Research on aging requires the ability to measure aging, and therein lies a challenge: it is impossible to measure every molecular, cellular, and physiological change that develops over time, but it is difficult to prioritize phenotypes for measurement because it is unclear which biological changes should be considered aspects of aging and, further, which species and environments exhibit “real aging.” Here, I propose a strategy to address this challenge: rather than classify phenotypes as “real aging” or not, conceptualize aging as the set of all age-dependent phenotypes and appreciate that this set and its underlying mechanisms may vary by population. Use automated phenotyping technologies to measure as many age-dependent phenotypes as possible within individuals over time, prioritizing organism-level (i.e., physiological) phenotypes in order to enrich for health relevance. Use those high-dimensional phenotypic data to construct dynamic networks that allow aging to be studied with unprecedented sophistication and rigor.

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Introduction “Aging” is a convenient label for a diverse set of biological changes that develop in a high proportion of individuals within a population over an average lifespan. The concept that aging is worth studying is essentially a hypothesis that these conditions share causal mechanisms and that working to identify and combat those shared mechanisms is a viable strategy to improve the quality and duration of life. However, research in this area is undermined by preconceived notions that aging is a universal, intrinsic process that is distinct from disease and transcends species and environments. This leads to an excess of effort spent attempting to define and measure “true aging.” There is no such thing. Instead, I propose de-emphasizing the classification of phenotypes as aging (or not) and, instead, measuring the multi-dimensional set of all possible age-dependent phenotypes, which can change between populations and environments. By measuring these phenotypes over time, aging can be modeled as a dynamic network. This eliminates the need for arbitrary cutoffs, such as dictating a distinction between normal aging and disease and trying to only measure the former. Many phenotypes progressively change with time, and we cannot measure everything; because the ultimate goal is to understand and ameliorate declines in health and well-being, I propose taking the “top-down” approach of initially focusing on organism-level phenotypes that change with age. The field now has the hardware to measure such phenotypes and the computational infrastructure to analyze them: tools such as metabolic and behavioral monitoring chambers and video monitoring paired with machine vision are making automated, high-dimensional longitudinal phenotyping of model organisms a reality. A network approach has several benefits: (1) it quantifies aging in high-dimensional space, allowing the nuanced assessment and comparison of interventions; (2) network structure may elucidate phenotypic clusters, suggesting shared mechanisms; and (3) comparison of the networks of different populations, e.g., mice and humans, may improve preclinical models of aging by identifying the preclinical phenotypes that are the most predictive of human phenotypes. Although a high-dimensional network is more complex than single endpoints such as lifespan or a handful of health-span parameters, it is also a more accurate and useful picture of reality.

Within a Population, There May Be Multiple Aging Mechanisms Peto and Doll, 1997 Peto R.

