Aging is not a disease: a new look from evolution and biology

Aging is the most universal and significant risk factor for numerous diseases in humans, including cancer (Trastus and d’Adda di Fagagna 2025), dementia (Feigin et al. 2019; Nichols et al. 2022), diabetes (Bellary et al. 2021), autoimmune diseases (Liu et al. 2023), and metabolic disorders (Amorim et al. 2022), among others. In other words, even if individuals lead healthy lives, as they age, their likelihood of developing diseases and infections increases, and their overall biological fitness declines.

This started debates about the definition of the aging process. Discussions on this topic feature a variety of perspectives and continue to grow as more empirical evidence contributes to the overall understanding (Rose et al. 2012; Lemoine 2020; Menassa et al. 2023). Nonetheless, most researchers generally agree that the aging process encompasses a series of complex, time-dependent changes occurring at multiple levels within an organism. These changes are typically viewed as damaging, leading to a gradual deterioration in the functionality and effectiveness of biochemical processes. As these processes deteriorate, the organism's ability to maintain homeostasis and respond to environmental challenges diminishes, contributing to a decline in overall health and vitality.

Because of its dynamic and multi-level nature, aging cannot be explained from a single perspective, leading to numerous theories to understand aging (da Costa et al. 2016). These theories seek to elucidate the mechanisms underlying physiological deterioration throughout life and to identify the factors that accelerate this process. While major theories of aging are only one aspect of a complex process, several key molecules and pathways repeatedly emerge as essential factors in the decline of cellular functions across organisms (López-Otín et al. 2013, 2023). Due to the consistent dysregulation of certain molecular pathways during the aging process across species, questions have emerged: Is aging programmed to occur? If not, can it be halted or reversed?

Given the complex dynamics, shared hallmarks, outcomes, and molecular changes of the aging process across organisms, the investigation of its origins has become a noteworthy focus of gerontology (Li et al. 2023; Pamplona et al. 2023), second only to our need to treat aging-associated diseases. Among the debated origins of aging, a discussion emerged that aging aligns with disease criteria, following five major converging arguments. First, is that aging shares molecular and cellular hallmarks with recognized pathologies, including genomic instability, chronic inflammation, and cellular senescence. Second, as the leading risk factor for most chronic conditions, aging is viewed as a primary target for prevention. Third, pharmacological interventions that modulate aging-associated biomarkers are considered evidence of therapeutic efficacy. Fourth, formal disease classification could facilitate regulatory approval and funding. Finally, it is argued that the progressive decline in physiological function may constitute a deviation from optimal biological performance, leading to suffering and impairment.

The answer divides the biomedical community into those who view aging as an irreversible, but manageable and delayable process, and those who seek to classify it as a treatable disease. This review engages these arguments. Rather than dismissing them, I evaluate whether they reflect the criteria required for disease classification. Examining the classification question does not imply opposition to interventions to extend healthspan, because it is clear that we must pursue ways to alleviate the physiological burden of aging. However, conceptual precision may strengthen translational efforts by clarifying targets and biological expectations.

The debate over what qualifies a condition as a disease has been ongoing for decades. While some authors have proposed that senescence can be understood as a syndrome of age-associated pathologies whose incidence increases with chronological age (Gems 2015), this does not logically imply that aging constitutes a disease. Conversely, proposals to classify aging as a disease within the context of ICD-11 frequently rely on institutional and economic arguments rather than biological criteria (Zhavoronkov and Bhullar 2015). Hence, the existence of pathological processes during aging does not, by itself, justify redefining aging as a disease, particularly when the boundaries, biomarkers, and onset of such a condition remain undefined. Likewise, the prospect of potential economic benefits does not override the need for conceptual clarity grounded in evolutionary and biological data.

