Problems of diagnosing aging

Methodological Problems of Diagnosing and Treating Degenerative Aging as a Medical Condition to Extend Healthy Lifespan

Ilia Stambler

The need for an integrated approach to healthy lifespan extension

The task of extending the healthy lifespan for the population is urgent for the well-being of the society. Due to the fast population aging in the developed countries, the prevalence of chronic non-communicable diseases and disabilities – such as cancer, ischemic heart disease, stroke, type 2 diabetes, Alzheimer’s disease, etc. – rises steeply.1 Thus, while 66% of deaths in the world occur from chronic age-related diseases, in the developed countries, this proportion reaches 90%, dramatically elevating the costs of healthcare and human suffering.2 Hence, it can be stated that the task of extending the healthy lifespan for the population is one of the most important healthcare, economic and humanitarian tasks.

In addition to the currently available lifestyle approaches (such as moderate exercise, moderate and balanced nutrition, and sufficient rest and sleep), the search for additional novel biomedical means and technologies for healthy lifespan extension is warranted. Moreover, insofar as the deteriorative aging process either precipitates or lies at the root of chronic age-related diseases, the search for novel means and technologies for healthy lifespan extension necessitates the maximal possible amelioration of the degenerative aging process. Such amelioration of the aging process should lead to better health and quality of life for the elderly.3 The possibility of therapeutic intervention into degenerative aging and the consequent significant healthy lifespan extension has been proven on both theoretical-biological grounds and experimental grounds in a variety of animal models. In particular, the ability of cell-based regenerative medicine, gene therapy, pharmacological therapy and nanomedicine to affect basic aging processes and extend healthy lifespan in animal models has been demonstrated, and even some encouraging preliminary results have been achieved in human experiments.4 This possibility has also been conclusively proven by the existence of a large and continuously growing long-lived population, including centenarians and super-centenarians, that exhibit not only a high longevity potential, but also a reduced rate of age-related diseases compared to the general population.5

Yet the pathway toward human healthy lifespan extension remains unclear and requires thorough elaboration, concerning many scientific problems that need to be clarified and technologies that need to be developed. There is a tremendous variety of studies and approaches toward healthy lifespan extension, and roadmaps indicating priority directions.6 Perhaps the most critical drawback in this variety is the lack of integration of the different approaches. The existing approaches often present lists of potential research directions, rather than coherent and coordinated entities. Hence the integration of the various approaches, shortening the pathways between the various disciplines, could be highly valuable for the fundamental and comprehensive understanding of aging and longevity, as well as for the further translation of this knowledge to practical integrative medical applications. Several important “gaps” may yet need to be “bridged” in the current variety of approaches to healthy lifespan extension. 

Longevity factors assessment and manipulation: Bridging the gap between “environmentalist” and “internalist” approaches

One of the main disparities in the current variety of approaches to healthy lifespan extension seems to be the perceived opposition between “external” or “environmental” factors for healthy lifespan extension, and “internal” or “genetically determined” factors. On the one hand, it is often assumed that environmental and lifestyle factors alone are sufficient to affect healthy lifespan, disregarding genetic composition, the inner structure and function of the body. On the other, there is a “genetic” or “biological deterministic” approach that assumes the strict genetic determination of the lifespan from birth that virtually cannot be influenced by environmental factors. There is a clear need to bridge this gap through the study of physiological, in particular metabolic, neuro-hormonal and epigenetic influences on the lifespan, which recognizes the vital regulatory role of the environment on gene expression and internal physiological function.

There are decisive practical implications of this gap, often producing conflicting therapeutic approaches, sometimes leading to struggle in terms of R&D priorities and funding. Thus, there is often a lack of connection between biotechnologies and biomedical technologies, on the one hand, and the so-called information and communication technologies (ICT) or assistive technologies for healthy aging, on the other. While biomedical approaches consider almost exclusively the “inside” of the aging organism, often disregarding the “outside” environmental influences, the ICT and other assistive geronto-technological applications often disregard the “inside” of the body. The study of physiological, in particular neuro-humoral regulation and homeostasis in response to changing environment can help build a bridge between those domains. The study of epigenetics (changes of gene function without changes in DNA sequence) can provide another link, due to the fact that the epigenetic signature of healthy functional longevity can be achieved not just by means of internal medicine, such as regenerative cell therapy and geroprotective small molecules, but in no lesser measure by changes of mental attitude, diet, exercise, the level of social involvement that can be induced not so much through biomedical therapies, as by external coaching and game-like ICT health applications, training the elderly subjects and prompting them to adopt a healthier life-style. The epigenetic mechanisms could provide the “internal/biological” basis for “external/environmental” interventions.7

“Multi-omics” and “frailty”: bridging the gap between molecular-biological, energy-metabolic and functional-behavioral evaluation and intervention

Within the general need for integration, it may be particularly important to bring together the domains of the so-called “multi-omics analysis” and “frailty evaluation.”

There has been an increasing discussion in the biotechnological and biomedical community about the need for “multi-omics” analysis.8 This implies a combined analysis of information about the human organism, aimed to diagnose, and if possible predict its condition, and analyze, and if possible predict the efficacy of specific types of treatment. The aim is to collect the information in a systemic way from different levels of biological organization (or “omes”), including: genome – genetic information, as presented by DNA sequence; epigenome – the epigenetic markers of gene regulation (such as methylation, phosphorylation or acetylation markers); transcriptome – the collection of messenger RNA participating in the transcription of genetic information into proteins; proteome – information on the proteins present in the organism, or in specific cells or tissues; metabolome – information on products of the organism’s metabolism (metabolites); physiome – information on the physiological, such as energetic or respiratory parameters of the organism, and other types of biomarkers. It is hoped that the information from the various levels (“omes”) is correlated with each other and with the clinical history (anamnesis) and therapeutic regimen to provide systemic, precise, predictive, preventive, personalized and participatory diagnosis and therapy.

On the other hand, the most common concept in geriatric evaluation and therapy is old-age “frailty” – a “geriatric syndrome” used to assess the health state of the elderly, alongside age-related diseases and other geriatric “syndromes” such as delirium, incontinence and falls. In the basic sense, frailty is not an evaluation of a defined present state, but an evaluation of a risk of future adverse events. Thus, according to the classical definition, “Frail individuals are perceived to constitute those older adults at highest risk for a number of adverse health outcomes, including dependency, institutionalization, falls, injuries, acute illness, hospitalization, slow or blocked recovery from illness and mortality.”9 It is also admitted that “although a clinical ‘sense’ of frailty exists, there is still no explicitly agreed-on, standard clinical definition of frailty or of failure to thrive that would assist identification of this high-risk subset of the population, prior to the onset of these adverse outcomes.”9 Hence, methods of predictive risk analysis can be most appropriate for the clinical definition and evaluation of old-age frailty.

