8
Biomarkers and the Genetics of Aging in Mice

Richard A.Miller

Laboratory mice, compared to people, present important advantages for investigations of the biological underpinnings of aging, late-life illness, and age-dependent physiological changes. Although they are far from ideal respondents in household surveys, they are relatively easy to persuade to donate samples of biological tissues, can be followed from birth to natural death in a few years, and can be coaxed to mate with spouses or consume most diets of the investigator’s choosing. They are more like people than worms and flies—those favorite invertebrate models for gerontogenetics—are like people, providing some basis for hope that aging of mice will resemble aging of people in helpful ways. The goal of this essay is to present, for the nonbiologist, a summary of how studies of age-sensitive biological traits can contribute to our knowledge of aging, its measurement, and its genetics. We will begin with a discussion about the nature of aging and whether its rate can be measured, present a set of proposed criteria for deciding if an age-sensitive trait provides a useful biomarker of aging, illustrate these validation criteria with examples drawn from T-cell subset testing, and show how genetic data can help to sort out the relation of age-related physiological changes to disease risk and life span. After digressions to consider the idea that genes affecting growth rates in early life have pleiotropic effects on longevity and late-life illnesses, and to introduce new, high-tech approaches that produce catalogs of gene expression data for biomarker and genetic analysis, we will conclude with three suggestions as to how biological data might best be integrated into population survey programs.



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Cells and Surveys: Should Biological Measures be Included in Social Science Research? 8 Biomarkers and the Genetics of Aging in Mice Richard A.Miller Laboratory mice, compared to people, present important advantages for investigations of the biological underpinnings of aging, late-life illness, and age-dependent physiological changes. Although they are far from ideal respondents in household surveys, they are relatively easy to persuade to donate samples of biological tissues, can be followed from birth to natural death in a few years, and can be coaxed to mate with spouses or consume most diets of the investigator’s choosing. They are more like people than worms and flies—those favorite invertebrate models for gerontogenetics—are like people, providing some basis for hope that aging of mice will resemble aging of people in helpful ways. The goal of this essay is to present, for the nonbiologist, a summary of how studies of age-sensitive biological traits can contribute to our knowledge of aging, its measurement, and its genetics. We will begin with a discussion about the nature of aging and whether its rate can be measured, present a set of proposed criteria for deciding if an age-sensitive trait provides a useful biomarker of aging, illustrate these validation criteria with examples drawn from T-cell subset testing, and show how genetic data can help to sort out the relation of age-related physiological changes to disease risk and life span. After digressions to consider the idea that genes affecting growth rates in early life have pleiotropic effects on longevity and late-life illnesses, and to introduce new, high-tech approaches that produce catalogs of gene expression data for biomarker and genetic analysis, we will conclude with three suggestions as to how biological data might best be integrated into population survey programs.

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? BASIC DEFINITIONS AND PREMISES What Is Aging? Does aging start at conception, at birth, at the age of lowest mortality risk (around puberty), or only when the effects of aging become obvious to clinicians and pathologists? Is it a single process or a group of processes that occur in parallel throughout life? Does it occur in tissue culture, in individual cells, or only in whole organisms? Is the “senescence” that causes oak leaves to turn red and die a form of aging? When a person gives up smoking and chocolate nut sundaes and takes up broccoli and jogging, thereby adding 10 years of healthy life to middle age, has “aging” been reversed? Terms in colloquial use bring with them dangerous baggage when they sneak into scientific discussions, and gerontologists quickly discover that most of their honorable and respected colleagues use the term “aging” in different, idiosyncratic, misleading, tricky, indefensible ways. To simplify my presentation, I will start with a formal definition, one defended in more detail elsewhere (Miller, 1997a, 1999). When I talk about aging, I mean the process that progressively converts healthy young adults (whole mice or whole people, for example) into frailer adults with increased risks of death and disability. It is important to note that this process does not occur only in old age: although 90-year olds are more aged than 20-year olds, there is no reason to think that 90-year olds are aging any more rapidly than they did when they were themselves young adults. Studies of senior citizens are needed to tell us what aging does, but studies of young and middle-aged adults may be at least as informative about how aging creates old people from young ones. Indeed, contrary to some versions of intuition, studies of young adults are likely to tell us more about aging than studies of old individuals, because the former are less affected by two important confounders: (a) the effects of specific serious diseases that often afflict the elderly, and (b) differential survival, which removes individuals from their age cohort and does so nonrandomly with respect to age-dependent physiological effects. Is There An “Aging Process” Per Se? Or does the conversion of young adults to noticeably different, more vulnerable, old adults reflect the simultaneous, more-or-less well-synchronized progression of multiple processes, some of which alter muscles and others bone marrow, some of which operate within cells and others on extra-cellular material, some of which alter DNA and others membranes or proteins or cell numbers or circuitry? The latter theory, conveniently termed the “multiple-clocks” model, is by far the most popu-