Doll R. There is no such thing as aging. Currie, 2009 Currie J. Healthy, wealthy, and wise: socioeconomic status, poor health in childhood, and human capital development. After asking whether and how mechanisms of aging are shared between populations, a next question might be whether and how the mechanisms of aging are shared between age-dependent phenotypes within a population. There is, of course, no a priori answer: as the mechanisms of aging have no formal requirement to be shared across populations, so too do they have no formal requirement to be shared across phenotypes within a population. The fact that multiple deleterious biological conditions develop with age does not necessitate that those conditions share causal mechanisms (). The statement “age is the largest risk factor for many diseases” is sometimes employed to argue that all diseases share a common cause (aging) that can be targeted for therapeutic benefit. This arises from the fallacious notion that chronological age represents a singular biological process rather than serving as a statistical proxy for a multitude of biological changes. Chronological age may be a good predictor of multiple morbidities simply because it is correlated with many biological changes and not because those morbidities are necessarily causally linked. Similarly, your family’s income during your childhood is an excellent predictor of your educational attainment, salary, and numerous health conditions because it is correlated with a large number of demographic factors, not because all of those outcomes share the same causal chain (). Figure 2 Hypothetical Models of How the Mechanisms of Aging Might Connect to the Phenotypes of Aging Show full caption (Left) Each aging phenotype has phenotype-specific mechanisms, but there is at least one mechanism that affects all phenotypes. Studying the set of aging phenotypes will eventually reveal this common mechanism, and targeting it will (at least partly) ameliorate all phenotypes of aging. (Middle) Each aging phenotype has phenotype-specific mechanisms and there are a few mechanisms that impinge on multiple phenotypes, but there is no single mechanism that affects all phenotypes. All else being equal, the shared mechanisms represent the most valuable therapeutic targets. (Right) There are no shared mechanisms, so studying the set of aging phenotypes in order to identify such mechanisms will not succeed. From a therapeutic perspective, each phenotype must be treated separately. In all panels, green circles and arrows represent mechanisms that impinge on more than one phenotype. Therefore, it is formally possible that each age-dependent phenotype is mechanistically independent ( Figure 2 , right). This is implausible, given the complex systems nature of biological processes, but if true, it could be argued that studying the biology of aging is frankly a waste of time: if a set of phenotypes has nothing in common, then studying them as a set would yield no greater (and likely less) insight than studying each individually. On the other extreme, all age-dependent phenotypes may share one or more underlying mechanisms ( Figure 2 , left). Note that there are still phenotype-specific mechanisms in this model—such mechanisms must exist, given that aging phenotypes do not perfectly correlate with one another. However, if pan-phenotype mechanisms do exist, a single therapy might at least partly ameliorate every age-dependent phenotype within a population. This seems overly optimistic, and indeed, such mechanisms have not been discovered to date despite extensive effort. The truth likely lies somewhere in between—age-dependent phenotypes probably have some common causal mechanisms that can be targeted for multi-phenotype benefit but also phenotype-specific mechanisms that are equally important yet less pleiotropic ( Figure 2 , middle), and we would like to understand that causal structure to inform therapeutic strategies. The set of age-dependent phenotypes we want to ameliorate might collapse to a single, ten, or a thousand causal processes. Whatever the actual number, it can only be found by studying the relationships between the phenotypes.

Aging versus Disease: Sound and Fury Blumenthal, 2003 Blumenthal H.T. The aging–disease dichotomy: true or false?. Caplan, 2005 Caplan A.L. Death as an unnatural process: why is it wrong to seek a cure for aging?. Gems, 2014 Gems D. What is an anti-aging treatment?. Gems, 2015 Gems D. The aging-disease false dichotomy: understanding senescence as pathology. Strehler, 1977 Strehler B. Time, Cells and Aging. To study the relationship between items in a set, one must first describe the set, and in this case, the set should include both aging phenotypes and age-related diseases. This is because there is no objective distinction between the two, and attempts to separate them risk obfuscating important mechanisms connections. The idea that aging and disease are fundamentally distinct relies on an arbitrary “normal” reference point in exactly the same manner as attempts to distinguish populations that exhibit “true aging” from those that do not (e.g., wild-type mice versus mutant backgrounds). For example, diseases are often contrasted with aging based on the perception that aging phenotypes, unlike diseases, exhibit complete penetrance within a population (aging happens to everyone) (). However, which phenotypes appear universal depends entirely on the population under study. A scientist confined to studying a population of people who all harbor presenilin-1 mutations would characterize Alzheimer’s disease before the age of 65 as “normal aging” because it would appear completely penetrant. The discovery of another population without the mutation would shift the scientist’s reference point, and early-onset Alzheimer’s would be relabeled a disease. If the human species as a whole harbors a similarly deleterious stretch (or stretches) of DNA that we have yet to discover, does that mean our perception of normal aging is actually a disease state, albeit one that happens to be fully penetrant within our population? It is a semantic question, and semantics should not dictate the structure of scientific inquiry. Blumenthal, 2003 Blumenthal H.T. The aging–disease dichotomy: true or false?. Caplan, 2005 Caplan A.L. Death as an unnatural process: why is it wrong to seek a cure for aging?. Strehler, 1977 Strehler B. Time, Cells and Aging. Another common argument is that aging, unlike disease, is fundamentally intrinsic, i.e., independent of environment (). This concept presupposes the existence of some optimal environment, or more correctly, the complete lack of an environment and claims that aging is most accurately assessed in that optimal, environment-less situation, lest it be overshadowed by non-aging pathology. This is nonsensical; life requires interaction with the environment. But even the less extreme version of this concept— attempting to create a less challenging environment in order to better measure aging—is flawed. Because there is no such thing as “true aging,” less challenging environments cannot better expose or more challenging environments mask “true” mechanisms of aging. Rather, the mechanisms that limit healthspan and lifespan simply differ from environment to environment, and the choice of environment, like the choice of genetic background, should be driven by relevance. For example, from a translation perspective, the preferred temperature at which to house mice for aging studies is not necessarily the one that stresses them the least; it is the one that renders their aging process the most similar to humans. Gladyshev and Gladyshev, 2016 Gladyshev T.V.

