is wellbeing, which refers to health only if the weight for survival were zero, and vice versa. For wellbeing as the state, we propose to name the associated time period the “wellspan”, and the underlying processes “wellspan-enhancing processes” (see Table 1). The processes that are reverse to “wellspan-enhancing processes”, that is, the biological processes that reduce wellbeing, have a standard term, which is aging. In other words, we propose that aging (which is a process) is simply the aggregate of all processes that reduce future wellbeing. The definition of aging is as contested as the definition of health (see, for example, [9394]). We think that our definition, as the aggregate of the processes that reduce health and survival, matches the intuitive meaning of the concept. We also claim that the concept of wellbeing matches closely the intuitive meaning of the WHO definition, and it covers any changes positive or negative for health and survival that are happening to an individual, including, e.g., the acquisition of “wisdom”.

The state that is the opposite of wellbeing, and that is caused by aging, may be called “illbeing”; the corresponding time period is the “illspan” (see Table 1). The sum of the wellspan and the illspan of an individual is its lifespan, and any predictor of illbeing as well as of wellbeing must predict the same entity. As we will see in the next section, the best possible integrative estimate predicting the future health and survival of an individual is biological age. In the literature, biological age also refers to any estimate of biological age, and not just to the idealized concept of its best, or perfect, estimate.Go to:

Predicting Health

The prediction of health, survival or wellbeing is often based on chronological age alone. Such a prediction is often a good one, but it is not the best one possible, as it cannot account for differences among individuals of the same age. Information about the actual state of the individual can make the prediction more precise. In our generic, simple and threshold-free framework, a biomarker is a feature of an individual that allows prediction of another feature of the same individual. This definition avoids hard-to-define terms such as “indicator”, which normally feature in definitions of “biomarker”, e.g., in the NIH definition (“a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention”) [95], or the definition by Merriam-Webster (“a distinctive biological or biologically derived indicator (such as a metabolite) of a process, event, or condition (such as aging, disease, or oil formation)”). Furthermore, adapting the approach of [13], a biomarker of health is any feature of an individual that predicts a temporally later feature of health better than chronological age, and a biomarker of aging is any feature of an individual that predicts a temporally later feature of illbeing better than chronological age. These definitions are not cyclic. Since wellbeing and illbeing are opposites on one and the same dimension, a biomarker of aging predicts the corresponding features of wellbeing equally well.

Further, considering that biological age is supposed to predict the future wellbeing of an individual (referring to the weighted average of health and survival) in the best possible way, it is a biomarker of aging, and it is the best composite biomarker imaginable if it could be estimated without error. Biological age is a concept found frequently in the literature (see, for example, [9697]), though it is often not explicitly and precisely defined. The closest approach to ours that we could find is provided by [16], where the authors define “biofunctional age”, in a similar fashion. Our definition thus fills a void, while preserving the intuitive meaning of the term. In our framework, aging increases the biological age of an individual, and biological age predicts wellspan (healthspan and lifespan) best. This is because we analyze aging as the aggregate of all processes reducing an individual’s wellbeing, and whatever changes a feature of wellbeing, also changes the ideal predictor for this feature. As biological age is the ideal predictor for wellbeing, aging must change biological age.

In practice, the biological age of an individual is represented by a specific numerical value that is estimated based on some features of the individual. It is often estimated in years – with the idea that in a baseline population of individuals, individuals with a similar biological age have a similar expected (residual) wellspan. To estimate it in the best possible way, all features of the individual that are contributing independently would need to be considered. Of cause, chronological age is an important contributor to this estimation, even though, by definition, it cannot be a biomarker of aging; a composite biomarker of aging such as biological age can therefore include a significant component that is not a biomarker of aging. And indeed, predicting a feature better than baseline best starts with that baseline, improving upon it.

In general, biomarkers are identified based on cross-sectional or (preferably) longitudinal cohort data, where features of individuals are measured over time. Whenever there is a time gap between measurements, the biomarker attribute (of predicting the future better than chronological age) may be tested. For any individual (which does not have to be a member of the cohort), the biomarkers we are interested in predict its wellbeing (health and survival) better than chronological age. Biomarker measurements that are predictive for some feature in a population do not have to be necessary, nor sufficient, for that same feature for a particular individual. For example, taking for granted that high blood pressure is a good biomarker for shorter lifespan and for cardiovascular disease, it is possible that a particular individual has high blood pressure but still enjoys a long lifespan without cardiovascular disease (because of other factors with protective influence), and another particular individual may feature short lifespan and cardiovascular disease without having high blood pressure (because of other factors with negative influence). However, a high or low biological age is based on the result of the measurement of the widest possible variety of molecules and functions, so that its prediction of wellbeing cannot be overridden.

Features, and biomarkers in particular, can further be classified on the basis of the following questions:

  1. Is it an intrinsic feature? Features may be intrinsic or extrinsic to the individual for which their value is measured. Intrinsic features include genetic and epigenetic ones; for humans, these also include behavior and lifestyle decisions. Extrinsic features include environmental (and social) ones, as well as prenatal ones. Both types of features are profoundly interconnected [79]. Given these interconnections, we designed our set of definitions to be valid for intrinsic and extrinsic features, even though their relevance is much higher for intrinsic features, see also the Discussion
  2. Is the feature time-invariant or role-switching? Features can be classified according to the periods in the life of the individual in which they are predictive. Thus, they may be biomarkers across the time axis of the entire life of an individual, or they may be predictive only during selected time periods. In fact, biomarkers may be time-dependent, up to the point that they may be “role-switching”, that is, predictions of health or survival based on a high biomarker measurement may first be negative, but then turn positive, or vice versa, as an individual gets older [98]. Generally, our definitions are supposed to be valid at young age, though their relevance is higher at middle and old age
  3. Is the feature predictive for itself? Biomarkers are usually reflexive, so that the current measurement of a biomarker predicts its own measurement in the future
  4. Is the feature diagnostic or theranostic? Features can be classified according to their role as “prognostic” or “predictive” tools. Diagnostic (also known as “prognostic”) biomarkers can help to set up a diagnosis, that is, they are simply predictors of future health or survival. Theranostic (also known as “predictive”) biomarkers can be used as a guide to find an appropriate therapy or intervention as well
  5. Does the feature have a causal influence? A causal relationship is necessary between a biomarker and the features of health, survival or wellbeing that it predicts, if our aim is the identification (but not necessarily the monitoring) of interventions. However, prediction “better than chronological age” does not necessarily imply causality, not even partial causality (that is, being one cause of many), with respect to the processes of aging. A biomarker may thus be purely correlative, but by Reichenbach’s common-cause-principle [99] it then should be the downstream consequence of another feature that is (partially) causal; otherwise it could not be a biomarker. A standard example for a pure correlation with age is the possession of grey hair, which is not supposed to cause aging processes in itself, even though, strictly speaking, it may do so by causing a depressed state or other psychological feedback effects that may be causal to aging. Guided by utility, we are most interested in features that are at least partially causal for aging, that is, features that are part of the causal basis of aging; popular examples are the so-called hallmarks of aging [100], or what became known as inflammaging [101]. There are a few examples of features related to age that are not predictive for any feature of wellbeing. These features are not biomarkers of aging, being not even partially causal, and not downstream of something causal for any feature of wellbeing. The racemization of amino acids in teeth may be cited as one of them [102]. Such racemization is due to the progression of chronological time, and it has no causal consequences. It is, thus, only a biomarker of chronological age. The accumulation of DNA mutations, however, must be considered to be a biomarker of aging, even if the underlying processes were purely chronological, because they have deleterious consequences. In general, we can expect strong correlations between wellbeing, health and survival, but any causal links will be complex, see also [103]
  6. Is the feature easy to measure in practice? A feature should be easily measurable repeatedly, and the measurement should not influence health or survival by itself, and it should yield comparable results in human and other animal species [13]

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Enhancing Health

As noted, any predictive feature of an individual can serve as a biomarker, which may be molecular (genetic variation, genomic methylation, gene/protein/metabolite abundance, etc.) or high-level phenotypic (blood count data, blood pressure, grip strength, anthropometry, etc.). Similarly, the healthspan-enhancing and lifespan-enhancing processes as defined above, just as their reverse, that is, aging, are associated with most aspects of its biology, so that it is impossible to strive for a comprehensive description. Nevertheless, we can define the causal molecular basis of aging as all features of aging that are both causal and molecular. In fact, both aspects of wellbeing, health and survival, have a molecular basis, and the processes leading to these states have such a molecular basis as well, and so does wellbeing. In case of processes, their molecular basis can consist of composite differential features that are measured as changes in the measurements of features [104]. Like all features, also differential ones may be biomarkers as defined above. Only biomarkers that are part of the causal molecular basis of aging can be molecular targets of intervention. Healthspan-enhancing or lifespan-enhancing processes entail maintenance, repair, rejuvenation, as well as the reversal of specific types of hypertrophy or damage, of unreliability and deterioration [102105107] that, as a consequence, move the state of the individual closer to complete health, as defined here, and as defined by the WHO, or that change the state of the individual so that death is occurring later. Naturally, there is a lot of overlap between healthspan-enhancing and lifespan-enhancing processes.