In this sense, modern definitions in evolutionary medicine, as broadly defined and discussed by Boorse, converge on the view that a pathological condition represent “a state of statistically species-subnormal biological part-function, relative to sex and age”, where “subnormal” means “far below the mean of function in a typical environment” and are, in principle, subject to negative selection (Boorse 1977, 2014). Boorse broadly discusses that there are specific physiological variations in physiological metrics (heart rate, temperature, etc.) within a species, depending on lifestyle (e.g., athletes, people living at different altitudes, night workers), but such variations do not constitute the whole class of the species, indicating that the negative impact of any physiological variation must be overall universal to be subnormal (Boorse 2014). Likewise, some variations in physiological parameters do not directly imply impairment, suffering, or physiological decline, as aging is experienced differently by each individual. For a complete assessment of the debate, refer to (Boorse 2014, 2025).

Based on ICD-11 and numerous age-related releases, the World Health Organization (WHO) makes a clear distinction between disease and aging. In this context, diseases are understood as conditions involving clinically relevant dysfunction of biological or psychological processes, associated with impairment, suffering, or increased risk of mortality, and amenable to clinical diagnosis and intervention. However, WHO did not add aging as a disease in ICD-11 and clearly states that biological aging is a normal part of life, not something to be diagnosed (World Health Organization 2015; The World Health Organization 2019). Importantly, WHO has explicitly avoided classifying aging itself as a disease and has explained that aging is experienced differently by each individual, without a defined progression and outcome, as is expected of a disease (The World Health Organization 2020, 2025, 2026). During the development of ICD-11, a proposal to include “old age” as a diagnostic category was withdrawn and replaced by “decline in intrinsic capacity,” which captures age-related functional changes without labeling them as pathological (The World Health Organization 2026).

The progressive decline in physiological homeostasis that we designate as aging, whether expressed through recognizable phenotypes or through molecular remodeling, is observed across virtually all animal taxa. Despite efforts to label aging as a disease, evolutionary theory consistently treats it as a byproduct of natural selection shaped by environmental contexts, making such a classification incompatible, even though aging strongly modulates disease risk. It should also be recognized that human aging occurs within complex social and environmental contexts that extend beyond the biological processes typically studied in experimental organisms. Socioeconomic conditions, psychological stress, cultural factors, and technological environments can influence health trajectories (Epel and Prather 2018; Araya-Ajoy et al. 2020; Kivimäki et al. 2025). These dimensions are determinants of human aging but are distinct from the biological mechanisms underlying organismal senescence. For this reason, the present discussion focuses primarily on the evolutionary and physiological aspects of aging biology, while acknowledging that human aging ultimately emerges from the interaction between biological processes and broader environmental and societal influences.

Medawar first proposed that the declining force of natural selection with age allows late-acting deleterious mutations to accumulate in populations (Medawar 1953). Williams extended this reasoning through the antagonistic pleiotropy hypothesis (APH), suggesting that alleles conferring early-life reproductive advantages may be favored even if they produce harmful effects later in life (Williams 1957). Kirkwood further formalized these ideas in the disposable soma theory (DST), arguing that organisms allocate limited energetic resources between reproduction and somatic maintenance, leading to gradual deterioration as an optimized evolutionary trade-off (Kirkwood 1977). These theories formed the backbone of the hundreds of theories that emerged in the ever-expanding universe of aging research and grounded aging as a selective trade-off, a theme echoed by all other theories (da Costa et al. 2016). A key dynamic, however, is recognizing that historical environmental context, whether social or ecological, plays a pivotal role in shaping an organism's functional decline. In this context, aging is highly heterogeneous and does not appear to be a universal downhill process of deterioration across all organisms.

Jones et al. examined age-specific mortality and fertility trajectories across 46 species, including mammals, birds, reptiles, amphibians, invertebrates, plants, and even algae (Jones et al. 2014). Contrary to the long-standing assumption that senescence universally involves progressively increased mortality after reproductive maturity, the authors found heterogeneity in aging patterns. In several species, mortality rates remain constant with age; in others, they decline, a phenomenon described as negative senescence. Fertility trajectories are equally diverse, with some species maintaining or even increasing reproductive output in later life stages (Jones et al. 2014). While some opinions use such variation of mortality as proof that aging can be reversed or halted, these interspecies patterns align with evolutionary predictions that aging reflects species-specific optimization strategies within given environmental contexts. Importantly, the authors explicitly caution against anthropocentric interpretations, emphasizing that modern humans are demographic outliers with unusually steep increases in late-life mortality. Hence, if aging were analogous to a disease, we would expect consistent deterioration across taxa and a defined onset.

