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O P E N A C C E S S S O U R C E : Mechanisms of Ageing and Development
Highlights
• Our mental categories tend to correspond to our linguistic categories.
• We argue there is no biological phenomenon that corresponds to the word “aging”.
• We show how the concept of “aging” misleads us from asking the right questions.
• We lay out a path for aging research without the concept of “aging” itself.
• We advocate gradual abandonment of the word “aging” in scientific contexts
Abstract
Are diseases caused by aging? What are the mechanisms of aging? Do all species age? These hotly debated questions revolve around a unitary definition of aging. Because we use the word “aging” so frequently, both colloquially and scientifically, we rarely pause to consider whether this word maps to an underlying biological phenomenon, or whether it is simply a grab-bag of diverse phenomena linked more by our mental associations than by any underlying biology. Here, we consider how the presence of the colloquial word “aging” generates a cognitive bias towards supposing there is a unitary biological phenomenon. We ask what kind of evidence would support or refute that idea, and subsequently show clear evidence at multiple levels that aging is not a unitary phenomenon. In particular, the known aging pathways lead to heterogeneous outputs, not a single coordinated phenomenon. From levels ranging from cellular/molecular to clinical to demographic to evolutionary, we show how the supposition that aging is a unitary phenomenon can mislead and distract us from asking the best questions. For major sub-disciplines of aging biology, we show how going beyond the notion of unitary aging can hone the paradigm and help advance the pace of discovery.
1. Introduction
Some 23 years ago, Peto and Doll (1997) argued that aging is not a biological phenomenon. Their argument – that there are not necessarily common mechanisms underlying the major aging-related chronic diseases, such as cancer, but rather a suite of individual disease processes synchronized via natural selection – would surely find little favor today. Common mechanisms, including inflamm-aging, mitochondrial dysfunction, and cellular senescence, are now thought to be well established (Kennedy et al., 2014; López-Otín et al., 2013). In retrospect, the argument seems ignorant of aging mechanisms. Here, we argue that Peto and Doll were right, but for the wrong reasons: that our more detailed knowledge of aging mechanisms is increasingly showing that there is no unitary phenomenon usefully summarized with this word.
What is aging? This question, at the heart of our field, has received a great deal of attention, and many definitions, implicit or explicit, have been proposed (Comfort, 1979; Gladyshev, 2016; Kuo et al., 2020; Rose et al., 2012; Shefferson et al., 2017). (Here, we use the term “aging,” though all our arguments equally apply to the term “senescence,” which is favored by some (e.g. Shefferson et al., 2017).) A coherent definition is even essential for the field: there are intensive efforts to measure aging, to slow aging, and to treat aging, and it will be impossible to know if they are succeeding without a clear definition of the subject of our research. Is it accumulation of molecular damage? Is it loss of function with increasing age? Is it increases in mortality (or decreases in reproductive rate) with age? Underlying the discussion to date is an assumption so basic it goes unnoticed: that there is an underlying biological phenomenon of aging. We have a word for aging, and therefore we assume that science will accommodate us, providing a phenomenon to match our word. And in a colloquial sense this is certainly the case: no one can doubt that we see ourselves, our relatives, and our friends age. But is this colloquial usage scientifically justified? Is there really a “thing” or a phenomenon we can call aging? We argue here that our understanding of the biology is now sufficient to say definitively that this is not the case, that from a scientific perspective there is no such thing as aging, but rather a collection of disparate phenomena and mechanisms – sometimes interacting with each other – that relate in one way or another to our colloquial sense of the word. Accordingly, our desire to find a single reality of aging has created a great deal of confusion in the field.
We are well aware that not all researchers in our field will like our thesis here: our identity as “aging researchers” is tightly wrapped around the notion that there is a phenomenon of aging. However, we do not believe there is a need to feel any existential threat from this idea, which is in some sense a natural extension of the multi-factorial hallmarks/pillars framework (though not an inevitable one – see below). Rather, we think that being more careful about our underlying assumptions, and how they do or do not conform to biological reality, can only make us better researchers. The field of aging research can still exist, but with a more nuanced understanding that we are not studying a single biological phenomenon, but an assortment of loosely related processes that we find convenient to lump together.