A stronger alliance between these fields may be desirable. There may accrue a great therapeutic benefit from introducing “multi-omics” type of analysis, its systemic, predictive and personalized philosophy, for old-age frailty evaluation and treatment. And conversely, the researchers and developers of multi-omics biomarkers may need to be more strongly involved in the problems of aging, to realize the critical need to address fundamental degenerative aging processes in order to alleviate virtually all health conditions, including those they are currently working on. Such an alliance is yet a rather rare occasion.

Currently, functional-behavioral assessments dominate the evaluations of frailty.10 For example, in the widely used “Study of Osteoporotic Fractures” (SOF) frailty index, there are 3 main diagnostic parameters: 1) “Weight loss,” 2) “Inability to rise from a chair,” and 3) “Poor energy” as identified by an answer “yes” or “no” to the question “Do you feel full of energy?” on the Geriatric Depression Scale.11 And in the even more widely used “Cardiovascular Health Study” (CHS) frailty index, the 5 parameters are: 1) “Shrinking” as shown by an unintentional weight loss, 2) “Weakness” as shown by a maximal grip strength, 3) “Poor energy” as determined by an answer to the question “Do you feel full of energy?” 4) “Slowness” as indicated by an average walk speed, and 5) “Low physical activity level” as identified by a Physical Activity Scale for the Elderly (PASE) score in the lowest quintile.12 It may be seen that biological markers of aging are assigned little significance in such scores.

To improve the frailty evaluation, to provide a reliable science-based proxy or indication for the aging process, it appears necessary to include more parameters measuring this process at its fundamental biological level. For example, the organism’s energy level can be objectively measured by such means as spirometry, oximetry, hemodynamic, electrochemical and spectroscopic energy metabolite measurements, etc., thus providing improved indication for therapy.13 The energy metabolism measurements may supplement molecular-biological measurements that are commonly employed in the research of biomarkers of aging (e.g. age-related changes in telomere length, advanced glycation endproducts – AGE, DNA repair capacity, aging-associated gene expression and epigenetic markers, stem cell populations and others).14 The more frequent and routine inclusion of old-age frailty evaluation into medical research and practice, and the greater addition of biological indicators to the common functional frailty assessments, in correlation with each other and reinforcing each other, may provide advanced diagnostic and therapeutic capabilities. 

Selecting candidates for therapeutic interventions: Bridging the gap between longevity factor analysis and therapeutic interventions

Despite the wide variety of approaches, there can be outlined a few basic generic fields in the study of longevity. One is the study of “aging biomarkers” and “longevity factors” (both external and internal). Large databases are being developed to collect various physiological, environmental, lifestyle, genetic and other factors associated with extended healthy lifespan as opposed to debilitating aging.14,15 On the other hand, there is the study of experimental “anti-aging” and “lifespan extension,” mainly associated with cell-based regenerative medicine and pharmacological geroprotective substances, that work to experimentally restore the physiological and functional state of the aging organism.16 Yet, there is often a deficit of interrelation between these approaches. The research of “biomarkers of aging” and “longevity factors” is often descriptive, with uncertain implications for clinical practice. The collected factors form large masses of data, yet it is often unclear how the different pieces of data are related to each other or to clinical outcomes, what factors or combinations of factors have the most weight in determining the healthy lifespan, or whether they can be therapeutically influenced either separately or in combinations to improve clinical outcomes. On the other hand, regenerative and geroprotective medicine approaches are often strongly empirical and “prescriptive,” testing for a variety of potential interventions, without a former comprehensive factor analysis, with the aim to empirically establish potentially effective treatments.

Often, the longevity factor analysis and experimental life extension research proceed as if they occupy separate “neighboring domains.” That is to say, a set of biomarkers and other diagnostic parameters of aging and longevity are being developed in one domain, and life-extending interventions in another. And then (in a part of the cases) an attempt is made to test the effects of the latter interventions domain on the former markers domain, rather than deriving the interventions directly from the markers. It may be possible to bridge this gap. It may be possible to conduct a thorough scan of “longevity factors” on a large population, including physiological, genetic, as well as environmental and epigenetic factors contributing to healthy lifespan. It will then be necessary to select the most informative factors contributing to healthy lifespan, for example, using advanced statistical, ontological and information-theoretical methodologies.17 These methodologies may increase the interoperability between model systems, and allow a precise and weighted estimate of the influence of various risk factors and therapeutic interventions, and their combinations, on the healthspan and age-related disease patterns.

The aging and longevity factor analysis should then not remain in a purely descriptive, analytical phase, but should move immediately and simultaneously to clinically relevant experiments on cell, tissue and animal models. For example, the special genetic and epigenetic factors, including gene candidates and epigenetic loci found to be associated with extended healthy lifespan, can form the initial targets for testing and manipulation in experimental models. A hallmark of epigenetic regulation of gene expression is its reversibility by environmental factors. Epigenetic markers (such as methylation) have been strongly associated with the aging process, and diverse pharmacological and cell-therapeutic interventions have been indicated to affect the epigenetic status.18 Moreover, various gene candidates have been associated with extended healthy longevity. Even though it may be practically difficult to directly modify those genes, their expression and activity can nonetheless be stimulated or mimicked via pharmacological and cell-based interventions.19 In case no known mimetics or stimulators of longevity factors exist, those can be designed using methods of synthetic biology or nanomedicine.20 Hence, by providing the input for therapeutic interventions from population-based aging and longevity factor analysis, it may be possible to provide a broad evidential database for further experimentation in regenerative and geroprotective medicine, as well as shorten the pathway between longevity factor analysis and experimentation. The results of experiments may in turn immediately feed back to refine data collection and analysis, accelerating the process of discovery. 

Testing interventions: Bridging the gap between research models

Yet another source of discrepancy among approaches to healthy lifespan extension is the deficit of inter-operability between various models, which may include population, individual, human, animal, culture, cell or molecular models. Often, studies are conducted at different levels of organization, with a disregard of other levels. There is an apparent need for an integrative approach, spanning across the relevant scales, using a wide array of physiological, environmental, genetic and epigenetic parameters. The human being as a whole should be the focus, with a special attention given to personalized factors characteristic of individual subjects, and selecting the most informative factors. Other models and levels could be studied as supplementary. Thus, an attempt at reconstitution of beneficial human characteristics could be made, with experimental testing on the level of human and animal cells and cell cultures and animal organism models. The latter tests could in turn help provide insights for further human studies.