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? lar among theoretically inclined gerontologists, from John Maynard Smith (1962) through Edward Masoro (1995) and many others. It is difficult to imagine how a single cellular or biochemical mechanism might lead to alterations in so many apparently disparate manifestations of aging. In my view, however, the notion that aging is indeed regulated by a single clock—or a very small collection of clocks—is supported by strong circumstantial evidence. The argument includes four elements: Caloric restriction, i.e., the imposition of diets containing about 40 percent fewer calories than a rodent would voluntarily consume, seems to retard nearly all aspects of aging, including changes in protein cross-linking, patterns of gene expression, loss of function in many organ systems, and risks of multiple forms of death (Weindruch and Walford, 1988). Restricted mice and rats stay active and vigorous at ages at which nearly all control animals have died (McCarter et al.,1997). This mass of evidence is very difficult to reconcile with models in which these aspects of aging are independently timed. Mutations at single genes can extend life span by about 50 percent in df/df and dw/dw dwarf mice (Brown-Borg et al., 1996; Miller, 1999), and by 200 percent or more in nematode worms (Kenyon et al., 1993). The dw/dw mice, at least, retain youthful levels of immune function into old age (Flurkey et al., unpublished results). Single gene changes that produce dramatic increases in longevity are again tough to reconcile with multiple-clock models of aging. Natural selection can rapidly generate subpopulations of mammals that live much longer than the founding population. The clearest example of this comes from Austad’s study of an opossum population, stranded for a few thousand years on a relatively risk-free island. Selective pressures in this new ecological niche favored slow-aging genotypes, and the resulting population survives much longer than their mainland cousins. Initial mortality rates do not differ greatly between the two populations, suggesting that differences in predation pressure per se contribute relatively little to these survival statistics, but changes in the slope of the mortality risk curve support the idea that the island opossums age more slowly than their mainland cousins by a factor of nearly two. Furthermore, island opossums not only exhibit life-span extension, but also show decelerated changes in collagen cross-linking and reproductive performance (Austad, 1993). Under a multiple-clock system, production of a new, longer-lived species or subspecies would require multiple adjustments in genes that time each of the multiple systems affected by aging, and would have to do so in a population of animals whose other protective systems continue to decay at the original, rapid rate. The single-clock model, in contrast, does not depend upon a rare concatenation of low-

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? impact mutations, but hypothesizes a fairly small number of genes in which mutation can alter multiple downstream processes in parallel. Natural selection has produced long-lived species and subspecies from shorter-lived ones dozens of times (e.g., turtles, tuna, elephants, bats, parrots, and chimps do not share a recent common ancestor!). Evolution of extended longevity seems to emerge over and over again from selective conditions that promote large body size, slowed development, and relatively small litter sizes. The single-clock model predicts that those individuals within a species who appear particularly “youthful” in one respect (e.g., immune function) are likely to appear relatively youthful in others (e.g., muscle function, collagen biochemistry, or mortality risk). Compelling evidence on this point is not yet available, but some recent data from our own laboratory, presented below, have begun to address this issue. These two viewpoints—aging as a single process or as a collection of processes—are not difficult to combine into a unified perspective. The electrical network in a residence can be viewed as a set of circuits, one running the toaster, another running the radio, a third running the light bulbs, each of which is subject to its own regulatory pathways (switches, rheostats), each engineered and fixable by specialists who need know little about the other appliances beyond their likely demands on the current. Each of these parallel processes, however, can be influenced by a reduction in the available voltage in a summer heatwave. The obvious fact that many systems break down, in different ways, in old people and old rodents does not by itself disprove the hypothesis that these multiple endpoints of aging might be timed, in part, by a shared mechanism. The question is an empirical one, with the evidence not yet fully assembled. Can Aging Be Measured? Alas, not yet. Researchers who wish to test whether a particular diet, exercise program, drug, or gene alters aging often use age at death as an indirect measure of the effects of aging itself. In these situations—for example, in studies of caloric restriction (Yu et al.,1985) —they determine if the experimental condition extends the mean life span of a test population, with particular attention to the longevity of longest-lived individuals (the “maximum life span,” conveniently estimated as the mean life span of the longest-lived 5 or 10 percent of the group), and conclude that aging has been slowed if the proportion achieving very old age increases. A related measure, the time over which mortality rate doubles, provides an index of actuarial aging useful when population size is high enough (Finch et al., 1990). These approaches have some face validity; because the risk