Healthspan and lifespan are often contrasted, and the causal processes resulting in health and survival overlap only partially ([20108] and references therein). For example, an individual may suffer from a serious neurodegenerative disease, but survive for a long time. Such a state of disease often consists of a long time spent in (subjectively) worse health. Thus, worse health does not necessarily imply shorter survival, and, in terms of time spent by the individual, “healthspan” and “lifespan” may differ. Naturally, at any time an individual is healthy, it must be alive. Hence, the healthspan of an individual is necessarily included in its lifespan. However, this inclusion relation is not preserved on the level of causal influences. While survival is influenced by affecting health, positive or negative influences on health may not influence survival with the same strength, or may not influence survival in the same direction, and vice versa. Thus, “causal feature for health and survival” is not synonymous with “causal feature for health”: Processes and interventions that positively influence lifespan may be detrimental to healthspan, and vice versa. For example, processes of antagonistic pleiotropy [109] improve health in early years, and reduce survival (and health) in later years. Aortic aneurysm has a much stronger influence on survival than on health, as it is often asymptomatic. On the other hand, dementia usually has a much stronger influence on health than on survival. In turn, treatment of aortic aneurysm by surgery may improve survival at the expense of health, and treatment of dementia may be indicated even if it causes a significant reduction of survival, but an improvement of health.

Finally, given relationships between features, such as molecular interactions, we can assemble sets of related features into (molecular) pathways. Although the boundaries between such pathways are ultimately arbitrary, the identification of pathways, and of the interaction (crosstalk) between these, has become a common concept. In our case, some pathways can be labeled as wellspan-enhancing, healthspan-enhancing [110], or lifespan-enhancing pathways, depending on whether the features making up the pathway are related to wellbeing, health or survival. In general, aiming to be threshold-free, a “health relatedness score” can be assigned to every pathway. Nevertheless, in practice, we may still label some pathways as being, e.g., healthspan-enhancing, and others as not being healthspan-enhancing. This labeling may be done based on a threshold on the predictiveness of the features making up the pathway. In turn, we may start with features of health, and construct pathways starting with these. The relationships between the (molecular) features may consist of sets of molecular interactions (for example, protein interaction or gene regulation, documented, e.g., in KEGG pathways [111] or other correlative or causal dependencies). As molecular pathways may interact themselves, their interactions can be described by pathway maps, yielding healthspan pathway maps. The net result of their interaction determines the progression, slowdown or reversal of wellbeing. Thus, [112] started with sets of molecular features (that is, genes that are likely involved in health in a causal fashion), and healthspan pathway maps were constructed for human (and C. elegans), based on molecular interaction data. A small number of interacting genes was added to the starting sets, so that at least the majority of genes can be assumed to be causal for health, and the pathways as well as the map between these were then based on a clustering algorithm applied to the molecular interaction network already known for the genes based on other public data.

Relationships between features, and specifically between biomarkers, may consist of relationships among higher-level phenotypic features as well as molecular ones at the same time, based on measuring their correlation [113]. Often, molecular biomarkers are used to predict higher-level phenotypic features. However, a higher-level phenotypic biomarker may also predict a molecular feature better than chronological age. Then again, for practical reasons, we define health by features of relevance to the individual, and these are usually phenotypic, and we strive to find biomarkers as predictors that are easy to measure and yet provide prediction potential for the future state of the individual, and these are often molecular.Go to:

Discussion

In this paper, we describe how health and healthspan can be operationalized for health and aging research. Based on a literature review, we provide a framework for generic, simple and threshold-free definitions of health and health-related terminology. Our definition of health comprises various elements that are often dispersed over distinct approaches to defining health, namely, (1) objective features like the lack of dysfunction and disease, (2) subjective weightings, and (3) the reference to the statistical average in a population. This way, we are able to integrate various operationalizations from the literature into a joint framework. We are optimistic that future operationalizations can be aligned to our framework, thus extending its scope and semantic expressivity. In particular, we expect to incorporate further feedback from the various research communities. We hope that such community feedback can also help to minimize our investigator bias.

We intend our framework as a means of integration for previous, present and future operationalizations of health. While we are striving for a framework of definitions that are as generic, simple and threshold-free as possible, we allow to design more restrictive frameworks by placing constraints on some of the definitions. In some cases, the more restrictive instantiations of our framework are more intuitive, but also less simple. In particular, we consider intrinsic as well as extrinsic features of health, while a restriction to intrinsic features that are contained within the individual may be more intuitive. Also, in our generic, simple and threshold-free framework, a biomarker is just an (intrinsic or extrinsic) feature of an individual that predicts another (intrinsic or extrinsic) feature of the same individual at a later time-point.

Our list of health features (Table 2) is long and complex, and it seems to be too unwieldy to be handled. We do, however, not want to imply that future studies of health or healthspan need to take into account all features in the list at the same time. To the contrary: As the features will be combined with a subjective weighting, those features that are not made use of in a given study can simply be combined with a zero weight in order to neutralize them. Indeed, a specific set of features can be selected, scored and weighted to yield a single score describing a specific aspect of health. Single scores can then be combined, considering various specific aspects of health, and eventually covering all features of Table 2. The more these specific sets of features and the subsequent scoring follow a standard based on a consensus among researchers, the higher the comparability of results from different experimental studies.

Admittedly, it is a difficult business to reduce the complex state of an individual to one single number, and it goes without saying that this comes at a price. First and foremost, a lot of information is lost on the way. For example, we do not capture the state of single organs or organ systems. An individual may have a biologically young skin, but a biologically old heart. But this is not problematic, as the point of the procedure is to distill the biological age of an individual, i.e., the best possible predictor for health and survival – and in this respect, the biologically old heart will probably weight more than the biologically young skin. While we try to avoid arbitrary thresholds, we do need to work with subjective weightings and reference populations, both of which allow for many variations. We see this as a benefit of our approach, as variation in both weighting and reference population may give rise to different kinds of analyses. The default choice of the reference population for health feature measurements is an age-matched population, i.e., we compare the data of an individual with a reference group whose members have similar chronological age as the individual under study. But it might also be very useful to compare results with a reference population that is matching closer the genetics of the individual under study. Still another option is to compare the results from individuals of any age with a reference population of a fixed standard age, e.g., a population of young adults (see Section 2). In any case, the choice of the reference population is an issue that shall be explored further.

Our definition of aging is very broad. According to our definition, any biological process that reduces health or survival will count as an aging process. We think that diverse processes such as the disease course of progeroid syndromes, preterm birth, the development from puberty to adulthood, traffic accidents, moving to a war zone, or losing one’s social interactions are all aging processes. This implies that we operate on a very broad definition of “biological” here, but we are convinced that all of these processes have at least a biological component. Specifically, there is no doubt that the disease course of progeroid syndromes such at Hutchinson-Gilford progeria consists of aging processes, reducing health and survival. Preterm birth and its consequences is actually quite alike progeroid syndromes, in that they include aging-related processes in basically the same way. In both cases, health and survival tend to be reduced, and the underlying molecular biology even features common molecular processes, e.g., in case of mandibuloacral dysplasia [114115] and Marfan lipodystrophy syndrome [116].

Taking the broad view, development from puberty (at which time human mortality is at its lowest in many countries) to adulthood also features some aspects of aging, that is, reductions of health or survival, e.g., due to risk-taking behavior that has at least in part a molecular or genetic determinant. In late adulthood, the relevance of risk-taking usually diminishes, but at the same time the effectivity of the response, in terms of cognitive abilities, goes down. By the same argument, almost all kinds of accidents, war- or crime-related death have biological components, even if non-biological external causes (like a brake malfunction) are more salient. Along the same lines, we can include social processes within our definition of aging processes – although social processes are extrinsic to the individual, and their effects on the individual are mediated by internal psychological processes in a fashion that may be specific for the individual, they are biological in the broad sense that they involve, in one way or other, genes, brains, and hormones. In the generic framework proposed here, the absence of social isolation, poverty, etc., are thus features of health, in line with the notion of functional ability [79] and the WHO “World Report on Ageing and Health” [117].

In fact, subjective aspects arise specifically for any definition of concepts relating to human. For example, aspects of social life are particularly prevalent features of human health, and we consider these as well as some of their cognitive prerequisites in Table 2, specifically as part of some of the integrative features. In particular, social contexts can turn the presence of a disease, which primarily has a negative effect, secondarily into an advantage, that is, into a secondary disease gain. For example, a certain disease may exempt from military service and thus indirectly prolong the life of the diseased, or it may lead to more attention by relatives and friends. Human beings are able as well as forced to integrate various (even pathological) circumstances into a dynamic system of judgements, decisions, values and goals. Considering an individual with a chronic condition, e.g., chronic heart failure, a disease which will progress over time, the goal of a long lifetime may be a function of a composite of – maybe contrary – wishes, beliefs, values, and goals that must be rationally and emotionally integrated into the current and future life course. Thus, quality of life is usually influenced by personality, life experience, cultural factors, personal (including financial) resources, social support networks and other unique life circumstances [118].

As we explained above, our definitions do not exclude that external accidents as well as war or crime-related misfortunes are aging processes, if they result in disease, dysfunction or death. Our main rationale for this broad view is to avoid arbitrary thresholds; in our view, all processes reducing health or survival should qualify as aging. To a certain extent, traffic accidents and war damages are externally caused, but they are not totally independent from intrinsic, biological features. For example, traffic accidents are more likely given risk-seeking behavior, and they are less likely given good cognitive abilities, both of which in turn may well have genetic roots and vary with age. Nevertheless, our framework is flexible to accommodate a restricted set of definitions, where all features considered must be intrinsic to the individual, and all extrinsic features are excluded. It would then fall to the proposer of such a restricted framework to delineate between these two classes of features.