Such interoperability is rare. Commonly, the data collected on humans remain as descriptive registers, with no transition to further experimentation. On the other hand, insights gathered at the level of cells, tissues and animal models remain at those levels, and their applicability for living human beings is unclear or even untenable. It is important to emphasize that the broadest possible collection of diverse biological, physiological and clinical human data, on every level of organization and on the widest possible populations, will be needed. And the human data will need to be compared and supplemented with the widest possible variety of animal data, also on all levels of organization. Such massive and diverse data could enable the creation of truly integrated, holistic models for predictive diagnostic evaluation and preventive therapeutic intervention. There may be a need to have a “common language” (e.g. non-dimensional measures) to describe the different model systems in common terms, for example using terms from information theory, such as entropy and normalized mutual information, that may be applicable for any system.21

Of course, it must be noted that the costs for such a comprehensive data collection and experimentation will likely be high, and funding will always be an issue. It may also be suspected that collecting and analyzing too much and too various data may become unwieldy (whatever the available computational power), and some simplification, abstraction and synthesis may be required. Yet, in any case, the more data can be available – the easier it will be to filter and simplify it. To paraphrase a proverb, ‘it is easier to make a hat from the entire sheep skin than from its tail.’ 

Designing interventions: Bridging the gap between Science and Technology

The research of aging and lifespan and healthspan extension is not just a theoretical scientific or purely biological subject, but in many ways a technological subject, where the capabilities of biological research and manipulation are largely determined by technological capabilities. Virtually all technological fields can be ultimately enlisted for solving the problem of degenerative aging and for extending healthy lifespan. These would include such technological areas as novel measurement modalities (including comprehensive physiological vitality measurements, as well as a vast array of cell-based and molecular measurements), synthetic biology, nanotechnology and micro-fabrication, as well as advanced computational, modeling and visualization capabilities. “Technological convergence” and “cross-fertilization” may be key concepts for tackling the problem of aging.

But the solutions should not remain at the stage of fundamental research in the lab. Another key concept may be “clinical translation” understood as the process of translating fundamental scientific research to its application in clinical practice, creating and utilizing actual medical technologies. The translation process includes all the stages of research and development: from studies on cells and tissues, through animal studies and human trials, up to marketing, production and distribution. The future translation into clinical practice should always be kept in mind as a primary objective. The studies of aging are not just academically intriguing (and they are), but also have a clear purpose – to improve health for the elderly, eventually for all of us. The translation from fundamental research to clinical practice is often difficult, and not only due to scientific and technological hurdles, but often also because of societal constraints, such as lack of social interest and investment or inefficient regulation and distribution. Careful thought should always be given for the facilitation and optimization of the translation process to make aging-ameliorating, life and health-extending therapies and technologies available to all of us. 

Social analysis: Bridging the gap between Science, Technology and Society

Indeed, biomedical aging amelioration and life and healthspan extension are often considered as just and only scientific or technological problems. Yet, in fact, the development, translation, application and access to treatments designed to ameliorate degenerative aging processes and extend healthy lifespan will involve a vast host of social issues and implications, including both hindering and facilitating impact factors that will require comprehensive analysis and debate.22 Hence, it will be necessary to give due consideration to social factors, such as legislative, administrative, communal, economic, demographic, educational and even ethical factors that largely determine the development of lifespan and healthspan extension research and translation of this research into practice. Some of the issues include: regulatory requirements for the short and long-term testing and approval of potential geroprotective treatments; criteria for their efficacy and safety; administrative and organizational requirements needed for the active promotion of healthspan extension research and practice; incentives for the rapid development and translation of the results of this research into medical and clinical practice; provisions for the universal distribution of healthspan-extending technologies to the public, and much more. All these issues will yet need to become the subject of a broad and intense academic and public debate, including political debate.23 

Knowledge dissemination: Bridging the gap between Research and Education

Within the general need for stronger social involvement, there is an urgent need to educate more specialists who will be able to contribute to the various areas of aging and healthspan extension research. There is an even prior need to educate the broader student body and wider public on the importance of such research to prepare the ground for further involvement. Thanks to such broad education, many more new promising studies may spring up. The increased knowledge of the field may increase the demand for therapies, which may in turn increase the offer. Even when the therapies are available, it should be the general public who should use them, hence their willingness to embark on and adhere to a preventive anti-aging and healthspan-improving regimen, their ability to intelligently choose and apply effective and safe therapy, will be vital for its successful application. Therefore comprehensive and wide-ranging “patient and consumer education,” and moreover “citizen scientist” and “do-it-yourself maker” education in the field of aging and healthspan extension will be necessary. Such education is currently very limited. In practical terms, globally there are very few centers or dedicated structures to promote and coordinate knowledge exchange and dissemination on biology of aging and healthy lifespan extension. There are even few courses in this field in university curricula around the world. There is a need for more courses and training materials on the subject, in order to make the narrative on biology of aging and healthy lifespan extension an integral part of academic curriculum and public discourse.

The problem of clinical definition of degenerative aging: bridging the gaps in scientific understanding and communication    

One of the major factors hindering the discussion of aging amelioration, lifespan and healthspan extension research, development and application may be the basic deficit of definitions. What is it exactly that we wish to ameliorate, and what is it exactly that we wish to extend? Such agreed definitions appear to be among the necessary conditions for the communication, dissemination and advancement of the field. But such agreed definitions are currently lacking.

Three is a growing realization that in order to combat the rising aging-related ill health and improve the healthy lifespan – the research, development and distribution of anti-aging and healthspan-improving therapies need to be accelerated.24 It was suggested that one of the accelerating factors could be the general recognition of the degenerative aging process itself as a medical problem to be addressed.25 It has been assumed that such a recognition may accelerate research, development and distribution in several aspects: 1) The general public would be encouraged to actively demand and intelligently apply aging-ameliorating, preventive therapies; 2) The pharmaceutical and medical technology industry would be encouraged to develop and bring effective aging-ameliorating therapies and technologies to the market; 3) Health insurance, life insurance and healthcare systems would obtain a new area for reimbursement practices, which would encourage them and their subjects to promote healthy longevity; 4) Regulators and policy makers would be encouraged to prioritize and increase investments of public funds into aging-related research and development; 5) Scientists and students would be encouraged to tackle a scientifically exciting and practically vital problem of aging. Here we would leave aside the question whether this medical condition should be called a “disease,” a “syndrome,” a “risk factor,” an “underlying cause” or some other trope. Here “the aging process as a medical condition” just means a processes that can be materially intervened into, improved (treated) and even eliminated (cured) by medical means.

