1. Contemporary methods of mortality analysis: Determinants of Exceptional Longevity Dr. Leonid A. Gavrilov, Ph.D. Dr. Natalia S. Gavrilova, Ph.D. Center on Aging NORC and The University of Chicago Chicago, Illinois, USA

2. Approach To study “success stories” in long-term avoidance of fatal diseases (survival to 100 years) and factors correlated with this remarkable survival success

3. Centenarians represent the fastest growing age group in the industrialized countries Yet, factors predicting exceptional longevity and its time trends remain to be fully understood In this study we explored the new opportunities provided by the ongoing revolution in information technology, computer science and Internet expansion to explore early-childhood predictors of exceptional longevity Jeanne Calment (1875-1997)

4. Revolution in Information Technology What does it mean for longevity studies? Over 75 millions of computerized genealogical records are available online now!

5. Computerized genealogies is a promising source of information about potential predictors of exceptional longevity: life-course events, early-life conditions and family history of longevity

6. Computerized Genealogies as a Resource for Longevity Studies Pros: provide important information about family and life-course events, which otherwise is difficult to collect (including information about lifespan of parents and other relatives) Cons: Uncertain data quality Uncertain validity and generalizability

7. For longevity studies the genealogies with detailed birth dates and death dates for long- lived individuals (centenarians) and their relatives are of particular interest In this study 1,001 genealogy records for centenarians born in 1875-1899 were collected and used for further age validation

9. Steps of Centenarian Age Verification 1. Internal consistency checks of dates 2. Verification of death dates – linkage to the Social Security Administration Death Master File (DMF) 3. Verification of birth dates – linkage to early Federal censuses (1900, 1910, 1920, 1930)

10. Results of Centenarian Age Verification 1001 records consistency checks 990 records used for further verification 990 records were linked to the SSA Death Master File Linkage success rate 77% (80% for centenarians born after 1890) In 3% of cases centenarian status was not confirmed 548 records found in DMF for persons born in 1890-1899 were then linked to early US censuses Linkage success rate 80% when using Genealogy.com and 91% after supplementation with Ancestry.com. In 8% of cases a 1-year disagreement between genealogy and census record was observed

11. Conclusions of the Age Verification Study Death dates of centenarians recorded in genealogies always require verification because of strong outliers (1.3%, misprints) Birth dates of centenarians recorded in genealogies are sufficiently accurate - 92% are correct; for the remaining 8% only one-year disagreements Quality of genealogical data is good enough if these data are pre-selected for high data quality

12. Predictors of Exceptional Longevity

13. Study 1: Compare centenarians with their siblings (within-family study)

14. Within-Family Study of Exceptional Longevity Cases - 198 Centenarians born in U.S. in 1890-1893 Controls – Their own siblings Method: Conditional logistic regression Advantage: Allows researchers to eliminate confounding effects of between- family variation

15. Design of the Study

16. A typical image of ‘centenarian’ family in 1900 census

17. First-born siblings are more likely to become centenarians (odds = 1.8) Conditional (fixed-effects) logistic regression N=950, Prob > chi2=0.0000 VariableOdds ratio95% CIP-value First-born status 1.771.18-2.660.006 Male sex0.400.28-0.58<0.001

18. Birth Order and Odds to Become a Centenarian

19. Can the birth-order effect be a result of selective child mortality, thus not applicable to adults? Approach: To compare centenarians with those siblings only who survived to adulthood (age 20)

20. First-born adult siblings (20+years) are more likely to become centenarians (odds = 1.95) Conditional (fixed-effects) logistic regression N=797, Prob > chi2=0.0000 VariableOdds ratio95% CIP-value First-born status 1.951.26-3.010.003 Male sex0.460.32-0.66<0.001

21. Are young fathers responsible for birth order effect? Conditional (fixed-effects) logistic regression N=950, Prob > chi2=0.0000 VariableOdds ratio95% CIP-value Born to young father 1.860.99-3.500.056 Male sex0.420.29-0.59<0.001

22. Birth order is more important than paternal age for chances to become a centenarian Conditional (fixed-effects) logistic regression N=950, Prob > chi2=0.0000 VariableOdds ratio95% CIP-value First-born status 1.641.03-2.610.039 Born to young father 1.290.63-2.670.484 Male sex0.410.29-0.58<0.001

