Body weight, hormones and T cell subsets as predictors of life span in genetically heterogeneous mice

Mechanisms of Ageing and Development 125 (2004) 381–390

Body weight, hormones and T cell subsets as predictors of life span in genetically heterogeneous mice James M. Harper a,c , Andrzej T. Galecki b,c , David T. Burke e , Richard A. Miller a,d,∗ a

e

Department of Pathology, University of Michigan School of Medicine, Ann Arbor, MI, USA b Geriatrics Center, University of Michigan School of Medicine, Ann Arbor, MI, USA c University of Michigan Institute of Gerontology, Ann Arbor, MI, USA d Ann Arbor DVA Medical Center, Ann Arbor, MI, USA Department of Human Genetics, University of Michigan School of Medicine, Ann Arbor, MI, USA Received 26 January 2004; received in revised form 27 February 2004; accepted 2 March 2004

Abstract Previous studies have shown that T cell subset levels, early life body weight, and levels of leptin and thyroid hormones can each serve, independently, as predictors of life span in populations of genetically heterogeneous mice. New data now confirm, in a replicate cohort, that T cell subset patterns predict longevity, and show that they can do so when measured in mice as young as 8 months of age. Individual T cell subsets, as well as composite indices that combine data from two or more T cell measures at 8 or 18 months, can be combined with 3- and 9-month body weight data to provide better prediction of life span than either immune or weight measures alone. Mice whose immune and weight measures are both in the lowest quartile have mean and maximal life spans that are 18% and 16–25% higher, respectively, than mice in the opposite quartiles for both traits. Thyroxine levels measured at 4 months lead to further improvement over models that combine weight and immune data only. A genome scan provided evidence for loci on chromosomes 2, 12, 13, and 17 that modulate age-sensitive T cell subset patterns at both 8 and 18 months of age. These data show that late-life mortality risks are influenced to a measurable degree by factors that modulate growth trajectory and hormone and immune status in the first third of the life span, and provide clues as to which early life systems deserve further scrutiny as potential mediators of late life disease risk. © 2004 Elsevier Ireland Ltd. All rights reserved. Keywords: Body weight; Thyroxine; Leptin; T cell subsets; Life span

1. Introduction Individual characteristics that predict remaining life expectancy fall into three broad and overlapping categories, each with its own role in aging research. The first such category consists of risk factors for specific lethal illnesses. A newborn with a severe heart defect, or a middle-aged person with severe obesity and diabetes, or someone at any age confronting a cobra has a short life expectancy compared to age-matched controls. Understanding the connections between risk factor and illness can be trivial and straightforward (cobra), or complex and of great medical value (diabetes). Some risk factors, including smoking his∗ Corresponding author. Present address: 1500 East Medical Center Drive, 5316 CCGCB, Box 0940, Ann Arbor, MI 48109-0940, USA. Tel.: +1-734-936-2122; fax: +1-734-936-9220. E-mail address: [email protected] (R.A. Miller).

tory, socioeconomic status, and body weight, predispose to multiple illnesses, and elucidating these connections is a major goal of medical research. Studies of disease-specific risk factors, however, are of only indirect relevance to clarification of the biology of aging, defined here as the process that converts healthy young adults into frail older ones, with diminished function in multiple cell and organ types, and an increasing risk of a wide range of distinct illnesses. There are two classes of longevity predictors of great interest to biogerontologists: midlife indicators of biological age (“biomarkers of aging”) and physiological variables, potentially detectable at very early ages, which modify the phenotype of juveniles and young adults and which also influence risks of multiple disease and multiple forms of functional decline much later in the life span. The biomarker class, whose very existence is controversial (Costa and McRae, 1988; Masoro, 1988) reflects a conceptual model in which individuals of identical chronologi-

0047-6374/$ – see front matter © 2004 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.mad.2004.03.003

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cal age are supposed to be at varying distances along a dimension of “biological age,” so that those whose biomarker profile resembles that of younger subjects are postulated to be less susceptible to multiple forms of disability and disease than subjects of the same chronological age whose biomarkers are typical of older subjects. Such a conceptual framework works well when considering members of different species: a 10-year-old dog is much more likely to exhibit cataracts, sarcopenia, immune senescence, arthritic changes, and multiple tumors than a 10-year-old horse or human, and so it is sensible to consider the 10-year-old horse biologically younger than a dog with the same birth date. It is still unclear, though, whether humans or mice or dogs that appear relatively aged in some respects (example: poor immune responses) are likely to appear relatively aged in other respects (examples: poor muscle strength, lens turbidity, joint degeneration, tumor burden) when compared to members of their own species with identical birth dates. The idea that a trait is useful as a biomarker of aging implies that the trait changes with age, and does so at a rate which is coupled, at least loosely, to other age-sensitive traits, including those that increase the risk of illness and death in the last half of the life span. It would not be sensible to expect biomarkers of aging to discriminate among juveniles or young adults, in whom aging has only just begun. The third possible class of life span predictors consists of processes, measurable in juveniles or young adults, that predict longevity either because they regulate aging rate directly or because they serve as indices of processes that influence aging per se. If, for example, mammalian aging rate is modulated by core body temperature, then individuals who maintain relatively low core temperatures throughout their life span might be expected to live longer, and just as importantly to show slower rates of change in multiple age-sensitive systems, than subjects with normal or higher core temperatures. Such a relationship does not depend on any assumptions about the effects of age on body temperature; body temperature, in this scenario, is not suitable as a biomarker of aging. As a second example, it seems plausible that exposure early in life to relatively low levels of IGF-I or poor IGF-I responsiveness, as seen in calorically restricted mice (Weindruch and Walford, 1988), several varieties of mutant mice (Brown-Borg et al., 1996; Coschigano et al., 2000; Holzenberger et al., 2003), and long-lived dogs (Eigenmann et al., 1984; Eigenmann et al., 1988; Miller, 1999), might lead both to small body size and also delay or decelerate the aging process. Were this the case, then measures of IGF-I or its surrogate body length in young subjects might allow prediction of life expectancy and the pace of aging regardless of whether aging itself led to alterations of IGF-I level. Evidence about biomarkers of aging, or about developmental factors that influence aging, is not easy to come by: it requires measurement of the alleged predictors in a large population of individuals that can then be followed until they become old and exhibit changes in age-sensitive