In this sense, demographic analyses of supercentenarians show that, after approximately age 110, the annual probability of death plateaus at around 50%, rather than continuing to rise exponentially as predicted by classical mortality models (Vaupel 2010). The sparse survival data beyond age 114 are consistent with the hypothesis that mortality virtually remains at this level. This suggests that individuals who survive to extreme ages arrive in a more robust physiological state. At advanced ages, remaining individuals appear to deteriorate at broadly similar rates.

Moreover, a study used multivariate biomarker data from large human cohorts and demonstrated that physiological dysregulation can be better understood as deviations in integrated biomarker networks rather than as abnormalities in single molecular variables (Cohen 2016). One key axis, consistent across cohorts, reflects coordinated variation in albumin, hemoglobin, inflammatory markers, and other clinical indicators. Importantly, this composite axis increases exponentially with age, predicts mortality and frailty, and remains significant even after adjusting for chronological age, yet it does not map cleanly onto specific chronic diseases (Cohen 2016). The authors interpret this pattern as evidence of an “emergent physiological process” (EPP) arising from network-level regulatory pathways, in which molecular systems gradually lose the ability to return to homeostasis due to the complex multilevel interactions. For example, another study discussed that although conserved pathways such as insulin/IGF-1 signaling and mTOR modulate lifespan in short-lived laboratory models, cross-species comparisons show that similar molecular networks operate within profoundly different outcomes (Cohen 2018). The same signaling architecture can produce steep senescence in mammals, negligible senescence in certain reptiles, or extended functional stability in colonial organisms.

Unlike a disease with a defined onset, aging instead represents a multivariate shift in organismal regulation. This also supports the hypothesis that, in humans, with extremely heterogeneous lifestyles, targeted therapeutic interventions might help delay aging but not halt or reverse it. Overall, the identification of molecular correlates of aging does not necessarily establish their causal role in organismal aging (Rattan 2020).

Moreover, Baudisch demonstrated that aging must be understood along two independent dimensions: the pace of aging, reflecting life expectancy and life-history tempo, and the shape of aging, describing how mortality changes relative to standardized lifespan (Baudisch 2011). Across species ranging from birds and mammals to primates, pace and shape do not covary in a predictable manner. For example, some long-lived birds exhibit relatively gradual increases in mortality, whereas certain short-lived mammals show steep acceleration in mortality after reaching maturity. Conversely, humans, despite being among the longest-lived mammals, show a pronounced proportional increase in late-life mortality when standardized to life expectancy (Baudisch 2011). In contrast, other taxa, such as certain reptiles, fish, and invertebrates, exhibit negligible or even negative senescence, with mortality remaining constant or declining across adult ages. Such results echo the work of Roper et al., which expanded on this concept. By recalculating age-specific selection gradients using a stable age distribution and reproductive values for 475 species, the authors demonstrated that the evolutionary force of selection does not universally decline with age (Roper et al. 2021). In many species, particularly those with stage-structured life histories or indeterminate growth, selection gradients can remain stable or even increase in later life stages – once again, reflecting the role of environmental context in shaping the aging process (Roper et al. 2021).

In humans, the emergence of long-lived populations provides a compelling demographic argument against reducing aging to a disease (Colchero et al. 2016). Analyzing extensive data across human populations and nonhuman primates, research showed that longevity follows a highly regular pattern linking pace and shape. In humans, increases in life expectancy are consistently accompanied by decreases in lifespan inequality, forming a remarkably linear relationship that spans populations with life expectancy as low as two years during crises to contemporary populations exceeding 85 years. Importantly, even under extreme mortality shocks, such as famines or epidemics, the relationship between life expectancy and lifespan equality remains intact. Mortality may temporarily surge, but the underlying coupling between pace and shape persists (Colchero et al. 2016).