Yet, in order for degenerative aging process to be recognized as such a diagnosable and treatable medical condition and therefore an indication for research, development and treatment, a necessary condition appears to be the development of evidence-based diagnostic criteria and definitions for degenerative aging. So far, there are still no such commonly accepted or formal criteria and definitions. Yet without such scientifically grounded and clinically applicable criteria, the discussions about “ameliorating” or even “curing” degenerative aging processes, will be mere slogans. Indeed, how can we “treat” or “cure” something that we cannot even diagnose? It may even be found that such criteria are explicitly or implicitly required by several major international and national regulatory and policy frameworks, such as the International Classification of Diseases (ICD), the WHO Global Strategy and Action Plan on Ageing and Health (GSAP), the European Medicines Agency (EMA), the US Food and Drug Administration (FDA), and others.23 Such frameworks are thirsting for evidence-based criteria for the effectiveness of interventions for “healthy aging.” Nonetheless, nobody has yet done the necessary work of devising such comprehensive evidential criteria. It may seem that the problem has not been solved just for the lack of enough trying. But it must be admitted that the problem is not at all easy even to dare to take on. Many formidable methodological challenges may arise in attempting to develop commonly acceptable diagnostic definitions and criteria for degenerative aging. But try we must!

A major challenge is related even to the semantic understanding of the term “degenerative aging.” The term “degenerative” may imply both the present state of degeneration and the process leading to the state of degeneration. This distinction may have major implications for intervention, respectively implying a curative approach to the already manifest state of degeneration (a late stage intervention) as opposed to a preventive approach to block a process leading to degeneration (an early stage intervention). It may be particularly helpful to explore “degenerative aging” in the latter sense, as a process leading to degeneration that can be prevented. Yet, many questions remain with such a definition. Obviously, not every time-related change leads to degeneration and disease, and some aging-related changes may be beneficial for the person (e.g. the proverbial “wisdom of age”26). Obviously also, many changes leading to age-related degeneration begin at conception, and may be necessary concomitants of the processes of growth and development. Then for which processes and at which stages is intervention warranted? In other words, which aging processes can be considered truly “degenerative” (leading to degeneration) that would require preventive intervention? Several sets of such candidate processes have been proposed,6 yet there is still little empirical evidence that intervention into them will have clinical benefits. The potential interrelation and regulation of these various processes are also uncertain. In this regard, a practical worry is that under the title of “prevention” and “early intervention” – drugs and other treatments will be sold to young and relatively healthy individuals without a real need and without proven benefits in actually preventing degenerative states and/or extending healthy lifespan. A more thorough, quantitative and formal understanding of old-age degeneration (frailty) as a physiological state is required as well. Should it be measured as a lack of function and adaptation to the environment, an impairment of homeostatic or homeodynamic stability?27 Should it be presented as an index or as physiological age?

Each of these options would raise a host of questions of its own, whose mere mentioning would go far beyond the scope of this work. To provide evidence-based answers to those questions, vast empirical and theoretical research yet appears to be needed to establish diverse age-related changes as predictors of adverse age-related outcomes (such as multi-morbidity and mortality) as well as evaluate the effects of various preventive and curative treatments on those outcomes. Based on such data, better formal, clinically applicable models and criteria of degenerative aging as a process and as a state can be developed.

It may be stated that the development of clinical definitions and criteria for degenerative aging, and the corresponding definitions and criteria for the effectiveness of anti-aging and healthspan-extending therapies would be the penultimate “gap” in the common scientific understanding of the problem that needs to be “bridged” before proceeding toward its practical solution. This would in fact mean bridging multiple “gaps” between multiple conceptions and approaches to the problem of aging amelioration and healthspan improvement, to achieve a good level of mutual understanding and agreement. With the current diversity of theories, approaches, models and prospective remedies, it may be yet a long road ahead before such a level of common understanding and agreement is reached. It may not be necessary that every researcher should accept a standard universal metrics and agree on most of the fundamental concepts and processes (as it has been accomplished in mathematics and physics), but at least some degree of commensurability for the field may be desirable. Such commensurability would not mean dictating the same approach to all, or even worse, prescribing the same measures and treatments for all, but rather providing a common language that would enrich general discourse and creativity in the field. The continuous active consultation and debate on these issues may be key to progress.

Some research areas to address in devising clinical diagnostic criteria for degenerative aging and for the effectiveness and safety of anti-aging and healthspan-improving interventions

The present work could not presume to even begin to provide any definitive answers for the above methodological problems. It does not provide any specific building blocks for the bridges between the various areas that may need to come into closer, more powerful synergistic contact. This work is only intended to attempt to emphasize some of those potential problems and stimulate their discussion (in addition to any discussions of these issues that may take place anywhere else). If it succeeds to enhance this discussion and improve this knowledge even slightly, then it has fulfilled its purpose.

 As a way of a conclusion, which is not a conclusion at all, but just an attempt to raise further discussion, a few particular challenges may be listed, including some of the earlier points, problems and gaps. This list includes some of the major concerns for the development of diagnostic and treatment criteria against degenerative aging and for healthy lifespan extension. These can be tentatively classified as follows: 1) establishing definitions, 2) minimizing confounding factors, 3) improving informative value, and finally 4) improving the practical utility of the criteria. This could also be the putative priority order at which the problems can be tackled. (It must be reemphasized that these propositions are only intended to stimulate academic and public debate.)

I. Establishing definitions:

1) Establishing basic terms and definitions. These may include the questions above. For example, should “degenerative aging” be understood as a process or as a state? Or is “healthy aging” a helpful term for developing clinical measurements of aging, considering that most aging processes increase morbidity? Should we instead speak in terms of “healthy longevity” as opposed to “degenerative aging”?

2) Defining clinical benefits. Just and only biomarkers of aging may not be sufficient to provide clinically applicable diagnostic criteria for “degenerative aging” or for interventions against it. For example, as many studies of Alzheimer’s disease have shown, treatments can modify “biomarkers” of the disease very well (in some types of models), but do little or nothing clinically beneficial for actual human patients.28 Hopefully, this problem can be avoided when addressing general aging as a medical condition. There is a need to precisely define measurable clinical end points, demonstrating evidential clinical benefits, especially for the reduction of age-related multimorbidity. The combination of structural biological and functional behavioral parameters may increase diagnostic capabilities. In practical terms, the establishment of clinical benefits would also mean more direct and fast transitions between descriptive measurements and experiments (in both directions), “bridging the gap between longevity factor analysis and therapeutic interventions.”

