Gender Specific Effects of Early-Life Events on Adult Lifespan

Testing Evolutionary Theories of Aging and Longevity Dr. Natalia S. Gavrilova, Ph.D. Dr. Leonid A. Gavrilov, Ph.D. Center on Aging NORC and The University of Chicago Chicago, Illinois, USA What are the data and the predictions of evolutionary theories of aging on Variability of age-related outcomes Old-age mortality trajectories

Trade-offs between longevity and fertility Part 1 Testing Predictions of Programmed vs. Stochastic Aging Opponents of programmed aging often argue that there is a too high variation in timing of aging-related outcomes, compared to much smaller variation in timing of programmed developmental outcomes (such as age of sexual maturation). In other words, aging just does not have an expected clock-wise accuracy, which is anticipated for programmed events.

Part 1 Testing Predictions of Programmed vs. Stochastic Aging To test the validity of this argument we compared relative variability (coefficient of variation) for parameters that are known to be determined by the developmental program (age at sexual maturity) with variability of characteristic related to aging (age at menopause). We used information on the ages at sexual maturation (menarche) and menopause from the nationally representative survey of the adult population of the United States (MIDUS) as well as published data for 14 countries.

Why use relative variability, coefficient of variation? "The fact that elephants, for instance, may have a standard deviation of 50 mm for some linear dimension and shrews a standard deviation of 0.5 mm for the same dimension does not necessarily mean that the elephants are more variable, in the essential zoological sense, than the shrews. The elephants are a hundred times the size of the shrews in any case, and we should expect the absolute variation also to be a hundred times as great without any essential difference in functional variability. The solution of this problem is very simple: it is necessary only to relate the measure of absolute variation to a measure of absolute size. The best measures to use for this purpose are the standard deviation and the mean, and since their quotient is always a very small number it is convenient to multiply it by 100. The resulting

figure is Roe a coefficient variation, orZoology: of variability" Simpson GG, A, Lewontin of RG. Quantitative Revised Edition. New York: Dover Publications, Inc.; 2003. Our results using the MIDUS study National survey conducted in 1994/95

Americans aged 25-74 core national sample (N=3,485) city oversamples (N=957) Additional samples: twins, siblings Subsample used in this study: women having natural menopause (no surgeries affecting the age at menopause) aged 60-74 A 30-40 minute telephone survey mail survey

Number of respondents: 4,242 respondents: 3,690 A 114 page Number of MIDUS SAMPLE POPULATION DISTRIBUTIONS (%) Women Aged 25-74 (n=2,087) AGE 25-54 55-64 65-74 RACE/ETHNICITY White African-American Other RELATIONSHIP STATUS Married Other intimate relationship

68.8 19.8 11.4 86.9 7.7 8.9 54.2 4.7 DISTRIBUTION OF AGE AT MENARCHE IN THE MIDUS SAMPLE DISTRIBUTION OF AGE AT MENOPAUSE IN THE MIDUS SAMPLE Variation for characteristics of human aging and development Characteristic

Mean age Coefficient (SD) years of variation Age at onset of menarche 12.9 (1.6) 12.4% MIDUS data Age at onset 49.7 (5.2) 10.5% of menopause MIDUS data Age at death USA, women, 1995. Human

mortality database 78.7 (16.1) 20.5% Source Variation of age at onset of menarche and age at death (in 2005) Country Mean age (SD) for onset of menarche CV

% Mean age (SD) at death CV % France 12.84 (1.40) 10.9 83.3 (13.8) 16.6 Italy 12.54 (1.46) 11.6

83.3 (13.1) 15.7 Sweden 13.59 (1.41) 10.4 82.3 (12.9) 15.7 UK 12.89 (1.54) 12.0 80.2 (14.0)

17.5 USA 12.9 (1.60) 78.7 20.5 12.4 Mean age (standard deviation, SD) at natural menopause Population Mean age (SD) at menopause, years

Source South Korean women 46.9 (4.9) Hong et al., MATURITAS, 2007 Viennese women aged 47 to 68 49.2 (3.6) Kirchengast et al., International Journal of Anthropology , 1999 Mexico: Puebla

Mexico city 46.7 (4.77) 46.5 (5.00) Sievert, Hautaniemi, Human Biology, 2003 49.5 (4.7) 48.9 (4.2) Walker et al., International Journal of Obstetrics & Gynaecology, 2005 Black women in South Africa: rural urban for Human Developmental