We defined aging as those processes that contribute to disease, dysfunction, or death. As these processes start early in life and accumulate over time, some authors propose to define aging as a disease [119120]. The ICD-11, the new release of the ICD, will contain an extension code “Ageing-Related” (XT9T) for ageing-related diseases. This in itself does not prejudicate whether aging is a disease or not: the diagnosis that some disease is aging-related is not tantamount to the claim that aging itself is a disease. We defined aging as the aggregate of all processes in an individual that reduce its wellbeing. As wellbeing aggregates health and survival, and health is defined as a lack of disease and dysfunction, this implies that aging is a cause of disease or dysfunction or death. Given our definition, it is thus much more natural to conceive of aging processes (as defined by us) as causes of disease, rather than being diseases themselves [121]. However, there would not be any logical inconsistency if aging were a disease, for among the causes of disease, dysfunction or death may well be diseases, while not every cause of these three need to be disease.

In our present analysis, we ignored most of the work on the demography of aging. For example, some demographers distinguish “true” progressive aging from linear processes related to wear and tear, or to disease. Some demographers thus investigate mortality patterns using Gompertz’ law, calculating, usually from small population samples, an initial mortality rate (IMR, also known as baseline vulnerability A) and a mortality rate doubling time (MRDT, also known as acceleration of mortality G, [122]). Then, the idea is that “true” aging is reflected by the MRDT, and there is an “aging-independent mortality” as reflected by the IMR. Implicitly, such a distinction sets a threshold at the transition from IMR and MRDT. Moreover, in an approach focusing on health and healthspan, it is the IMR that counts and that we wish to extend, and we may even aim for a high MRDT, to compress the period of morbidity. Consequently, in our framework, there is no such thing as an “aging-independent mortality” [122], consistent with the notion that a biomarker of aging is just a predictor of health and survival that is better than chronological age.Go to:

Conclusion and future work

We here present a framework which defines often used terms in life science research in an integrative manner. We differentiate between states, time periods, underlying biological processes and predictors of the future. We propose to create a framework which enables researchers to apply the terms (and the concepts behind these terms) to different species, for human beings as well as for model organisms used in research on aging and diseases. Taking into account the huge steps basic research has taken in the last years, we also aimed to create the framework as an open and dynamic one which will progress with the growing knowledge on health, aging and disease mechanism and processes. Therefore, the proposed framework should be seen as a starting point because without a precise definition of what we are studying, the results will be less easy to interpret, also from a translational point of view. With this in mind we hope that the proposed framework will help basic researchers and clinicians to gain a deeper understanding of the field and it enables trans- and interdisciplinary research. It will be work of the future to enrich our table of health features by taking into account more operationalizations of health and healthspan from past, present and future studies.

In order to handle the complexity of health features, it would be desirable to develop a formal ontology of these features, in order to enhance interoperability and automated integration of experimental data derived with different subsets of these features. In such a future ontology of health, the features need to be aligned to appropriate top-level classes. It seems to be promising to analyze many physiological functions as processes, considering that the respective dysfunctions consist in the lack of the dispositions to realize these processes [123]. Moreover, our framework is again flexible enough to allow for subtypes of aging, like linear aging, or progressive aging, in order to single out those aging processes that contribute to a specific kind of decline of health or survival.Go to:

Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant agreement No 633589 (Aging with Elegans). This publication reflects only the authors’ views and the Commission is not responsible for any use that may be made of the information it contains. AAC is supported by a CIHR New Investigator Salary Award and is a member of the Fonds de recherche du Québec – Santé funded Centre de recherche du CHUS and Centre de recherche sur le vieillissement.Go to:

Footnotes

Competing interests

The authors declare that they have no competing interests.Go to:

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Aging Dis. 2018 Dec 4;9(6):1165-1184. doi: 10.14336/AD.2018.1026. eCollection 2018 Dec.

Emerging Anti-Aging Strategies – Scientific Basis and Efficacy.

Shetty AK1,2Kodali M1,2Upadhya R1,2Madhu LN1.

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Abstract

The prevalence of age-related diseases is in an upward trend due to increased life expectancy in humans. Age-related conditions are among the leading causes of morbidity and death worldwide currently. Therefore, there is an urgent need to find apt interventions that slow down aging and reduce or postpone the incidence of debilitating age-related diseases. This review discusses the efficacy of emerging anti-aging approaches for maintaining better health in old age. There are many anti-aging strategies in development, which include procedures such as augmentation of autophagy, elimination of senescent cells, transfusion of plasma from young blood, intermittent fasting, enhancement of adult neurogenesis, physical exercise, antioxidant intake, and stem cell therapy. Multiple pre-clinical studies suggest that administration of autophagy enhancers, senolytic drugs, plasma from young blood, drugs that enhance neurogenesis and BDNF are promising approaches to sustain normal health during aging and also to postpone age-related neurodegenerative diseases such as Alzheimer’s disease. Stem cell therapy has also shown promise for improving regeneration and function of the aged or Alzheimer’s disease brain. Several of these approaches are awaiting critical appraisal in clinical trials to determine their long-term efficacy and possible adverse effects. On the other hand, procedures such as intermittent fasting, physical exercise, intake of antioxidants such as resveratrol and curcumin have shown considerable promise for improving function in aging, some of which are ready for large-scale clinical trials, as they are non-invasive, and seem to have minimal side effects. In summary, several approaches are at the forefront of becoming mainstream therapies for combating aging and postponing age-related diseases in the coming years.

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Aging; antioxidants; astragalus; autophagy; curcumin; intermittent fasting; neurogenesis; physical exercise; plasma transfusion; resveratrol; senescent cells; senolytics; stem cell therapy; stem cells; telomeresPMID: 30574426 PMCID: PMC6284760 DOI: 10.14336/AD.2018.1026Free PMC Article

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Aging Dis. 2019 Aug; 10(4): 883–900.Published online 2019 Aug 1. doi: 10.14336/AD.2018.1030PMCID: PMC6675520PMID: 31440392

Health and Aging: Unifying Concepts, Scores, Biomarkers and Pathways

Georg Fuellen,1,*Ludger Jansen,2,*Alan A Cohen,3Walter Luyten,4Manfred Gogol,5Andreas Simm,6Nadine Saul,7Francesca Cirulli,8Alessandra Berry,8Peter Antal,9,10Rüdiger Köhling,11Brecht Wouters,12 and Steffen Möller1Author informationArticle notesCopyright and License informationDisclaimerThis article has been cited by other articles in PMC.Go to:

Abstract

Despite increasing research efforts, there is a lack of consensus on defining aging or health. To understand the underlying processes, and to foster the development of targeted interventions towards increasing one’s health, there is an urgent need to find a broadly acceptable and useful definition of health, based on a list of (molecular) features; to operationalize features of health so that it can be measured; to identify predictive biomarkers and (molecular) pathways of health; and to suggest interventions, such as nutrition and exercise, targeted at putative causal pathways and processes. Based on a survey of the literature, we propose to define health as a state of an individual characterized by the core features of physiological, cognitive, physical and reproductive function, and a lack of disease. We further define aging as the aggregate of all processes in an individual that reduce its wellbeing, that is, its health or survival or both. We define biomarkers of health by their attribute of predicting future health better than chronological age. We define healthspan pathways as molecular features of health that relate to each other by belonging to the same molecular pathway. Our conceptual framework may integrate diverse operationalizations of health and guide precision prevention efforts.Keywords: terminology, health, aging, biological age, wellbeing, biomarkerGo to:

Introduction

For some years, the concepts of health and healthspan have been advocated as the primary goal of medical diagnosis and intervention [14]. Given their importance for national and international allocation of resources in research and care, it is important to define these terms as precisely as possible. In this paper, we suggest a set of operational definitions, including definitions of health and related terms such as wellbeing, biological age, and aging, and we place these into a consistent systematic framework. Our aim in presenting these definitions is to support empirical studies, in particular in health and aging research, and to facilitate the comparability of results. For this reason, we aim for a coherent set of definitions that are practical in the sense that they can be used in actual research contexts. This requires that the definitions can be operationalized, that they are based on a sufficient consensus in the research communities and are sufficiently robust to be applied to different experimental and clinical settings covering molecular as well as higher-level phenotypic phenomena common for a variety of biological species – in particular human and model organisms such as C. elegans and mouse.

Specifically, we dissect health into a hierarchical system of its various aspects, allowing to analyze its features in detail, and to identify the biomarkers, molecular pathways and corresponding supportive interventions for the various aspects of health. While beyond the scope of the present paper, the inter-related aspects of health that we describe can in principle be scored and weighted, and thus provide a way for the overall measurement and comparison of the health of different individuals. Defining health based on disease and dysfunction, we follow a consensus approach by means of a literature survey. For disease, we employ the World Health Organization (WHO) International Statistical Classification of Diseases and Related Health Problems, and for dysfunction, we start with the WHO International classification of functioning, disability and health. The latter will then be utilized as background for the review of pertinent papers from health and healthspan research, to systematize our findings. From this consensus, we then derive appropriate definitions of healthspan, healthspan-enhancing processes and biomarkers of health, as well as wellbeing, aging, and biological age. In order to allow the step from prediction to enhancement, we finally distinguish between correlative features on the one hand, and causal features which are potential targets of interventions in order to increase healthspan on the other hand. Our definitions are designed to apply to most animal species, although the literature we surveyed, and thus the operationalization of health we suggest, is specifically targeted to human and the model organisms C. elegans and mouse. Overall, we arrive at a framework of definitions, covering states, time periods, associated processes and predictors of future states, as given in Table 1. We suggest that this generic framework of simple and threshold-free definitions of common terms places these into context while still preserving, to a maximum degree, their intuitive meaning.