Gladyshev V.N. A disease or not a disease? Aging as a pathology. This rationale applies to any other criterion used to demarcate aging from disease. “Aging” and “disease” represent cultural and clinical judgments about normality applied to non-discrete realities (). Similar concepts exist in other fields: there is no such thing as a “true planet” but simply an agreed-upon set of rules for categorizing celestial bodies. Although those rules might aide certain discussions, the idea that Neptune must obey a different set of physical principles than Pluto because the former is christened a planet and the latter is not is obviously fallacious. Mann, 1997 Mann D.M. Molecular biology’s impact on our understanding of aging. Rattan, 2014 Rattan S.I.S. Aging is not a disease: implications for intervention. Not only is it impossible to draw distinct lines between aging and disease, but such an exercise provides little value. Others have argued that maintaining a distinction between aging and disease is important because aging is mechanistically distinct from disease, and thus, they require different approaches for research and intervention (), but this argument begs the question. We don’t know how age-dependent phenotypes with complete or high population penetrance (i.e., “aging”) are mechanistically related to age-dependent phenotypes with lower population penetrance (i.e., “age-related diseases”), thus taking fundamentally different approaches to studying and treating them is currently unwarranted. We should seek to understand the causal chains within and between as many age-dependent phenotypes as possible in order to treat them, irrespective of whether we label them aging or disease. One might argue that a distinction between aging and age-related disease is helpful because the latter should be prioritized for treatment. Such a prioritization may indeed be medically useful, but it is relatively straightforward to prioritize age-dependent phenotypes without reference to any label: the more common and more deleterious a phenotype is (i.e., the more it impacts quality or duration of life), the more important it is to ameliorate. Although hair graying and skin wrinkling are nearly universal, compared to conditions such as cognitive decline, sarcopenia, bone loss, and cardiovascular disease, they have marginal influence on overall health and thus are less important to treat. The more deleterious phenotypes ameliorated and the more people whose quality or duration of life is improved, the more valuable a therapy becomes, regardless of whether it is branded a disease therapy or an aging therapy. Imagine a clinical trial that finds that a drug reduces the incidence of the most common age-related diseases (without deleterious side effects): cardiovascular disease, diabetes, Alzheimer’s and Parkinson’s, chronic kidney disease, sarcopenia, osteoporosis, arthritis, and cancer. Should these data be sufficient to label that pill an “anti-aging therapy,” or is it a disease therapy? It doesn’t really matter—irrespective of what we call it, it would clearly be an incredibly valuable therapy and virtually everyone would want to take it.

The Utility of a Network Model Helfman and Bada, 1976 Helfman P.M.