23. Are young mothers responsible for the birth order effect? Conditional (fixed-effects) logistic regression N=950, Prob > chi2=0.0000 VariableOdds ratio95% CIP-value Born to young mother 2.031.33-3.110.001 Male sex0.410.29-0.59<0.001

24. Maternal Age at Person’s Birth and Odds to Become a Centenarian

25. Birth order effect explained: Being born to young mother! Conditional (fixed-effects) logistic regression N=950, Prob > chi2=0.0000 VariableOdds ratio95% CIP-value First-born status 1.360.86-2.150.189 Born to young mother 1.761.09-2.850.021 Male sex0.410.29-0.58<0.001

26. Even at age 75 it still helps to be born to young mother (age <25) (odds = 1.9) Conditional (fixed-effects) logistic regression N=557, Prob > chi2=0.0000 VariableOdds ratio95% CIP-value Born to young mother 1.861.15-3.050.012 Male sex0.460.31-0.69<0.001

27. Question Families were quite large in the past, particularly those covered by genealogical records (large family size bias). Is the "young mother effect" robust to the family size, and is it observed in smaller families too? Or is it confined to extremely large families only? Approach: To split data in two equal parts by median family size (9 children) and re-analyze the data in each group separately.

28. Results In smaller families (less than 9 children) the effect of young mother is even larger: Odds ratio = 2.23, P=0.004; 95%CI = 1.30 - 3.98 Compare to larger families (more than 9 children): Odds ratio = 1.72, P=0.11; 95%CI = 0.88 - 3.34 Conclusion: "Young mother effect" is not confined to extremely large family size

29. Being born to Young Mother Helps Laboratory Mice to Live Longer Source: Tarin et al., Delayed Motherhood Decreases Life Expectancy of Mouse Offspring. Biology of Reproduction 2005 72: 1336-1343.

30. Possible explanation These findings are consistent with the 'best eggs are used first' hypothesis suggesting that earlier formed oocytes are of better quality, and go to fertilization cycles earlier in maternal life.

31. Study 2: Compare centenarians when they were young adults to their peers using WWI Civil Draft Registration Cards

32. Physical Characteristics at Young Age and Survival to 100 A study of height and build of centenarians when they were young using WWI civil draft registration cards

33. Height – What to Expect 1. Height seems to be a good indicator of nutritional status and infectious disease history in the past. 2. Historical studies showed a negative correlation between height and mortality. 3. Hence we may expect that centenarians were taller than average

34. Build – What to Expect 1. Slender build may suggest a poor nutrition during childhood. We may expect that centenarians were less likely to be slender when young. 2. On the other hand, biological studies suggest that rapid growth may be harmful and somewhat delayed maturation may be beneficial for longevity.

35. Small Dogs Live Longer Miller RA. Kleemeier Award Lecture: Are there genes for aging? J Gerontol Biol Sci 54A:B297–B307, 1999.

36. Small Mice Live Longer Source: Miller et al., 2000. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 55:B455-B461

37. Data Sources 1. Social Security Administration Death Master File 2. WWI civil draft registration cards (completed for almost 100 percent men born between 1873 and 1900)

38. Study Design Cases: men centenarians born in 1887 (randomly selected from the SSA Death Master File) and linked to the WWI civil draft records. Out of 120 selected men, 19 were not eligible for draft. The linkage success for remaining 101 records was 75% (76 records) Controls: men matched on birth year, race and county of WWI civil draft registration

39. Design of the Study

40. WWI Civilian Draft Registration In 1917 and 1918, approximately 24 million men born between 1873 and 1900 completed draft registration cards. President Wilson proposed the American draft and characterized it as necessary to make "shirkers" play their part in the war. This argument won over key swing votes in Congress.

41. WWI Draft Registration Registration was done in three parts, each designed to form a pool of men for three different military draft lotteries. During each registration, church bells, horns, or other noise makers sounded to signal the 7:00 or 7:30 opening of registration, while businesses, schools, and saloons closed to accommodate the event.