Table 1

Framework of definitions.

 StateTime periodUnderlying biological processesPredictor of future state
Single conceptshealthhealthspanhealthspan-enhancing processeshealth biomarkers
survivallifespanlifespan-enhancing processessurvival biomarkers
Integrative concepts…wellbeing“wellspan”wellspan-enhancing processesbiological age
… and their oppositesillbeing“illspan”aging processes
Baseline referencebaseline organismal statechronological timeaverage biological processeschronological age

Frequently used terminology that we can fit into our framework is marked in boldface. The terms in the last row, and specifically the term “average biological processes” refer to a specifically selected reference population.

In this paper, we will first present a framework for the different kinds of terminological categories (states, time periods, processes, predictors). We then define the key term health and closely related terms such as healthspan. We define the term survival, contrast its meaning with health, and propose to integrate both terms under the integrative concept of wellbeing. Often used indicators of health such as quality of life, activities of daily living, lack of frailty, or self-reported health (in case of human), and indices such as the Healthy Aging Index can then be viewed as projections or surrogates of wellbeing. We further define aging as the set of all processes in an individual that reduce its wellbeing, that is, its health or survival or both. Regarding predictors, we define the term biomarker (for features of health, survival, or wellbeing) as generically as possible, as a predictor for these features that is better than chronological age. Such a biomarker is a feature itself, and as any feature, it may be composed of more elementary features. We discuss various classes of biomarkers (of aging), considering, for example, causality of various kinds. We define healthspan pathways as molecular features of health that relate to each other, specifically by belonging to the same molecular pathway. Precise definitions of other standard concepts such as biological age follow naturally.Go to:

How to Define Health with Respect to a Reference

Therapeutic interventions affecting aging and health may have different goals. Often, the emphasis of preventive as well as curative interventions was on the extension of lifespan. But for most people the mere extension of life is not desirable: if it were possible to live for several hundred years in a vigilant coma, hardly anyone would prefer such a long enduring vegetative state to a normal human life with a much shorter lifespan. For this and other reasons, emphasis has shifted to increasing healthspan, i.e., the time period that an individual spends in a state of health. Lifespan is relatively easy to be operationalized. While, from a theoretical perspective, life is both intensionally and extensionally vague at its borders [5], this does not matter much in the context of medical research. For practical purposes, “being alive”, that is, survival, can be modelled as a binary state: any individual, as a whole, is either alive or it is not. (We consider only the survival of an individual as a whole, not the life status of body parts like organs, tissues, or single cells). The time period of an individual spent alive is its lifespan. Death is the irreversible end of biological life.

In contrast, it is much more difficult to operationalize health and healthspan. For one, the definition of health itself is contested: Is it an intrinsic property of an individual, or is it the extrinsic statistical property of instantiating certain features better than the average of a relevant reference group? Is it a subjective value-judgement, or is it ascribed to individuals in a socio-constructive way [67]? We will argue that an operational definition of health needs to incorporate elements from all these approaches. Second, as one may expect, the features of health turn out to be quite different in humans and model organisms like C. elegans or mouse. Third, it is not clear whether there is an irreversible end of healthspan other than death. Often, the healthspan of an individual is assumed to begin with conception or birth and end at some later time. But individuals can have diseases or dysfunctions during their life and then recover; they may even be born with a disease or a dysfunction and then have their health (re-)gained. We thus do not define healthspan as a single coherent time-interval, but allow it to stretch over unconnected intervals, and simply define healthspan as the time an individual spends in a state of health, where “health” is, in turn, operationalized as described below. This allows us to stay uncommitted to the question whether it is possible in principle to (re-)gain the state of health.

These problems of the WHO definition motivate an approach in which the severity of any deviation from health is weighted on an individual basis, taking into account that goals may change with time and circumstances. This also holds for the time period in which health is desired. Possibly, some individuals (such as athletes) may want to trade better physical function in the short term for worse health in the long term. Thus, there may be trade-offs between different features of health, as well as between the intensity and the extension of health, given that both cannot necessarily be optimized at the same time. We will introduce the concept of wellbeing (different from the WHO term “well-being” featuring in the WHO definition of health) in order to integrate health (healthspan) and survival (lifespan) according to the subjective weighting of individuals. As our operationalizations of health and wellbeing make use of more features than one, these features have to be weighted in order to be integrated into a single score. Such weighting is necessarily subjective, and different weights may reflect different preferences of different people. In case of non-human animals, weighting is (implicitly or explicitly) carried out by the researcher; here, the subjective view of the researcher replaces the preferences of the individual.

Our definition of health will thus contain a subjective element with respect to certain weighting factors, but the very features that are weighted comprise objectively measurable aspects. Thus, we will not advertise a subjective definition of health [6]. Subjective theories of health define health in subjective terms: Individuals are healthy, according to these theories, if they feel healthy or report to be healthy. Feeling healthy is usually considered a necessary aspect of health, and self-reports are often used to operationalize health. However, subjective aspects cannot be the whole story: Individuals can feel healthy although they have diseases or dysfunctions still unknown to them or ignored by them. In addition, coping strategies and compensation as well as a change in goals and values may influence the subjective assessment of one’s health. Moreover, subjective theories are not feasible for other species like worm or mouse: even if worms or mice had a subjective self-conception of being healthy, they would not be able to self-report their health status at an interview.

In contrast to a subjective approach to defining health, we will define health as a state of an individual based on specific objective features, namely the absence of disease and dysfunction. As most of these features can be realized in a gradual manner, the question arises where exactly to put the threshold: We need to introduce thresholds in order to distinguish between healthy and unhealthy individuals. In order not to have to introduce arbitrary thresholds, we will refer to the average realization of the features in a certain reference population. Thus, our approach is threshold-free in the sense that we do not set any thresholds a priori; we only provide a recipe for setting these in a generic way.

Furthermore, in view of the controversial discussion found in the literature, we refrain from starting top-down with a new definition of health, for which we then have to find means to operationalize it. Instead, we opt for a bottom-up strategy and first look at how health is de facto operationalized in the research literature, and then systematize the findings. A near-consensus in the health literature is that health is a state of an individual that lacks dysfunction and is free from diseases (while it is a matter of debate what exactly counts as a dysfunction or a disease). However, the following issues arise: Which functions are these, dependent on species? While the health of humans will in part consist of their capability to exercise higher cognitive functions, these will be irrelevant for worms. To which extend does a function need to be exercised, or to which degree does a disease have to lack its manifestation, in order to count an individual as healthy? Finally, which weight should be assigned to each of these criteria? As noted, different human individuals will decide on this in different ways.

In order to address these issues, we start with two well-established codified classification systems provided by the WHO. Using the ICD, the International Statistical Classification of Diseases and Related Health Problems, [9], disease is operationalized by criteria to establish that an individual is affected by disease. Using the ICF, the International classification of functioning, disability and health, [10], dysfunction is operationalized by criteria to establish that an individual is affected by dysfunction. While taking the ICD as a given, we filter the definitions of the ICF by their follow-up in the literature on health and healthspan, arriving at a pragmatic community consensus. Starting from the ICF classification, we reviewed pertinent papers from health and healthspan research with respect to how they operationalize health, systematizing our findings according to the ICF classification. In some cases, our review gave us reason to modify the default presented by the ICF classification.

Once the different features have been selected and measured, we can compare the values measured for these with the reference values that are the average in a reference population. For example, we can compare the grip strength of a 60-year old individual with the average grip strength of 60-year olds in the reference population. Depending on whether the value measured is below or above average, optionally considering the variability of values in the reference population, we can assign a score to this feature, and we can consider this individual to be in bad or good health with respect to this feature. E.g., a very simple (and often inadequate) scoring would assign -1 to values below average and 0 to values above average.

Using some subjective weighting, we can then integrate the scores for all features into one overall health score. This would be done in a standardized way, which reflects the different aspects of health. Such an approach mirrors the use of qualifiers in the ICF. The simplest health score would employ equal weighting; if it were based on binary scores, it would amount to just counting the number of diseases or dysfunctions of an individual, based on a list of measured ones. Indeed, dysfunctions are often listed, scored and summed up in the literature, yielding frailty and health scores (as detailed below). In case of disease, the ICD considers disease severity in some cases, but the idea of scoring diseases by severity can be implemented in principle for most if not all diseases. In fact, such severity scores can be based on a calculation of dysfunction due to disease (more precisely, of disability-related sequelae of disease and injury). On this basis, as part of the worldwide GBD (Global Burden of Disease) studies, YLDs (years lived with disability) and HALEs (healthy life expectancies), equal to the sum of the prevalence multiplied by the general public’s assessment of the severity of health loss, were calculated, establishing country-age-sex-year reference population data for sequelae, where same country may or may not imply similar genetics [11].