traits or die. We have conducted two such studies using a population of genetically heterogeneous mice. The first experiment (called “LAG1”) focused on T cell subsets in midlife as potential biomarkers of aging. The data showed that 18-month-old mice with high levels of CD4 memory cells, CD8 memory cells and CD4 P-glycoprotein cells and low levels of CD4 and CD4 na¨ıve cells tended to be shorter lived than age-matched controls with the opposite pattern of T cell subsets (Miller, 2001). A composite index, developed by principal component calculations to reflect the overall T cell subset pattern, was found to be a statistically significant predictor of life span not only in the population as a whole, but also in subpopulations dying of various forms of illness (Miller and Chrisp, 2002). Experiments using a second cohort (“LAG2”), produced evidence that body weight (Miller et al., 2002) and early life levels of the hormones thyroxine (T4 ) and leptin (Harper et al., 2003) can predict life span. We have now carried out a series of analyses of the LAG2 cohort to ask three questions: (a) can prediction of longevity by T cell subset patterns be detected in this replicate population, and if so at what age?; (b) can models that combine T cell data with either hormone or body weight results provide better prediction of longevity than models based on one class of data alone?; and (c) can we identify quantitative trait loci (QTL) for indices of immune function using this population of 4-way cross mice?

2. Methods 2.1. Mice The animals used in this study were of the UM-HET3 stock, bred at the University of Michigan as the offspring of (BALB/cJ × C57BL/6J)F1 (CB6F1) females and (C3H/HeJ × DBA/2J)F1 (C3D2F1) males. They were weaned at 3–4 weeks of age, housed in cages containing mice of the same sex, initially at 3–4 mice per cage, and given free access to food and water. To ensure the specific pathogen free (spf) status of the study population, groups of sentinel mice were exposed to spent bedding from the study population on a quarterly basis, and were later evaluated serologically for the presence of specific viral and bacterial pathogens. The animals were also examined for pinworm. At one point three animals were euthanized due to a positive result for mouse parvo virus (MPV), but all other test results were negative over the course of the study. 2.2. Determination of Life span Mice were entered into the study in cohorts of approximately 30 per month beginning in March of 1998, and housed in the University of Michigan Cancer Center and Geriatrics Center Building. Weights were recorded to the nearest gram at monthly intervals from two months of age until the death of the animal. Mice were examined at least

J.M. Harper et al. / Mechanisms of Ageing and Development 125 (2004) 381–390

once each day to record date of death, and they were euthanized if found to be so severely ill that in the opinion of an experienced caretaker they were thought unlikely to survive more than another few days. Initially, the population consisted of 311 females and 287 males. Of these, 7 females and 49 males were removed from the population prior to their natural death because of either: (a) damage inflicted by fighting (37 males), (b) unintended pregnancy (4 females), (c) death during an injection or surgical procedure (11 animals), (d) record keeping errors (1 mouse), or (e) they were found to be positive for MPV (see above). These culled animals were included in the survival analysis, and treated as censored observations. Using non-censored life spans, the median survival in this population was 864 days for females and 859 days for males. In the context of another study, each mouse was immunized at the ages of 4 and 15 months using either sheep or turkey erythrocytes, and then bled by tail venipuncture 2 weeks later. Serum from these samples was used for the quantification of circulating T4 , leptin and IGF-I as described elsewhere (Harper et al., 2003). In addition, each mouse was subjected to brief metofane anesthesia at age 12 months as part of a separate investigation. 2.3. T cell subset levels Two-color flow cytometry analyses were used in the evaluation of individual T cell subset levels in blood samples collected by tail venipuncture at age 8 and 18 months using previously described methods (Miller et al., 1997). See Table 1 for a description of the parameters used to define each subset. 2.4. Principal component calculation A principal component calculation was done to combine immune subset measures into smaller numbers of composite factors, similar to the approach taken in (Miller and Chrisp, 2002). The principal component extraction was done twice, one using data from all six T cell subsets measured at 8 months of age, and then a second time using the T cell subset data from the mice at 18 months of age. For mice in which a specific T cell subset measurement was unavailable because of technical error, the mean level for the subset at that age was used. The principal component scores were calculated for each mouse without any factor rotation.