42. Registration Day Parade

44. Information Available in the Draft Registration Card age, date of birth, race, citizenship permanent home address occupation, employer's name height (3 categories), build (3 categories), eye color, hair color, disability

46. SAMPLE CHARACTERISTICS (%) CentenariansControls Foreign born20.522.2 Married68.463.7 Had children52.642.1 Farmers31.623.4 African Am.5.3

47. 171 pairs of men born in 1887 Centenarians Controls Body Height short 6.4 8.8 medium 65.5 56.7 tall 28.1 34.5 Body Build slender 25.2 medium 67.8 60.2 stout 7.0 14.6 BODY HEIGHT AND BODY BUILD DISTRIBUTIONS (%)

48. Height Distribution Among Centenarians and Controls

49. Body Build Distribution Among Centenarians and Controls

50. Multivariate Analysis Conditional multiple logistic regression model for matched case-control studies to investigate the relationship between an outcome of being a case (extreme longevity) and a set of prognostic factors (height, build, occupation, marital status, number of children, immigration status) Statistical package Stata-10, command clogit

51. Results of multivariate study VariableOdds Ratio P-value Medium height vs short and tall height 1.350.260 Slender and medium build vs stout build 2.63*0.025 Farming2.20*0.016 Married vs unmarried0.680.268 Native born vs foreign b. 1.130.682

52. Results of multivariate study Significant predictors only VariableOdds Ratio P-value ‘Slender’ body build reference: stout build 2.540.040 ‘Medium’ body build reference: stout build 2.640.017 Farming1.990.025

53. Other physical characteristics VariableOdds Ratio P-value Blue eye color1.620.069 ‘Short’ body height reference: tall height 1.020.967 ‘Medium’ body height reference: tall height 1.430.212 Other variables include body build and farming

54. Having children by age 30 and survival to age 100 Conditional (fixed-effects) logistic regression N=171. Reference level: no children VariableOdds ratio95% CIP-value 1-3 children1.620.89-2.950.127 4+ children2.710.99-7.390.051

55. Conclusion The study of height and body build among men born in 1887 suggests that obesity at young adult age (30 years) has strong long-lasting effect in preventing longevity

56. Other Conclusions Both farming and having large number of children (4+) at age 30 significantly increased the chances of exceptional longevity by 100-200%. The effects of immigration status, marital status, and body height on longevity were less important, and they were statistically insignificant in the studied data set.

57. Study 3: Compare centenarians found in computerized genealogies with general population

58. Case-Control Study of Early-Life Conditions and Exceptional Longevity Cases - 382 ‘white’ households where centenarians (born in 1890-1899) were raised (from centenarian records linked to 1900 census) Controls – 1% random sample of ‘white’ households with children below age 10 enumerated by 1900 census (from Integrated Public Use Microdata Sample, IPUMS: http://www.ipums.umn.edu/usa/index.html )

59. Statistical Approach: Logistic regression Dependent variable: Households with child-future centenarian (y=1) vs control households (y=0) Predictor variables: childhood residence, household property status, paternal immigration status, etc.

60. Childhood Residence and Survival to Age 100 Odds for household to be in a ‘centenarian’ group A – New England and Middle Atlantic (reference group) B – Mountain West and Pacific West C – Southeast and Southwest D – North Central

61. Household Property Status During Childhood and Survival to Age 100 Odds for household to be in a ‘centenarian’ group A – Rented House B – Owned House C – Rented Farm D – Owned farm (reference group)

62. Paternal Immigration Status and Survival to Age 100 Odds for household to be in a ‘centenarian’ group A – Father immigrated B – Father native-born (reference group)

63. No Association was Found (so far) Between Chances to Become a Centenarian and Paternal literacy Child mortality of siblings

64. Limitations Reporting bias in genealogies People mentioned in genealogies may be not representative to the whole population: more fertile, longer-living (?), wealthier (?), more educated (?)

65. General Conclusion of Centenarian Studies The shortest conclusion was suggested in the title of the New York Times article about our previous related study

67. 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://longevity-science.blogspot.com/

68. What are the Links Between Human Longevity and Fertility? Testing the evolutionary theory of aging

69. Founding Fathers Beeton, M., Yule, G.U., Pearson, K. 1900. Data for the problem of evolution in man. V. On the correlation between duration of life and the number of offspring. Proc. R. Soc. London, 67: 159-179. Data used: English Quaker records and Whitney Family of Connectucut records for females and American Whitney family and Burke’s ‘Landed Gentry’ for males.

