Toward a Unified Framework for Healthspan and Lifespan: Bridging Mechanistic and Functional Paradigms of Aging

Jasmine Smith
3 days ago
10 min read

The Evolution of Aging Biology

The biology of aging is complex, and our grasp of it is still in its early days. Yet a handful of landmark advances—from the Human Genome Project (2003) to today’s AI-guided drug-discovery engines—have rapidly expanded what we can measure and, increasingly, what we can change. Epigenetics revealed age-related shifts in gene activity without DNA mutations; AlphaFold brought near-instant protein-structure predictions; partial cellular reprogramming showed that molecular age can be reset; and the Biomarkers of Aging Consortium is building agreed-upon measures to tell whether any of these ideas actually make people biologically younger.

Over the past two decades, aging biology has moved from cataloging curiosities to engineering interventions. Amid this experimental whirlwind, scientists reached for conceptual maps to keep the field navigable. Three, in particular, have shaped scientific discourse and research priorities: the Hallmarks of Aging, the Seven Pillars, and Strategies for Engineered Negligible Senescence (SENS).

While these frameworks overlap considerably and share similar scientific foundations, the field lacks a unified approach that integrates them. Researchers cite different papers, emphasize different aspects, and pursue different intervention strategies based on their chosen framework. This fragmentation has practical consequences: hallmark-focused approaches often drive industry toward interventions that may optimize current biological systems within intrinsic biological limits rather than transcending them, while structural damage repair approaches pursue more comprehensive but technically challenging solutions that require substantial time and resources to reach clinical viability. This divergence shapes not just research priorities, but how the public perceives aging interventions—conservative healthspan enhancement versus "radical" life extension.

This article examines what each framework captures, where each leaves gaps, and how a layered synthesis could provide a shared roadmap for understanding aging and inventing therapies to slow, stop, or even reverse it. We also explore how AI systems—like those being developed at Rejuve.AI—could leverage such integration to identify hidden relationships between mechanistic processes and functional interventions, suggesting novel intervention strategies that address both healthspan and lifespan goals simultaneously. For researchers and AI developers working in aging biology, understanding these frameworks—and their potential for integration—is essential for moving beyond piecemeal approaches toward comprehensive solutions.

Current Aging Frameworks: Mechanistic vs Functional

Since the formal emergence of geroscience and efforts to formally understand and intervene in aging, the three aforementioned frameworks have formed the basis of scientific (and downstream clinical) approaches to slowing or reversing the effects of aging.

I will describe these frameworks broadly in terms of “mechanistic” (meaning describing the why and how of the processes and the ways in which they interact and intertwine) and “functional” (describing the what of the processes and the means by which they may be intervened in).

Hallmarks of Aging

The key frame of reference for describing aging and thus developing interventions is currently the Hallmarks of Aging, a landmark paper originally published in 2013 with nine identified drivers and effects of aging, being expanded in 2023 with three additional Hallmarks, with assumed room for continued expansion. The Hallmarks are divided into three main categories: Primary, Antagonistic, and Integrative. The idea is that these cascade into each other, and this describes aging. The Hallmarks of Aging are a starting point for any deep dive into aging biology, but are they enough?

Rejuve.AI’s premier AI model Bayes Expert (featured in our recently launched Longevity App) is the first model of its kind to aggregate the known scientific consensus of this framework into the context of everyday health and wellness. It leverages these Hallmarks for health insights, education, and systems biology modelling. These Hallmarks are important for quantifying biological age, benchmarking treatments, and learning more about biomolecular activity.

However, there are some key limitations of this framework:

1) It is unclear if/when it is ever complete.

Drivers of aging damage and their effects continue to be debated among experts. The standards for being a Hallmark are: (1) the association of aging with its manifestation over time, (2) an acceleration of the aging process due to its experimental accentuation, and (3) a deceleration of biological aging resulting from its experimental attenuation. However, these criteria can potentially be met by literally dozens of factors. In fact, just last month, two new Hallmarks were added with explicit mention that they are inherently incomplete given ongoing aging research.

2) Attenuation of all of the Hallmarks does not necessarily equate to rejuvenation.

While a great effect on all Hallmarks presumably can demonstrate some degree of "reversal of biological age" (which also has no clear endpoints and is a separate issue), it does not reflect total restoration to a youthful state or indefinite postponement of age-related ill health. If it were the case, the ultra-healthy would already be living indefinitely.

Prominent examples of this phenomenon are known anti-aging compounds, these days commonly referred to as “Geroprotectors” (ex. resveratrol, NMN, even coffee) that may improve multiple or even all Hallmarks of Aging to some degree. Yet, these substances, even in combination, will not lead to living beyond known limits or even guarantee living to 100.

Seven Pillars of Aging

Seven colorful circles linked by lines, labeled Metabolism, Epigenetics, Inflammation, etc., representing interconnected biological processes.

