Search%20for%20mechanisms%20of%20exceptional%20human%20longevity

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Search for mechanisms of exceptional human
longevity

• Dr. Natalia S. Gavrilova, Ph.D.
• Dr. Leonid A. Gavrilov, Ph.D.
• Center on Aging
• NORC and The University of Chicago
• Chicago, USA

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Approach

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

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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)
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Revolution in Information TechnologyWhat does
it mean for longevity studies?

• Millions of official census, birth, marriage,
  death and other records are available online now!

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Predictors of Exceptional Longevity
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Study 1

• How centenarians are different from their
  shorter-lived sibling?

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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
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Design of the Study
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A typical image of centenarian family in 1900
census
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First-born siblings are more likely to become
centenarians (odds 1.8)
Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000
Variable Odds ratio 95 CI P-value
First-born status 1.77 1.18-2.66 0.006
Male sex 0.40 0.28-0.58 lt0.001
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Birth Order and Odds to Become a Centenarian
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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)

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First-born adult siblings (20years) are more
likely to become centenarians (odds
1.95)
Conditional (fixed-effects) logistic regression N797, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N797, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N797, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N797, Prob gt chi20.0000
Variable Odds ratio 95 CI P-value
First-born status 1.95 1.26-3.01 0.003
Male sex 0.46 0.32-0.66 lt0.001
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Are young fathers responsible for birth order
effect?
Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000
Variable Odds ratio 95 CI P-value
Born to young father 1.86 0.99-3.50 0.056
Male sex 0.42 0.29-0.59 lt0.001
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Birth order is more important than paternal age
for chances to become a centenarian
Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000
Variable Odds ratio 95 CI P-value
First-born status 1.64 1.03-2.61 0.039
Born to young father 1.29 0.63-2.67 0.484
Male sex 0.41 0.29-0.58 lt0.001
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Are young mothers responsible for the birth order
effect?
Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000
Variable Odds ratio 95 CI P-value
Born to young mother 2.03 1.33-3.11 0.001
Male sex 0.41 0.29-0.59 lt0.001
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Maternal Age at Persons Birth and Odds to Become
a Centenarian
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Birth order effect explainedBeing born to young
mother!
Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N950, Prob gt chi20.0000
Variable Odds ratio 95 CI P-value
First-born status 1.36 0.86-2.15 0.189
Born to young mother 1.76 1.09-2.85 0.021
Male sex 0.41 0.29-0.58 lt0.001
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Even at age 75 it still helps to be born to young
mother (age lt25)(odds 1.9)
Conditional (fixed-effects) logistic regression N557, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N557, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N557, Prob gt chi20.0000 Conditional (fixed-effects) logistic regression N557, Prob gt chi20.0000
Variable Odds ratio 95 CI P-value
Born to young mother 1.86 1.15-3.05 0.012
Male sex 0.46 0.31-0.69 lt0.001
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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.

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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.

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Study 2

• How centenarians are different from their
  shorter-lived peers when compared at young adult
  age?

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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
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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

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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.

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Small Dogs Live Longer

Miller RA. Kleemeier Award Lecture Are there
genes for aging? J Gerontol Biol Sci
54AB297B307, 1999.
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Small Mice Live Longer

Source Miller et al., 2000. The Journals of
Gerontology Series A Biological Sciences and
Medical Sciences 55B455-B461
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Design of the Study
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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)

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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.

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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 700 or 730 opening
of registration, while businesses, schools, and
saloons closed to accommodate the event.
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Registration Day Parade

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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

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Draft Registration CardAn Example

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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

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Height and Survival to 100
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Body Build and Survival to 100
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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

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Results of multivariate study
Variable Odds Ratio P-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 0.268
Native born vs foreign b. 1.13 0.682
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Results of multivariate studySignificant
predictors only
Variable Odds Ratio P-value
Slender body build reference stout build 2.54 0.040
Medium body build reference stout build 2.64 0.017
Farming 1.99 0.025
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Other physical characteristics
Variable Odds Ratio P-value
Blue eye color 1.62 0.069
Short body height reference tall height 1.02 0.967
Medium body height reference tall height 1.43 0.212
Other variables include body build and farming
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Having children by age 30 and survival to age 100
Conditional (fixed-effects) logistic regression N171. Reference level no children Conditional (fixed-effects) logistic regression N171. Reference level no children Conditional (fixed-effects) logistic regression N171. Reference level no children Conditional (fixed-effects) logistic regression N171. 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
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Conclusion

• The study of height and build among men born in
  1887 suggests that rapid growth and overweight at
  young adult age (30 years) might be harmful for
  attaining longevity

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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

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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.

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Acknowledgments

• This study was made possible thanks to
• generous support from the National Institute on
  Aging and the Society of Actuaries

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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/

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Multivariate AnalysisConditional logistic
regression

• For 11 matched study, the conditional likelihood
  is given by
• Where xi1 and xi0 are vectors representing the
  prognostic factors for the case and control,
  respectively, of the ith matched set.

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Final Conclusion

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

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New Striking Findings Invitation for discussion
and brain-storming!

• The favorable "Young Mother Effect" is
  particularly strong when parents have
  particularly large differences in their lifespan

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Odds Ratio to live to 100 years if born to young
motheras a function of maternal and paternal
lifespans (tertiles)
MOTHER FATHER FATHER FATHER
MOTHER Shorter-lived Medium-lived Longer-lived
Shorter-lived 0.93 1.29 4.04
Medium-lived 3.49 3.01 1.50
Longer-lived 11.62 1.21 0.36
plt0.05
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Month of Birth and the Likelihood
to Become a Centenarian
among WWI draft participants
Method Conditional logistic regression for odds
to become a centenarian. Controls men matched
on birth year, race and county of draft
registration. Adjusted for occupation, height and
build. 76 observations
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Month of Birth and the Likelihood
to Become a Centenarian
for Adult Siblings
Method Conditional logistic regression for odds
to become a centenarian, using siblings as
within-family control. 787 observations
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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.

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Results

• In smaller families (less than 9 children) the
  effect of young mother is even larger
• Odds ratio 2.23, P0.004 95CI 1.30 - 3.98
• Compare to larger families (more than 9
  children)
• Odds ratio 1.72, P0.11 95CI 0.88 - 3.34
• Conclusion
• "Young mother effect" is not confined to
  extremely large family size