1. Early-Life Predictors of Exceptional Longevity in the United States: Why Centenarians are Different From Their Shorter-Lived Siblings Leonid A. Gavrilov, Ph.D. Natalia S. Gavrilova, Ph.D. Center on Aging NORC and The University of Chicago Chicago, USA Leonid A. Gavrilov, Ph.D. Natalia S. Gavrilova, Ph.D. Center on Aging NORC and The University of Chicago Chicago, USA

2. Some results from our previous studies of exceptional longevity

3. Parental longevity is an important predictor of the offspring longevity

4. Daughter's Lifespan (Mean Deviation from the Birth 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 Source: Gavrilova, Gavrilov, JAAM, 2001

5. Study of the U.S. centenarians based on computerized family histories linked to early U.S. censuses

6. 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)

7. 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

8. Study of the U.S. male centenarians linked to the WWI draft registration cards: Socio-demographic and physical characteristics at age 30 and survival to age 100

9. Body Build and Survival to 100

10. 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

11. Having children by age 30 and survival to age 100 Conditional (fixed-effects) logistic regression N=171. Reference level: no children Variable Odds ratio 95% CIP-value 1-3 children1.620.89-2.950.127 4+ children2.710.99-7.390.051

12. Study based on individual records from the Social Security Death Index

13. 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.

14. The role of early-life conditions in shaping late-life mortality is now well recognized

15. New Vision of Aging-Related Diseases

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

17. Within-Family Approach Allows researchers to eliminate between-family variation including the differences in genetic background and childhood living conditions

18. 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

19. Internet Resources Used for Centenarian Data Collection and Verification Computerized genealogies available at the Rootsweb website Social Security Administration Death Master File is publicly available at the Rootsweb website Individual indexes of enumerated persons by 1900, 1910, 1920 and 1930 federal censuses and census page images are provided by Ancestry.com

20. Steps of the study  23,127 records of centenarians born in 1880-1895 with known information about parents were identified using the Rootsweb genealogical website  2,834 centenarians having detailed information on their 21,893 siblings were selected  1,711 centenarians had their death dates verified using the Social Security Death Index  Finally 1,081 centenarians born in a more narrow window of 1880-1889 were used for further analyses

21. 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 shorter-lived siblings (5,778 survived to age 50) Method: Conditional logistic regression Advantage: Allows researchers to eliminate confounding effects of between-family variation

22. Design of the Study

24. Multivariate Analysis: Conditional logistic regression  For 1:n matched study, the likelihood for N matched sets is given by: Where u i is the covariate vector for the case and v i1, v i2, …, v in(i) are covariate vectors for the n i controls, respectively, of the ith matched set.

25. 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

26. Does maternal age effect depend on the gender of siblings? Data were split by the gender of siblings (‘daughters only’ and ‘sons only’ analyses)

27. Maternal age and odds to live to 100, by gender. Odds ratios (p-value) Conditional (fixed-effects) logistic regression Controlled for month of birth and paternal. Paternal and maternal lifespan >50 years Maternal age Daughters n=4732 Sons N=1681 <201.43 (0.121)1.72 (0.162) 20-241.37 (0.067)1.77 (0.042) 25-291.57 (0.006)1.24 (0.435) 30-341.07 (0.708)1.29 (0.360) 35-391.10 (0.0552)0.92 (0.769) 40+Reference

28. 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.

29. 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

30. 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

31. 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.

32. 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.

33. Siblings Born in November Have Twice Higher Chances to Live to 100 Within-family study of 5,698 centenarians and their siblings survived to age 50

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

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