2. Linguistics, the definition of categories, and the word “aging”
Linguists have long recognized that mental classifications, which can vary across cultures, are reflected in vocabulary, and that the presence of words in a vocabulary creates the perception that reality is organized in line with lexical ontology (Goddard and Wierzbicka, 2013; Hussein, 2012; Kay and Kempton, 1984). For example, in Japanese, water does not exist. What Westerners would call water, the Japanese divide into oyu (warm-to-hot water) and mizu (water-that-is-not-hot). You take a bath in oyu and use it to make tea. Mizu fills the oceans and is refreshing to drink when it’s hot out. Japanese scientists certainly do acknowledge that there is a single chemical structure, H2O, that is shared by both oyu and mizu, and everyone knows that there’s a bit of ambiguity when you put some mizu on the stove and start to boil it. But for a Japanese person in her daily life, there’s no such thing as water (in our sense), and all H2O encountered gets mentally classified as oyu or mizu.
Conversely, in Japanese there are neither rats nor mice, only nezumi, a single word that covers mouse-and-rat-like rodents. Here, science would seem to side a bit more with the Japanese: while there are two distinct genera of the muridae family that describe mice and rats, Mus and Rattus respectively, they are relatively closely related, and there are many other rodents that most people would classify as a mouse or a rat that are relatively distantly related to both genera, enough so that Wikipedia has an entire section on “Types of animals known as mice” (https://en.wikipedia...s_known_as_mice). Yet English speakers who are not mammologists will nonetheless immediately classify any relevant rodent as a mouse or a rat, despite the fact that neither is a real biological category.
In the cases of both water (oyu/mizu) and rodents (nezumi), science has provided clear answers as to the underlying reality, and colloquial uses of the relevant terms can coexist with the technical understanding with a minimum of tension or confusion. In the case of aging, the scientific consensus on the underlying reality is still evolving and fast moving. The subject is substantially more complex than water chemistry or rodent phylogeny, and there are not (to our knowledge) major linguistic differences for translations of “aging” across languages/cultures. Perhaps for these reasons, we still use the words “aging” and “senescence” as if these corresponded to a single underlying reality, a single phenomenon to be understood. While some definitions of aging might be able to englobe a wide array of these phenomena (e.g., “inexorable declines in organismal function associated increasing chronological age”), this doesn’t mean they describe a useful scientific concept. As we make clear below, the attempt to arrive at a general definition does more harm than good.
Accordingly, we are not arguing that the term “aging” should never be used. It will certainly continue to be used colloquially, and it may be useful to retain it to describe our field, or in certain situations when the very broad set of phenomena is really of some interest. But we believe scientists should move away from the term, particularly when they are only referring to a subset of the phenomena in question. For example, when we mean “damage accumulation,” we should say this rather than “aging.” Likewise, when we mean age-associated increase in mortality risk, we should find a term such as “demographic aging” or “Gompertzian mortality patterns.”
Below, we show why aging is not a single phenomenon, we show how the use of a single term has led to confusion about the underlying reality in a number of subfields, and then discuss the implications.
3. What would it mean for aging to exist?
Philosophers have debated whether there is an objective reality that is knowable by humans independent of cultural and psychological biases (Boghossian, 2010). The enterprise of science assumes that there is, but this does not imply that science is free from psychological and cultural biases (Boghossian, 2010). We wish to argue that the concept of “aging” does not have an existence independent of such biases, and that it may in fact be misleading us into missing key aspects of the underlying biology that might indeed be understood free of such biases. In order to make that argument, we first need to establish what it would mean for aging to exist as a single, objective phenomenon, what we refer to as a “unitary phenomenon.”