II. Minimizing confounding factors:

1) Focus on older persons. The clinical benefits need to be evaluated in the primary target population – the older frail persons, rather than the younger and healthier ones who may exhibit entirely different biological responses.29

2) Long term consideration. The clinical criteria and biomarkers, as well as resources available to the organism, need to be considered for the long term. Thanks to long-term evaluation it may be possible to control for effects of over-stimulation, as well as rule out transient compensatory and psychosomatic effects and seeming short-term benefits that may arrive at the expense of long-term deterioration. In particular, seeming short-term “rejuvenation effects” may increase mortality and shorten the actual lifespan.30

III. Improving informative value:

1) Selection. As almost any age-related biological parameter may be considered a “biomarker of aging,” there is a need to select the most predictive and economic biomarkers, for the population as well as for individuals.31

2) Integration. Criteria for degenerative aging may not be only molecular and cellular, but at every level of biological organization – from the molecular to cellular to tissues and organs, to the entire organism and to the organism’s interrelation with the environment – that need to be integrated.32 Moreover, these criteria may not necessarily be chemical and biological, but can also be physical, in particular as relates to various resuscitation technologies as applied to the elderly, such as hypothermia and suspended animation,33 oxygenation and energy metabolism,34 electromagnetic stimulation.35 Social (engagement) and psychological (motivation) criteria also need to be added. Among other implications, this drive for integration would also mean “bridging the gaps” between “environmental” and “internal” evaluations and interventions, between “multi-omics” and “frailty,” and between different, currently often incomparable “research models.”

Individual biomarkers may not be indicative of the process or state of degeneration, and need to be considered in combinations, or ideally in a systemic balanced way – otherwise interventions on particular biomarkers and pathways may exacerbate other biomarkers and pathways, and disrupt the system as a whole. The general methodology for the evaluation of the effects of multiple integrated therapeutic agents and risk factors (including biomarkers of aging) on multiple integrated adverse effects and age-related diseases (multimorbidity) need to be improved, to allow the evaluation of non-linear, cumulative or synergistic effects.36

IV. Improving practical utility:

1) Pluralism and rigor. Particular batteries of assays and interventions are usually related (and potentially biased) to particular theories, research agendas, academic schools and commercial interests. There is an apparent need to allow pluralism of investigation, discovery and application, while maintaining standards of the scientific method. Consensus standards often emerge as a result of data-sharing,37 which may become a practical challenge of its own.

2) Affordability. Costs of diagnostic biomarkers assays and therapeutic interventions may become prohibitive or even impractical for use by most people in the world. There is a need to focus on such therapies, biomarkers and functional assays that may be most cost-effective, especially those that are already routinely used in clinical practice, while still encouraging the development of more sophisticated assays and therapies, that may become more accessible in time, and specifically devising means to increase their accessibility.38

The issue of “affordability” actually involves most of the problems and “gaps” between “science and technology” (the problem of translating fundamental research to practical affordable therapies), between “science, technology and society” (making the therapies widely available, and not only “for the rich and powerful”), as well as between “research and education” (making the knowledge of the field more accessible and wider spread, to catalyze even more knowledge generation). The main overarching question to ask in this regard is: “How can we make the best, most effective therapies available (affordable) as fast as possible to as many as possible?” The details are to be established in a broad academic, public and political discussion.

Motivation for further discussion

All these issues must become a subject of massive and pluralistic consultation, involving scientists, policy makers and other stakeholders. Thanks to such a consultation it may be possible to develop agreeable scientific clinical criteria for degenerative aging that could improve diagnostic capabilities and allow better informed clinical decisions, as well as stimulate further research and development of effective, evidence-based anti-aging and healthspan-extending therapies, treating the underlying processes of aging-related diseases rather than their particular symptoms. In such a broad consultation, various diagnostic and therapeutic approaches to aging amelioration and healthy lifespan extension may be brought together, their relative merits and drawbacks may be compared, and points of their convergence may be clarified. Such a discussion may facilitate the creation of a comprehensive and actionable roadmap toward healthy lifespan extension. It is hoped that the present work will contribute to raising the demand to expand such discussion and research.

References and notes 

  1. Kunlin Jin, James W. Simpkins, Xunming Ji, Miriam Leis, Ilia Stambler, “The critical need to promote research of aging and aging-related diseases to improve health and longevity of the elderly population,” Aging and Disease, 6, 1-5, 2015,
  2. Rafael Lozano, Mohsen Naghavi, Kyle Foreman, Stephen Lim, Kenji Shibuya, Victor Aboyans, et al., “Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010,” Lancet, 380, 2095-2128, 2012.
  3. Nathan Keyfitz, “Improving life expectancy: An uphill road ahead,” American Journal of Public Health, 68, 954-956, 1978,;

Michael J. Rae, Robert N. Butler, Judith Campisi, Aubrey D.N.J. de Grey, Caleb E. Finch, Michael Gough, George M. Martin, Jan Vijg, Kevin M. Perrott, Barbara J. Logan, “The demographic and biomedical case for late-life interventions in aging,” Science Translational Medicine, 2, 40cm21, 2010,

  1. Gregory M. Fahy, Michael D. West, L. Stephen Coles, Steven B. Harris, (Eds.), The Future of Aging: Pathways to Human Life Extension, Springer, New York, 2010;

Alexander Vaiserman (Ed.), Anti-aging Drugs: From Basic Research to Clinical Practice, Royal Society of Chemistry, London, 2017.

  1. Swapnil N. Rajpathak, Yingheng Liu, Orit Ben-David, Saritha Reddy, Gil Atzmon, Jill Crandall, Nir Barzilai, “Lifestyle factors of people with exceptional longevity,” Journal of the American Geriatrics Society, 59(8), 1509-1512, 2011;

Sofiya Milman, Nir Barzilai, “Dissecting the mechanisms underlying unusually successful human health span and life span,” Cold Spring Harbor Perspectives in Medicine, 6(1), a025098, 2015;

Natalia S. Gavrilova, Leonid A. Gavrilov, “Search for mechanisms of exceptional human longevity,” Rejuvenation Research, 13(2-3), 262-264, 2010;

Miguel A. Faria, “Longevity and compression of morbidity from a neuroscience perspective: Do we have a duty to die by a certain age?” Surgical Neurology International, 6, 49, 2015.