383

2.5. Statistical analyses Due to differences in scale and units of measure, the raw data were standardized prior to analysis such that the mean value was 0 and the standard deviation was 1 for each T cell subset, body weight, and serum hormone measure. The strength of the association between individual T cell subset levels and principal component factors and life span was evaluated using both Cox proportional hazards and standard linear regression. In order to assess whether including body weight and/or serum hormone measures significantly improves the fit of a Cox regression model over that of a model using only an immune measure as a predictor, a likelihood ratio test, denoted by G, was performed, and is calculated as twice the absolute difference between the log likelihood score of the model containing the variable of interest and the log likelihood score of the model that does not contain this variable (Hosmer and Lemeshow, 1999). When performing the log likelihood ratio tests, all missing cases were excluded in a casewise manner prior to analysis in order to ensure that the log likelihood score for each of the models were generated using the same set of data. For the detection of quantitative trait loci (QTL) for specific immune measures a single point genome-wide search was performed for each of the phenotypes. In brief, individual mice are scored for their genotype at each of the marker loci, as well as for each of the immune measures, with a difference in the mean of the immune measure between the different marker loci being indicative of a QTL linked to that marker. In a single point search, marker loci are considered one a time, rather than simultaneously with other markers. To ensure that the analysis was consistent for all partially and fully informative markers, four-way informative markers were split into two sets of bi-allelic markers that were informative for either the maternally or paternally transmitted alleles. One-way ANOVA models, with each measure as the dependent variable and a bi-allelic marker as the independent variable, were used to perform a genome-wide search for each of the 164 bi-allelic markers. The statistical significance for each marker-trait combination was then calculated empirically using a permutation based technique that allows for the generation of an “experiment-wise” acceptance criterion that takes multiple comparisons into account and avoids type I error inflation. A null distribution for permutation analysis was generated based on 1000 random shuffles of the original phenotypic data.

Table 1 Description of T cell subsets measured in 8- and 18-month-old UM-HET3 Mice Subset

Description

Measure

CD4 CD8 CD4M CD8M CD4P CD8P

Class II restricted helper T cells Class I restricted cytotoxic T cells Memory CD4 cells Memory CD8 cells P-glycoprotein-(+) CD4 cells P-glycoprotein-(+) CD8 cells

CD4(+), CD8(+), CD4(+), CD8(+), CD4(+), CD8(+),

CD3(+) as % of CD3 CD3(+) as % of CD3 CD44(high) as % of CD4 CD44(high) as % of CD8 R123 extruding as % of CD4 R123 extruding as % of CD8

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3. Results 3.1. Age-dependent changes in T cell subset levels Table 2 summarizes the effect of age on each of the six T cell subsets measured in this group of mice, referred to as the “LAG2” cohort. In agreement with a previous report on a separate population of these mice (the “LAG1” cohort), generated using same breeding scheme as for the LAG2 population (Miller, 2001), there was a significant increase in the proportion of CD4 and CD8 memory cells, a significant increase in the proportion of P-glycoprotein positive cells, and a significant decline in the proportion of CD4 cells. These effects were seen regardless of sex. The age-dependent increase in CD8 cells was not apparent in the previous LAG1 cohort (Miller, 2001), but is consistent with the decrease in CD4 cells noted in both studies. 3.2. Individual T cell subsets as predictors of life span A previous study (Miller, 2001), based on the LAG1 cohort, documented a significant association between several T cell subsets and life expectancy, including CD8M cells at 8 and 18 months, and CD4M and CD4 cells at 18 months. The current study in the LAG2 cohort provided independent confirmation of three of these relationships (CD8M at ages 8 and 18 months, and CD4 at 18 months) using a Cox regression model (Table 3), as well as two of these relationships (CD8M at ages 8 and 18 months) using standard linear regression (Table 3). CD8 cells at 18 months of age were also significant predictors of life span in the current, LAG2, cohort, although not in the previous LAG1 study. Fig. 1 ilTable 2 Summary statisticsa for T cell subsets at two ages Subset

Age at measure (months)

Male

Female

CD4

8 18

68.2 ± 5.1 (234) 57.7 ± 7.6 (211)b

67.3 ± 5.7 (305) 57.8 ± 8.5 (285)b

CD8

8 18

31.4 ± 5.2 (234) 41.0 ± 6.7 (211)b

31.9 ± 5.6 (305) 42.2 ± 8.2 (285)b

CD4M

8 18

30.5 ± 10.9 (228) 38.6 ± 11.8 (211)b

30.3 ± 9.1 (296) 43.0 ± 13.4 (269)b,c

CD8M

8 18

40.2 ± 12.7 (226) 55.6 ± 15.1 (209)b

34.9 ± 10.6 55.1 ± 15.6 (266)b

CD4P

8 18

16.3 ± 6.3 (215) 21.7 ± 11.3 (211)b

18.2 ± 7.4 (269)c 24.5 ± 11.6 (272)b,c

CD8P

8 18

56.7 ± 15.3 (221) 62.3 ± 13.6 (209)b

56.9 ± 15.7 (275) 63.2 ± 14.4 (272)b

3.3. T cell subsets and body weight as joint predictors of life span We have shown previously that body weight (Miller et al., 2002) was a significant predictor of life span in the LAG2 population. Thus, we considered whether regression models that included combinations of immune and body weight variables might be better predictors of life span than models that made use of immune measures only. Table 4 shows the results of regression analyses using T cell subset levels together with body weight data, for mice weighed at 3 or at 9 months of age. For CD8M cells at either 8 or 18 months, and for the CD8 subset at 18 months, the addition of either body weight measure significantly increased the predictive power of the Cox regression model Table 3 Univariate regression analyses: individual T cell subsets as predictors of life span in UM-HET3 mice Subset