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? of many forms of lethal injury or illness increases so dramatically at advanced ages, interventions that influence only one cause of death have relatively small influence on overall mortality rates (Olshansky et al., 1990), and dramatic reduction in overall mortality at very old ages implies effects on many types of diseases—good grounds for concluding that aging itself has been retarded. The actuarial approach to quantification of aging presents several problems, however. For one thing, age at death is influenced by multiple factors, among which biological aging will in some cases play an insignificant role. An individual unlucky enough to inherit the genes for cystic fibrosis, be infected by HIV, or hit by a bus may die at an age at which aging has yet to cause much increase in vulnerability. Although changes in the mean longevity of a test population may be altered by interventions with little effect on aging, the slope of the mortality curve (or the mortality rate doubling time) and the age at death of the final few percent of the population seem more likely to be useful indicators of interpopulation differences in aging rate. These indices may be less valuable for invertebrate species, in which large population studies have documented a small subset of extremely long-lived individuals among which the mortality rate may stabilize in very old age (Carey et al., 1992; Curtsinger et al., 1992). It is worth noting, however, that longevity rate deceleration in mammals, if it exists at all, is much less dramatic than in the insect populations in which it was first noted. In Mediterranean fruit flies, for example, one fly per million lives to an age that is fivefold higher than the age at which 90 percent of the cohort has died (Carey et al., 1992). A similar life table in humans would create approximately 300 U.S. citizens aged 400 years or older; this discrepancy is thus good news for the Social Security Administration, but bad news for those who seek to develop and test general principles about mammalian aging from demographic studies of invertebrates. BIOMARKERS OF AGING Assessment of Interindividual Differences in Aging Rate Developing measures of aging that can assess interindividual differences is a tougher nut to crack, but in my view deserves a high priority among the issues facing research gerontologists. The concept of biomarkers has earned a useful niche in toxicology: measuring some effect of the toxin may be more convenient than trying to measure the integrated levels of the agent itself. Thus, for example, measures of glycated hemoglobin provide a valuable surrogate marker for exposure of cells to ill-

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? regulated glucose levels, and measures of DNA damage may provide a useful way to judge exposure to specific mutagens (e.g., after Chernobyl) or to whole classes of mutagenic agents (e.g., in those living close to oil refineries). Attempts to derive analogous indices of “biological age” have been cogently criticized on methodological grounds (Costa and McCrae, 1988, 1995), and indeed those who are convinced philosophically that there is no such thing as the biological age of an individual would argue that biomarker measurement has no likelihood of success and correspondingly little importance. But in my view—from the perspective that multiple aspects of aging are indeed likely to be coordinated in their timing by a single factor—developing a reliable way to measure interindividual differences in aging would have great importance in two respects: it would help to settle the debate as to whether aging can be treated as a unitary process, and it would provide a yardstick with which to measure the effects of genetic or environmental factors thought to have an effect on aging. Development of a replicable, validated set of tests that could discriminate between individuals aging at different rates would have the same effect in biogerontology as the effect of the sphygmomanometer on studies of hypertension or the invention of the clock on studies of celestial motion. How Would We Know if We Had Successfully Validated One or More Biomarkers of Aging? It is regrettably safe to agree that “it is too early to constitute a definitive panel of biomarkers for either animal models or humans” (Sprott, 1999:464). Proposed validation rules for candidate biomarkers vary widely among different experts, and sometimes between different formulations from the same experts. Some definitions (Lipman et al., 1999) propose that a biomarker of aging must be able to distinguish physiologically young from old, but such a formulation merely rewords the problem without offering an operational solution. I would propose an alternate definition, specifically, that a biomarker of aging should meet both of the following criteria simultaneously: It should correlate well, among middle-aged adults, with a wide range of other age-sensitive traits in multiple domains, after statistical adjustment for the effects of calendar age per se; and It should be a good predictor of life expectancy for individuals whose death is due to any of a wide range of lethal illnesses.