  1. Ilia Stambler, A History of Life-Extensionism in the Twentieth Century, Longevity History, 2014,;

Gregory M. Fahy, Michael D. West, L. Stephen Coles, Steven B. Harris, (Eds.), The Future of Aging: Pathways to Human Life Extension, Springer, New York, 2010;

Alexander Vaiserman (Ed.), Anti-aging Drugs: From Basic Research to Clinical Practice, Royal Society of Chemistry, London, 2017.

In the quite famous SENS program (Strategies for Engineering Negligible Senescence), the priority research and intervention areas include: 1) eliminating damage from cell loss and tissue atrophy by adding stem cells and tissue engineering (RepleniSENS); 2) neutralizing nuclear (epi-) mutations leading to cancer by the removal of telomere-lengthening machinery (OncoSENS); 3) backing up mutant mitochondria by allotopic expression of 13 proteins in the nucleus (MitoSENS); 4) elimination of death-resistant cells by targeted ablation (ApoptoSENS); 5) preventing tissue stiffening by substances breaking Advanced Glycation End-products – AGE-breakers (GlycoSENS) and by tissue engineering; 6) cleaning up extracellular aggregates by immunotherapeutic clearance (AmyloSENS); 7) dissolving intracellular aggregates by novel lysosomal hydrolases (LysoSENS). See:

Aubrey D.N.J. de Grey, Michael Rae, Ending Aging. The Rejuvenation Breakthroughs That Could Reverse Human Aging in Our Lifetime, St. Martin’s Press, New York, 2007;

SENS Research Foundation, “A Reimagined Research Strategy for Aging,” accessed June 2017,

As another example, at the 2013 US NIH Geroscience Summit, the following priority research areas were identified: 1) adaptation to stress, 2) epigenetics, 3) inflammation, 4) macromolecular damage, 5) metabolism, 6) proteostasis, 7) stem cells/regeneration. See:

Healthspan Campaign, “NIH Geroscience Interest Group (GSIG) Releases Recommendations from the October 2013 Advances in Geroscience Summit,” 2013,;

Brian K. Kennedy, Shelley L. Berger, Anne Brunet, Judith Campisi, Ana Maria Cuervo, Elissa S. Epel, Claudio Franceschi, Gordon J. Lithgow, Richard I. Morimoto, Jeffrey E. Pessin, Thomas A. Rando, Arlan Richardson, Eric E. Schadt, Tony Wyss-Coray, Felipe Sierra, “Geroscience: linking aging to chronic disease,” Cell, 59(4), 709-713, 2014,

In yet another popular classificatory roadmap, the “hallmarks of aging” that need to be therapeutically addressed, include: 1) genomic instability, 2) telomere attrition, 3) epigenetic alterations, 4) loss of proteostasis, 5) deregulated nutrient sensing, 6) mitochondrial dysfunction, 7) cellular senescence, 8) stem cell exhaustion, 9) altered intercellular communication. See:

Carlos López-Otín, Maria A. Blasco, Linda Partridge, Manuel Serrano, Guido Kroemer, “The hallmarks of aging,” Cell, 153(6), 1194-1217, 2013,

  1. Anne Brunet, Shelley L. Berger, “Epigenetics of aging and aging-related disease,” Journal of Gerontology: Biological Sciences, 69 Suppl 1, S17-20, 2014,;

Maria Manukyan, Prim B. Singh, “Epigenetic rejuvenation,” Genes to Cells, 17(5), 337-343, 2012,;

Alejandro Ocampo, Pradeep Reddy, Paloma Martinez-Redondo, …, Juan Carlos Izpisua Belmonte, “In Vivo Amelioration of Age-Associated Hallmarks by Partial Reprogramming,” Cell, 167(7), 1719-1733.e12, 2016,

  1. Yehudit Hasin, Marcus Seldin, Aldons Lusis, “Multi-omics approaches to disease,” Genome Biology, 18, 83, 2017,
  2. Linda P. Fried, Jeremy Walston, “Frailty and failure to thrive,” in William R. Hazzard, John P. Blass, Walter H. Ettinger, Jeffrey B. Halter, Joseph G. Ouslander (Eds.), Principles of Geriatric Medicine and Gerontology, Fourth Edition, McGraw Hill, New York, 1999, pp. 1387-1402.
  3. Frailty Net, Frailty toolkit, Diagnostic tools,
  4. Kristine E. Ensrud, Susan K. Ewing, Peggy M. Cawthon, Howard A. Fink, Brent C. Taylor, Jane A. Cauley, Thuy-Tien Dam, Lynn M. Marshall, Eric S. Orwoll, Steven R. Cummings, the Osteoporotic Fractures in Men Research Group, “A comparison of frailty indexes for the prediction of falls, disability, fractures, and mortality in older men,” Journal of the American Geriatrics Society, 57(3), 492-498, 2009.
  5. Linda P. Fried, Catherine M. Tangen, Jeremy Walston, Anne B. Newman, Calvin Hirsch, John Gottdiener, Teresa Seeman, Russell Tracy, Willem J. Kop, Gregory Burke, Mary Ann McBurnie, Cardiovascular Health Study Collaborative Research Group, “Frailty in older adults: evidence for a phenotype,” Journal of Gerontology: Medical Sciences, 56(3), M146–M156, 2001.
  6. Johannes H.G.M. van Beek, Thomas B.L. Kirkwood, James B. Bassingthwaighte, “Understanding the physiology of the ageing individual: computational modelling of changes in metabolism and endurance,” Interface Focus, 6(2), 20150079, 2016,

Gennady G. Rogatsky, Edward G. Shifrin, Avraham Mayevsky, “Physiologic and biochemical monitoring during hyperbaric oxygenation,” Undersea and Hyperbaric Medicine, 26(2), 111-122, 1999;

Nili Zarchin, Sigal Meilin, Joseph Rifkind, Avraham Mayevsky, “Effect of aging on brain energy-metabolism,” Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 132(1), 117-120, 2002.

  1. Thomas Craig, Chris Smelick, Robi Tacutu, Daniel Wuttke, Shona H. Wood, Henry Stanley, Georges Janssens, Ekaterina Savitskaya, Alexey Moskalev, Robert Arking, João Pedro de Magalhães, “The Digital Ageing Atlas: integrating the diversity of age-related changes into a unified resource,” Nucleic Acids Research, 43, D873-878, 2015,;

Georg Fuellen, Paul Schofield, Thomas Flatt, Ralf-Joachim Schulz, Fritz Boege, Karin Kraft, Gerald Rimbach, Saleh Ibrahim, Alexander Tietz, Christian Schmidt, Rüdiger Köhling, Andreas Simm, “Living Long and Well: Prospects for a Personalized Approach to the Medicine of Ageing,” Gerontology, 62(4), 409-416, 2016.