n

(293)c

Mean ± S.D. for each of the six different subsets at two ages. The number in parentheses indicates sample size. b Indicates a significant difference for 8-month-old vs. 18-month-old individuals by paired t-test. c Indicates a significant difference for male vs. female mice via two sample t-test. a

lustrates these relationships in the form of scatterplots for each of the four associations. The figure shows that longer life span is associated with lower levels of CD8 cells in 18-month-old mice, with lower levels of CD8M cells in both 8- and 18-month-old individuals, and with increased levels of CD4 cells at 18 months of age. Thus, in each case improved longevity is seen in mice whose T cell subset distribution resembles that seen in younger mice (see Table 2). The earlier study (Miller, 2001) had found that the CD4M level was a strong predictor of life span in 18-month-old LAG1 UM-HET3 mice. However in the LAG2 cohort this predictor did not achieve statistical significance by Cox regression (risk ratio = 1.01, P = 0.12) or by linear regression (r = −0.08, P = 0.07). In the LAG2 cohort, we also found that the CD8P level in 18-month-old mice was a significant predictor of longevity, but in a sex-specific fashion (Table 3 and Fig. 2). High levels of CD8P cells were associated with longer life in female mice, but with a significantly shorter life span in males. These data thus do not replicate the findings in the LAG1 study (Miller, 2001), in which CD8P levels were not predictors of life span in either male or female mice, and in which CD4P cells were significantly associated with longevity.

Cox regression

Linear regression

Risk ratio

P-value

Correlation coefficient

P-value

CD8M (8)a CD8M (18) CD4 (18) CD8 (18)

519 474 495 495

1.12 1.14 0.92 1.14

0.006 0.004 0.05 0.005

−0.13b −0.12 0.07 −0.10

0.002 0.007 0.13 0.03

CD8P (18) Males Females

208 272

1.17 0.86

0.03 0.01

−0.15 0.13

0.03 0.04

a

Number in parentheses indicates the age at which the measure was taken. b Values given are Pearson product moment correlation coefficients.

J.M. Harper et al. / Mechanisms of Ageing and Development 125 (2004) 381–390 1400

1400

Age at Death (days)

Age at Death (days)

1200 1000 800 600 400 200

1200

1000

800

600 0

20

40

60

80

100

0

20

CD8Mat 8 Months

60

40

80

100

CD8M at18 Months

1400

1400

1200

1200

Age at Death (days)

Age at Death (days)

385

1000

800

1000

800

600

600 0

20

40

60

80

CD4 at 18Months

0

20

40

60

80

CD8 at18 Months

Fig. 1. Scatterplots showing longevity as a function of T cell subset levels measured in male (䊉) and female () UM-HET3 mice. CD8M T cell levels were significant predictors of longevity at both 8 and 18 months of age (top panels) while CD4 and CD8 levels were significant predictors of longevity only in 18-month-old mice (bottom panels). Lines were calculated using simple linear regression for both sexes of mice combined.

(P ≤ 0.02 by likelihood ratio test). In contrast, models combining body weight measures with the 18-month CD4 subset data are not significantly better than those using the weight data alone (P < 0.07 for 3-month weight), although they are better than models that use only the data on CD4 cells at 18 months (P < 0.001 for either weight measure). The results of combining the subset and weight data for CD8P levels in 18-month-old mice are also included in

Table 4. For female mice, in which high CD8P values predict increased life span, inclusion of either body weight measure with the CD8P subset measure significantly improves the fit of the final model (P = 0.01 by likelihood ratio test). For male mice, in which high CD8P values are associated with shorter life span, the combination of weight and CD8P levels is only slightly better than weight alone, and does not reach statistical significance (P < 0.08 by likelihood ratio test). 3.4. Principal components analysis

Age at Death (days)

1400 1200 1000 800 600 400

0

20 40

60 80 100 0

20 40 60 80 100

CD8P at 18 Months Fig. 2. Scatterplot showing longevity as a function of P-glycoprotein positive CD8 cells measured in 18-month-old male (left panel) and female (right panel) UM-HET3 mice. Lines were calculated using simple linear regression for each sex separately.

