Gavrilov-PAA-2011-poster by ajizai

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									 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
              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.
      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.
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
New Vision of Aging-Related Diseases
          Approach


To study “success stories” in
long-term avoidance of fatal
diseases (survival to 100 years)
and factors correlated with this
remarkable survival success
How centenarians are
 different from their
shorter-lived sibling?
         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
Design of the Study
A typical image of ‘centenarian’
     family in 1900 census
       Multivariate Analysis:
    Conditional logistic regression

   For 1:1 matched study, the
    conditional likelihood is given by:


         (1  exp(   ( x
          i
                                        i1    xi 0 ))   1


   Where xi1 and xi0 are vectors representing the
    prognostic factors for the case and control, respectively,
    of the ith matched set.
   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 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
    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
    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.
       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.

                2.6         p=0.020

                2.4
                                      p=0.013
                2.2
                 2
   Odds ratio




                                                   p=0.043
                1.8
                1.6
                1.4
                1.2
                 1
                0.8
                      <20         20-24      25-29         30-34        35-39          40+
                                          Maternal Age at Birth
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.
     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.
Season-of-birth effect on
       longevity
       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
    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)
   Season of birth and odds to live to 100
      Within-family study of siblings
Variable          All siblings    Siblings survived to age   Siblings survived to age   Siblings survived to age
                                  30                         50                         70


Month of birth:

January           1.13 (0.387)    1.11 (0.472)               1.11 (0.463)               1.09 (0.537)
February          1.25 (0.101)    1.25 (0.109)               1.24 (0.124)               1.16 (0.303)
March             Reference       Reference                  Reference                  Reference
April             1.15 (0.320)    1.15 (0.337)               1.16 (0.320)               1.09 (0.567)
May               1.20 (0.218)    1.17 (0.288)               1.19 (0.251)               1.15 (0.373)
June              1.20 (0.229)    1.00 (0.254)               1.18 (0.284)               1.11 (0.486)
July              1.03 (0.855)    1.19 (0.991)               1.01 (0.941)               1.00 (0.990)
August            1.25 (0.110)    1.24 (0.125)               1.27 (0.100)               1.21 (0.198)
September         1.44 (0.006)    1.43 (0.009)               1.45 (0.007)               1.39 (0.022)
October           1.43 (0.008)    1.37 (0.021)               1.37 (0.022)               1.27 (0.099)
November          1.51 (0.003)    1.48 (0.005)               1.47 (0.006)               1.41 (0.017)
December          1.17 (0.266)    1.13 (0.380)               1.17 (0.283)               1.11 (0.486)


Female sex        3.77 (<0.001)   3.82 (<0.001)              3.80 (<0.001)              3.41 (<0.001)


Pseudo R2         0.0811          0.0861                     0.0871                     0.0766
Number of         12,132          10,393                     9,724                      8,123
observations
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
   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

                                  2.4
                                                                                         p=0.004     p=0.004
                                  2.2


                                   2
      Odds Ratio to Live to 100




                                  1.8


                                  1.6


                                  1.4


                                  1.2


                                   1


                                  0.8
                                        Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec
                                   Life Expectancy and Month of Birth
                                   7.9                                                     Data source:
                                                                                           Social Security
                                                                    1885 Birth Cohort      Death Master File
                                                                    1891 Birth Cohort
life expectancy at age 80, years




                                   7.8                                                     Published in:
                                                                                           Gavrilova, N.S.,
                                                                                           Gavrilov, L.A. Search
                                                                                           for Predictors of
                                                                                           Exceptional Human
                                   7.7
                                                                                           Longevity. In: “Living
                                                                                           to 100 and Beyond”
                                                                                           Monograph. The
                                                                                           Society of Actuaries,
                                                                                           Schaumburg, Illinois,
                                                                                           USA, 2005, pp. 1-49.
                                   7.6



                                         Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

                                                        Month of Birth
      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.
Acknowledgments
This study was made possible
          thanks to:

  generous support from the
  National Institute on Aging
    grant #R01AG028620
     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/
        Final Conclusion

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

								
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