Around the same time as the Hallmarks of Aging paper was published, the Trans-NIH Geroscience Interest Group (GSIG) published another namely mechanistic framework specifically aimed at exploring the relationship between aging and disease, supporting the geroscience hypothesis which posits that addressing aging as a root cause also mitigates other chronic diseases and thus extends overall total healthy lifespan. The Seven Pillars of Aging were the result of a meeting sponsored by the United States National Institute of Aging (NIA) in 2014, with the goals of developing a systematic understanding of aging, understanding mechanistic links between aging and chronic disease, and recommending pathways to develop therapies or preventative approaches for age-associated diseases.

These pillars remain key foci of aging research and technology today, from mTOR inhibition to stem cell therapies to epigenetic reprogramming. This framework overlaps heavily with the Hallmarks of Aging, sharing the goal of understanding drivers of aging in order to more narrowly direct research into potential therapies and mitigation strategies. However, it shares similar limitations in that it presents more questions than answers (How do we optimize metabolism to a greater extent? How do we deal with macromolecular damage? What are the best ways to prevent or reduce inflammation? What determines adaptation to stress and how malleable is it?), and in that modestly addressing all of these points could potentially delay the onset of chronic diseases but does not comprehensively (at least not in a direct manner) point to ways in which to overcome the 120-year lifespan barrier or stave off these diseases indefinitely.

SENS

Table listing "seven deadly things" with fix strategies. Colorful rows detail aging issues and maintenance approaches under a yellow title.

Eleven years prior to the Hallmarks of Aging, a revolutionary framework was proposed by renowned biomedical gerontologist Dr. Aubrey de Grey, which instead of focusing on mechanisms and processes to explain aging, defined seven (reduced from originally nine) distinct categories/consequences of aging damage, along with corresponding approaches on how to address them. While it shares some overlap with other frameworks (ex. highlighting cellular senescence, defunct mitochondria, and genetic and epigenetic mutations as key problems), this framework differs considerably in that “damage” is defined as phenomena that cannot be mitigated by natural processes once they exceed a certain threshold, therefore representing actual root causes of death from aging. Appropriately, these were referred to as "The Seven Deadly Things". To combat each of these deadly things were general strategies (the Strategies for Engineered Negligible Senescence), which represented an engineering approach and the first conceptualization of rejuvenation biotechnology.

Theoretically, fixing all seven of these damage types could restore a person who is old to a more youthful state by way of restoring the structural integrity of the body at the cellular and molecular level in order to restore the function.The key limitation of this framework is that it is easier said than done. While the idea of not needing to know exactly how aging works in order to intervene is straightforward, there are clear reasons why this has not already succeeded in defeating aging. The original SENS platform proposed relatively ‘brute force’ approaches that require deep engineering of fundamental biology. For example, the MitoSENS approach aims to express the 13 unique mitochondrial genes inside the nucleus as a “back-up copy” (so far, scientists have been able to express 8 out of 13 of them). Another particularly ambitious approach is OncoSENS, which proposed to completely remove telomere lengthening capability entirely in order to eliminate cancer (by virtue that the majority of cancers express/need telomerase to thrive).

Fortunately, significant advancements have occurred since the time of the publishing of this framework that have offered alternative and perhaps easier ways of targeting these damage types. One prominent example is Cyclarity (which was originally spun out from SENS Research Foundation), a company developing a drug to remove atherosclerotic plaques (an intracellular aggregate) for the first time in history, which was approved for human clinical trials in Australia this year.

Other examples include new cancer drugs. Rather than poisoning or irradiating the body at large to target specific cells, approaches are becoming more precise to target only the offending cells, such as immunotherapies like CAR-T, immune‑checkpoint inhibitors, and investigations into gene therapies like oncolytic virus-based suicide gene therapy.

Partial reprogramming has also become one of the most popular and promising approaches to replacing lost cells and even addressing other damage types by resetting cells back to a more youthful state. The first human trials could begin as early as this year.

Replacement , or the idea of replacing worn-out biological components with lab-grown or synthetic tissues, is also gaining recent momentum, which arguably can be seen as a sweeping solution for all of these types of damage should it be successful.

Along with the technical (and not to mention funding) challenges of achieving these breakthroughs, the complementariness of understanding more about aging clearly benefits the safety, speed, and precision of the resulting therapies. Learning more about the aging process (i.e. the mechanistic approaches) better informs the strategies and expands their profiles, leading to more innovative therapeutic approaches.

A Unified Framework: Bridging the Divide

These frameworks view the aging problem in slightly differing ways, with the Hallmarks and Pillars of Aging concepts focused on explaining aging and slowing it down, and SENS focused on direct targets and all-out rejuvenation. Consequently, this ties directly to the "healthspan versus lifespan" debate. Should we focus primarily on extending healthspan in light of a world filled with growing aging populations in poor health, or instead on extending overall lifespan with emerging technologies?