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? Each of these criteria deserves some amplification. A biomarker validation program would start with a list of candidate biomarkers, each known to be age-sensitive (by cross-sectional and/or longitudinal analyses) in adults. By hypothesis, some of these traits would reflect interindividual differences in the aging process, but each would also be sensitive to genetic and nongenetic factors that also vary among individuals, statistical “noise” that would interfere with the extraction of the “signal” attributable to aging itself. A correlation between age-sensitive immune parameters— for example, T-cell proliferation and T-cell cytokine production—would be relatively unhelpful in evaluating each of these parameters as potential biomarkers of aging, because the two assays are closely related and likely to be influenced by many factors unrelated to aging (e.g., recent infection, vaccination history, polymorphisms in immune system genes). However, a correlation between T-cell proliferation and, for example, muscle strength, or reflex speed, or lens protein cross-linking, or age at menopause, would be difficult to attribute to any obvious metabolic or pathophysiological mechanism other than linkage to some fundamental aging rate that might by hypothesis retard or accelerate changes in a wide range of age-sensitive traits. Because each such candidate biomarker is already known to be age-sensitive, merely showing that pairs of these traits are mutually correlated would be unsurprising and uninteresting. However, correlations that remain strong after adjustment for the effects of calendar age would not be as easy to explain away and could provide good prima facie evidence that the components of the test battery measure, even if indirectly and imprecisely, some common factor that contributes to impairment of function in multiple cells and organ systems. The second proposed criterion—correlation of the candidate biomarker with life expectancy regardless of the specific cause of death—is designed to link the assays to physiological decline. Loss of physiological reserve is not the only outcome of aging, but it is the outcome that causes most concern, and has furthermore been extensively studied in previous intervention studies. Loss of physiological reserve therefore serves, imperfectly, as the “gold standard” for most demonstrations of decelerated aging. An ability to predict subsequent longevity is not by itself sufficient to evaluate biomarker candidates, because risk factors (genetic or nongenetic) for specific forms of death would otherwise qualify: unstable angina, high titers of serum HIV, or a consistent history of cigarette or drug use are all predictors of subsequent longevity because of their association with specific causes of death, but these traits do not provide information about the kind of underlying aging processes that would, by hypothesis, increase risk factors for the wide range of late-life causes of death.

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? Secondary Criteria for Biomarker Evaluation A test that is strongly correlated with other age-dependent traits and also predicts life span would merit further evaluation as an indirect index of aging per se. To be practical, such a test would have to meet a number of secondary criteria relevant to its incorporation into research protocols. The test should be relatively inexpensive to perform and show good agreement between closely spaced replicate assays of the same individual. Tests that meet the two primary criteria in multiple subpopulations (e.g., different human population groups, different rodent stocks, preferably different mammalian species) would also have practical advantages and meet our theoretical expectations that aging should be similar in many ways among mammals. The test should also be innocuous, neither harming the subject nor influencing the outcome of later tests of the same property at more advanced ages. CD4 Memory T-Cells: A Candidate Biomarker in Aging Mice In our own work we have attempted to test whether age-sensitive T-cell subsets might be useful biomarkers of aging in mice. There are several varieties of T-lymphocyte in the blood, spleen, and lymph nodes of mice (most of them with human counterparts), and their relative proportions change dramatically with age in adult animals and people. The CD4 lymphocytes—so called because they express the CD4 protein on their surface—act as “helper” cells in immune responses, assisting other cells in making antibodies and protective cytokines. The CD8 T-cells specialize in killing tumor cells and virus-infected cells. These are mutually exclusive classes: to a first approximation, all peripheral T-cells are either CD4+ or CD8+ but not both. The CD4 pool can be further subdivided into two mutually exclusive subsets: the naive CD4 cell (sometimes called the “virgin” CD4 cell, CD4V), and the memory CD4 cell (CD4M). Newborn CD4 cells, fresh from the thymus, are considered naive and can be identified by a specific pattern of surface proteins. Once stimulated by a virus or a bacterial antigen, they can produce clones of CD4M cells that are specific for the antigen in question and can help to protect against later encounters with the same or closely related pathogens. CD8 cells, too, can be divided into naive CD8 cells and the CD8M memory cells to which they give rise. Aging of both rodents and humans leads to a progressive increase in the proportion of CD4 cells in the CD4M subset, with a corresponding fall in the proportion of CD4 cells with the CD4V phenotype. The shift from CD4V to CD4M can be seen in blood and internal lymphoid tissues such as lymph nodes and spleen (Lerner et al.,1989), and is decelerated by calorically restricted diets that postpone aging and

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? death (Miller, 1997b). The ability to measure CD4M levels in blood cells allows for repeated assessment of each individual mouse and could facilitate comparisons with human populations. For these reasons, we assessed in several ways the possibility that CD4M levels could serve as a biomarker of aging. A group of genetically heterogeneous mice was used for these analyses, to diminish the possibility that our findings would later prove to be applicable only to a single genotype of inbred or F1 hybrid mouse (Miller et al., 1999). Each mouse in a group of 46 males and 83 females was tested at 8 months of age, and the survivors (93 percent) tested again at 18 months of age. Each animal was then observed until it died or became severely ill (and was euthanized on humane grounds). Regression analysis was then used to examine the dependence of longevity on CD4M values, with gender as a covariate. The results (Figure 8–1) showed a strong association between high CD4M values at 18 months of age and lower longevity; after adjustment for FIGURE 8–1 Correlation of mouse longevity with the percentage of CD4M cells measured at 18 months of age. The filled circles and darker line represent female mice, and the open circles and lighter line represent males. There is a significant correlation between CD4M levels and longevity; R2=0.18, p=0.0003 after adjustment for gender effects. SOURCE: Miller et al. (1997).

