1. Early Life Biodemographic Influences on Exceptional Longevity: Parental Age at Person's Birth and the Month of Birth Are Important Predictors Leonid A. Gavrilov, Ph.D. Natalia S. Gavrilova, Ph.D. Center on Aging NORC and The University of Chicago Chicago, USA

2. High Initial Damage Load (HIDL) Idea "Adult organisms already have an exceptionally high load of initial damage, which is comparable with the amount of subsequent aging-related deterioration, accumulated during the rest of the entire adult life." Source: Gavrilov, L.A. & Gavrilova, N.S. 1991. The Biology of Life Span: A Quantitative Approach. Harwood Academic Publisher, New York.

3. Practical implications from the HIDL hypothesis: "Even a small progress in optimizing the early-developmental processes can potentially result in a remarkable prevention of many diseases in later life, postponement of aging-related morbidity and mortality, and significant extension of healthy lifespan." Source: Gavrilov, L.A. & Gavrilova, N.S. 1991. The Biology of Life Span: A Quantitative Approach. Harwood Academic Publisher, New York.

4. Why should we expect high initial damage load in biological systems? General argument: -- biological systems are formed by self-assembly without helpful external quality control. Specific arguments: 1.Most cell divisions responsible for DNA copy-errors occur in early development leading to clonal expansion of mutations 2.Loss of telomeres is also particularly high in early-life 3.Cell cycle checkpoints are disabled in early development

5. New Vision of Aging-Related Diseases

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

7. How centenarians are different from their shorter-lived sibling?

8. Within-Family Study of Exceptional Longevity Cases - 1,081 centenarians born in the U.S. in 1880-1889 with known information about parental lifespan Controls – 6,413 their own siblings Method: Conditional logistic regression Advantage: Allows researchers to eliminate confounding effects of between- family variation

9. Design of the Study

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

11. Multivariate Analysis: Conditional logistic regression For 1:1 matched study, the conditional likelihood is given by: Where x i1 and x i0 are vectors representing the prognostic factors for the case and control, respectively, of the ith matched set.

12. Maternal age and odds to live to 100 for siblings survived to age 50 Conditional (fixed-effects) logistic regression N=5,778. Controlled for month of birth, paternal age and gender. Paternal and maternal lifespan >50 years Maternal ageOdds ratio95% CIP-value <201.731.05-2.880.033 20-241.631.11-2.400.012 25-291.531.10-2.120.011 30-341.160.85-1.600.355 35-391.060.77-1.460.720 40+1.00Reference

13. Results In smaller families (less than 9 children) the effect of young mother is even larger (for siblings survived to age 50 and maternal age 20-24 years vs 40+ years): Odds ratio = 2.23, P=0.013; 95%CI = 1.18 – 4.21 Compare to larger families (more than 9 children): Odds ratio = 1.39, P=0.188; 95%CI = 0.85 – 2.27 Conclusion: "Young mother effect" is not confined to extremely large family size

14. 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.

15. People Born to Young Mothers Have Twice Higher Chances to Live to 100 Within-family study of 2,153 centenarians and their siblings survived to age 50. Family size <9 children. p=0.020 p=0.013 p=0.043

16. 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.

17. 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.

18. Season-of-birth effect on longevity

19. Season-of-birth Study of Exceptional Longevity Cases - 1,574 centenarians born in the U.S. in 1880-1895 Controls – 10,885 their own siblings and 1,083 spouses Method: Conditional logistic regression Advantage: Allows researchers to eliminate confounding effects of between- family variation

20. Distribution of individuals by month of birth in percent: centenarians, their shorter-lived siblings survived to age 30 and U.S. population born in our study window (1880-1895) according to the 1900 U.S. Census (IPUMS data)

21. Season of birth and odds to live to 100 Within-family study of siblings VariableAll siblingsSiblings survived to age 30 Siblings survived to age 50 Siblings survived to age 70 Month of birth: January1.13 (0.387)1.11 (0.472)1.11 (0.463)1.09 (0.537) February1.25 (0.101)1.25 (0.109)1.24 (0.124)1.16 (0.303) MarchReference April1.15 (0.320)1.15 (0.337)1.16 (0.320)1.09 (0.567) May1.20 (0.218)1.17 (0.288)1.19 (0.251)1.15 (0.373) June1.20 (0.229)1.00 (0.254)1.18 (0.284)1.11 (0.486) July1.03 (0.855)1.19 (0.991)1.01 (0.941)1.00 (0.990) August1.25 (0.110)1.24 (0.125)1.27 (0.100)1.21 (0.198) September1.44 (0.006)1.43 (0.009)1.45 (0.007)1.39 (0.022) October1.43 (0.008)1.37 (0.021)1.37 (0.022)1.27 (0.099) November1.51 (0.003)1.48 (0.005)1.47 (0.006)1.41 (0.017) December1.17 (0.266)1.13 (0.380)1.17 (0.283)1.11 (0.486) Female sex3.77 (<0.001)3.82 (<0.001)3.80 (<0.001)3.41 (<0.001) Pseudo R 2 0.08110.08610.08710.0766 Number of observations 12,13210,3939,7248,123

22. Siblings Born in September-November Have Higher Chances to Live to 100 Within-family study of 9,724 centenarians born in 1880-1895 and their siblings survived to age 50

23. Spouses Born in October-November Have Higher Chances to Live to 100 Within-family study of 1,800 centenarians born in 1880-1895 and their spouses survived to age 50

24. Life Expectancy and Month of Birth Data source: Social Security Death Master File Published in: Gavrilova, N.S., Gavrilov, L.A. Search for Predictors of Exceptional Human Longevity. In: “Living to 100 and Beyond” Monograph. The Society of Actuaries, Schaumburg, Illinois, USA, 2005, pp. 1-49.

25. Possible explanations These are several explanations of season-of birth effects on longevity pointing to the effects of early-life events and conditions: seasonal exposure to infections, nutritional deficiencies, environmental temperature and sun exposure. All these factors were shown to play role in later-life health and longevity.

26. Acknowledgments This study was made possible thanks to: generous support from the National Institute on Aging grant #R01AG028620

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28. Final Conclusion The shortest conclusion was suggested in the title of the New York Times article about this study