Classifying aging as a disease would imply redefining species-typical trajectories as pathological states. This conclusion becomes problematic when considering species that exhibit negligible senescence or no clear functional decline with age.

A useful contrast to understand the nuances of classifying aging as a disease is obesity (Table 1). WHO classifies obesity as a disease because it represents a measurable physiological deviation that increases the risk of morbidity and mortality, even when shaped by environmental context. While it may sound similar to aging, obesity is neither species-typical (i.e., expected) nor inevitable within a population. Animals in their natural habitat will not eventually become obese.

While it is associated with increased disease risk, aging does not constitute a deviation from normative physiological and molecular strategies, nor does it present a clearly demarcated pathological onset. In this sense, Rattan proposed that organismal health is better conceptualized in terms of “homeodynamic space”, defined as the dynamic capacity of biological systems to tolerate, repair, and adapt to molecular and physiological disturbances (Rattan 2013). Within this context, aging reflects the gradual reduction of this buffering capacity, which is better aligned with evolutionary perspectives. Unlike obesity, which can occur independently of age and may be prevented or reversed without altering the organism's fundamental structure, aging unfolds as an intrinsic temporal dimension of that trajectory.

Moreover, another example of the heterogeneity of aging trajectories is that even tissues do not age at the same rate or exhibit the expected molecular response to functional stress. The brain is a particularly illustrative case.

Despite its high metabolic demand and sustained exposure to reactive oxygen species, neurons maintain tightly regulated redox control, suggesting that oxidative stress in the aging brain acts less as a primary driver and more as a threshold-dependent amplifier once compensatory capacity is exceeded (Feltes 2025a). This is further supported by a study showing that although oxidative stress can increase α-synuclein accumulation in oligodendroglial cells, it cannot drive this process (Riedel et al. 2009). Hence, even under experimentally induced cytotoxic stress, canonical molecular signatures commonly associated with neurodegeneration do not invariably emerge in a self-sustaining manner. Moreover, from an energetic standpoint, the persistence of adult hippocampal neurogenesis (Moreno-Jiménez et al. 2019), NAD⁺ recycling (Xie et al. 2020), ketone body utilization (Cunnane et al. 2020), pyruvate-mediated antioxidant buffering (Desagher et al. 1997), and compensatory hormonal strategies, well beyond the reproductive peak (Lord et al. 2008; Mosconi et al. 2024), challenges the expectation that post-reproductive maintenance should be evolutionarily minimized, as proposed by the DST (Feltes 2025b). Thus, the brain does show a hierarchically organized redistribution of metabolic resources, selectively preserving circuits essential for cognition and social adaptation in key brain regions. Importantly, neurons do not exhibit a defined replicative lifespan, as they are uncoupled from an organism's lifespan (Magrassi et al. 2013).

Similarly, DNA damage, traditionally viewed as purely deleterious (Freitas and De Magalhães 2011), has been functionally repurposed in neurons, where controlled DNA double-strand breaks mediated by TOP2B facilitate rapid transcriptional responses required for memory formation (Madabhushi et al. 2015). Despite continuous exposure to endogenous genotoxic stress and repeated cycles of activity-induced DNA breaks, neuronal populations can remain viable and functionally integrated for decades. Not only do they remain viable, but nuclear DNA fragments can also activate TLR9-mediated inflammatory pathways, further aiding memory formation (Jovasevic et al. 2024).