Some 20–30 years ago, many researchers in the biology of aging believed aging was due to a single mechanistic process, such as oxidative stress (Harman, 1956), inflammation (Franceschi et al., 2000), or telomeres (Levy et al., 1992). Currently, views have changed substantially, with the emergence of a near-consensus that aging is multifactorial and heterogeneous (Taffett, 2003), as described by the hallmarks (López-Otín et al., 2013) and pillars (Kennedy et al., 2014) frameworks, among others. Indeed, it could be argued that much of the field already agrees with our core thesis, having largely accepted the framework of the hallmarks/pillars, though few of the researchers that accept that framework would likely be willing to state that “there is no such thing as aging.” However, even if aging is multifactorial and heterogeneous, it could still be considered a unitary phenomenon if any of the following applied:
(1) There were a single upstream mechanistic cause, universally present wherever aging is considered to exist.
(2) There were a single gene or pathway that exerted exclusive control over aging, wherever aging is considered to exist.
(3) The heterogeneous/multifactorial mix were identical across all species considered to age, and absent in those that do not.
(4) There were a uniform demographic signature across all species considered to age, and absent in those that do not.
(5) A number of genes or pathways had evolved specifically to jointly adjust aging in a coordinated fashion, in the evolutionarily teleological sense.
4. Biological reasons to believe aging is not a unitary phenomenon
While our linguistic argument is, to our knowledge, novel, our biological argument is not. Many authors have remarked on the heterogeneity of aging in different ways (e.g. Franceschi et al., 2017a; Mitnitski et al., 2017; Rattan, 2008). Medvedev (1990), in reviewing more than 300 theories of aging, stated:
“It is obvious now that the expectation that a really unified, or a single ‘main cause’, theory of ageing would eventually emerge is not realistic. Many theories co-exist because they do not contradict each other, or because they try to explain different and independent forms of senescence.”
Similarly, many more recent papers, notably the hallmarks/pillars framework, highlight the multi-factorial nature of aging (Kennedy et al., 2014; Kirkwood, 2005; López-Otín et al., 2013). For example, Rattan (2006) integrates many of the known mechanisms into a multi-level model of homeodynamic loss in aging. Gladyshev (2016) argues that aging can be considered as the accumulation of the deleteriome – the set of “cumulative, deleterious age-related changes.” In some sense we cannot disagree, and the scientific substance of Gladyshev’s arguments is excellent, but the definition is in the end circular: it is essentially a concession that the biological nature of these deleterious processes is too heterogeneous to be defined by any biological common features, mechanisms, or control switches. Whether it is the hallmarks/pillars, the deleteriome, or some other multi-factorial framework, the question then is whether this means that there is no such thing as biological aging.
To answer this, we should evaluate the five propositions above. Several can be excluded summarily, or nearly so. For #1, it seems highly unlikely (though not impossible) that so many biologists working so hard for so long have missed a single upstream cause that would neatly unify our field. For #2, multiple genes and pathways have been identified that interact to influence aging-related processes (Bitto et al., 2015). For #3, it is clear that the mix of mechanisms varies dramatically across species (Cohen, 2017).
The fourth proposition is not so easily excluded. In fact, there is a reasonable argument to be made that aging could be defined as an exponential increase in age-specific mortality. Substantial work has gone into showing how aging-like demographic patterns can be simulated (e.g. Gavrilov and Gavrilova, 2001), or emerge from simple underlying processes (Karin et al., 2019). Nonetheless, an increasing number of studies are publishing demographic data for a wide array of species across the tree of life, and these data are showing that the demographics of age-specific mortality are highly heterogeneous (Baudisch et al., 2013; Jones et al., 2014; Shefferson et al., 2017). Even among species that show increases in mortality with age, the increase is not necessarily exponential, and not necessarily smooth. While this might be explained by appropriate models of upstream processes (Le Cunff et al., 2014), the resulting demography is still heterogeneous. Accordingly, by all indications, either there is not a stable emergent demographic process that could be labeled aging, or the definition of which species “age” would need to be restricted to the point where the word would lose most correspondence to our intuitive sense of it.