Characteristics Comparison of mean ages at menarche (1), menopause (2), and death (3) as well as their standard deviations for studied human populations. Source: Gavrilova N.S., Gavrilov L.A., Severin, F.F. and Skulachev, V.P. Testing predictions of the programmed and stochastic theories of aging: Comparison of variation in age at death, menopause, and sexual maturation. Biochemistry (Moscow), 2012, 77(7), 754-760. Conclusions Relative variability, coefficients of variation, for ages at onset of menarche and ages at death for contemporary populations are of the same order of magnitude Theories of programmed aging are fruitful in suggesting new testable predictions. "Although any claim that humans are programmed to age and die would be highly speculative, we

believe that as a hypothesis it suggests fruitful avenues for biological and even medical research." Longo VD, Mitteldorf J, Skulachev VP. Programmed and altruistic ageing. Nature Review Genetics. 2005 Nov;6(11): 866-72. To read more about this part of our study see: Gavrilova NS, Gavrilov LA, Severin FF, Skulachev VP. Testing predictions of the programmed and stochastic theories of aging: comparison of variation in age at death, menopause, and sexual maturation. Biochemistry (Moscow). 2012 Jul;77(7):754-60. http://www.ncbi.nlm.nih.gov/pubmed/22817539 Part 2 Testing the

Prediction of Late-Life Mortality Plateau Many evolutionary biologists believe that aging can be readily understood in terms of the declining force of selection pressure with age. At extremely old postreproductive ages when the force of natural selection reaches a zero plateau, some evolutionary biologists (i.e. Michael Rose) believe that the mortality plateau should also be observed (no further increase in mortality rates with age). To test the validity of this argument we analyzed mortality data for humans, rats and mice.

Some evolutionary theories predict late-life mortality plateau Source: Presentation by Michael Rose When the force of natural selection reaches a zero plateau, the mortality plateau is also expected Problems with Hazard Rate Estimation At Extremely Old Ages 1. Mortality deceleration in humans may be an artifact of mixing different birth cohorts with different mortality (heterogeneity effect) 2.

Standard assumptions of hazard rate estimates may be invalid when risk of death is extremely high 3. Ages of very old people may be highly exaggerated Monthly Estimates of Mortality are More Accurate Simulation assuming Gompertz law for hazard rate Stata package uses the NelsonAalen estimate of hazard rate: x = H(x ) H(x 1) =

dx nx H(x) is a cumulative hazard function, dx is the number of deaths occurring at time x and nx is the number at risk at time x before the occurrence of the deaths. This method is equivalent to calculation of probabilities of death: qx = dx lx Social Security Administrations Death Master File (SSAs DMF) Helps to Alleviate the First Two Problems

Allows to study mortality in large, more homogeneous single-year or even single-month birth cohorts Allows to estimate mortality in one-month age intervals narrowing the interval of hazard rates estimation What Is SSAs DMF ? As a result of a court case under the Freedom of Information Act, SSA is required to release its death information to the public. SSAs DMF contains the complete and official SSA database extract, as well as updates to the full file of

persons reported to SSA as being deceased. SSA DMF is no longer a publicly available data resource (now is available from Ancestry.com for fee) We used DMF full file obtained from the National Technical Information Service (NTIS). Last deaths occurred in September 2011. SSA DMF birth cohort mortality Nelson-Aalen monthly estimates of hazard rates using Stata 11 Conclusions from our earlier study of SSA DMF

Mortality deceleration at advanced ages among DMF cohorts is more expressed for data of lower quality Mortality data beyond ages 106-107 years have unacceptably poor quality (as shown using female-to-male ratio test). The study by other authors also showed that beyond age 110 years the age of individuals in DMF cohorts can be validated for less than 30% cases (Young et al., 2010) Source: Gavrilov, Gavrilova, North American Actuarial Journal, 2011, 15(3):432-447 Selection of competing mortality models using DMF data

Data with reasonably good quality were used: non-Southern states and 85-106 years age interval Gompertz and logistic (Kannisto) models were compared Nonlinear regression model for parameter estimates (Stata 11) Model goodness-of-fit was estimated using AIC and BIC Fitting mortality with Kannisto and Gompertz models Gompertz

model Kannisto model Akaike information criterion (AIC) to compare Kannisto and Gompertz models, men, by birth cohort (nonSouthern states) U.S. Males Gompertz Kannisto -250000 Akaike criterion -270000 -290000 -310000 -330000

