Summary: A recent paper outlines a practical strategy for creating new anti‑aging therapies and reliable biomarkers by mining large medical studies and population biobanks.
Source: GERO.
Aging is the principal single factor driving chronic disease and mortality. As global populations age rapidly, health care and social systems face mounting challenges. By 2050, the number of older adults worldwide is expected to more than double compared with 2015. In a new article published in Frontiers in Genetics, MIPT scientist Peter Fedichev describes a data‑driven strategy for systematic discovery of anti‑aging therapeutics and biomarkers using large medical studies and biobank resources.
Human mortality rises roughly exponentially with age: the mortality rate approximately doubles every eight years. Incidence rates for many major conditions — including cancer, stroke, and other age‑related diseases — also accelerate after about age 40 at a rate that mirrors overall mortality growth. While physical decline is often treated as an unavoidable consequence of getting older, biology does not mandate an inevitable, fixed trajectory. In nature, some species show very slow increases in mortality risk, prolonged plateaus, or even reductions in risk with age. Species such as the naked mole‑rat and several bat species exhibit little or no mortality acceleration, suggesting that the rate of aging is a tunable biological characteristic rather than an immutable law. This observation opens the possibility that interventions can alter the pace of aging.
The article applies the concept of criticality, borrowed from the physics of complex dynamic systems, to aging research. Criticality is used to describe how complex systems approach tipping points and how collective behavior emerges from many interacting components — ideas that have proven useful in fields from weather modeling to financial markets. Fedichev and colleagues propose that similar mathematical and computational frameworks can be applied to human biology to reconstruct the underlying dynamics that give rise to the Gompertz law of mortality. By analyzing large, diverse datasets from medical cohorts and biobanks with these tools, researchers can build predictive models of biological age and aging rate and identify molecular signatures and potential intervention points for therapies that slow or reverse aspects of aging.
Practical applications of this strategy are already emerging. Fedichev’s team has developed an aging and frailty biomarker that leverages data from wearable devices and smartphones, illustrating how continuous, real‑world physiological signals can inform accurate estimations of biological age. In another example, the group used transcriptomic signatures associated with longevity to identify experimental compounds that extend lifespan in model organisms. These successes demonstrate how combining large datasets with systems theory and machine learning can accelerate candidate discovery and prioritize interventions for experimental testing.
Regulatory and clinical landscapes are also evolving in ways that could support trials of therapies targeting aging‑related decline. The 11th Revision of the International Classification of Diseases (ICD‑11) added classifications for several aging‑related conditions, including age‑associated cognitive decline. These changes should make it easier to design clinical trials and seek regulatory approval for therapies aimed at the functional declines that accompany aging. Among promising early targets are circulating blood factors, because experimental evidence from plasma transfusion studies indicates that blood‑borne molecules play a significant role in modulating tissue function and resilience during aging.
“The growing ability to mine large medical datasets and biobanks, combined with concepts from complex systems science, provides a realistic path toward generating clinically relevant biomarkers and discovering therapeutic targets that influence the aging process,” explains Peter Fedichev, the paper’s author and founder of Gero LLC, a longevity biotechnology company focused on extending healthy human lifespan.
Funding: GERO LLC funded this study.
Source: Ksenia Tsvetkova – GERO
Publisher: Organized by NeuroscienceNews.com.
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Original Research: Open access research: “Hacking Aging: A Strategy to Use Big Data From Medical Studies to Extend Human Life” by Peter O. Fedichev, published in Frontiers in Genetics on October 23, 2018. doi: 10.3389/fgene.2018.00483
GERO. “Hacking the Aging Code.” NeuroscienceNews, November 26, 2018.
Abstract
Hacking Aging: A Strategy to Use Big Data From Medical Studies to Extend Human Life
Age is the dominant risk factor for chronic disease and death. The human mortality curve follows the Gompertz law, with mortality roughly doubling every eight years. The incidence of many specific age‑related diseases accelerates with age at a similar pace, suggesting a common underlying process that increases vulnerability to disease and ultimately leads to death: aging itself. Yet exponential increases in morbidity and mortality are not mandated by a physical law; they are emergent properties of biological systems that may be modified.
The acceleration of mortality with age is therefore a central measurable feature of aging and varies widely across species, even among closely related mammals. This variability indicates that aging rate is a tunable phenotype. In the paper, Fedichev outlines how large‑scale human medical datasets, longitudinal cohort studies, and population biobanks, combined with analytical methods from the physics of complex dynamical systems, can be used to reverse‑engineer the biological processes behind the Gompertz mortality pattern. By building predictive models of biological age and the dynamics of physiological decline, researchers can identify robust biomarkers of aging and prioritize molecular targets for therapeutic intervention.
This data‑driven framework emphasizes integration of heterogeneous data types — clinical measures, molecular profiles, wearable sensor outputs, and longitudinal health records — and the use of systems‑level modeling to detect early signs of destabilization in physiological networks. The approach aims to provide a systematic pipeline for discovery: extract aging signatures from large datasets, validate biomarkers that track biological age and frailty, and use those signatures to nominate candidate interventions for experimental testing. Ultimately, this strategy seeks to accelerate the transition from descriptive aging science to actionable therapies that extend healthspan and reduce the burden of age‑related disease.