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? gender, the CD4M effect had p(t)=0.0003, and 18 percent of the variance in longevity could be accounted for by gender and CD4M alone (R2= 0.18). The direction of the association was consistent with theoretical expectations, in that high levels of CD4M cells are characteristic (in crosssectional and longitudinal studies) of older mice, and in the biomarker analysis were seen to be associated with diminished longevity. Seven other age-sensitive T-cell subsets were also tested in this initial set of mice (Miller et al., 1997), and four of these were found in univariate analyses to be associated with differences in longevity, but only at marginal significance levels that could have resulted from chance in a series of multiple comparisons. If CD4M levels are indeed useful as biomarkers of an underlying aging process, then their association with longevity should be equally apparent for a wide range of terminal diseases. Indeed, the strength of this association was found to be similar in subgroups of the mice dying of different causes, including fibrosarcoma, lymphoma, mammary carcinoma, and a miscellaneous group of other neoplasms (Miller et al., 1997); the association reached statistical significance in the two subgroups with the largest numbers of cases. The number of deaths attributable to nonneoplastic causes was too low (N=13) to provide adequate statistical power, although in these mice, too, high CD4M levels were associated with poor survival. A stronger test of the potential status of CD4M as a biomarker would be to determine whether mice with high levels of CD4M cells also displayed relatively extreme levels of age-dependent traits outside the immune system. A group of 59 genetically heterogeneous mice was tested for CD4M levels and also on several age-sensitive measures of muscle strength between 9 and 12 months of age, and then the surviving 42 mice were tested again at ages 20–22 months. Because muscle force generation declines with age, the hypothesis was that within each age group, force generation would be inversely proportional to CD4M levels. Figure 8–2 presents the results: mice with high CD4M levels did indeed, as predicted, tend to have significantly lower muscle strength at each of these ages (Faulkner et al., unpublished results). Similar tests conducted on these mice at intermediate ages (14–16 months), however, did not show this relationship (R=–0.06), contrary to hypothesis. Additional work is now in progress to see whether CD4M levels correlate, in these heterogeneous mouse populations, with age-sensitive measures of bone density, cataract formation, protein oxidation, and spontaneous activity, as well as tests of functional immunity.

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? FIGURE 8–2 Correlation of CD4M cell percentage with muscle force generation in mice. CD4M levels were measured in each mouse at 9 and (in survivors) at 20 months of age, and muscle force measured at 12 and 22 months. There is a significant inverse correlation at each age: mice with high levels of CD4M cells tend to have low levels of muscle force. SOURCES: Faulkner et al. (unpublished results); Miller et al. (1996).

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? FIGURE 8–8 Genetic influences on age-sensitive T-cell subsets in mice: two representative examples. The top panel shows the association between the BALB allele at D12Mit34 and relatively high levels of CD4M cells; the association is apparent in mice as young as 8 months, and is equally significant in mice at age 18 months. The bottom panel shows the relationship between genotype at D9Mit110 and the CD4P subset: mice inheriting the C3H allele have higher levels of CD4P cells at 18 months of age, but there is no genotypic effect at 8 months. SOURCE: Jackson et al. (1999).

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? coid levels, etc. The mapping project should thus help to guide the search for human genes that regulate these interesting phenotypes and at the same time spark new investigations, in animal models, for the biochemical differences that mediate the genetic effects we detect. At the same time, the dataset that emerges should also allow us to test more general questions about the nature of aging and its genetic control. We may, for example, be able to identify QTLs that not only retard the development of one or more age-sensitive T-cell subsets, but also retard age-dependent changes in protein conformation, bone matrix turnover, and brain GFAP levels. Such a finding would imply that these changes are influenced, together, by a common biochemical pathway, and the corresponding QTLs would be excellent candidates for genes that regulate aging per se, rather than merely one among the many more age-sensitive traits. In the same way, it will be of particular interest to determine if QTLs that regulate age-sensitive traits also are associated with differences in life span, and conversely if QTLs identified on the basis of longevity effects modify one (or nearly all?) of the age-sensitive traits in our test battery. Mice selected on the basis of longevity QTLs may be of particular value for testing hypotheses about the role of specific biochemical and cellular mechanisms in aging and disease. It should be possible to identify mice that inherit combinations of genetic alleles associated with optimal longevity, and to do so within a few weeks of birth. Mice with this particular combination of alleles, together with randomly chosen sibling controls, could then be tested, at any age, for factors—telomere length, fibroblast growth patterns, levels of protein oxidation, immunological vigor—hypothesized to play a role in controlling age-sensitive physiological changes or disease risks. By providing a way to identify siblings destined for longer or shorter lives, the gene-selected mouse populations could provide an approach to sorting the long list of potential aging mechanisms into those that do or do not show the expected association with longevity. The Wrong Lamppost—A Brief Technical Digression It is worth mentioning here a technical, but potentially fatal, objection to the entire QTL-plus-biomarker enterprise just outlined. The objection is based upon the concern that the laboratory-adapted inbred mouse lines used for these (and most other) biomarker and mouse QTL studies of aging, may have lost, over their many generations of inadvertent selection for laboratory lifestyles, exactly those genetic variations that are most informative for biogerontological work. The argument, set forth in more detail elsewhere (Miller et al., 1999, 2000b), notes that the mouse stocks