Another work argues that physiological liver aging is also characterized by distinct molecular alterations compared to those in other organs (Morsiani et al. 2019). Specifically, it reports increased expression of miR-31-5p, miR-141-3p, and miR-200c-3p after age 60, along with genome-wide DNA methylation remodeling that progresses up to approximately that age and then plateaus. Clinically, multiple transplantation studies indicate that livers from aged donors exhibit function and long-term survival comparable to those from younger donors, with successful use of organs from donors ≥ 80 years old and even centenarian livers (Morsiani et al. 2019). Finally, the article notes that liver aging is accelerated by chronic inflammation, which, in turn, might lead to pathological conditions. Hence, it reinforces the distinction between physiological aging and pathology arising from sustained inflammatory stress.

In the absence of a consistent pathological trajectory, the molecular alterations observed during aging are better interpreted as context-dependent regulatory shifts rather than as markers of a pathology (Rattan 2014). This argument becomes even more compelling when one recognizes that the hallmarks of aging do not correspond to the hallmarks of pathology.

The identification of conserved molecular hallmarks of aging has been interpreted by some as evidence that aging is a disease.

Hallmarks are easily identifiable characteristics used to understand a given biological context. Aging-associated pathways, including genomic instability, epigenetic alterations, and altered nutrient sensing, are components of evolved life-history architecture (López-Otín et al. 2013, 2023). Their modulation may influence healthspan, but their existence does not redefine aging as pathology. Furthermore, the same molecular pathways operate across species exhibiting profoundly different senescence trajectories. If identical hallmarks can underlie negligible senescence in one taxon and steep mortality acceleration in another, then these mechanisms cannot constitute disease per se.

A good example is the hallmarks of cancer. The hallmarks of cancer were formulated to summarize the capabilities that enable malignant cells to overcome organismal control. These include sustained proliferative signaling, evasion of growth suppressors, resistance to apoptosis, replicative immortality, angiogenesis, invasion, metastasis, immune evasion, and phenotypic plasticity (Hanahan 2022). In sum, they expand cellular autonomy at the expense of tissue integrity and organismal survival and have a clear onset; hence, cancer clearly qualifies as a disease (Table 1).

From an evolutionary perspective, cancer has been described as a deviation from the normal functioning of multicellular systems (Athena Aktipis et al. 2015; Maley et al. 2017). Normal tissues enforce regulatory boundaries that limit proliferation and maintain differentiation. Neoplasia emerges when such dynamics fail. Tumorigenesis then proceeds through clonal evolution, driven by recurrent, fitness-enhancing somatic alterations and selection among competing cellular lineages (Greaves and Maley 2012; Vogelstein et al. 2013).

The hallmarks of aging describe something fundamentally different. The original basis identified genomic instability, telomere attrition, epigenetic alterations, proteostasis loss, dysregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication as recurrent biological features of aging (López-Otín et al. 2013). However, such hallmarks reflect the cumulative consequences of imperfect maintenance under declining selection gradients and energetic trade-offs. Even when aging increases vulnerability to disease, the trajectory itself reflects species-typical patterns.

The therapeutic logic that reinforces most arguments classifying aging as a disease further exposes the asymmetry. Clinical oncology seeks to block proliferative signaling, restore apoptosis, inhibit angiogenesis, prevent metastasis, and reactivate immune surveillance (Hanahan 2022). These interventions target a pathological process with a clear onset and progression, both of which are absent in aging. Thus, the existence of identifiable molecular traits in aging is not analogous to a disease.

Another line of argument frequently invoked in support of classifying aging as a disease draws on the literature on epigenetic clocks. DNA methylation–based estimators of biological age have emerged as among the most precise quantitative predictors. The original multi-tissue epigenetic clock developed by Horvath demonstrated that chronological age can be predicted with high accuracy across a wide range of human tissues using methylation levels at a defined set of CpG sites (Horvath 2013). Importantly, deviations between predicted epigenetic age and chronological age, often termed epigenetic age acceleration, have been associated with mortality risk, multiple age-related conditions, and functional decline. Because such parameters appear to quantify biological aging and be used to predict mortality risk, they are sometimes interpreted as evidence that aging behaves like a pathological process. As such, they capture correlational signatures of aging rather than identifying causal molecular mechanisms underlying aging. Indeed, in later works, the authors explain that the molecular mechanisms underlying these methylation changes remain incompletely understood and may reflect a combination of developmental programs, cumulative environmental exposures, and systemic physiological remodeling (Horvath and Raj 2018). Consequently, epigenetic clocks should be interpreted primarily as measures of organismal aging dynamics rather than as direct evidence of a discrete pathological process.

