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Record W2517242151 · doi:10.1093/gerona/glw087

Moving Geroscience Into Uncharted Waters: Table 1.

2016· editorial· en· W2517242151 on OpenAlexaff
Felipe Sierra

Bibliographic record

VenueThe Journals of Gerontology Series A · 2016
Typeeditorial
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsTable (database)OceanographyEnvironmental scienceGeologyComputer scienceData mining

Abstract

fetched live from OpenAlex

Research into the basic biology of aging has undergone a seismic shift in the last 10–20 years, moving rapidly from the very descriptive approach focused on the aged that was the predominant focus by the end of the last century, to a more mechanistic (and primarily genetics-driven) phase, focused less on describing the aging phenotype in different models, and more on a definition of the molecular and cellular drivers of the process. This progression was accompanied by an evolution in the concepts and ideas that have dominated the field in the past, namely free radicals, cell senescence, and caloric restriction, each of which became the seed upon which the modern foci of research now stands. Progress in a variety of research areas has crystallized into the beginnings of a conceptualization of the process, including seminal publications that described the major hallmarks or pillars of aging in 2013 and 2014 ( 1 , 2 ). This knowledge is far from complete, and much more research is needed to further refine these pillars, as well as the interactions among them and the interactions between aging and other Gene × Environment elements that, together with aging, determine the health status of the elderly. I would even venture that the description of the major pillars of aging represents the first baby steps in the right direction, but nevertheless, it begins to allow researchers to envision the process of aging as a whole. Aging research is not simply an academic pursuit, it actually holds more promise in terms of helping mankind than most or all other biomedical fields. In terms of health and human suffering, it is well known that four out of five older Americans suffer from at least one chronic disease, and more than half suffer from multiple comorbidities. Aging being the major risk factor for all those diseases, it follows that research into aging could be pivotal in our efforts to reduce the suffering associated with the ravages of old age. In addition to the direct health issues, it has been calculated that care for the elderly currently accounts for 43% of the total health care spending in the United States, or approximately 1 trillion dollars a year ( 3 ), and this number is expected to rise as baby boomers reach retirement age. Reducing these costs is critical for the survival of society as we know it, and indeed it has been calculated that a modest increase in life span and health span (2.2 years) could reduce those expenses by 7 trillion dollars by 2050 ( 4 ). Thus, by delaying aging even by a lesser degree than currently achieved in animal models, there will be significant gains both in terms of health and wealth. The enormous advances in basic aging research, coupled with the promises described in the previous paragraphs, led to the concept of geroscience, a field that aims to understand the molecular and cellular mechanisms responsible for aging being the major risk factor and driver of common chronic conditions and diseases of the elderly. Of course, there is considerable work to be done in order to bring the field forward and move aging biology towards translation. Major areas in need of further development include, in the preclinical space, the development of better, reliable, and predictive biomarkers, as well as development of metrics for health, including resilience. On the clinical side, a major roadblock is the fact that life span—the traditional gold standard in aging research in animal models—is not an option in human studies, and therefore reliable surrogate endpoints need to be developed and characterized. In an effort to take advantage of recent developments in the field, and trying to catalyze further conceptualization of emerging areas, the NIA published in 2011 a Program Announcement (PAR 11–266) titled “Network Infrastructure Support for Emerging Areas of Research in the Basic Biology of Aging.” The PAR called for the establishment of teams that could develop overarching ideas on how to move the field forward, using the R24 mechanism (a resource-related NIH mechanism designed to enhance the capability of resources to serve biomedical research). Several important areas were selected for funding through the usual peer review system, including projects focused on developing the domestic dog as a model system for aging research, a geropathology consortium, and a team, led by Drs. Kirkland, Austad, and Barzilai, to develop a Geroscience Network (see Table 1 ). This latter team tackled the central issue of the geroscience hypothesis: by delaying the aging process, it should be possible to delay not just one, but most chronic diseases affecting the elderly, all at once. Geroscience Network Geroscience Network The accompanying papers describe the work done thus far by this group, as well as a report from an NIA-sponsored workshop on “Resilience in Aging Animal Models”, held at the NIH Campus on August 27, 2014. They Geroscience Network group’s work consisted of 4 separate workshops, each involving between 18 and 35 basic scientists, clinicians, and others. The workshops, held within the space of about 2 years, focused on specific aspects needed to move geroscience from a hypothesis into a reality. While each Geroscience Network workshop focused on specific aspects within the continuum between basic aging biology and clinical practice, many elements of the discussions intersected extensively and consequently, there is considerable overlap in the reports. The paper by Burd and coworkers analyzes the barriers to translation and preclinical development of interventions. The authors discuss possible best approaches along the early stages of the pipeline, starting from drug discovery and lead compound development. In addition to suggestions on best practices at those stages, a discussion is presented concerning existing barriers such as the current lack of efficient communication between basic scientists and clinicians (a gap that geroscience is attempting to narrow), and the difficult issue of funding for these