Early-Life Predictors of Exceptional Early-Life Predictors of Exceptional Longevity in the United States:Longevity in the United States:

Why Centenarians are Different From Their Why Centenarians are Different From Their

Shorter-Lived SiblingsShorter-Lived Siblings

Leonid A. Gavrilov, Ph.D.Natalia S. Gavrilova, Ph.D.

Center on Aging

NORC and The University of Chicago Chicago, USA

Some results from our Some results from our previous studies of previous studies of

exceptional longevityexceptional longevity

Parental longevity is an Parental longevity is an important predictor of the important predictor of the

offspring longevityoffspring longevity

Daughter's LifespanDaughter's Lifespan(Mean Deviation from the Birth Cohort Life Expectancy)(Mean Deviation from the Birth Cohort Life Expectancy)

as a Function of Paternal Lifespanas a Function of Paternal Lifespan

Paternal Lifespan, years40 50 60 70 80 90 100

Dau

ghte

r's L

ifesp

an (d

evia

tion)

, yea

rs

-2

2

4

6

0

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

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

censusescensuses

Household Property Status During Childhood Household Property Status During Childhood and Survival to Age 100and Survival to Age 100

Odds for household to be in a ‘centenarian’ groupOdds for household to be in a ‘centenarian’ group

00.10.20.30.40.50.60.70.80.9

1

A B C D

MalesFemales

A – Rented House

B – Owned House

C – Rented Farm

D – Owned farm(reference group)

Childhood Residence Childhood Residence and Survival to Age 100and Survival to Age 100

Odds for household to be in a ‘centenarian’ groupOdds for household to be in a ‘centenarian’ group

00.5

11.5

22.5

33.5

A B C D

MalesFemales

A – New England and Middle Atlantic (reference group)

B – Mountain West and Pacific West

C – Southeast and Southwest

D – North Central

Study of the U.S. male Study of the U.S. male centenarians linked to the WWI centenarians linked to the WWI

draft registration cards:draft registration cards:

Socio-demographic and physical Socio-demographic and physical characteristics at age 30 and characteristics at age 30 and

survival to age 100survival to age 100

Body Build and Survival to 100Body Build and Survival to 100

Results of multivariate studyResults of multivariate studyVariable Odds

RatioP-value

Medium height vs short and tall height

1.35 0.260

Slender and medium build vs stout build

2.63* 0.025

Farming 2.20* 0.016 Married vs unmarried 0.68 0.268Native born vs foreign b.

1.13 0.682

Having children by age 30 and survival Having children by age 30 and survival to age 100to age 100

Conditional (fixed-effects) logistic regressionN=171. Reference level: no children

Variable Odds ratio 95% CI P-

value

1-3 children 1.62 0.89-2.95 0.127

4+ children 2.71 0.99-7.39 0.051

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

Month of BirthJan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

life

expe

ctan

cy a

t age

80,

yea

rs

7.6

7.7

7.8

7.9

1885 Birth Cohort1891 Birth Cohort

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.

The role of early-life conditions The role of early-life conditions in shaping late-life mortality is in shaping late-life mortality is

now well recognizednow well recognized

New Vision of Aging-Related DiseasesNew Vision of Aging-Related Diseases

How centenarians are How centenarians are different from their different from their

shorter-lived sibling?shorter-lived sibling?

Within-Family ApproachWithin-Family ApproachAllows researchers to eliminate

between-family variation including the differences in

genetic background and childhood living conditions

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

Internet Resources Used for Internet Resources Used for Centenarian Data Collection and Centenarian Data Collection and

VerificationVerificationComputerized 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

Steps of the studySteps 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

Within-Family Study Within-Family Study of Exceptional Longevityof 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

Design of the StudyDesign of the Study

Multivariate Analysis:Multivariate Analysis:Conditional logistic regressionConditional logistic regression

For 1:n matched study, the likelihood for N matched sets is given by:

Where ui is the covariate vector for the case and vi1, vi2 , …, vin(i) are covariate vectors for the ni controls, respectively, of the ith matched set.

N

in

jij

ii

v

u1

1

)exp(

)exp(

Maternal age and odds to live to 100 for Maternal age and odds to live to 100 for siblings survived to age 50siblings survived to age 50

Conditional (fixed-effects) logistic regressionN=5,778. Controlled for month of birth, paternal age and gender. Paternal and maternal lifespan >50 years

Maternal age

Odds ratio 95% CI P-value

<20 1.73 1.05-2.88 0.033

20-24 1.63 1.11-2.40 0.012

25-29 1.53 1.10-2.12 0.011

30-34 1.16 0.85-1.60 0.355

35-39 1.06 0.77-1.46 0.720

40+ 1.00 Reference

Does maternal age effect Does maternal age effect depend on the gender of depend on the gender of

siblings?siblings?Data were split by the gender of

siblings (‘daughters only’ and ‘sons only’ analyses)

Maternal age and odds to live to 100, by Maternal age and odds to live to 100, by gender. Odds ratios (p-value)gender. Odds ratios (p-value)

Conditional (fixed-effects) logistic regressionControlled for month of birth and paternal. Paternal and maternal lifespan >50 years

Maternal age

Daughters n=4732

Sons N=1681

<20 1.43 (0.121) 1.72 (0.162)

20-24 1.37 (0.067) 1.77 (0.042)

25-29 1.57 (0.006) 1.24 (0.435)

30-34 1.07 (0.708) 1.29 (0.360)

35-39 1.10 (0.0552) 0.92 (0.769)

40+ Reference Reference

QuestionQuestion

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.

ResultsResults

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 – 2.27Conclusion:"Young mother effect" is not confined to extremely large

family size

People Born to Young Mothers Have Twice People Born to Young Mothers Have Twice Higher Chances to Live to 100Higher Chances to Live to 100

Within-family study of 2,153 centenarians and their siblings survived to age 50. Family size <9 children.Within-family study of 2,153 centenarians and their siblings survived to age 50. Family size <9 children.

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

<20 20-24 25-29 30-34 35-39 40+

Odds

ratio

Maternal Age at Birth

p=0.020

p=0.013

p=0.043

Being born to Young Mother Helps Being born to Young Mother Helps Laboratory Mice to Live LongerLaboratory Mice to Live Longer

Source:

Tarin et al., Delayed Motherhood Decreases Life Expectancy of Mouse Offspring.

Biology of Reproduction 2005 72: 1336-1343.

Possible explanationPossible explanationThese 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.

Siblings Born in November Have Twice Higher Siblings Born in November Have Twice Higher Chances to Live to 100Chances to Live to 100

Within-family study of 5,698 centenarians and their siblings survived to age 50Within-family study of 5,698 centenarians and their siblings survived to age 50

AcknowledgmentsAcknowledgmentsThis study was made possible

thanks to:

generous support from the National Institute on Aging grant #R01AG028620

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Final ConclusionFinal Conclusion

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