In an attempt to reduce the dimensionality of the dataset, a principal components analysis (PCA) was performed using the T cell subset data, similar to the approach taken in (Miller and Chrisp, 2002). Using data from 8-month-old mice, the first principal component factor had an eigenvalue of 1.84, and accounted for 31% of the inter-mouse variation; in other words, this factor explained 1.84 times as much variance as the average T cell subset value taken separately. The factor loadings (correlations between the factor and the individual T cell subset measures, not shown) indicated that its value was related largely to the CD4 and CD8 cell levels; mice with high levels of this factor were those with high numbers of CD4 cells and low numbers of CD8 cells. This factor was not a significant predictor of longevity in 8-month-old mice (P = 0.09). The second factor, with an eigenvalue of 1.51,

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Table 4 Cox regression: T cell subsets plus body weight as predictors of life span in UM-HET3 mice Final modela

Likelihood ratio test, G (P-value)b

Trait 1

n

Risk ratio

P-value

Trait 2

CD8M (8)c

564

CD8M (18)

479

CD4 (18)

500

CD8 (18)

500

1.11 1.11 1.14 1.13 0.92 0.93 1.13 1.12

0.02 0.02 0.004 0.01 0.06 0.11 0.007 0.01

Weight Weight Weight Weight Weight Weight Weight Weight

CD8P (18) Males

213

Females

272

1.13 1.14 0.84 0.85

0.08 0.07 0.006 0.009

Weight Weight Weight Weight

Risk ratio

P-value

T cell subset vs. subset + weight

Body weight vs. subset + weight

(3) (9) (3) (9) (3) (9) (3) (9)

1.14 1.21 1.19 1.23 1.18 1.23 1.17 1.23

0.004 <0.0001 0.002 <0.0001 0.007 <0.0001 0.001 <0.0001

8.01 (0.005) 16.95 (<0.0001) 10.83 (0.001) 16.77 (<0.0001) 11.15 (0.001) 19.1 (<0.0001) 10.82 (0.001) 18.5 (<0.0001)

5.45 5.51 8.34 6.67 3.29 2.41 7.07 5.94

(0.02) (0.02) (0.004) (0.01) (0.07) (0.12) (0.008) (0.02)

(3) (9) (3) (9)

1.21 1.32 1.18 1.18

0.007 0.0001 0.01 0.01

6.93 14.42 6.21 6.17

3.11 3.30 7.16 6.59

(0.08) (0.07) (0.01) (0.01)

(0.01) (0.0001) (0.01) (0.01)

a Cox regression was used to predict longevity as a function of the T cell subset + body weight measures. The risk ratio is indicative of the change in hazard rate attributable to each variable in the final model and the P-values indicates whether each variable is a significant, independent predictor of life span in the final model. b A P-value < 0.05 indicates that the two variable regression model (T cell subset + body weight) is a better fit than the indicated single variable model (left hand column: T cell subset only; right hand column: body weight only). c Number in parentheses indicates the age at which the measure was taken for each trait.

accounted for 25% of the variation among the mice. High values of this second factor were seen in mice with high levels of both CD4M and CD8M cells. We will refer to this second principal component as “MemFac 8” because it is an indicator of memory cell levels at 8 months of age. MemFac 8 was found to be a significant predictor of longevity by standard linear regression (R2 = 0.018, P = 0.002), consistent with the association of high memory cell numbers to shorter life expectancy in this and in the LAG1 study. The first two lines of Table 5 show the results of calculations evaluating the usefulness of body weight data to improve the predictive power of the MemFac 8 measure. Weight at 3 months was an independent, statistically significant predictor of longevity (P = 0.003) in models also incorporating MemFac 8, as was weight at 9 months (P < 0.0001). The results of the likelihood ratio tests indicated that in each case the two-variable model was a significantly better predictor of life span than either measure was alone (P ≤ 0.03 for all cases). A similar principal component analysis was conducted using the T cell subset data from 18-month-old mice. At

this age, the first principal component (“ImFac 18”) was correlated with four T cell subsets: high values of ImFac 18 were associated with high levels of CD8, CD4M, CD8M and low levels of CD4 cells. Thus mice with high values of ImFac 18 had immune systems that resembled those of older mice for all four of these subsets. The eigenvalue of ImFac 18 was 2.40, and it accounted for 40% of the total available variance among the mice. ImFac 18 was by itself a significant predictor of life expectancy by standard linear regression (R2 = 0.012, P = 0.019). Table 5 shows that models combining ImFac 18 values with body weight at either 3 or at 9 months of age were superior to models containing only immune or only weight data as predictors of life span (P < 0.01 for either weight measure). Fig. 3 (top) shows survival curves for groups of mice that differed from one another both in body weight at 3 months of age and in T cell subset patterns as estimated by MemFac 8. The 56 mice that were culled from the experimental group because of fighting, record keeping error, or unintended pregnancy, or which died because of experimental accidents, were not included in this graphic because

Table 5 Cox regression: immunological PC factor scores plus body weight as predictors of life span in UM-HET3 mice Final model

Likelihood ratio test, G (P-value)

Trait 1

n

Risk ratio

P-value

Trait 2

Risk ratio

P-value

Immune factor vs. factor + weight

Body weight vs. factor + weight

MemFac 8

500

1.11 1.11

0.03 0.02

Weight (3) Weight (9)

1.16 1.22

0.003 <0.0001

8.4 (0.004) 16.58 (0.0001)

4.75 (0.03) 5.14 (0.02)

ImFac 18

460

1.15 1.14

0.003 0.01

Weight (3) Weight (9)

1.19 1.23

0.001 <0.0001

10.84 (0.001) 16.24 (0.0001)

8.4 (0.004) 6.87 (0.009)

See Table 4 for explanation of details.