efforts, where a focus on generic drugs may limit financial incentives for industry, despite the potentially large market for these interventions, and where both Federal and philanthropy monies are very tight. Importantly, this report emphasizes the need to develop biomarkers and clinical trial strategies relevant to frailty and resilience, both in humans and in animal models, thus appropriately serving as a nexus between the more basic focus of Huffman and coworkers, and the more clinical ones by Newman and coworkers and Justice and coworkers. The piece by Huffman and coworkers focuses on health span evaluation in preclinical models, with an emphasis on non- or minimally invasive measurements that can be conducted in biomedicine’s animal model of choice, the mouse. This report complements other efforts in the same domain that have appeared recently ( 5 , 6 ), and which aim at attempting a consensus among basic scientists to define what constitutes “health” in mouse models. The focus is on physiological parameters of overall health, including neuromuscular, cognitive, cardiovascular, metabolic, and inflammatory domains. Significantly, Huffman and coworkers also discuss the importance of using stressors to challenge these different physiological domains, so as to assess resilience, rather than simply frailty. The importance of defining and characterizing a panel of health measures that is reliable and robust cannot be overemphasized, since such measurements will form the basis for assessing, at the preclinical stage, whether an intervention is worth considering for a first-in-humans trial. The report by Newman and coworkers deals primarily with strategies needed for translation into the clinic. Purposely, the focus was on how to design studies to delay aging by drugs already approved for human use. Therefore, in addition to discussing the general principles involved in clinical trial design, the participants also discussed the merits and drawbacks of different candidate drugs including metformin, acarbose, rapamycin, resveratrol, and others that are currently being actively investigated. The roadblocks to designing a clinical trial focused on aging as an endpoint are formidable, starting from the obvious impossibility of using life span—the gold standard in preclinical models—in human studies, unless they are observational Phase IV (postmarketing) as was recently reported for metformin ( 7 ). Another major hurdle is the need to define what surrogate measurements might or might not be acceptable to regulatory agencies such as the FDA in the United States. The paper discusses in certain detail some possible approaches to surrogate measurements, including health span, resilience, and diagnosis of a large panel of chronic conditions, which could be used as an endpoint for clinical studies. The report by Justice and coworkers described specific issues and elements that need to be taken into consideration while developing potential clinical trials for aging in humans, including all the elements that characterize a well-designed Phase II clinical trial, and how such a design can be modeled when the goal is to obtain an FDA certification against aging, rather than the more commonly standard of issuing such certifications against disease. Many obstacles will need to be addressed as the field moves from basic biology observations and into the clinical realm, and some of these obstacles differ on whether the intervention involves a previously FDA-approved drug, or a new entity requiring an IND. Interestingly, Justice and coworkers envision at least three possible paradigms that can be used: targeting age-related diseases, targeting geriatric syndromes, and targeting resilience. While many considerations are common to all three scenarios, some are unique, and each scenario presents specific advantages and disadvantages that need to be balanced as the field moves forward. Finally, it is worth noticing that, as stated in the reports, all the discussions of the Geroscience Network involved, to different degrees, an element related to measurements of resilience, either in animal models or in humans. For that reason, and in spite of it being independent of the Geroscience Network, the report from the NIA workshop on Resilience in Aging Animal Models is included in this package as well. The workshop concentrated on identifying practical methods for measuring resilience in mouse models, as a way of accelerating the testing of potential interventions before a full-length longevity analysis was attempted. The group identified a limited number of potential tests that are simple, cheap, and to an extent, measure overall physiological response, as opposed to focusing on a single or few tissues or systems. These potential tests are here being offered to the research community to elicit further studies on optimization of both the stresses and the responses. As a result of the workshop, in 2016, the NIA published RFA AG 16-006, “Short-term Measurements of Improved Physical and Molecular Resilience in Pre-clinical Models.” The reports represent the core of the discussions, which included multiple viewpoints, and are not meant to be the definitive answer to all the related questions. Rather, they represent an offer for the research community to discuss and hopefully improve upon. As stated at the beginning, basic aging research has travelled a long way and the current efforts by several research teams, including those funded through the R24 mechanism, represent the latest—but by no means the last—stage in the progression from the early descriptive phase to finally being able to reap the benefits of basic aging research and apply this knowledge to improve the health and well-being of the burgeoning older population. Drs. Austad, Barzilai, Kirkland, and Sierra are grateful to all the attendees of the R24 retreats for their participation and contributions ( Supplementary Table ).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.025
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.044
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.003
Science and technology studies0.0040.004
Scholarly communication0.0120.009
Open science0.0040.004
Research integrity0.0130.022
Insufficient payload (model declined to judge)0.0250.013

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.273
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations34
Published2016
Admission routes1
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