-350000 -370000 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 Birth Cohort Conclusion: In all ten cases Gompertz model demonstrates better fit than Kannisto model for men in age interval 85-106 years Akaike information criterion (AIC) to compare Kannisto and Gompertz models, women, by birth cohort (non-Southern states) U.S. Females Gompertz Kannisto Akaike Criterion -600000 -650000 -700000

-750000 -800000 -850000 -900000 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 Birth Cohort Conclusion: In all ten cases Gompertz model demonstrates better fit than Kannisto model for men in age interval 85-106 years The second studied dataset: U.S. cohort death rates taken from the Human Mortality Database Selection of competing mortality models using HMD data

Data with reasonably good quality were used: 80-106 years age interval Gompertz and logistic (Kannisto) models were compared Nonlinear weighted regression model for parameter estimates (Stata 11) Age-specific exposure values were used as weights (Muller at al., Biometrika, 1997) Model goodness-of-fit was estimated using AIC and BIC Fitting mortality with Kannisto and Gompertz models, HMD U.S. data

Fitting mortality with Kannisto and Gompertz models, HMD U.S. data Akaike information criterion (AIC) to compare Kannisto and Gompertz models, men, by birth cohort (HMD U.S. data) U.S.Males Akaike Criterion Gompertz Kannisto -150 -170 -190 -210 -230 -250

1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 Birth Cohort Conclusion: In all ten cases Gompertz model demonstrates better fit than Kannisto model for men in age interval 80-106 years Akaike information criterion (AIC) to compare Kannisto and Gompertz models, women, by birth cohort (HMD U.S. data) U.S. Females A kaike C riterio n Gompertz Kannisto -150 -160 -170

-180 -190 -200 -210 -220 -230 -240 -250 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 Birth Cohort Conclusion: In all ten cases Gompertz model demonstrates better fit than Kannisto model for men in age interval 80-106 years Compare DMF and HMD data Females, 1898 birth cohort 1 0.1 0.01 60 70 80A

9 0 1 0 1 0 ge,years log Hazard rate DMF HMD Hypothesis about two-stage Gompertz model is not supported by real data What about other mammals? Mortality data for mice:

Data from the NIH Interventions Testing Program, courtesy of Richard Miller (U of Michigan) Argonne National Laboratory data, courtesy of Bruce Carnes (U of Oklahoma) Mortality of mice (log scale) Data by Richard Miller males females Actuarial estimate of hazard rate with 10-day age intervals Bayesian information criterion (BIC) to compare the Gompertz and Kannisto models, mice data Dataset

Miller data Controls Miller data Exp., no life extension Carnes data Early controls Carnes data Late controls Sex M F

M F M F M F Cohort size at age one year 1281 1104 2181

1911 364 431 487 510 Gompertz 597.5 496.4 -660.4 -580.6

-585.0 -566.3 639.5 549.6 -556.3 -558.4 -638.7 -548.0 -565.6 -495.4 -571.3 -577.2 Kannisto

Better fit (lower BIC) is highlighted in red Conclusion: In all cases Gompertz model demonstrates better fit than Kannisto model for mortality of mice after one year of age Laboratory rats Data sources: Dunning, Curtis (1946); Weisner, Sheard (1935), Schlettwein-Gsell (1970) Mortality of Wistar rats males females

Actuarial estimate of hazard rate with 50-day age intervals Data source: Weisner, Sheard, 1935 Bayesian information criterion (BIC) to compare Gompertz and Kannisto models, rat data Line Wistar (1935) Sex M F M Cohort size

1372 1407 Gompert z -34.3 Kannisto 7.5 Wistar (1970) Copenhagen Fisher

Backcrosses F M F M F M F 1372 2035 1328

1474 1076 2030 585 672 -10.9 -34.3 -53.7 -11.8 -46.3

-17.0 -13.5 -18.4 -38.6 5.6 7.5 1.6 2.3 -3.7 6.9 9.4

2.48 -2.75 Better fit (lower BIC) is highlighted in red Conclusion: In all cases Gompertz model demonstrates better fit than Kannisto model for mortality of laboratory rats Simulation study of the Gompertz mortality Kernel smoothing of hazard rates Hazard, log scale .2 .4 .6

.8 Smoothed hazard estimate 80 90 100 age 110 120 Recent developments none of the agespecific mortality relationships in our nonhuman primate analyses demonstrated the

type of leveling off that has been shown in human and fly data sets Bronikowski et al., Science, 2011 " Conclusions for Part 2 of our Study We found that mortality rates increase exponentially with age (the Gompertz law), and no expected late-life mortality plateaus are observed in humans, mice, and rats.