J.M. Harper et al. / Mechanisms of Ageing and Development 125 (2004) 381–390

Proportion Remaining

1.2

Low 25 % MemFac_8, Light25% Middle50 %MemFac_8, Middle 50% High25% MemFac_8, Heavy 25%

1.0 0.8 0.6 0.4 0.2 0.0 200

400

600

800

1000

1200

1400

Age at Death (days)

Proportion Remaining

1.2

Low 25% ImFac_18,Light 25% Middle 50%ImFac_18, Middle 50% High 25%ImFac_18, Heavy 25%

1.0 0.8 0.6 0.4 0.2 0.0 200

400

600

800

1000

1200

1400

Age at Death (days) Fig. 3. Kaplan-Meier survival curves for UM-HET3 mice stratified by body weight at 3 months plus the principal components factors MemFac 8 (top panel) and ImFac 18 (bottom panel; see text for details). Each symbol represents an individual mouse.

our interest was in the aging process and biological contributions to late life diseases. Three subgroups are included: (a) those which were in the heaviest quartile for weight at 3 months and also in the quartile with the highest values of MemFac 8, (b) those which were in the lightest quartile for weight at 3 months and also in the quartile with the lowest values of MemFac 8, and (c) those which were in the middle half of the distribution for both the weight measure and MemFac 8. The bottom panel shows similar plots for groups

387

defined by ImFac 18 and weight at 3 months. The graphic shows that mice with a low body weight and a low score in either immune status measure live significantly longer than mice with high or intermediate scores for body weight and either immune index. Log-rank tests were used to test the significance of these differences among groups. Mice in the low-weight, low-score group were significantly longer lived than those in the opposite group (P = 0.0002 for MemFac 8 and P < 0.0001 for ImFac 18), as well as in comparison to mice whose measures fell in the middle half of the distribution (P = 0.03 and P = 0.02 for MemFac 8 and ImFac 18, respectively). Interestingly, those mice that had body weight and immune factor scores in the middle half of the distribution were also significantly longer lived than those mice with high body weight and a high immune factor score (P = 0.02 for MemFac 8 and P = 0.0005 for ImFac 18). Table 6 collects summary statistics from these life tables. Mice with the optimal combination of weight and immune predictors had a mean life span that was approximately 18% longer than those with the opposite combination, while the difference in maximal longevity between these groups was 16–25%. These trends were also apparent for groups of mice that were stratified in the same manner for the 9-month body weight measure, as well as for groups of mice stratified by individual subset measures and body weight (data not shown). 3.5. Inclusion of early-life hormone values as predictors of longevity Because our previous studies of this LAG2 population showed that T4 levels (in male mice) and leptin levels (in female mice) were significant predictors of life span at 4 months of age, we evaluated whether models that added information on these hormones improved the predictive power of regressions using either immune markers alone or immune and weight data together. We found that a model combining T4 levels with MemFac 8 was a better predictor of longevity than either of the models using these predictors

Table 6 Mean and maximal life span of UM-HET3 mice stratified by body weight at 3 months and immune factor scoresa Immune measure

Age (months)

PCF score quartile

Body weight quartile at 3 months

n

Mean (±S.D.) life span (days)

%Different from shortest-lived

Max (±S.D.) life span (days)b

%Different from shortest-lived

MemFac 8

8

High 25% Middle 50% Low 25%

Heavy 25% Middle 50% Light 25%

37 120 28

807 (±162) 856 (±199) 949 (±189)c

– 6 18

1066 (±45) 1171 (±52) 1232 (±76)c,d

– 10 16

ImFac 18

18

High 25% Middle 50% Low 25%

Heavy 25% Middle 50% Light 25%

30 118 30

823 (±97) 899 (±147)c 964 (±180)c

– 9 17

1010 (±21) 1160 (±49)c 1261 (±45)c,d

– 15 25

a

Censored mice were excluded from these analyses. See text for explanation of PCF and body weight score designations. Maximum life span was calculated as the mean life span of the longest-lived 10% for each combination of measures. c Indicates that the value is significantly different from the high weight, high PCF score group at P < 0.05 by one-way ANOVA followed by a Tukey–Kramer post hoc test. d Indicates that the value is significantly different from the middle weight, middle PCF score group at P < 0.05 by one-way ANOVA followed by a Tukey–Kramer post hoc test. b

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Table 7 Genome scan for QTL modulating T cell subset patterns in UM-HET3 mice Locusa

MGIb

Position cMc

Position bpd

Trait

%Variancee

P(e)
Nominal P-value for alternate traitg

D2Mit58p D13Mit26m D12Mit46m D17Mit185p

92194 89753 89454 100867

51 38 16 41

109,197,094 69,309,785 29,163,393 67,585,893

MemFac 8 MemFac 8 ImFac 18 ImFac 18

3.0 4.1 4.6 2.4

<0.003 <0.001 <0.001 <0.023

0.005 0.08 0.001 0.003

a The symbol ‘P’ indicates an allelic difference between C3H/HeJ and DBA/2. The symbol ‘m’ indicates an allelic difference between BALB/c and C57BL/6. b Sequence reference number for Mouse Genome Informatics database. c Distance in cM from centromere. d Physical position, build version 3 (MGSv3; GenBank accession number CAAA01000000). e Percentage of variance explained for the indicated trait. f Experiment-wise probability. g Nominal P-value (point-wise, not experiment-wise) significance level for the indicated locus evaluated for the other trait; i.e. D2Mit58p and D13Mit26m evaluated for linkage to ImFac 18 and D12Mit46m and D17Mit185p evaluated for associated with MemFac 8.