70. Findings and Conclusions by Beeton et al., 1900 They tested predictions of the Darwinian evolutionary theory that the fittest individuals should leave more offspring. Findings: Slightly positive relationship between postreproductive lifespan (50+) of both mothers and fathers and the number of offspring. Conclusion: “fertility is correlated with longevity even after the fecund period is passed” and “selective mortality reduces the numbers of the offspring of the less fit relatively to the fitter.”

71. Other Studies, Which Found Positive Correlation Between Reproduction and Postreproductive Longevity Bettie Freeman (1935): Weak positive correlations between the duration of postreproductive life in women and the number of offspring borne. Human Biology, 7: 392-418. Bideau A. (1986): Duration of life in women after age 45 was longer for those women who borne 12 or more children. Population 41: 59-72. Telephone inventor Alexander Graham Bell (1918): “The longer lived parents were the most fertile.”

72. Studies that Found no Relationship Between Postreproductive Longevity and Reproduction Henry L. 1956. Travaux et Documents. Gauter, E. and Henry L. 1958. Travaux et Documents, 26. Knodel, J. 1988. Demographic Behavior in the Past. Le Bourg et al., 1993. Experimental Gerontology, 28: 217-232.

73. 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.

74. 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

75. “… it is not a matter of reduced fertility, but a case of 'to have or have not'.“ Source: Toon Ligtenberg & Henk Brand. Longevity — does family size matter? Nature, 1998, 396, pp 743-746

76. 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

78. 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).

79. 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.

80. 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 ?

81. 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, under- reporting 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

82. 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 cross-checked 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 (1807­1882), 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).

83. 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

84. 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

85. Characteristics of Our Data Sample for ‘Reproduction-Longevity’ Studies 3,723 married women born in 1500-1875 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.

86. 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

88. 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.

89. 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.

90. Short Conclusion: Exceptional human longevity is NOT associated with infertility or childlessness

91. 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 under-reporting of children. After data cleaning, cross-checking 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.

92. 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.

93. Parental-Age Effects in Humans (accumulation of mutation load in parental germ cells) What are the Data and the Predictions of Evolutionary Theory on the Quality of Offspring Conceived to Older Parents? Does progeny conceived to older parents live shorter lives?

94. Evolutionary Justification for Parental-Age Effects "The evolutionary explanation of senescence proposes that selection against alleles with deleterious effects manifested only late in life is weak because most individuals die earlier for extrinsic reasons. This argument also applies to alleles whose deleterious effects are nongenetically transmitted from mother to progeny, that is, that affect the performance of progeny produced at late ages rather than of the aging individuals themselves. … a decline of offspring quality with parental age should receive more attention in the context of the evolution of aging.” Stearns et al. "Decline in offspring viability as a manifestation of aging in Drosophila melianogaster." Evolution, 2001, Vol. 55, No. 9, pp. 1822–1831.

95. Genetic Justification for Paternal Age Effects Advanced paternal age at child conception is the main source of new mutations in human populations. James F. Crow, geneticist Professor Crow (University of Wisconsin-Madison) is recognized as a leader and statesman of science. He is a member of the National Academy of Sciences, the National Academy of Medicine, The American Philosophical Society, the American Academy of Arts and Sciences, the World Academy of Art and Science.

96. Paternal Age and Risk of Schizophrenia Estimated cumulative incidence and percentage of offspring estimated to have an onset of schizophrenia by age 34 years, for categories of paternal age. The numbers above the bars show the proportion of offspring who were estimated to have an onset of schizophrenia by 34 years of age. Source: Malaspina et al., Arch Gen Psychiatry.2001.

97. Paternal Age as a Risk Factor for Alzheimer Disease MGAD - major gene for Alzheimer Disease Source: L. Bertram et al. Neurogenetics, 1998, 1: 277-280.

98. Daughters' Lifespan (30+) as a Function of Paternal Age at Daughter's Birth 6,032 daughters from European aristocratic families born in 1800-1880 Life expectancy of adult women (30+) as a function of father's age when these women were born (expressed as a difference from the reference level for those born to fathers of 40-44 years). The data are point estimates (with standard errors) of the differential intercept coefficients adjusted for other explanatory variables using multiple regression with nominal variables. Daughters of parents who survived to 50 years.