The predictive capacity of epigenetic clocks was further extended with the development of second-generation models such as GrimAge, which integrates methylation-based proxies for plasma proteins and smoking exposure to predict lifespan and healthspan outcomes (Lu et al. 2019). However, this predictive power should not be conflated with evidence that aging itself constitutes a disease. GrimAge and related models quantify risk and physiological remodeling associated with aging, and that individuals differ in the rate at which these changes accumulate. Yet these associations remain largely statistical, as they indicate correlation between methylation signatures and health outcomes rather than demonstrating that the underlying molecular changes constitute a pathological process. In this sense, epigenetic clocks reinforce that aging unfolds gradually over time and leaves detectable molecular traces that can be quantified.

Hence, measurement and classification are conceptually distinct problems. Just as blood pressure predicts cardiovascular events without being identical to cardiovascular disease, epigenetic age acceleration can signal elevated risk or altered physiological trajectories without implying that aging itself constitutes a pathological condition.

Although aging is fundamentally a biological and cognitive challenge, it is experienced primarily as a social phenomenon, since individuals age alongside those around them. As a result, considerations about evolutionary mechanisms or cascades of molecular events rarely occupy a central place in public discourse.

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) from 1990–2021 found that age-associated diseases ranked among the leading causes of non-communicable disease mortality worldwide (Naghavi et al. 2024). The scientific literature is full of evidence demonstrating that aging is the primary driver of increased risk for a wide range of diseases, making an exhaustive listing of supporting studies unnecessary. Nevertheless, substantial research indicates that socioeconomic factors, particularly income, play a critical role in shaping mortality from age-related diseases (Marengoni et al. 2011). Variables such as housing stability, access to healthcare resources, and familiarity with digital platforms can significantly influence health-related decisions and outcomes (Feigin et al. 2019; Cheng et al. 2020; Jenkins Morales and Robert 2022; Lu et al. 2022; Naghavi et al. 2024; Chen et al. 2024). Hence, you cannot discuss aging without considering that economic factors determine whether an individual can afford a given treatment or even have access to adequate healthcare, both at the individual and governmental levels.

In this sense, conditions in which the risk increases greatly with age, such as dementia, heart failure, kidney failure, and osteoarthritis, account for billions of dollars in health system expenditure across numerous countries, and it is expected to continue to rise (Huang et al. 2022; Hawker and King 2022; Savarese et al. 2023; Chesnaye et al. 2024). The economic impact of caring for elderly patients within the health system is so great that it correlates negatively with a country's economic growth (Prince et al. 2015; Tang et al. 2022).

Therefore, it is not surprising that debates about how health and insurance institutions should support older adults are widespread. Aging populations use more health services and incur higher care costs, raising concerns about the sustainability of current insurance models and premium structures, especially as demographic shifts increase the proportion of older beneficiaries, often translating into higher premiums or out-of-pocket costs for older individuals and families (Kallestrup-Lamb et al. 2024). In Brazil’s private insurance market, for example, pricing rules that limit premium variation by age can lead to intergenerational transfers and strain the system as policyholders age (Santos et al. 2019).

Studies indicate that insurance coverage and financial assistance can lower out-of-pocket costs and improve access to care among older adults, emphasizing the role of policy in mitigating economic barriers to health care (Chen et al. 2023; Kallestrup-Lamb et al. 2024). Hence, promoting healthier lifestyles and effective preventive care is critical not only for individual health but also for reducing the economic burden on insurers and public health systems.