alone (P < 0.04 by likelihood ratio test). Substitution of ImFac 18 as the immune predictor did not meet our significance criterion (P = 0.09 compared to models using T4 levels alone). A model combining leptin values with ImFac 18 was significantly better (P = 0.04) than models using leptin or ImFac 18 alone, but models combining MemFac 8 with leptin levels did not reach significance (P = 0.08 for the comparison with MemFac 8 by itself). We also evaluated models in which T4 levels were considered together with both immune and weight variables. We found that a three variable model using MemFac 8, weight at 3 months, and T4 provided improved predictive power than any of the corresponding single-variable or two variable models; P = 0.01 for each comparison, except P = 0.06 for the comparison to a model combining weight and T4 without MemFac 8. Very similar results were obtained using weight at 9 months in models including MemFac8 and T4 levels (P < 0.02 for all comparisons except P = 0.06 for comparison to the model using weight and T4 without MemFac 8). Three factor models including leptin, weight, and immune status did not improve significantly on models incorporating two of these three factors, presumably because of the strong correlation between body weight and leptin. 3.6. Mapping quantitative trait loci (QTL) for immune status indicators The availability of genotypic data on the mice in our population allowed us to seek evidence for QTL that influenced the MemFac 8 and ImFac 18 scores. Using stringent experiment-wise significance criteria, we found loci on chromosomes 2 and 13 that modulated the MemFac 8 score, and loci on chromosomes 12 and 17 with effects on ImFac 18. Table 7 presents summary statistics from this genome scan, leaving out marker loci on the same chromosomes with similar but weaker associations (the paternally-derived marker D2Mit434 for MemFac 8 and the maternally-derived marker D12Mit105 for ImFac 18). Each of the four associations noted in the table has an experiment-wise P-value < 0.05, showing that the association noted is unlikely to have oc-

curred by chance even in a genome scan that considered many marker loci at the same time. The final column in Table 7 shows the nominal (point-wise, not experiment-wise) P-value for the locus in question evaluated for its association with the other trait; for example the paternally-derived marker D2Mit58 evaluated for its association with differences in the levels of ImFac 18. Three of these associations have a nominal P < 0.05 suggesting that loci on chromosomes 2, 12, and 17 may modulate these age-sensitive immune patterns at both 8 and 18 months of age.

4. Discussion The objectives of this study were threefold. First, we wanted to confirm, in an independent cohort, that measurements of peripheral T cell subset levels in 8- and 18-month-old animals could provide information regarding individual differences in life expectancy among genetically heterogeneous mice. Second, we wanted to determine whether regression models that included combinations of immune, body weight, and hormone variables might be better predictors of life span than models that made use of each of these variables alone. Last, we wanted to determine whether these individual differences in the pace of age-dependent T cell subset changes were under genetic control. We were able to provide independent confirmation that the proportion of three T cell subsets (CD8M at ages 8 and 18 months and CD4 at 18 months) is significantly associated with individual life expectancy. Our study failed to confirm the observation, from the LAG1 cohort, that CD4M and CD4P subset levels in 18-month-old individuals were also predictors of life span. CD8 cells at 18 months of age were found to predict life span in the LAG2 cohort, although this association did not reach statistical significance among LAG1 mice (Miller, 2001). Taken together, the data from the LAG1 and LAG2 cohorts suggest that age-sensitive T cell subsets are indeed predictors of subsequent life expectancy, but that the strength of the associations is weak enough

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that regressions based on single subsets do not always reach statistical significant in studies of 300–600 mice. The relationship between CD8P cells and life span, noted for the first time in the LAG2 cohort, is noteworthy in that the direction of the effect is sex-specific: high CD8P levels at 18 months predict short life expectancy in males but long life span in females. The effect in male mice is consistent with the observation, based on the other subsets, that shorter life expectancy is typically associated with T cell subset levels found in older mice, but the association of high longevity with high CD8P levels in females is inconsistent with these other observations. The previous study (Miller, 2001) of LAG1 mice documented an association between high CD4P levels and short life span in males, with no association in females. Thus the relationships among sex, P-glycoprotein subsets, and life span are at this point difficult to interpret. As mice age there is a significant increase in the proportion of CD4 and CD8 memory cells, a significant increase in the proportion of P-glycoprotein positive cells, and a significant decline in the proportion of CD4 cells in the LAG2 cohort, consistent with previous reports in the earlier LAG1 cohort (Miller, 2001), as well as in other stocks of mice (Miller, 1997; Witkowski and Miller, 1993). Because each of the individual T cell subsets could potentially be influenced by genetic and environmental factors independent of the aging process, we used a principal components calculation to try to develop scores that combine individual subset measures into more informative composites. In the LAG2 cohort, we found two such principal component factors that were useful predictors of life span: (a) MemFac 8, which is an indicator of CD4 and CD8 memory cell levels at 8 months of age; and (b) ImFac 18, which was correlated with high levels of CD8, CD4M, CD8M and low levels of CD4 cells. For both factors there is a significant relationship between its score and individual life expectancy in that high factor scores are associated with a decline in life expectancy. In a previous study (Miller and Chrisp, 2002) in which data from LAG1 mice were combined with data from a group of mated female UM-HET3 animals, a factor similar to ImFac 18 had been shown to predict life expectancy in the population as a whole, as well as in subgroups whose death was attributable to specific forms of neoplasia (lymphoma, breast cancer, and fibrosarcoma). At 8 months of age, a factor similar to ImFac 18 had also been shown to predict longevity, but with strong associations in the mated female mice only. Thus the current study is broadly consistent with the work in LAG1 mice, but is the first to show convincing evidence for longevity prediction by T cell subset patterns as early as 8 months of age in virgin mice. A second objective was to determine if models that predict life span from T cell subset values could be improved by adding information about body weight, hormones, or both. We found that for three individual T cell subsets (Table 4), as well as for MemFac 8 and ImFac 18 (Table 5), the addition of body weight measures to Cox regression models consistently, and significantly, improved the predictive abil-