The answer is simple: We are and should continue to do both. Preserving health at the population level is beneficial for society in many ways, including economically and socially. But without a unified definition and framework for approaching aging, such divides form within the scientific community, influencing public opinion, which subsequently leads to funding being suboptimally allocated and applied.

This article proposes and advocates for the creation of a unified framework that clearly focuses on both healthspan and lifespan extension by virtue of grounding in the sevsn SENS categories with explanatory variables and mechanisms such as those identified in the Hallmarks of Aging and Seven Pillars as an emergent layer, allowing for expansion and interconnection. Therefore, novel relationships between mechanisms of action on select processes and their potential for effect or revealing information on ultimate rejuvenation strategies can be teased out and modeled via AI.

Practical Applications of Unification

Continuing an example mentioned earlier, while it is known that present Geroprotectors, even in combination, cannot remove certain intracellular aggregates (ex. atherosclerotic plaque, beta amyloid), we can still learn more about how macroautophagy (e.g. the cellular "self-eating" process that removes damaged components) itself, as well as other associated Hallmarks like loss of proteostasis (maintenance of proper protein function) and mitochondrial dysfunction affect the aggregation of these presently indestructible waste products in order to inform new therapies and mechanisms by which to target them.

We can take these insights so far as to even identify molecular and atomic properties of these pathways and mechanisms in order to synthesize improved ones that both have a stronger effect than is currently possible while maintaining an adequate safety profile (i.e. not interfering with normal metabolism).

Applied to data organized within this unified framework, AI systems can yield novel insights into the connections between factors that merely slow aging versus those with restorative potential. This distinction is crucial for moving beyond interventions with diminishing returns toward those that fundamentally reset biological age.

Such systems can precisely analyze how existing interventions impact repair processes and to what degree, while simultaneously mapping co-effects—both beneficial and detrimental. For example, AI could determine how a specific senolytic compound affects not just cellular senescence but also mitochondrial function, stem cell activation, and extracellular matrix composition. This comprehensive assessment enables the design of improved therapies that maximize repair mechanisms while minimizing off-target effects.

Through cross-referencing and transfer learning across species datasets, AI can suggest high-impact studies in both animal models and humans. This could guide Rejuve.AI's decentralized N-of-1 trials that incentivize human data provision, or projects like the LEV Foundation's Robust Mouse Rejuvenation (RMR ) study, which is testing whether combination therapies can significantly extend remaining lifespan in normal, middle-aged mice—with the ambitious goal of adding 12 months of life—tripling the gains seen with caloric restriction, the current benchmark. A unified framework that bridges mechanistic and functional paradigms of aging could help prioritize such interventions by predicting which combinations are most likely to modulate both Hallmarks and structural damage repair—while ensuring safety and personalization across populations. This vision aligns with the network-based approach described by Duong et al. (2023), where AI-guided synthesis of diverse biological models and crowdsourced datasets forms a Generative Cooperative Network (GCN) capable of adapting to complex systems-level aging dynamics.

AI analysis within this unified framework could also suggest entirely new Hallmarks of Aging or emergent categories that transcend current classification systems. Beyond identifying biomarkers of aging, such work could lead to specific biomarkers of rejuvenation—signals that distinguish between slowed deterioration and actual biological age reversal.

Conclusion: Moving Beyond Fragmentation

While aging researchers continue to disagree about the limits of age reversal, maximum human lifespan, and the true definition of aging, it is clear that these mechanistic and functional frameworks have prominent and significant relevance in the overall study of aging. No matter where one stands on the healthspan to lifespan spectrum, what definition of aging they are subscribed to, or even whether or not they believe aging is programmed, all approaches target some or all of these shared attributes in their efforts to develop methods and benchmarks proposed to defeat aging.

Rather than choosing between frameworks or developing yet another competing model, this article proposes viewing them as complementary layers of understanding. By viewing these in a unified framework of letting mechanistic descriptions and modest mitigations of aging (ex. autophagy, microbiome improvement) inform more advanced approaches (ex. partial reprogramming, intra-arterial plaque removal), we can accelerate therapeutic development across the entire spectrum of aging interventions.

A unified framework, enhanced by AI analysis, could transform aging research from tracking individual biomarkers to orchestrating comprehensive restoration strategies. By synthesizing mechanistic understanding with functional interventions, we open pathways to therapies that address both the gradual decline of healthspan and the fundamental limits of lifespan. This framework can be applied to existing data scattered across studies and institutions while simultaneously guiding the design of better studies and more targeted data collection—creating a virtuous cycle of understanding and intervention.

The promise of such synthesis extends beyond academic clarity. As AI systems begin aggregating insights across all frameworks, they could identify intervention combinations and timing protocols that no single approach would discover in isolation. This unification of conceptual models is not merely an intellectual exercise—it is a pathway toward therapies that could ultimately defeat aging, restore vitality indefinitely, and fundamentally redefine the boundaries of human health and longevity.