Bada J.L. Aspartic acid racemisation in dentine as a measure of ageing. A network model easily absorbs situations that would pose problems for the traditional view of aging as a universal process, distinct from disease. For example, a process that occurs in a relatively small fraction of the population, e.g., gout, is not removed from the network as “not aging”; rather, its low probability of occurring as part of any given individual’s aging process is encoded by its largely invariant distribution (mostly zero) over age and its low connectivity to more common phenotypes. Phenotypes driven primarily by physical processes and effectively independent of biology, e.g., the D/L ratio (the ratio of right- and left-handed configurations) of amino acids in teeth, which increases with age via spontaneous racemization but is thought to have no functional consequence (), will have dynamic distributions over age but will poorly correlate with clinically relevant phenotypes (or at least, correlate no better than chronological age), and their relatively unconnected positions in the network will demonstrate this lack of common mechanism. Dakos et al., 2012 Dakos V.

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Kohanski R. Geroscience and the trans-NIH Geroscience Interest Group, GSIG. However, another use of the network, elucidating causal hypotheses, may provide insight in this regard. The structure of the network (e.g., the emergence of nodal clusters linked by correlation) could suggest, at least to an order of magnitude, how many mechanistically independent sets of phenotypes likely exist. Processes and phenotypes that are highly correlated are more likely to share common mechanisms; moreover, the earlier in life such correlations can be identified, the closer one comes to identifying the common mechanism. If the network incorporates molecular data, its structure could even help to identify the mechanisms themselves. Using a network approach to identify the links between age-dependent phenotypes is similar to, albeit somewhat broader than, the recently described geroscience concept, which emphasizes the need to systematically investigate the links between aging and age-related diseases (). Although I do not subscribe to the distinction between aging and age-related disease, from a geroscience perspective, one could examine the network edges between traditional age-related disease nodes and cell or molecular nodes to propose causal hypotheses. For example, if sarcopenia and chronic kidney disease have a higher partial correlation than one would expect by chance, it might suggest they share a common mechanism. Further, if the incidence of sarcopenia and chronic kidney disease were both highly correlated with the value of a node representing serum TNFα from several years prior to their diagnosis, it might suggest that TNFα level represents an earlier step in the causal chain leading to those conditions. These hypotheses could then be tested. Berg and Lässig, 2006 Berg J.

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Amit I. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. A final use of a network model of aging is to compare networks of different populations or species, thereby allowing identification of analogous phenotypes and mechanisms that might exist. As discussed above, although it is tempting to believe that the age-dependent changes of another species are relevant to humans, there is no reason it must be so; we must identify and optimize human-relevant models and readouts, not assume we already know what they are. Alignment between networks from different species, which will generally consist of different phenotypic nodes, is a challenging problem (), but this type of analysis has the potential to clarify which phenotypes should be measured in model organisms to yield the greatest insight into human aging. For example, by comparing the network of mice to that of humans, we might identify an age-dependent phenotype in mice (e.g., decreased social interaction) that shares many of the same nodal neighbors as a clinically relevant human phenotype that is difficult to directly model in rodents (e.g., loss of higher cognitive functions). This association would suggest that the mouse phenotype of decreased social interaction represents a useful preclinical readout even if it is not health-limiting in mice. Network alignment can most readily be accomplished by identifying common nodes to help anchor the alignment (e.g., body temperature, the serum concentration of conserved proteins), but in the absence of common nodes, it can also be accomplished by measuring the response of both networks to the same perturbation (e.g., rapamycin-treated humans versus rapamycin-treated mice) and aligning nodes that respond similarly. Data from an increasing number of perturbations yield an increasingly accurate alignment, and this approach can be extended by using pre-existing genetic variation as the perturbation; e.g., by comparing the network response to a genetic polymorphism in mice with the network response to polymorphisms in the orthologous gene in humans, we may be able to identify murine surrogates of human phenotypes. This is largely analogous to inferring gene regulatory pathways from transcriptomic data by grouping genes that respond similarly to perturbations (), except that in this case, many of the phenotypes are organism-level states instead of mRNA abundances.