99. Contour plot for daughters’ lifespan (deviation from cohort mean) as a function of paternal lifespan (X axis) and paternal age at daughters’ birth (Y axis) 7984 cases 1800-1880 birth cohorts European aristocratic families Distance weighted least squares smooth

100. Daughters’ Lifespan as a Function of Paternal Age at Daughters’ Birth Data are adjusted for other predictor variables Daughters of shorter-lived fathers (<80), 6727 cases Daughters of longer-lived fathers (80+), 1349 cases

101. Conclusions Being conceived to old fathers is a risk factor, but it is moderated by paternal longevity It is OK to be conceived to old father if he lives more than 80 years Methodological implications: Paternal lifespan should be taken into account in the studies of paternal-age effects

102. Heritability of Longevity

103. Mutation Accumulation Theory of Aging (Medawar, 1946) From the evolutionary perspective, aging is an inevitable result of the declining force of natural selection with age. So, over successive generations, late-acting deleterious mutations will accumulate, leading to an increase in mortality rates late in life.

104. Predictions of the Mutation Accumulation Theory of Aging Mutation accumulation theory predicts that those deleterious mutations that are expressed in later life should have higher frequencies (because mutation- selection balance is shifted to higher equilibrium frequencies due to smaller selection pressure). Therefore, ‘expressed’ genetic variability should increase with age (Charlesworth, 1994. Evolution in Age-structured Populations). This should result in higher heritability estimates for lifespan of offspring born to longer-lived parents.

105. Linearity Principle of Inheritance in Quantitative Genetics Dependence between parental traits and offspring traits is linear

106. The Best Possible Source on Familial Longevity Genealogies of European Royal and Noble Families Charles IX d’Anguleme (1550-1574) Henry VIII Tudor (1491-1547) Marie-Antoinette von Habsburg-Lothringen (1765-1793)

107. Characteristic of our Dataset Over 16,000 persons belonging to the European aristocracy 1800-1880 extinct birth cohorts Adult persons aged 30+ Data extracted from the professional genealogical data sources including Genealogisches Handbook des Adels, Almanac de Gotha, Burke Peerage and Baronetage.

108. Daughter's Lifespan (Mean Deviation from Cohort Life Expectancy) as a Function of Paternal Lifespan Offspring data for adult lifespan (30+ years) are smoothed by 5-year running average. Extinct birth cohorts (born in 1800-1880) European aristocratic families. 6,443 cases

109. “The Heritability of Life-Spans Is Small” C.E. Finch, R.E. Tanzi, Science, 1997, p.407 “… long life runs in families” A. Cournil, T.B.L. Kirkwood, Trends in Genetics, 2001, p.233 Paradox of low heritability of lifespan vs high familial clustering of longevity

110. Heritability Estimates of Human Lifespan Author(s)Heritability estimate Population McGue et al., 19930.22Danish twins Ljungquist et al., 1998<0.33Swedish twins Bocquet-Appel, Jacobi, 1990 0.10-0.30French village Mayer, 19910.10-0.33New England families Cournil et al., 20000.27French village Mitchell et al., 20010.25Old Order Amish

111. Is the effect of non-linear inheritance remain valid after controlling for other explanatory variables? Lifespan of other parent Parental ages at child’s conception Ethnicity Month of birth

112. Offspring Lifespan at Age 30 as a Function of Paternal Lifespan Data are adjusted for other predictor variables Daughters, 8,284 casesSons, 8,322 cases

113. Offspring Lifespan at Age 30 as a Function of Maternal Lifespan Data are adjusted for other predictor variables Daughters, 8,284 casesSons, 8,322 cases

114. Is the effect of non-linear inheritance observed for non-biological relatives? We need to test an alternative hypothesis that positive effects of long-lived parents on the offspring survival may be non-biological and caused by common environment and life style What about lifespan of spouses?

115. Person’s Lifespan as a Function of Spouse Lifespan Data are adjusted for other predictor variables Married Women, 4,530 casesMarried Men, 5,102 cases

116. Acknowledgments This study was made possible thanks to: generous support from the National Institute on Aging and the Society of Actuaries

117. 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://longevity-science.blogspot.com/