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? typically used for laboratory work have gone through two very strong sets of selective pressures. First, the development of laboratory mouse stocks from wild-caught animals tends to favor genetic alleles and allele combinations that promote rapid breeding, i.e., the production of large litters as early in life as possible. Selection for large body size—the better to bear large litters with—typically accompanies this selection process (Roberts, 1961). These selection pressures are thus the opposite of those thought to lead to development of races or new species with slower development and longer life span. Genetic variants that decelerate aging and development, if they exist at all in natural mouse populations, may well be lost during the first few dozen laboratory generations. Second, nearly all rodent gerontology is conducted with inbred animals or their progeny, despite the risks that the inbreeding process may select for rare combinations of alleles that preserve viability and reproductive performance in fully homozygous individuals. We have elsewhere described the case (Miller et al., 1999) for seeking the keys to mammalian aging under a different lamppost, one infested with mice freshly derived from wild populations, and preferably from populations (Miller et al., 2000b) that exhibit the characteristics (small body size, small litter size, and modestly elevated glucocorticoid levels) one expects to see in slow-aging races. Genetic and biomarker analysis of mice whose great-grandparents include both wild-derived and laboratory-derived stocks could help to determine which early life traits—growth rate, reproductive scheduling, early or sustained immune function—are best associated with differences in life span and disease risk, and set the stage for mapping the corresponding QTL that distinguish wild-derived from laboratory-derived mice in these respects. The Height-Life Span Nexus Several observations and lines of experimentation have raised the issue of whether interindividual differences in aging rate are influenced by genes that modulate body size and early-life growth patterns. These include (a) the association between small stature and exceptional longevity in calorically restricted rodents (Yu et al., 1985), methionine-restricted rats (Orentreich et al., 1993), and mutant dwarf mice (Brown-Borg et al., 1996; Miller, 1999); and (b) the association between small body size and longer life span in natural populations of mice (Falconer et al., 1978), flies (Hillesheim and Stearns, 1992), dogs (Li et al., 1996), and, possibly, people (Samaras and Storms, 1992). The correlation in dogs is particularly striking: selective breeding for dogs of different body size has produced breeds varying in size from Chihuahua to Irish wolfhound. These breeds also vary greatly in mean longevity, from approximately 7 to 10.5 years, and

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? the correlation between breed longevity and breed body weight (Miller, 1999) is a remarkable R2=0.56. These differences are genetic and affect stature rather than obesity: no amount of overeating will convert a West Highland white terrier to a St. Bernard. The selective pressures applied were designed to create dogs of specific sizes and temperaments and were not intended to influence aging rate or life span. The clear implication is that the effects on longevity are pleiotropic, i.e., that genes selected for their effect on body size and conformation influenced life span as a side effect. It is of interest to note that the few analyses (Eigenmann et al., 1984, 1988) of the hormonal basis for interbreed differences in body size have shown that the genes in question influence levels of IGF-1, the most likely mediator of the life-span effects in the long-lived df/df and dw/dw mouse mutants. Could it be mere coincidence that long-lived mutant nematode worms (Kimura et al., 1997) also show mutations in genes related to insulin and IGF-1 receptors? To carry out a more general test of the hypothesis that early-life differences in growth rate might influence life span, we have conducted a study of mouse lines selectively bred for differences in weight gain between 0–10 days of age or between 28–56 days. Starting from a group of genetically heterogeneous mice, Atchley and his colleagues (Atchley et al., 1997) created six lines of large-sized mice by picking breeding pairs that had shown rapid early-life weight gain, and six other stocks of smaller mice whose ancestors were chosen based on slow rates of growth early in life. Three unselected stocks served as controls. These mouse stocks proved to be remarkably different in mean longevity (Miller et al., 2000a): the longest-lived stock, with mean life span of 941 days, lived 36 percent longer than the median for the 15 tested stocks and 1.7-fold longer than the shortest-lived stock. Among the 15 stocks, weight was strongly correlated with stock longevity measured at six months of age (R=0.69, p=0.004). The differences reflected stature rather than obesity, in that the correlation was strong when weight was measured at early ages (3 months), and became smaller, though still significant, when peak weight was considered. Differences among these stocks in longevity did not reflect major differences in the cause of death, except for one large, short-lived stock in which most animals died of pituitary adenoma, potentially a source of growth hormone overproduction. These results are thus consistent with the idea that genetic effects on life span may, to an important degree, be the side effects of genes selected (naturally by ecological forces or artificially by selective breeding schemes) because they alter rates of early-life growth and maturation. The findings offer many opportunities for followup studies. Where are the relevant genes, and how many are there? In crosses between long-lived and short-lived stocks, is there a good correlation between individual longevity and various measures of growth, size,