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ity of the final model over models using weight or T cells alone. Models that incorporated T4 levels in 4-month-old mice with MemFac 8 or that combined ImFac 18 with leptin were also superior to models that used immunity or hormone levels alone, and models that included predictors of all three types (immune factors, body weight and T4 ) were better predictors of life span than virtually all of the possible single or two variable models. For each of the regression models where multiple independent variables were a significantly better predictor of life span that any of the variables was by itself, the effect of each of each variables appears to be additive since the interaction term in the full Cox regression model was not significant in any case (data not shown). The graphics of Fig. 3 and the summary statistics of Table 6 illustrate the size of the effects attributable to these variations in immune status and body weight, and provide points of comparison to systems that manipulate longevity by caloric restriction or single gene mutations. Groups defined by quartiles of immune status at 8 months and weight at 3 months differ from one another by 18% in mean life span and 16% in an estimate of maximal life span. When immune status at 18 months is combined with weight at 3 months, the differences in mean and maximal life span are, respectively, 17 and 25%. These effects are about half of those seen with optimally effective caloric restriction (Weindruch and Walford, 1988), and are similar to those seen by alteration of the genes for Ghrhr, insulin receptor, and IGF-R (Flurkey et al., 2001; Bluher et al., 2003; Holzenberger et al., 2003), though only about half of those seen in dw/dw, df/df and GHR-KO mice (Brown-Borg et al., 1996; Coschigano et al., 2000). However, the long-lived mice in the LAG2 population have not been subjected to any type of experimental treatment, nor do they harbor mutations in genes known to influence life span. We cannot determine from this observational study whether the differences among these groups of mice are the result of the natural variation in patterns of growth and immune system development, or instead if growth and immune status are merely surrogate indicators of some underlying process that also modulates aging and life expectancy. Nonetheless, our data suggest that the biochemical and physiological pathways that influence immune maturation, hormone levels, and growth trajectories may be attractive targets for intervention studies aimed at extending life span. A previous paper (Jackson et al., 2003) reported QTL on chromosomes 4 and 13 that influenced T cell subset patterns, calculated by principal component methods, in a cohort of UM-HET3 mice evaluated at 18 months of age. The chromosome marker locus used in that earlier genome scan, D13Mit21, was located at 35 cm on maternal chromosome 13, in close proximity to the maternally-derived D13Mit26 marker found to be a significant predictor of MemFac 8 in the current study. The chromosome 4 locus documented in the earlier cohort (paternally-derived D4Mit55, at 20 cm on the paternal chromosomes) did not achieve experiment-wise significance

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in the current, LAG2, population. A post-hoc test, however, showed an association between the D4Mit55 marker and both MemFac 8 and ImFac 18 met the nominal significance criterion at P = 0.04 for each variable. Thus the chromosome 4 locus, too, seems to have a replicable influence on age-sensitive T cell subsets in both the current and the earlier study population. The maternally-derived chromosome 12 marker D12Mit46 had a nominal P < 0.01 for association with the 18 month T cell subset pattern in the earlier cohort, thus providing a replicate for this assignment in Table 7. The association to the paternally-derived marker D17Mit185 listed in Table 7 was not apparent (P = 0.35) in the earlier study, and may represent a chance association in the current population. Overall these data and analyses are relevant to a fundamental question in biological gerontology: to what extent are life expectancy and late life disease risks influenced by events that occur in the developmental and early adult periods of the life span? Our data show that three kinds of data—weight, hormone levels, and T cell subset patterns—can predict longevity in mice even when measured in the first third of the life span, i.e. the first 3–9 months of life, and thus show that some of the factors that modulate life expectancy can be detected in young adult animals. An important next step is to discover the mediators that are responsible for the observed differences in body weight gain, circulating hormone levels, and changes in T cell subset distributions, and to learn how these also modulate late life disease risks and life expectancy.

Acknowledgements We thank Emily Gray and Dana Knutzen for their technical assistance with the genotyping, Gretchen Buehner, Maggie Vergera and Stephen Pinkosky for their technical and husbandry assistance, and Shu Chen for her assistance with the statistical analyses. We would also like to thank Robert Arking whose thoughtful question was the impetus behind this study. This work was supported by NIH Grants AG16699, AG11687, and AG08808 (to RA Miller). J.H. was supported by NIA training grant T32-AG00114. References Bluher, M., Kahn, B.B., Kahn, C.R., 2003. Extended longevity in mice lacking the insulin receptor in adipose tissue. Science 299, 572–574.

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