Late-life mortality deceleration and mortality plateau observed in some earlier studies may be related to problems with data quality and biased estimates of hazard rates at extreme old ages It seems unreasonable to explain aging (Gompertz law of mortality) by declining force of natural selection, because aging continues at the same pace at extremely old postreproductive ages when the force of natural selection already reaches a zero plateau To read more about this part of our study see: Gavrilov L.A., Gavrilova N.S. Mortality measurement at advanced ages: A study of the Social Security Administration

Death Master File. North American Actuarial Journal, 2011, 15(3): 432-447. http://www.ncbi.nlm.nih.gov/pmc/articles/ PMC3269912/ Part 3 Testing the Prediction of a Trade-off between Longevity and Fertility One of the predictions of the disposable soma theory and the antagonistic pleiotropy theory is that exceptional longevity should come with the price of impaired fertility (longevity-fertility trade-off ). This prediction seems to be confirmed by a high profile study published by Nature, which claimed that almost half of long lived women were childless.

Here we re-evaluate this study with more complete data Study that Found a Trade-Off Between Reproductive Success and Postreproductive Longevity Westendorp RGJ, Kirkwood TBL. 1998. Human longevity at the cost of reproductive success. Nature 396: 743-746. Extensive media coverage including BBC and over 100 citations in scientific literature as an established scientific fact. Previous studies were not quoted and discussed in this article.

Point estimates of progeny number for married aristocratic women from different birth cohorts as a function of age at death. The estimates of progeny number are adjusted for trends over calendar time using multiple regression. Source: Westendorp, Kirkwood, Human longevity at the cost of reproductive success. Nature, 1998, 396, pp 743-746 it is not a matter of reduced fertility, but a case of 'to have or have not'. Table 1 Relationship between age at death and number of children for married aristocratic women Age at death Proportion childless

(years) Number of children mean for all women mean for women having children <20 0.66 0.45 1.32 21-30 0.39 1.35

2.21 31-40 0.26 2.05 2.77 41-50 0.31 2.01 2.91 51-60

0.28 2.4 3.33 61-70 0.33 2.36 3.52 71-80 0.31 2.64 3.83

81-90 0.45 2.08 3.78 >90 0.49 1.80 3.53 Source: Toon Ligtenberg & Henk Brand. Longevity does family size matter? Nature, 1998, 396, pp 743-746 Number of progeny and age at first childbirth dependent on the age at

death of married aristocratic women Source: Westendorp, R. G. J., Kirkwood, T. B. L. Human longevity at the cost of reproductive success. Nature, 1998, 396, pp 743-746 Source: Westendorp, R. G. J., Kirkwood, T. B. L. Human longevity at the cost of reproductive success. Nature, 1998, 396, pp 743-746 Do longevous women have impaired fertility ? Why is this question so important and interesting? Scientific Significance This is a testable prediction of some evolutionary theories of aging disposable soma theory of aging (Kirkwood) "The disposable soma theory on the evolution of ageing states that longevity requires investments in somatic maintenance that

reduce the resources available for reproduction (Westendorp, Kirkwood, Nature, 1998). Do longevous women have impaired fertility ? Practical Importance. Do we really wish to live a long life at the cost of infertility?: the next generations of Homo sapiens will have even longer life spans but at the cost of impaired fertility Rudi Westendorp Are we becoming less disposable? EMBO Reports, 2004, 5: 2-6. "... increasing longevity through genetic manipulation of the mechanisms of aging raises deep biological and moral questions. These questions should give us pause before we embark on the enterprise of extending our lives Walter Glennon "Extending the Human Life Span", Journal of Medicine and Philosophy, 2002, Vol. 27, No. 3, pp. 339-354. Educational Significance

Do we teach our students right? Impaired fertility of longevous women is often presented in scientific literature and mass media as already established fact (Brandt et al., 2005; Fessler et al., 2005; Schrempf et al., 2005; Tavecchia et al., 2005; Kirkwood, 2002; Westendorp, 2002, 2004; Glennon, 2002; Perls et al., 2002, etc.). This "fact" is now included in teaching curriculums in biology, ecology and anthropology world-wide (USA, UK, Denmark). Is it a fact or artifact ? General Methodological Principle: Before making strong conclusions, consider all

other possible explanations, including potential flaws in data quality and analysis Previous analysis by Westendorp and Kirkwood was made on the assumption of data completeness: Number of children born = Number of children recorded Potential concerns: data incompleteness, underreporting of short-lived children, women (because of patrilineal structure of genealogical records), persons who did not marry or did not have children. Number of children born >> Number of children recorded Test for Data Completeness Direct Test: Cross-checking of the initial dataset with other data sources