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? and body proportions? Do the long- and short-lived stocks differ systematically in production of or response to growth regulatory hormones in early life, and do these differences persist throughout adult life? Would temporary growth retardation at specific periods of the developmental process produce increases in longevity similar to those seen in mice whose short stature reflects inherited genes? Would studies of humans, carefully controlled for the many potential confounding variables, also reveal a connection between early-life growth trajectory and later susceptibility to the perils of old age? GENE EXPRESSION SCREENING Gene mapping methods deal with questions of inherited characteristics—the variations in DNA sequences that influence differences between people in body size, eye color, life span, and personality characteristics. Gene expression studies, in contrast, generate lists of which genes are expressed in specific cell types. The liver cells, brain cells, and T-cells of any one person all possess the same inherited DNA sequences, but express different subsets of these genes. Among the 40,000 genes expressed in a neuron, for example, perhaps 20,000 will also be expressed by a liver cell, and a different subset of 20,000 or so will be expressed by a CD4 memory T-cell. Until about five years ago, producing a catalog of genes expressed by any one kind of cell—a T-lymphocyte, for example—was a tedious process, proceeding one gene at a time, and the list of genes known to be expressed by even well-studied cell types was only a few hundred genes long. The recent development of methods for screening large numbers of genes simultaneously has placed the production of gene catalogs well within the grasp of even modest sized laboratories. The technology is undergoing rapid evolution, and the amount of information produced for a given investment of time and money is growing quickly, with today’s advanced techniques sure to be obsolete within a year or two. Even today’s immature methods, however, permit research laboratories to ask and answer many important questions that would have been hopelessly ambitious a few years ago. A few examples—concocted for relevance to biogerontology and geriatrics—may be of use to those unfamiliar with this “high-throughput” approach: A list of genes expressed by T-cells of young adults but not of old adults, and the complementary list of genes expressed only by T-cells from older people, is likely to provide insights into the molecular basis for immune senescence. A list of gene expression differences between calorically restricted mice and those on a normal caloric intake would include important clues

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? about the ways in which the restricted diet retards aging rate. A similar comparison between long-lived dw/dw mice and their nonmutant siblings would be equally informative. Genes that found their way onto both lists—i.e., overexpressed or underexpressed both by restricted mice and by dw/dw animals—would merit special attention. Would some of these genes also prove to be differentially expressed by mice inheriting the genetic alleles associated with longer life span in the QTL analyses? Would a subset also prove to distinguish longer-lived mouse stocks in the Atchley set or to discriminate wild mice from those born into laboratory stocks? Gene expression data could also, in principle, help in the selection of bioindicators of specific clinical states of interest. If, for example, a group of investigators wished to develop a method to evaluate the level of chronic psychological stress to which individual subjects had been exposed, they might begin with a validation study in which control subjects and those known to have experienced highly stressful circumstances were asked to donate blood samples for analysis. Screening these samples for expression of 1,000–5,000 genes might identify genes whose expression was dramatically different between the two subject groups. A list of such genes—perhaps 5–15 among the set of thousands examined—would be valuable in two respects: as inexpensive quantifiable measures of a biological state of clinical interest, and as pointers to the underlying biology of psychological stress and its putative connections to health. Although the cost of screening thousands of genes simultaneously is too high to make the method appropriate for population surveys—a situation not likely to change for a decade—the cost is now sufficiently low to be useful for picking and validating smaller subsets of genes whose expression levels are informative and affordable to employ in population surveys. Moreover, these gene products are not mere anonymous markers of the underlying clinical state, but are embedded in a network of biologically interpretable relationships. Thus, for example, a list of genes associated with psychological stress might be found to contain several genes known to respond, in other tissues, to endorphins or catecholamines or sex steroids, providing links to new areas for investigation and, perhaps, intervention. BIOLOGICAL MATERIALS SUITABLE FOR LARGE-SCALE HUMAN STUDIES Other chapters in this volume have given a great deal of thought to the question of how best to exploit biological data for population surveys, and how best to exploit the elaborate and expensive infrastructure developed for population studies to address biological questions. My own