Given a health score, it is thus straightforward to compare the score of two individuals, or to compare the score of an individual at different times. We can then talk about health in comparative terms, i.e., we can talk about “better” or “worse” health. However, as noted, to define “good” or “bad” health in absolute terms, we need a reference value as a threshold dividing good health from bad health. Full scores of 100% on all features would be required by the WHO definition of health. As this is too strong a requirement, we would like to say that an individual is in good health, if the health score of the individual is above a specific threshold. However, already in the simple case of grip strength a threshold is not straightforward to define in such a way that a grip strength below threshold must necessarily be considered unhealthy. As we strive for definitions that do not require the setting of (arbitrary) thresholds, we refer to a baseline as the reference value also for the health score itself and take note of any deviations. As noted, our standard baseline for defining the health of an individual is the age-matched population average, as it develops along the time axis. Thus, for a reference population, we will consider that its average health develops as a function of chronological time, driven by “average” biological processes (see also Table 1). We take the reference population to be fixed once and invariant thereafter (though we consider various age groups within the reference population); also, we expect that it matches sufficiently well in terms of the years (or, more generally, time period(s)) during which the samplings and measurements are done. (A reference population from the 19th century would not be considered to be a good match for individuals of the 20th century).

An alternative choice is an age-invariant reference population that does not change as the individual gets older, for example, a “young adult” reference population. This choice would allow us to follow an individual on the same scale over time. If the features of this individual stay constant, this may be interpreted positively as “stability”. If an age-matched reference population were used, the change of features then observed for such a “stable” individual would instead be interpreted positively or negatively (depending on how the measurements in the age-matched reference population changed along the time axis, and on how these measurements are interpreted as features of health). For example, if the grip strength of an individual does not change, this observation would indicate “stability”. If grip strength deteriorates in the age-matched reference, however, the relative change in grip strength would indicate an improvement in relative terms [12]. (A related aspect that is beyond the scope of this paper is the need to consider all biomarker measurements on an individual basis, not just with respect to the average in a reference population. One idea here is to employ factors such as genetics/ethnicity or sex to define specific reference populations that are a better match for certain individuals, but their size and therefore the robustness of the average feature measurements based on these subpopulations necessarily becomes smaller, and missing values become more of a problem. For example, to compare two individuals of different regional origin, two different reference populations may be employed, and the resulting relative measurements be compared. Another idea is the consideration of specific composite features consisting of the feature F1 that was measured to estimate health, and, based on some scientific evidence, another feature F2 that is used to elaborate on the difference between the measurement of F1 and the population average given for F1. For example, a genetic feature reflecting low cardiac risk (F2) may suggest that a blood pressure measurement (F1) higher than average does not contribute to a below-average health score for some specific individual, following [12]).

Our threshold-free definition of health is matched, in a natural way, by our definition of a biomarker of health as a predictor for health (see section 4). Quite simply, a predictor for health has to predict the future state of health of an individual better than chronological age. This threshold-free definition allows flexibility in the same way as the standard definition of a biomarker of aging with respect to predictions that are better than chronological age [13]. A level of (statistical) significance may be required for the improvement in predictive accuracy, by a more restrictive yet compatible definition. As described, the thresholds for the measurements are by default based on a reference population (see also [1415]). Our relative definition of health is compatible with the definition of predictors relative to the baseline of chronological age. Moreover, we do not distinguish linear and progressive aspects of aging; these may be considered in more restrictive definitions (see section 6).

Traditionally, aging researchers were concerned with increasing lifespan; we call the underlying biological processes lifespan-enhancing processes. Instrumental for this goal is the search for features that are correlated with the lifespan of an individual, and can thus be used as predictors of survival, that is, as biomarkers of future (residual) lifespan. Such predictors are usually found based on statistical reasoning: What is the statistical life expectancy of an individual with the biomarkers in question? Similarly, the goal of health researchers is to uncover biological processes that enhance health and, thereby, the healthspan of individuals. We call the (molecular) processes resulting in health healthspan-enhancing processes. Just as there are predictors of survival (residual lifespan), there are predictors of future health (residual healthspan); naturally, there is a lot of overlap. Furthermore, ideal predictors are to be distinguished from the estimates that may be calculated for these. Along these lines, we suggest that the ideal predictor of both residual lifespan and healthspan may simply be called “biological age” (see Section 4). A similar approach was taken by [16], and the resulting predictor was referred to as “biofunctional age”.Go to:

Defining, Operationalizing and Measuring Health

Operationalizing health by dissecting it into a hierarchy of its various aspects is a difficult task. However, as described, in the literature on human health the two main aspects of health are dysfunction and disease; both have been codified by standard classifications, the most visible ones being the ICD and the ICF published by the WHO. We wish to do justice to both aspects – absence of disease and dysfunction – by considering both as contributors to health, using an integrated approach.

Based on the ICF and the ICD as a guide, we surveyed the literature and assembled the various ways to operationalize health in both humans and model organisms. The results of this review, presented in Table 2, is an operational consensus definition of health, which encompasses the aspects of both disease and dysfunction, and includes integrative concepts such as quality of life as well as pathological and prodromal features. Each feature can be operationalized in order to be a useful object of inquiry (see the references in Table 2). Each such operationalization gives rise to a score, possibly a binary one (yes/no). Each feature can be weighted, in order to be integrated with the other features, where the weights may reflect the subjective preferences of the individual or the researcher. The features of Table 2, distilled from our digest of the literature, represent a current, yet limited and biased understanding of health, so they are also subject to change in the light of new scientific findings, and they shall be refined by feedback from the scientific community. Our operational consensus definition of health allows to describe the state of health of an individual, characterized by the features listed in Table 2 and their measurements for that individual.

Table 2

Features contributing to a definition of health.

Featurelimited to speciespathologicalReferences
physiological function   
stress resistance  [1721], cf.
 thermo-tolerance (=heat shock tolerance)  [2223]
 hypoxic stress tolerance  [2425]
 osmotic stress tolerance  [26]
 oxidative stress tolerance  [192327]
 metabolic status / homeostasis xcf.[2], cf. [28 29]
 redox status / homeostasis x[3031]
 immune status / homeostasis xcf. [2032]
 
physical & cognitive function (=strength and cognition)
 motivated/stimulated locomotion(worm) [33]
 (motor) balance, dexterityhuman/mouse [3438]
 muscle/neuronal/intestinal integrity x[3941]
 
 physical function (=strength)
  [unmotivated/unstimulated] locomotion  cf. [18204243]
  grip strengthhuman/mouse cf. [204445]
  pharyngeal pumpingworm [18224647]
  gait speed, chair risinghuman/ (mouse) cf. [204849]
  muscle integrity x[4041]
 
 cognitive function (=cognition)
  sensory perception  cf. [205052]
  (short-term) memory,
processing speed
(human/ mouse) [5356]
  sleep, cardiac rhythm  cf. [2057]
  executive/verbal functionhuman/ mouse [5859]
  neuronal integrity x[60]
 
reproductive function
 number of offspring  [6164]
 offspring health/survival  [6566]
 
lack of frailty, Healthy Aging Index (and similar), allostatic load; lack of physiological dysregulation, self-reported health, quality of life(human) [26774]
 
(prodromal) organ/physiological function (heart/cardiovascular, neurological, etc.)
(prodromal) paralysis, protein aggregation/plaques
human/animal modelxcf. [207576]
 
lack of disease and medications(human) e.g., [7778]

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Synonyms are marked by “=”, given in parentheses. Species-specificity noted in parentheses is debatable. Pathological features are features that are predictive of future health problems, but they are not usually regarded as features of health per se.

When defining health by absence of disease and dysfunction, our pragmatic approach to defining disease is based on the adoption of the ICD, using all codes. This may be deemed problematic, because many items in the ICD do not represent diseases. For example, chapter XV (codes O00-O99) concerns “pregnancy, birth and puerperium”, chapter XIX (S00-T98) deals with “injury, poisoning and certain other consequences of external causes”, and chapter XX (V01-Y84) lists “external causes of morbidity and mortality”. For this reason, the ICD is, despite its name, not so much a classification of diseases, but a classification of diagnoses. Nevertheless, such permissiveness is not problematic for us, since we weight the various features of health. While some ICD codes are indeed irrelevant in the light of most non-operational definitions of health, we do not need to exclude these codes beforehand, as this is already accounted for by the fact that these codes are likely to have zero weight in any specific implementation of our approach.

Our pragmatic approach to defining dysfunction consists of adopting the ICF, but only as the first step. Since we are concerned with dysfunction, we only consider the part of the ICF concerning “body functions”, not the other parts on “body structures”, “activities and participation” or “environmental factors”. In fact, the chapter on “activities and participation” is redundant for our purposes, because its entries are mirrored in the chapter on “body functions” except for some specific human-related aspects (see also [79]). With the ICF list of body functions in mind, we surveyed the literature, and collected the hierarchical framework of features of Table 2 that can be mapped to the ICF codes in a consistent fashion.