Consequently, contemporary discourse has become increasingly focused on the concept of “delayed” aging, which is more accurately understood as extending the period of life spent in good health rather than slowing the biological process of aging itself. From an economic perspective, delaying the onset of age-related diseases is projected to yield substantial financial benefits, with estimates suggesting trillions of dollars in savings by 2050 (Goldman 2016). Nonetheless, while age-associated diseases impose significant economic burdens on healthcare systems and public institutions, they may simultaneously generate economic benefits for other sectors, highlighting the sometimes-conflicting incentives embedded in aging-related health policies.

It is clear that the emergence of new diagnoses is closely linked to economic expansion in the healthcare sector, as numerous examples show that the recognition of new conditions drives the development of novel drugs, medical devices, and therapeutic interventions (MacIlwaine 2004). However, while this perspective may open new avenues for treating well-characterized diseases, it does not translate seamlessly to aging. Slowing specific biological processes associated with aging through pharmacological interventions is fundamentally different from “curing” aging itself, a notion that lacks a clear biological foundation.

Most arguments in favor of classifying aging as a disease rely on the premise that it is driven by identifiable biological factors, which we discussed before, that are not linear correlations with disease hallmarks. These arguments are further supported by the availability of robust model organisms and experimental approaches that enable systematic investigation of aging phenotypes and their modulation (Bulterijs et al. 2015). Nevertheless, despite the proliferation of startups and enterprises claiming to be on the verge of curing aging, no substantial clinical breakthroughs have been achieved to date. Instead, the most consistent and reproducible gains have come from interventions that modulate healthspan, most of which are based on lifestyle changes, which can be supported by some pharmacological interventions (Kennedy et al. 2014; The Lancet Healthy Longevity 2022).

Most compounds under investigation, including rapamycin and its analogs, senolytics, and NAD⁺ precursors, are being evaluated through repurposing strategies and are primarily tested for effects on age-associated diseases (Guarente et al. 2024). Consequently, while some interventions show context-specific health benefits, current human evidence does not demonstrate a direct halting or reversal of the biological aging process. In this sense, the growing medicalization of age-associated chronic conditions expands healthcare demand and costs without proportional gains in population health, raising serious concerns about the sustainability of public health and entitlement systems (Olshansky et al. 2005; Bloom et al. 2015). Similarly, classifying aging as a disease does not imply that insurance will cover medical expenditures based on “old age” simply because it was labeled a pathology (Boorse 2025).

Hence, lifestyle interventions remain the most effective, safe, and translationally validated strategies for improving healthspan in humans (Longo et al. 2015; Rattan 2020). Framing aging as a disease risks expanding medicalization and healthcare costs without proportional gains in population health, particularly in already strained public systems. A more pragmatic and evidence-based approach is to address age-associated diseases, reduce socioeconomic barriers to care, prioritize policies that promote healthy aging, and narrow the debate between longevity science and clinicians rather than pursuing the ill-defined goal of curing aging itself (The Lancet Healthy Longevity 2022).

Finally, while the growing interest in classifying aging as a disease is partly driven by the notion that doing so could facilitate access to dedicated funding mechanisms and clinical trial pathways traditionally structured around clearly defined pathologies, the proposal may also reflect increased institutional incentives to accelerate translational research. Nevertheless, these classification issues should remain distinct from biological criteria, because they lack a scientific basis. The absence of formal disease classification does not diminish the importance of studying aging as a biologically consequential process. Age-associated functional decline remains the primary risk factor for most chronic conditions and represents a major determinant of morbidity and mortality worldwide. Understanding its mechanisms and identifying strategies to preserve physiological resilience, therefore, remain central objectives of biomedical research, irrespective of whether aging is formally defined as a disease.