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? contribution to this aspect of the discussion would be to urge colleagues to consider three points: Tests of specific research questions. In some cases, biological information should be included in population surveys on the grounds that the data are necessary to answer specific well-formulated hypotheses. Sociologists who become familiar with the current, but rapidly changing, toolkit of biological analyses will be best positioned to formulate and then answer questions linking clinical and psychological issues to their biological substrates. The specific kind of materials needed will depend on the specific questions to be answered. Some questions about genetic influences on traits of interest will require genotyping at a very small number of loci and thus require no more DNA than that available from a small blood sample; other genetic questions may require a stable, larger supply of DNA and thus justify the expense of setting up long-term cultures from blood cells or cheek scrapings. Questions that require information about transient hormonal levels, chronic exposure to blood chemicals, patterns of gene expression, or responsiveness of specific cell types will each imply very different sample collection (and in some cases sample preparation and archiving) requirements. Archives for future exploitation. In other cases, managers of largescale population surveys may be tempted to collect and archive biological materials from their study subjects “on spec,” that is, in the hope that future technical developments will someday allow them to make good use of these materials, associated as they are with highly valuable datasets of clinical, psychological, and economic information. I hope that leaders of such projects give in to such temptations, but then give careful attention to the key specifics—which samples, from which population subsets, stored in what circumstances—that are likely to preserve or undermine the future utility of the resulting archives. If the samples are to be used, for example, for analyses of genetic variation and its effects on traits of interest, future users of the materials will be particularly grateful for archives that include sets of siblings or parent/offspring pairs. As gene mapping strategies come, in the next decade, to rely less on large pedigrees and more on large-sample analysis of sequence polymorphisms linked to effector loci, samples that provide sufficient DNA for extensive study (or live cells that can be grown in culture for DNA extraction) will be of particular value. If the samples are to be used for analyses of bioindicators that provide transient markers of clinical or psychological status, archives that include live cells may be more valuable than those that bank only serum samples or tissue extracts. Animal studies as stalking horses for human biogerontology. For the most part, studies on the biology of aging are as difficult and impractical in humans as are studies of health insurance in rodents. It is fairly

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? easy, of course, to study aged humans, but much harder to study the 50–80 year process that creates aged people from teenagers. Analytical strategies that depend upon correlations between variables measured in middle age and those tested in old age are reasonably straightforward in rodent colonies, but exceptionally difficult in human populations. The hypothesis that T-cell subsets predict future health status, or that diets high in a specific antioxidant might help to postpone neoplasia or cataracts, or that the rate of skeletal growth in the first 10th of the life span influences cancer risk at late ages, or that mildly elevated serum glucocorticoid levels in young adulthood are associated with higher mean longevity, can be tested first in mouse or rat populations; those hypotheses that still appear promising after such a screening process will thus earn consideration for incorporation into studies of human populations. Mapping human genes that influence the aging process can be guided by mouse studies in a similar way, because nearly all segments of the human genome correspond to known sections of mouse chromosomes. Thus a demonstration that a given section of mouse chromosome 12 carries genes that confer resistance to age-dependent neoplastic and non-neoplastic illnesses could justify analyses of the corresponding human chromosome segment in family-based or polymorphism-dependent mapping projects. ACKNOWLEDGMENTS The original work presented here represents a collaboration with Drs. David Burke, Andrzej Galecki, Clarence Chrisp, Jack van der Meulen, Kevin Flurkey, and John Faulkner and Ms. Anne Jackson, with technical assistance from Luann Linsalata and Gretchen Buehner. This research was supported by NIA grant AG11687, AG16699, AG08808, and AG13283, and by the Ann Arbor DVA Medical Center. REFERENCES Atchley, W.R., S.Xu, and D.E.Cowley 1997 Altering developmental trajectories in mice by restricted index selection. Genetics 146:629–640. Austad, S.N. 1993 Retarded senescence in an insular population of Virginia opossums (Didelphis virginiana). Journal of Zoology 229:695–708. Bining, N., and R.A.Miller 1997 Cytokine production by subsets of CD4 memory T-cells differing in P-glycoprotein expression: Effects of aging . Journals of Gerontology, Series A, Biological Sciences and Medical Sciences 52A:B137-B145. Brown-Borg, H.M., K.E.Borg, C.J.Meliska, and A.Bartke 1996 Dwarf mice and the ageing process. Nature 384:33.

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