Specifically, in Table 2, the notion of physiological function as found in the literature includes many ICF body functions, such as functions of the cardiovascular, hematological, immunological and respiratory systems (ICF, Body functions, chapter 4), functions of the digestive, metabolic and endocrine systems (ICF, Body functions, chapter 5), genitourinary function (ICF, Body functions, part of chapter 6), and functions of the skin and related structures (ICF, Body functions, chapter 8). The notions of physical and cognitive function as found in the literature include neuromusculoskeletal and movement-related functions (ICF, Body functions, chapter 7) and, more specific to cognitive function, mental functions, sensory functions and pain (ICF, Body functions, chapters 1+2) as well as voice and speech functions (ICF, Body functions, chapter 3). Finally, the notion of reproduction from the literature includes, naturally, reproductive functions (ICF, Body functions, part of chapter 6). Notably, the ICF does not list any important body functions that we miss in our framework, which indicats that our list of features is likely to be complete for our purposes.

In summary, our literature survey of health and healthspan shows that health is operationalized in terms of stress resistance and homeostasis (which we summarize as physiological function), strength (physical function), cognition (cognitive function), and reproduction, as well as in disease-related and integrative terms, see Table 2. Reassuringly, this set of higher-level terms matches closely the NIH toolbox approach that distinguishes four major domains of function: cognition, motor, sensation, and emotion [80]. It also resembles closely the five domains constituting intrinsic capacity: locomotion, sensation, cognition, psychological issues and vitality, where vitality unfolds into hormonal and cardio-respiratory function and energy metabolism [79]. Similar to our approach, the latter approach is also making use of the ICF, with a focus on body functions, and it can be extended by extrinsic factors, defining the more general term of “functional ability”.

Recently, the combination of strength and cognition (physical and cognitive function) displayed in Table 2 gained popularity. In C. elegans healthspan research, health is now often operationalized in the form of “stimulated locomotion”, which can be clearly distinguished from locomotion that is just due to the search for food [43]. In human, “cognitive frailty” was proposed to cover both aspects [81]. The corresponding term strength and cognition refers to both, strength and cognition, giving rise to a hierarchy reflected by the table. The other hierarchy-generating properties in Table 2 are the various specializations of physiological, physical, cognitive and reproductive function. We also included histological and molecular features that are called “pathological” and that are predictive of future health problems, although they are not usually regarded as features of (worse) health per se. For example, no individual suffers directly from microscopic lesions in muscle tissue, or from elevated values of cholesterol or prostate-specific antigen, or from some specific variant of the APOE or BRCA gene; instead, “healthy” measurements of these features are biomarkers of health (cf. Section 4). Along the same lines, we consider pathological features that are early indicators of the onset of specific diseases (“prodromal” features, e.g., protein plaques indicative of Alzheimer’s disease).

Table 2 includes dysfunctions, as well as various integrative approaches towards listing and indexing them, such as (lack of) frailty, “healthy aging” indices and the like. Such indices often include features on various levels of abstraction, but a rigorous justification for a specific selection of features is usually lacking. For example, frailty is defined as a state of reduced physiological fitness that includes multimorbidity, functional limitation, and geriatric syndromes, representing a compendium of interacting factors contributing to poorer health outcomes [80]. There are two more widespread definitions of frailty by [6768], but there is still a lack of consensus [82]. Further indices were introduced with an emphasis on “healthy” or “successful” aging, for example, the Healthy Aging Index by [69], the Successful Aging Index by [70], or the Healthy Aging Score by [71]. These indices include features from the sociodemographic domain partly based on self-assessment, disease-related scores such as disease counts, some laboratory markers such as blood pressure, and some examination scores such as the Mini-Mental State Examination test result. As another example of an integrative concept, allostatic load is based on laboratory markers [72]. A lot more indexes were developed, recently reviewed by [83], most recently encompassing multiple blood-based biomarkers [84], clinical and blood-based biomarkers [8586], functional measures and questionnaires [87], multimorbidity [88], or combinations of these [89].

Although the ICD and the ICF are intended to be complementary [90], some overlaps between features of disease and dysfunction may be identified by careful inspection. (For example, the German modification of the ICD comprises several codes for dysfunctions (“Funktionseinschränkungen”, functional limitations), in its chapter XII, codes U50-U52. These codes are not contained in the international WHO version of the ICD, and they are intended to be applied for the initial period of inpatient treatment only). Thus, there is threat of double counting dysfunctions by having codes for them not only in the ICF, but also in ICD. This can be avoided by identifying the respective terms and mapping them to each other. The same holds true for overlaps among single features and integrative ones, and for overlaps among the latter.

In the last row of Table 2, we consider disease and medication. As described, we define diseases pragmatically as anything that is codified by the ICD. Intake of medications is, of course, neither necessary nor sufficient for having a disease. It can, however, be used as a proxy for information about health. Moreover, it is possible to map the feature-based descriptions available for many diseases to animal species if these can also be identified and scored in these species [91]. Similarly, the feature-based descriptions available for human dysfunctions can often be mapped to similar or even identical descriptions of animal dysfunctions.

While health and survival may be contrasted, these two concepts may also be integrated by taking a weighted average, as motivated by [92]. We suggest that the weighted average of an individual’s healthspan and lifespan is the best objective measurement of success of any healthcare intervention. Naturally, the weighting factor for health on the one hand and survival on the other hand will be subjective (as described in Section 1). Our short term for the weighted average of health and survival is wellbeing, which refers to health only if the weight for survival were zero, and vice versa. For wellbeing as the state, we propose to name the associated time period the “wellspan”, and the underlying processes “wellspan-enhancing processes” (see Table 1). The processes that are reverse to “wellspan-enhancing processes”, that is, the biological processes that reduce wellbeing, have a standard term, which is aging. In other words, we propose that aging (which is a process) is simply the aggregate of all processes that reduce future wellbeing. The definition of aging is as contested as the definition of health (see, for example, [9394]). We think that our definition, as the aggregate of the processes that reduce health and survival, matches the intuitive meaning of the concept. We also claim that the concept of wellbeing matches closely the intuitive meaning of the WHO definition, and it covers any changes positive or negative for health and survival that are happening to an individual, including, e.g., the acquisition of “wisdom”.

The state that is the opposite of wellbeing, and that is caused by aging, may be called “illbeing”; the corresponding time period is the “illspan” (see Table 1). The sum of the wellspan and the illspan of an individual is its lifespan, and any predictor of illbeing as well as of wellbeing must predict the same entity. As we will see in the next section, the best possible integrative estimate predicting the future health and survival of an individual is biological age. In the literature, biological age also refers to any estimate of biological age, and not just to the idealized concept of its best, or perfect, estimate.Go to:

Predicting Health

The prediction of health, survival or wellbeing is often based on chronological age alone. Such a prediction is often a good one, but it is not the best one possible, as it cannot account for differences among individuals of the same age. Information about the actual state of the individual can make the prediction more precise. In our generic, simple and threshold-free framework, a biomarker is a feature of an individual that allows prediction of another feature of the same individual. This definition avoids hard-to-define terms such as “indicator”, which normally feature in definitions of “biomarker”, e.g., in the NIH definition (“a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention”) [95], or the definition by Merriam-Webster (“a distinctive biological or biologically derived indicator (such as a metabolite) of a process, event, or condition (such as aging, disease, or oil formation)”). Furthermore, adapting the approach of [13], a biomarker of health is any feature of an individual that predicts a temporally later feature of health better than chronological age, and a biomarker of aging is any feature of an individual that predicts a temporally later feature of illbeing better than chronological age. These definitions are not cyclic. Since wellbeing and illbeing are opposites on one and the same dimension, a biomarker of aging predicts the corresponding features of wellbeing equally well.

Further, considering that biological age is supposed to predict the future wellbeing of an individual (referring to the weighted average of health and survival) in the best possible way, it is a biomarker of aging, and it is the best composite biomarker imaginable if it could be estimated without error. Biological age is a concept found frequently in the literature (see, for example, [9697]), though it is often not explicitly and precisely defined. The closest approach to ours that we could find is provided by [16], where the authors define “biofunctional age”, in a similar fashion. Our definition thus fills a void, while preserving the intuitive meaning of the term. In our framework, aging increases the biological age of an individual, and biological age predicts wellspan (healthspan and lifespan) best. This is because we analyze aging as the aggregate of all processes reducing an individual’s wellbeing, and whatever changes a feature of wellbeing, also changes the ideal predictor for this feature. As biological age is the ideal predictor for wellbeing, aging must change biological age.

In practice, the biological age of an individual is represented by a specific numerical value that is estimated based on some features of the individual. It is often estimated in years – with the idea that in a baseline population of individuals, individuals with a similar biological age have a similar expected (residual) wellspan. To estimate it in the best possible way, all features of the individual that are contributing independently would need to be considered. Of cause, chronological age is an important contributor to this estimation, even though, by definition, it cannot be a biomarker of aging; a composite biomarker of aging such as biological age can therefore include a significant component that is not a biomarker of aging. And indeed, predicting a feature better than baseline best starts with that baseline, improving upon it.

In general, biomarkers are identified based on cross-sectional or (preferably) longitudinal cohort data, where features of individuals are measured over time. Whenever there is a time gap between measurements, the biomarker attribute (of predicting the future better than chronological age) may be tested. For any individual (which does not have to be a member of the cohort), the biomarkers we are interested in predict its wellbeing (health and survival) better than chronological age. Biomarker measurements that are predictive for some feature in a population do not have to be necessary, nor sufficient, for that same feature for a particular individual. For example, taking for granted that high blood pressure is a good biomarker for shorter lifespan and for cardiovascular disease, it is possible that a particular individual has high blood pressure but still enjoys a long lifespan without cardiovascular disease (because of other factors with protective influence), and another particular individual may feature short lifespan and cardiovascular disease without having high blood pressure (because of other factors with negative influence). However, a high or low biological age is based on the result of the measurement of the widest possible variety of molecules and functions, so that its prediction of wellbeing cannot be overridden.