Aristotle framed aging as a natural process marked by the gradual loss of an internal “vital heat” or life force (Aristotle 1984). Although modern biology has long moved beyond this simplified explanation, Aristotle’s formulation captured an essential intuition of his time. Despite centuries of scientific progress, most people's everyday understanding of aging remains, as expected, grounded in lived experience rather than in molecular or evolutionary explanations. While this notion has little impact on longevity science itself, it strongly shapes how aging is portrayed to the public and to institutions, influencing expectations and fueling often enthusiastic and overly optimistic promises about what will “soon” be available to treat aging-related conditions. Moreover, oversimplifying aging profoundly influences how flawed theories spread rapidly among the public, fostering unrealistic expectations about which interventions can genuinely extend lifespan.

A good example is the often mistaken view that aging is a rate-limited process governed by a single physiological feature, such as the long-discredited notion of a fixed number of heartbeats per lifetime. In fact, rate-of-living theories have long been discredited and lack an evolutionary basis, yet they somehow found their way into the present (Speakman et al. 2002; Speakman 2005). Such reductionist views impose artificial limits on a multifactorial process and misguide expectations.

Unfortunately, this recurring pattern is not confined to ancient worldviews. Modern biomedical history provides multiple examples of aging theories that gained rapid visibility before being substantially revised. The free radical theory of aging, once presented as a near-universal explanatory theory (Harman 1956, 2003), gradually lost its status as a unifying mechanism due to no increased life expectancy in model organisms after modulating antioxidant defenses (Liochev 2013) and when antioxidant supplementation failed to reproducibly extend lifespan in humans and often yielded neutral or even adverse outcomes in clinical trials (Miller et al. 2005; Ristow et al. 2009). It is clear that aging is intrinsically associated with increased reactive oxygen species generation (Barja 2014; Viña 2019), but aging is not driven by redox imbalance, which reflects a recurring tendency to elevate individual molecular pathways to disproportionately central roles in aging biology.

Across evolutionary data, aging emerges not as a pathology but as a species-typical, time-dependent remodeling process centered within an environmental/social context. Although aging increases vulnerability to disease and shares certain molecular features with pathological states, these overlaps do not collapse the conceptual boundary between biological progression and pathological deviation. Aging unfolds as a near-universal trajectory, lacking a discrete onset, a singular mechanistic architecture, and a clear therapeutic target.

Recent ethical analyses of longevity science have compellingly argued that efforts to mitigate age-related decline should be grounded in respect for autonomy, self-ownership, and the intrinsic value of life, rather than solely in economic projections (Han and de Magalhães 2026). Such reasoning rightly places the protection of human flourishing at the center of the debate. At the same time, however, the ethical legitimacy of pursuing healthspan-extending interventions does not eliminate the need for conceptual precision. Whether aging fulfills the biological criteria required for disease classification remains a distinct debate.

Likewise, a recent study identified several unresolved questions spanning evolutionary theory, organismal survival dynamics, and the heterogeneity of aging phenotypes (Rattan 2024). These include the unclear identity and universality of longevity-assurance pathways, the mechanisms governing biological time across species, and the determinants of variability in aging trajectories from the molecular to the organismal level. Importantly, age-associated changes cannot always be interpreted as harmful processes, as many alterations may reflect adaptive remodeling, compensatory responses, or hormetic stress responses that contribute to organismal resilience. Hence, the scientific community must recognize that the architecture of aging remains incompletely characterized, and caution against premature attempts to frame aging within rigid pathological classifications.

Recognizing this distinction does not diminish the importance of geroscience or of pursuing interventions to extend healthspan. On the contrary, theoretical clarity strengthens translational efforts by preserving the distinction between intrinsic biological aging and age-related diseases. Under current evolutionary and biological evidence, classifying aging itself as a disease lacks scientific basis. Even well-intentioned narratives can divert scientific effort and inflate public expectations.

Feltes, BC: Conceptualization, Data Curation, Investigation, Writing—Original Draft, Reviewing & Editing, Project Administration, and Funding Acquisition.

The Article Processing Charge (APC) for the publication of this research was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) (ROR identifier: 00x0ma614). This work was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Brazil; Finance code 001) and the Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS) [24/2551–0001277-0].

No datasets were generated or analysed during the current study.