  1. Marian Beekman, Hélène Blanché, Markus Perola, Anti Hervonen, Vladyslav Bezrukov, Ewa Sikora, …, Claudio Franceschi, the GEHA consortium, “Genome-wide linkage analysis for human longevity: Genetics of Healthy Aging Study,” Aging Cell, 12(2),184-193, 2013;

Alexander Bürkle, María Moreno-Villanueva, Jürgen Bernhard, María Blasco, Gerben Zondag, Jan H.J. Hoeijmakers, Olivier Toussaint, Beatrix Grubeck-Loebenstein, Eugenio Mocchegiani, Sebastiano Collino, Efstathios S. Gonos, Ewa Sikora, …, Richard Aspinall, “MARK-AGE biomarkers of ageing,” Mechanisms of Ageing and Development, 151, 2-12, 2015;

Gregory K. Farber, “Can data repositories help find effective treatments for complex diseases?” Progress in Neurobiology, 152, 200-212, 2017.

  1. John C. Newman, Sofiya Milman, Shahrukh K. Hashmi, Steve N. Austad, James L. Kirkland, Jeffrey B. Halter, Nir Barzilai, “Strategies and Challenges in Clinical Trials Targeting Human Aging,” Journal of Gerontology: Biological Sciences, 71(11), 1424-1434,

Anthony Atala, “Extending life using tissue and organ replacement,” Current Aging Science, 1(2), 73-83, 2008.

  1. David Blokh, Ilia Stambler, “The application of information theory for the research of aging and aging-related diseases,” Progress in Neurobiology, 157, 158-173, 2017, doi:;

David Blokh, Ilia Stambler, “The use of information theory for the evaluation of biomarkers of aging and physiological age,” Mechanisms of Ageing and Development, 163, 23-29, 2017, doi:;

Keren Yizhak, Orshay Gabay, Haim Cohen, Eytan Ruppin, “Model-based identification of drug targets that revert disrupted metabolism and its application to ageing,” Nature Communications, 4, 2632, 2013,

Georg Fuellen, Melanie Boerries, Hauke Busch, Aubrey de Grey, Udo Hahn, Thomas Hiller, …, Daniel Wuttke, “In Silico Approaches and the Role of Ontologies in Aging Research,” Rejuvenation Research, 16(6), 540-546, 2013,

  1. Anne Brunet, Shelley L. Berger, “Epigenetics of aging and aging-related disease,” Journal of Gerontology: Biological Sciences, 69 Suppl 1, S17-20, 2014;

Maria Manukyan, Prim B. Singh, “Epigenetic rejuvenation,” Genes to Cells, 17(5), 337-343, 2012;

Alejandro Ocampo, Pradeep Reddy, Paloma Martinez-Redondo, …, Juan Carlos Izpisua Belmonte, “In Vivo Amelioration of Age-Associated Hallmarks by Partial Reprogramming,” Cell, 167 (7), 1719-1733.e12, 2016;

Shuji Kishi, Peter E. Bayliss, Jun-ichi Hanai, “A prospective epigenetic paradigm between cellular senescence and epithelial-mesenchymal transition in organismal development and aging,” Translational Research, 165(1), 241-249, 2014;

Steve Horvath, “DNA methylation age of human tissues and cell types,” Genome Biology, 14, R115, 2013,;

Danny Ben-Avraham, Radhika H. Muzumdar, Gil Atzmon, “Epigenetic genome-wide association methylation in aging and longevity,” Epigenomics, 4(5), 503-509, 2012.

  1. Konrad T. Howitz, Kevin J. Bitterman, Haim Y. Cohen, Dudley W. Lamming, Siva Lavu, Jason G. Wood, Robert E. Zipkin, Phuong Chung, Anne Kisielewski, Li-Li Zhang, Brandy Scherer, David A. Sinclair, “Small molecule activators of sirtuins extend Saccharomyces cerevisiae lifespan,” Nature, 425(6954), 191-196, 2003;

Yariv Kanfi, Shoshana Naiman, Gail Amir, Victoria Peshti, Guy Zinman, Liat Nahum, Ziv Bar-Joseph, Haim Y. Cohen, “The sirtuin SIRT6 regulates lifespan in male mice,” Nature, 483(7388), 218-221, February 22, 2012;

Yan Sun, Jia Li, Na Xiao, Meng Wang, Junping Kou, Lianwen Qi, Fang Huang, Baolin Liu, Kang Liu, “Pharmacological activation of AMPK ameliorates perivascular adipose/endothelial dysfunction in a manner interdependent on AMPK and SIRT1,” Pharmacological Research, 89, 19-28, 2014;

Laurent Mouchiroud, Laurent Molin, Nicolas Dallière, Florence Solari, “Life span extension by resveratrol, rapamycin, and metformin: The promise of dietary restriction mimetics for an healthy aging,” Biofactors, 36(5), 377-382, 2010.

  1. Weijie You, Dante Rotili, Tie-Mei Li, Christian Kambach, Marat Meleshin, Mike Schutkowski, Katrin F. Chua, Antonello Mai, Clemens Steegborn, “Structural Basis of Sirtuin 6 Activation by Synthetic Small Molecules,” Angewandte Chemie International Edition, 56(4), 1007-1011, 2017;

Sriram Kosuri, George M. Church, “Large-scale de novo DNA synthesis: technologies and applications,” Nature Methods, 11(5), 499-507, 2014;

Shawn M. Douglas, Ido Bachelet, George M. Church, “A Logic-Gated Nanorobot for Targeted Transport of Molecular Payloads,” Science, 335(6070), 831-834, February 17, 2012.

  1. David Blokh, Ilia Stambler, “The application of information theory for the research of aging and aging-related diseases,” Progress in Neurobiology, 157, 158-173, 2017, doi:
  2. Ilia Stambler, “The pursuit of longevity – The bringer of peace to the Middle East,” Current Aging Science, 6, 25-31, 2014.
  3. Ilia Stambler, “Recognizing degenerative aging as a treatable medical condition: methodology and policy,” Aging and Disease, 8(5), 583-589, 2017,;

Ilia Stambler, “Human life extension: opportunities, challenges, and implications for public health policy,” in Alexander Vaiserman (Ed.), Anti-aging Drugs: From Basic Research to Clinical Practice, Royal Society of Chemistry, London, 2017, pp. 535-564.