Features, and biomarkers in particular, can further be classified on the basis of the following questions:

  1. Is it an intrinsic feature? Features may be intrinsic or extrinsic to the individual for which their value is measured. Intrinsic features include genetic and epigenetic ones; for humans, these also include behavior and lifestyle decisions. Extrinsic features include environmental (and social) ones, as well as prenatal ones. Both types of features are profoundly interconnected [79]. Given these interconnections, we designed our set of definitions to be valid for intrinsic and extrinsic features, even though their relevance is much higher for intrinsic features, see also the Discussion
  2. Is the feature time-invariant or role-switching? Features can be classified according to the periods in the life of the individual in which they are predictive. Thus, they may be biomarkers across the time axis of the entire life of an individual, or they may be predictive only during selected time periods. In fact, biomarkers may be time-dependent, up to the point that they may be “role-switching”, that is, predictions of health or survival based on a high biomarker measurement may first be negative, but then turn positive, or vice versa, as an individual gets older [98]. Generally, our definitions are supposed to be valid at young age, though their relevance is higher at middle and old age
  3. Is the feature predictive for itself? Biomarkers are usually reflexive, so that the current measurement of a biomarker predicts its own measurement in the future
  4. Is the feature diagnostic or theranostic? Features can be classified according to their role as “prognostic” or “predictive” tools. Diagnostic (also known as “prognostic”) biomarkers can help to set up a diagnosis, that is, they are simply predictors of future health or survival. Theranostic (also known as “predictive”) biomarkers can be used as a guide to find an appropriate therapy or intervention as well
  5. Does the feature have a causal influence? A causal relationship is necessary between a biomarker and the features of health, survival or wellbeing that it predicts, if our aim is the identification (but not necessarily the monitoring) of interventions. However, prediction “better than chronological age” does not necessarily imply causality, not even partial causality (that is, being one cause of many), with respect to the processes of aging. A biomarker may thus be purely correlative, but by Reichenbach’s common-cause-principle [99] it then should be the downstream consequence of another feature that is (partially) causal; otherwise it could not be a biomarker. A standard example for a pure correlation with age is the possession of grey hair, which is not supposed to cause aging processes in itself, even though, strictly speaking, it may do so by causing a depressed state or other psychological feedback effects that may be causal to aging. Guided by utility, we are most interested in features that are at least partially causal for aging, that is, features that are part of the causal basis of aging; popular examples are the so-called hallmarks of aging [100], or what became known as inflammaging [101]. There are a few examples of features related to age that are not predictive for any feature of wellbeing. These features are not biomarkers of aging, being not even partially causal, and not downstream of something causal for any feature of wellbeing. The racemization of amino acids in teeth may be cited as one of them [102]. Such racemization is due to the progression of chronological time, and it has no causal consequences. It is, thus, only a biomarker of chronological age. The accumulation of DNA mutations, however, must be considered to be a biomarker of aging, even if the underlying processes were purely chronological, because they have deleterious consequences. In general, we can expect strong correlations between wellbeing, health and survival, but any causal links will be complex, see also [103]
  6. Is the feature easy to measure in practice? A feature should be easily measurable repeatedly, and the measurement should not influence health or survival by itself, and it should yield comparable results in human and other animal species [13]

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Enhancing Health

As noted, any predictive feature of an individual can serve as a biomarker, which may be molecular (genetic variation, genomic methylation, gene/protein/metabolite abundance, etc.) or high-level phenotypic (blood count data, blood pressure, grip strength, anthropometry, etc.). Similarly, the healthspan-enhancing and lifespan-enhancing processes as defined above, just as their reverse, that is, aging, are associated with most aspects of its biology, so that it is impossible to strive for a comprehensive description. Nevertheless, we can define the causal molecular basis of aging as all features of aging that are both causal and molecular. In fact, both aspects of wellbeing, health and survival, have a molecular basis, and the processes leading to these states have such a molecular basis as well, and so does wellbeing. In case of processes, their molecular basis can consist of composite differential features that are measured as changes in the measurements of features [104]. Like all features, also differential ones may be biomarkers as defined above. Only biomarkers that are part of the causal molecular basis of aging can be molecular targets of intervention. Healthspan-enhancing or lifespan-enhancing processes entail maintenance, repair, rejuvenation, as well as the reversal of specific types of hypertrophy or damage, of unreliability and deterioration [102105107] that, as a consequence, move the state of the individual closer to complete health, as defined here, and as defined by the WHO, or that change the state of the individual so that death is occurring later. Naturally, there is a lot of overlap between healthspan-enhancing and lifespan-enhancing processes.

Healthspan and lifespan are often contrasted, and the causal processes resulting in health and survival overlap only partially ([20108] and references therein). For example, an individual may suffer from a serious neurodegenerative disease, but survive for a long time. Such a state of disease often consists of a long time spent in (subjectively) worse health. Thus, worse health does not necessarily imply shorter survival, and, in terms of time spent by the individual, “healthspan” and “lifespan” may differ. Naturally, at any time an individual is healthy, it must be alive. Hence, the healthspan of an individual is necessarily included in its lifespan. However, this inclusion relation is not preserved on the level of causal influences. While survival is influenced by affecting health, positive or negative influences on health may not influence survival with the same strength, or may not influence survival in the same direction, and vice versa. Thus, “causal feature for health and survival” is not synonymous with “causal feature for health”: Processes and interventions that positively influence lifespan may be detrimental to healthspan, and vice versa. For example, processes of antagonistic pleiotropy [109] improve health in early years, and reduce survival (and health) in later years. Aortic aneurysm has a much stronger influence on survival than on health, as it is often asymptomatic. On the other hand, dementia usually has a much stronger influence on health than on survival. In turn, treatment of aortic aneurysm by surgery may improve survival at the expense of health, and treatment of dementia may be indicated even if it causes a significant reduction of survival, but an improvement of health.

Finally, given relationships between features, such as molecular interactions, we can assemble sets of related features into (molecular) pathways. Although the boundaries between such pathways are ultimately arbitrary, the identification of pathways, and of the interaction (crosstalk) between these, has become a common concept. In our case, some pathways can be labeled as wellspan-enhancing, healthspan-enhancing [110], or lifespan-enhancing pathways, depending on whether the features making up the pathway are related to wellbeing, health or survival. In general, aiming to be threshold-free, a “health relatedness score” can be assigned to every pathway. Nevertheless, in practice, we may still label some pathways as being, e.g., healthspan-enhancing, and others as not being healthspan-enhancing. This labeling may be done based on a threshold on the predictiveness of the features making up the pathway. In turn, we may start with features of health, and construct pathways starting with these. The relationships between the (molecular) features may consist of sets of molecular interactions (for example, protein interaction or gene regulation, documented, e.g., in KEGG pathways [111] or other correlative or causal dependencies). As molecular pathways may interact themselves, their interactions can be described by pathway maps, yielding healthspan pathway maps. The net result of their interaction determines the progression, slowdown or reversal of wellbeing. Thus, [112] started with sets of molecular features (that is, genes that are likely involved in health in a causal fashion), and healthspan pathway maps were constructed for human (and C. elegans), based on molecular interaction data. A small number of interacting genes was added to the starting sets, so that at least the majority of genes can be assumed to be causal for health, and the pathways as well as the map between these were then based on a clustering algorithm applied to the molecular interaction network already known for the genes based on other public data.

Relationships between features, and specifically between biomarkers, may consist of relationships among higher-level phenotypic features as well as molecular ones at the same time, based on measuring their correlation [113]. Often, molecular biomarkers are used to predict higher-level phenotypic features. However, a higher-level phenotypic biomarker may also predict a molecular feature better than chronological age. Then again, for practical reasons, we define health by features of relevance to the individual, and these are usually phenotypic, and we strive to find biomarkers as predictors that are easy to measure and yet provide prediction potential for the future state of the individual, and these are often molecular.Go to:

Discussion

In this paper, we describe how health and healthspan can be operationalized for health and aging research. Based on a literature review, we provide a framework for generic, simple and threshold-free definitions of health and health-related terminology. Our definition of health comprises various elements that are often dispersed over distinct approaches to defining health, namely, (1) objective features like the lack of dysfunction and disease, (2) subjective weightings, and (3) the reference to the statistical average in a population. This way, we are able to integrate various operationalizations from the literature into a joint framework. We are optimistic that future operationalizations can be aligned to our framework, thus extending its scope and semantic expressivity. In particular, we expect to incorporate further feedback from the various research communities. We hope that such community feedback can also help to minimize our investigator bias.

We intend our framework as a means of integration for previous, present and future operationalizations of health. While we are striving for a framework of definitions that are as generic, simple and threshold-free as possible, we allow to design more restrictive frameworks by placing constraints on some of the definitions. In some cases, the more restrictive instantiations of our framework are more intuitive, but also less simple. In particular, we consider intrinsic as well as extrinsic features of health, while a restriction to intrinsic features that are contained within the individual may be more intuitive. Also, in our generic, simple and threshold-free framework, a biomarker is just an (intrinsic or extrinsic) feature of an individual that predicts another (intrinsic or extrinsic) feature of the same individual at a later time-point.