The fifth proposition is the most problematic and will be treated in detail. Our increasing knowledge of the aging process, genetically and mechanistically, allows us to build a model of how different aging pathways integrate signals. The starting point is to consider that almost all biological regulation occurs in the context of the complex systems formed by biological networks, either (a) to structure development, or (b) to maintain homeostasis/allostasis, in the broad sense of allowing an organism to develop and to adjust appropriately to its internal and external conditions (Cohen et al., 2012). (Here we use “homeostasis” in this broad sense, well aware that it is not static, and can be predictive as well as reactive (Sterling, 2020).) Accordingly, aging pathways such as IGF signaling and sirtuins also almost certainly evolved to have roles in maintenance of homeostasis (though also with roles in development, which we will not consider here).
Martin (1997), expanded on by Partridge and Gems (2002) proposed the model of public versus private aging mechanisms, in which the conserved signaling pathways (IGF, sirtuins, etc.) are upstream regulators of multiple downstream processes. Each species could thus have its own set of aging mechanisms, but the conserved pathways are the upstream regulators adjusting aging rate. This is much like the vertebrate and arthropod eyes, which evolved independently but are nonetheless controlled during development by an orthologous gene (PAX-6) that likely controlled development of a light-sensitive patch of cells in a common ancestor (Gehring, 1996). The underlying assumption is that there are trade-offs modulated by the public aging pathways: for example, that either species or individuals might wish to accelerate aging in order to increase reproduction, and thereby maximize fitness.
The public/private mechanism distinction maps nicely on the “bowtie” model of regulation that has emerged from complex systems theory (Csete and Doyle, 2004), subsequently applied to immune aging by Franceschi and colleagues (Cevenini et al., 2008; Franceschi et al., 2018; Tieri et al., 2010). Applied to aging pathways, the bowtie model suggests that multiple input signals converge on a limited number of aging pathways, which then integrate this signal and use it to adjust a number of downstream mechanisms. Fig. 1 shows a schematic of the bowtie model; while simplified in terms of the network structure and hierarchical relationships of some elements, this schematic preserves the key elements of how information is processed. This elegant model would appear at first glance to support the public/private distinction, and thus to support the fifth proposition above, suggesting that aging is a suite of downstream mechanisms, specific to a given species, that are coordinated by the signaling pathways to produce a coherent aging process.
Fig. 1. A model of how pathways thought to influence aging integrate information. On the left are various signals from the internal and external environment. In the center are known aging pathways. Obviously, they influence each other, though this is not shown. On the right are outputs – downstream targets. Many of the potential links are well-documented, though others remain hypothetical. Crucially, this model permits differential and flexible adjustment of the outputs.
However, closer inspection in fact suggests the opposite. The structure shown in Fig. 1, though not identical, bears a remarkable resemblance to neural networks, both real networks of neural connections and the statistical tool derived from them (Fig. 2A), which might be more precisely termed a cybernetic network (Wiener, 1948). More specifically, they resemble a type of neural network known as autoencoders (Fig. 2B). In all cases, the structure can be understood as a framework for processing information, and there is a reason for the apparent convergence: just like in other areas of convergent evolution, this particular structure is highly efficient at performing its task. The presence of multiple pathways ensures (a) that an error in one pathway will have a modest impact on the overall result of the regulatory pathways, generating robustness; (b) that diverse information can be synthesized in a flexible way to generate a globally optimized metabolic response; and © that multiple downstream targets can be optimized simultaneously and differentially as a function of multiple upstream input signals.
Fig. 2. A. An artificial neural network, as represented on the Wikipedia page of that name as of May 17, 2020. B. A sparse autoencoder, as shown in (Ng, 2011). Note the similarity to Fig. 1.
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