We examined 335 claims of childlessness in the dataset used by Westendorp and Kirkwood. When we crosschecked these claims with other professional sources of data, we found that at least 107 allegedly childless women (32%) did have children! At least 32% of childlessness claims proved to be wrong ("false negative claims") ! Some illustrative examples: Henrietta Kerr (16531741) was apparently childless in the dataset used by Westendorp and Kirkwood and lived 88 years. Our cross-checking revealed that she did have at least one child, Sir William Scott (2nd Baronet of Thirlstane, died on October 8, 1725). Charlotte Primrose (17761864) was also considered childless in the initial dataset and lived 88 years. Our cross-checking of the data revealed that in fact she had as many as five children: Charlotte (18031886), Henry (18061889), Charles (1807 1882), Arabella (1809-1884), and William (18151881). Wilhelmina Louise von Anhalt-Bernburg (17991882), apparently childless, lived 83 years. In reality, however, she had at least two children, Alexander (18201896) and Georg (18261902). Point estimates of progeny number for married aristocratic

women from different birth cohorts as a function of age at death. The estimates of progeny number are adjusted for trends over calendar time using multiple regression. Source: Westendorp, R. G. J., Kirkwood, T. B. L. Human longevity at the cost of reproductive success. Nature, 1998, 396, pp 743-746 Antoinette de Bourbon (1493-1583) Lived almost 90 years She was claimed to have only one child in the dataset used by Westendorp and Kirkwood: Marie (1515-1560), who became a mother of famous Queen of Scotland, Mary Stuart. Our data cross-checking revealed that in fact Antoinette had 12 children!

Marie 1515-1560 Francois Ier 1519-1563 Louise 1521-1542 Renee 1522-1602 Charles 1524-1574 Claude 1526-1573 Louis 1527-1579 Philippe 1529-1529

Pierre 1529 Antoinette 1531-1561 Francois 1534-1563 Rene 1536-1566 Characteristics of Our Data Sample for Reproduction-Longevity Studies 3,723 married women born in 15001875 and belonging to the upper European nobility. Women with two or more marriages (5%) were excluded from the analysis in order to facilitate the interpretation of

results (continuity of exposure to childbearing). Every case of childlessness has been checked using at least two different genealogical sources. Childlessness is better outcome than number of children for testing evolutionary theories of aging on human data Applicable even for population practicing birth control (few couple are voluntarily childless)

Lifespan is not affected by physiological load of multiple pregnancies Lifespan is not affected by economic hardship experienced by large families Source: Gavrilova et al. Does exceptional human longevity come with high cost of infertility? Testing the evolutionary theories of aging. Annals of the New York Academy of Sciences, 2004,

1019: 513-517. Source: Gavrilova, Gavrilov. Human longevity and reproduction: An evolutionary perspective. In: Grandmotherhood The Evolutionary Significance of the Second Half of Female Life. Rutgers University Press, 2005, 59-80. Short Conclusion: Exceptional human longevity is NOT associated with infertility or childlessness

More Detailed Conclusions We have found that previously reported high rate of childlessness among long-lived women is an artifact of data incompleteness, caused by underreporting of children. After data cleaning, crosschecking and supplementation the association between exceptional longevity and childlessness has disappeared. Thus, it is important now to revise a highly publicized scientific concept of heavy reproductive costs for human longevity. and to make corrections in related teaching curriculums for students. More Detailed Conclusions (2) It is also important to disavow the doubts and concerns over further extension of human lifespan,

that were recently cast in biomedical ethics because of gullible acceptance of the idea of harmful side effects of lifespan extension, including infertility (Glannon, 2002). There is little doubt that the number of children can affect human longevity through complications of pregnancies and childbearing, as well as through changes in socioeconomic status, etc. However, the concept of heavy infertility cost of human longevity is not supported by data, when these data are carefully reanalyzed. Acknowledgments This study was made possible thanks to: generous support from the National Institute on Aging (R01 AG028620)

Stimulating working environment at the Center on Aging, NORC/University of Chicago For More Information and Updates Please Visit Our Scientific and Educational Website on Human Longevity: http://longevity- science.org And Please Post Your Comments at our Scientific Discussion Blog: http://longevityscience.blogspot.com/

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