Our list of health features (Table 2) is long and complex, and it seems to be too unwieldy to be handled. We do, however, not want to imply that future studies of health or healthspan need to take into account all features in the list at the same time. To the contrary: As the features will be combined with a subjective weighting, those features that are not made use of in a given study can simply be combined with a zero weight in order to neutralize them. Indeed, a specific set of features can be selected, scored and weighted to yield a single score describing a specific aspect of health. Single scores can then be combined, considering various specific aspects of health, and eventually covering all features of Table 2. The more these specific sets of features and the subsequent scoring follow a standard based on a consensus among researchers, the higher the comparability of results from different experimental studies.

Admittedly, it is a difficult business to reduce the complex state of an individual to one single number, and it goes without saying that this comes at a price. First and foremost, a lot of information is lost on the way. For example, we do not capture the state of single organs or organ systems. An individual may have a biologically young skin, but a biologically old heart. But this is not problematic, as the point of the procedure is to distill the biological age of an individual, i.e., the best possible predictor for health and survival – and in this respect, the biologically old heart will probably weight more than the biologically young skin. While we try to avoid arbitrary thresholds, we do need to work with subjective weightings and reference populations, both of which allow for many variations. We see this as a benefit of our approach, as variation in both weighting and reference population may give rise to different kinds of analyses. The default choice of the reference population for health feature measurements is an age-matched population, i.e., we compare the data of an individual with a reference group whose members have similar chronological age as the individual under study. But it might also be very useful to compare results with a reference population that is matching closer the genetics of the individual under study. Still another option is to compare the results from individuals of any age with a reference population of a fixed standard age, e.g., a population of young adults (see Section 2). In any case, the choice of the reference population is an issue that shall be explored further.

Our definition of aging is very broad. According to our definition, any biological process that reduces health or survival will count as an aging process. We think that diverse processes such as the disease course of progeroid syndromes, preterm birth, the development from puberty to adulthood, traffic accidents, moving to a war zone, or losing one’s social interactions are all aging processes. This implies that we operate on a very broad definition of “biological” here, but we are convinced that all of these processes have at least a biological component. Specifically, there is no doubt that the disease course of progeroid syndromes such at Hutchinson-Gilford progeria consists of aging processes, reducing health and survival. Preterm birth and its consequences is actually quite alike progeroid syndromes, in that they include aging-related processes in basically the same way. In both cases, health and survival tend to be reduced, and the underlying molecular biology even features common molecular processes, e.g., in case of mandibuloacral dysplasia [114115] and Marfan lipodystrophy syndrome [116].

Taking the broad view, development from puberty (at which time human mortality is at its lowest in many countries) to adulthood also features some aspects of aging, that is, reductions of health or survival, e.g., due to risk-taking behavior that has at least in part a molecular or genetic determinant. In late adulthood, the relevance of risk-taking usually diminishes, but at the same time the effectivity of the response, in terms of cognitive abilities, goes down. By the same argument, almost all kinds of accidents, war- or crime-related death have biological components, even if non-biological external causes (like a brake malfunction) are more salient. Along the same lines, we can include social processes within our definition of aging processes – although social processes are extrinsic to the individual, and their effects on the individual are mediated by internal psychological processes in a fashion that may be specific for the individual, they are biological in the broad sense that they involve, in one way or other, genes, brains, and hormones. In the generic framework proposed here, the absence of social isolation, poverty, etc., are thus features of health, in line with the notion of functional ability [79] and the WHO “World Report on Ageing and Health” [117].

In fact, subjective aspects arise specifically for any definition of concepts relating to human. For example, aspects of social life are particularly prevalent features of human health, and we consider these as well as some of their cognitive prerequisites in Table 2, specifically as part of some of the integrative features. In particular, social contexts can turn the presence of a disease, which primarily has a negative effect, secondarily into an advantage, that is, into a secondary disease gain. For example, a certain disease may exempt from military service and thus indirectly prolong the life of the diseased, or it may lead to more attention by relatives and friends. Human beings are able as well as forced to integrate various (even pathological) circumstances into a dynamic system of judgements, decisions, values and goals. Considering an individual with a chronic condition, e.g., chronic heart failure, a disease which will progress over time, the goal of a long lifetime may be a function of a composite of – maybe contrary – wishes, beliefs, values, and goals that must be rationally and emotionally integrated into the current and future life course. Thus, quality of life is usually influenced by personality, life experience, cultural factors, personal (including financial) resources, social support networks and other unique life circumstances [118].

As we explained above, our definitions do not exclude that external accidents as well as war or crime-related misfortunes are aging processes, if they result in disease, dysfunction or death. Our main rationale for this broad view is to avoid arbitrary thresholds; in our view, all processes reducing health or survival should qualify as aging. To a certain extent, traffic accidents and war damages are externally caused, but they are not totally independent from intrinsic, biological features. For example, traffic accidents are more likely given risk-seeking behavior, and they are less likely given good cognitive abilities, both of which in turn may well have genetic roots and vary with age. Nevertheless, our framework is flexible to accommodate a restricted set of definitions, where all features considered must be intrinsic to the individual, and all extrinsic features are excluded. It would then fall to the proposer of such a restricted framework to delineate between these two classes of features.

We defined aging as those processes that contribute to disease, dysfunction, or death. As these processes start early in life and accumulate over time, some authors propose to define aging as a disease [119120]. The ICD-11, the new release of the ICD, will contain an extension code “Ageing-Related” (XT9T) for ageing-related diseases. This in itself does not prejudicate whether aging is a disease or not: the diagnosis that some disease is aging-related is not tantamount to the claim that aging itself is a disease. We defined aging as the aggregate of all processes in an individual that reduce its wellbeing. As wellbeing aggregates health and survival, and health is defined as a lack of disease and dysfunction, this implies that aging is a cause of disease or dysfunction or death. Given our definition, it is thus much more natural to conceive of aging processes (as defined by us) as causes of disease, rather than being diseases themselves [121]. However, there would not be any logical inconsistency if aging were a disease, for among the causes of disease, dysfunction or death may well be diseases, while not every cause of these three need to be disease.

In our present analysis, we ignored most of the work on the demography of aging. For example, some demographers distinguish “true” progressive aging from linear processes related to wear and tear, or to disease. Some demographers thus investigate mortality patterns using Gompertz’ law, calculating, usually from small population samples, an initial mortality rate (IMR, also known as baseline vulnerability A) and a mortality rate doubling time (MRDT, also known as acceleration of mortality G, [122]). Then, the idea is that “true” aging is reflected by the MRDT, and there is an “aging-independent mortality” as reflected by the IMR. Implicitly, such a distinction sets a threshold at the transition from IMR and MRDT. Moreover, in an approach focusing on health and healthspan, it is the IMR that counts and that we wish to extend, and we may even aim for a high MRDT, to compress the period of morbidity. Consequently, in our framework, there is no such thing as an “aging-independent mortality” [122], consistent with the notion that a biomarker of aging is just a predictor of health and survival that is better than chronological age.Go to:

Conclusion and future work

We here present a framework which defines often used terms in life science research in an integrative manner. We differentiate between states, time periods, underlying biological processes and predictors of the future. We propose to create a framework which enables researchers to apply the terms (and the concepts behind these terms) to different species, for human beings as well as for model organisms used in research on aging and diseases. Taking into account the huge steps basic research has taken in the last years, we also aimed to create the framework as an open and dynamic one which will progress with the growing knowledge on health, aging and disease mechanism and processes. Therefore, the proposed framework should be seen as a starting point because without a precise definition of what we are studying, the results will be less easy to interpret, also from a translational point of view. With this in mind we hope that the proposed framework will help basic researchers and clinicians to gain a deeper understanding of the field and it enables trans- and interdisciplinary research. It will be work of the future to enrich our table of health features by taking into account more operationalizations of health and healthspan from past, present and future studies.

In order to handle the complexity of health features, it would be desirable to develop a formal ontology of these features, in order to enhance interoperability and automated integration of experimental data derived with different subsets of these features. In such a future ontology of health, the features need to be aligned to appropriate top-level classes. It seems to be promising to analyze many physiological functions as processes, considering that the respective dysfunctions consist in the lack of the dispositions to realize these processes [123]. Moreover, our framework is again flexible enough to allow for subtypes of aging, like linear aging, or progressive aging, in order to single out those aging processes that contribute to a specific kind of decline of health or survival.Go to:

Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant agreement No 633589 (Aging with Elegans). This publication reflects only the authors’ views and the Commission is not responsible for any use that may be made of the information it contains. AAC is supported by a CIHR New Investigator Salary Award and is a member of the Fonds de recherche du Québec – Santé funded Centre de recherche du CHUS and Centre de recherche sur le vieillissement.Go to:

Footnotes

Competing interests

The authors declare that they have no competing interests.Go to:

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  2. I’m not sure exactly why but this weblog is loading incredibly slow for me. Is anyone else having this issue or is it a problem on my end? I’ll check back later and see if the problem still exists.

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  4. How did we win the election in the year 2000? We talked about a humble foreign policy: No nation-building; don’t police the world. That’s conservative, it’s Republican, it’s pro-American – it follows the founding fathers. And, besides, it follows the Constitution. — Ron Paul

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