  1. Michael J. Rae, Robert N. Butler, Judith Campisi, Aubrey D.N.J. de Grey, Caleb E. Finch, Michael Gough, George M. Martin, Jan Vijg, Kevin M. Perrott, Barbara J. Logan, “The demographic and biomedical case for late-life interventions in aging,” Science Translational Medicine, 2, 40cm21, 2010,;

Luigi Fontana, Brian K. Kennedy, Valter D. Longo, Douglas Seals, Simon Melov, “Medical research: treat ageing,” Nature, 511(7510), 405-407, 2014,;

Kunlin Jin, James W. Simpkins, Xunming Ji, Miriam Leis, Ilia Stambler, “The critical need to promote research of aging and aging-related diseases to improve health and longevity of the elderly population,” Aging and Disease, 6, 1-5, 2015,;

Dana P. Goldman, David M. Cutler, John W. Rowe, Pierre-Carl Michaud, Jeffrey Sullivan, Jay S. Olshansky, Desi Peneva, “Substantial health and economic returns from delayed aging may warrant a new focus for medical research,” Health Affairs, 32(10), 1698-1705, 2013,

  1. Alex Zhavoronkov, Bhupinder Bhullar, “Classifying aging as a disease in the context of ICD-11,” Frontiers in Genetics, 6, 326, 2015,;

Sven Bulterijs, Raphaella S. Hull, Victor C.E. Björk, Avi G. Roy, “It is time to classify biological aging as a disease,” Frontiers in Genetics, 6, 205, 2015,;

Ilia Stambler, “Has aging ever been considered healthy?” Frontiers in Genetics, 6, 202, 2015,

  1. Joshua K. Hartshorne, Laura T. Germine, “When does cognitive functioning peak? The asynchronous rise and fall of different cognitive abilities across the life span,” Psychological Science, 26(4), 433-443, 2015.
  2. Alan A. Cohen, “Complex systems dynamics in aging: new evidence, continuing questions,” Biogerontology, 17(1), 205-220, 2016,;

David Blokh, Ilia Stambler, “The application of information theory for the research of aging and aging-related diseases,” Progress in Neurobiology, 157, 158-173, 2017, doi:;

Alexey Moskalev, Elizaveta Chernyagina, Vasily Tsvetkov, Alexander Fedintsev, Mikhail Shaposhnikov, Vyacheslav Krut’ko, Alex Zhavoronkov, Brian K. Kennedy, “Developing criteria for evaluation of geroprotectors as a key stage toward translation to the clinic,” Aging Cell, 15(3), 407-415, 2016,;

Alexey Moskalev, Elizaveta Chernyagina, Anna Kudryavtseva, Mikhail Shaposhnikov, “Geroprotectors: a unified concept and screening approaches,” Aging and Disease, 8(3), 354-363, 2017,

  1. Eric M. Reiman, Jessica B.S. Langbaum, Adam S. Fleisher, Richard J. Caselli, Kewei Chen, Napatkamon Ayutyanont, Yakeel T. Quiroz, Kenneth S. Kosik, Francisco Lopera, Pierre N. Tariot, “Alzheimer’s Prevention Initiative: A plan to accelerate the evaluation of presymptomatic treatments,” Journal of Alzheimer’s Disease, 26(Suppl 3), 321-329, 2011;

Jeremy Toyn, “What lessons can be learned from failed Alzheimer’s disease trials?” Expert Review of Clinical Pharmacology, 8(3), 267-269, 2015.

  1. Morrison D.H., Rahardja D., King E., Peng Y., Sarode V.R., “Tumour biomarker expression relative to age and molecular subtypes of invasive breast cancer,” British Journal of Cancer, 107, 382-387, 2012.
  2. David G. Le Couteur, Stephen J. Simpson, “Adaptive senectitude: the prolongevity effects of aging,” Journal of Gerontology: Biological Sciences, 66, 179-182, 2011,
  3. David Blokh, Ilia Stambler, “Applying information theory analysis for the solution of biomedical data processing problems,” American Journal of Bioinformatics, 3(1), 17-29, 2015,
  4. Alexander N. Khokhlov, “From Carrel to Hayflick and back or what we got from the 100 years of cytogerontological studies,” Biophysics, 55(5), 859-864, 2010.
  5. 33. Ronald Bellamy, Peter Safar, Samuel Tisherman, …, Harvey Zar, “Suspended animation for delayed resuscitation,” Critical Care Medicine, 24(2Suppl), S24-47, 1996;

Peter Safar, “On the future of reanimatology,” Academic Emergency Medicine, 7(1), 75-89, 2000.

  1. Gennady G. Rogatsky, Ilia Stambler, “Hyperbaric oxygenation for resuscitation and therapy of elderly patients with cerebral and cardio-respiratory dysfunction,” Frontiers In Bioscience (Scholar Edition), 9, 230-243, 2017,;

Gennady G. Rogatsky, Avraham Mayevsky, “The life-saving effect of hyperbaric oxygenation during early-phase severe blunt chest injuries,” Undersea Hyperbaric Medicine, 34(2), 75-81, 2007;

John N. Kheir, Laurie A. Scharp, Mark A. Borden, …, Francis X. McGowan Jr., “Oxygen gas-filled microparticles provide intravenous oxygen delivery,” Science Translational Medicine, 4(140), 140ra88, 2012.

  1. Yury P. Gerasimenko, Daniel C. Lu, Morteza Modaber, …, V. Reggie Edgerton, “Noninvasive Reactivation of Motor Descending Control after Paralysis,” Journal of Neurotrauma, 32(24), 1968-1980, 2015;

Max Schaldach, Electrotherapy of the Heart: Technical Aspects in Cardiac Pacing, Springer-Verlag, Berlin, 2012.

  1. David Blokh, Ilia Stambler, “Estimation of heterogeneity in diagnostic parameters of age-related diseases,” Aging and Disease, 5, 218-225, 2014,; David Blokh, Ilia Stambler, “Information theoretical analysis of aging as a risk factor for heart disease,” Aging and Disease, 6, 196-207, 2015,;

David Blokh, Ilia Stambler, “The use of information theory for the evaluation of biomarkers of aging and physiological age,” Mechanisms of Ageing and Development, 163, 23-29, 2017, doi:

  1. Gregory K. Farber, “Can data repositories help find effective treatments for complex diseases?” Progress in Neurobiology, 152, 200-212, 2017,

38. Ilia Stambler, “Human life extension: opportunities, challenges, and implications for public health policy,” in Alexander Vaiserman (Ed.), Anti-aging Drugs: From Basic Research to Clinical Practice, Royal Society of Chemistry, London, 2017, pp. 535-564.