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Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of Chicago, 1155 East 60th Street, Chicago, IL 60637

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Page 1: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Testing Biological Theories of Aging with Demographic and Genealogical Data

Natalia S. GavrilovaLeonid A. Gavrilov

Center on Aging, NORC/University of Chicago, 1155 East 60th Street, Chicago, IL 60637

Page 2: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

What are the data and the predictions of the evolutionary theory on

Links between human longevity and fertility

Lifespan heritability in humans

Quality of offspring conceived to older parents

Page 3: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Founding Fathers Beeton, M., Yule, G.U., Pearson,

K. 1900. Data for the problem of evolution in man. V. On the correlation between duration of life and the number of offspring. Proc. R. Soc. London, 67: 159-179.

Data used: English Quaker records and Whitney Family of Connectucut records for females and American Whitney family and Burke’s ‘Landed Gentry’ for males.

Page 4: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Findings and Conclusions by Beeton et al., 1900

They tested predictions of the Darwinian evolutionary theory that the fittest individuals should leave more offspring.

Findings: Slightly positive relationship between postreproductive lifespan (50+) of both mothers and fathers and the number of offspring.

Conclusion: “fertility is correlated with longevity even after the fecund period is passed” and “selective mortality reduces the numbers of the offspring of the less fit relatively to the fitter.”

Page 5: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Other Studies, Which Found Positive Correlation Between Reproduction and Postreproductive Longevity

Bettie Freeman (1935): Weak positive correlations between the duration of postreproductive life in women and the number of offspring borne. Human Biology, 7: 392-418.

Bideau A. (1986): Duration of life in women after age 45 was longer for those women who borne 12 or more children. Population 41: 59-72.

Telephone inventor Alexander Graham Bell (1918): “The longer lived parents were the most fertile.”

Page 6: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Studies that Found no Relationship Between Postreproductive Longevity and Reproduction

Henry L. 1956. Travaux et Documents.

Gauter, E. and Henry L. 1958. Travaux et Documents, 26.

Knodel, J. 1988. Demographic Behavior in the Past.

Le Bourg et al., 1993. Experimental Gerontology, 28: 217-232.

Page 7: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Study that Found a Trade-Off Between Reproductive Success and Postreproductive Longevity

Westendorp RGJ, Kirkwood TBL. 1998. Human longevity at the cost of reproductive success. Nature 396: 743-746.

Extensive media coverage including BBC and over 100 citations in scientific literature as an established scientific fact. Previous studies were not quoted and discussed in this article.

Page 8: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Point estimates of progeny number for married aristocratic women from different birth cohorts

as a function of age at death. The estimates of progeny number are adjusted for trends over

calendar time using multiple regression.

Source: Westendorp, Kirkwood, Human longevity at the cost of reproductive success. Nature, 1998, 396, pp 743-746

Page 9: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

“… it is not a matter of reduced fertility, but a case of 'to have or have not'.“

Table 1 Relationship between age at death and number of children for married aristocratic women

Age at death Proportion childless Number of children

(years) mean for all women mean for women having children

<20 0.66 0.45 1.32

21-30 0.39 1.35 2.21

31-40 0.26 2.05 2.77

41-50 0.31 2.01 2.91

51-60 0.28 2.4 3.33

61-70 0.33 2.36 3.52

71-80 0.31 2.64 3.83

81-90 0.45 2.08 3.78

>90 0.49 1.80 3.53

Source: Toon Ligtenberg & Henk Brand. Longevity — does family

size matter? Nature, 1998, 396, pp 743-746

Page 10: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Number of progeny and age at first childbirth dependent on the age at death of married aristocratic women

Source: Westendorp, R. G. J., Kirkwood, T. B. L. Human longevity at the cost of reproductive success. Nature, 1998, 396, pp 743-746

Page 11: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Source: Westendorp, R. G. J., Kirkwood, T. B. L. Human longevity at the cost of reproductive success. Nature, 1998, 396, pp 743-746

Page 12: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Do longevous women have impaired fertility ?Why is this question so important and interesting?

Scientific Significance

This is a testable prediction of some evolutionary theories of aging - disposable soma theory of aging (Kirkwood)

"The disposable soma theory on the evolution of ageing states that longevity requires investments in somatic maintenance that reduce the resources available for reproduction“ (Westendorp, Kirkwood, Nature, 1998).

Page 13: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Do longevous women have impaired fertility ?

Practical Importance. Do we really wish to live a long life at the cost of infertility?: “the next generations of Homo sapiens will have even

longer life spans but at the cost of impaired fertility” Rudi Westendorp “Are we becoming less disposable? EMBO

Reports, 2004, 5: 2-6.

"... increasing longevity through genetic manipulation of the mechanisms of aging raises deep biological and moral questions. These questions should give us pause before we embark on the enterprise of extending our lives“ Walter Glennon "Extending the Human Life Span", Journal of Medicine and Philosophy, 2002, Vol. 27, No. 3, pp. 339-354.

Page 14: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Educational Significance Do we teach our students right?

Impaired fertility of longevous women is often presented in scientific literature and mass media as already established fact (Brandt et al., 2005; Fessler et al., 2005; Schrempf et al., 2005; Tavecchia et al., 2005; Kirkwood, 2002; Westendorp, 2002, 2004; Glennon, 2002; Perls et al., 2002, etc.).

This "fact" is now included in teaching curriculums in biology, ecology and

anthropology world-wide (USA, UK, Denmark). Is it a fact or artifact ?

Page 15: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

General Methodological Principle:

Before making strong conclusions, consider all other possible explanations, including potential flaws in data quality and analysis

Previous analysis by Westendorp and Kirkwood was made on the assumption of data completeness:Number of children born = Number of children recorded

Potential concerns: data incompleteness, under-reporting of short-lived children, women (because of patrilineal structure of genealogical records), persons who did not marry or did not have children.Number of children born   >> Number of children recorded

Page 16: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Test for Data CompletenessDirect Test: Cross-checking of the initial dataset with

other data sources We examined 335 claims of childlessness in the dataset used by

Westendorp and Kirkwood. When we cross-checked these claims with other professional sources of data, we  found that at least 107 allegedly childless women (32%) did have children!

At least 32% of childlessness claims proved to be wrong ("false negative claims") !

Some illustrative examples:

Henrietta Kerr (1653 1741) was apparently childless in the dataset used by Westendorp and Kirkwood and lived 88 years. Our cross-checking revealed that she did have at least one child, Sir William Scott (2nd Baronet of Thirlstane, died on October 8, 1725).

 Charlotte Primrose (1776 1864) was also considered childless in the initial dataset and lived 88 years. Our cross-checking of the data revealed that in fact she had as many as five children: Charlotte (1803 1886), Henry (1806 1889), Charles (1807 1882), Arabella (1809-1884), and William (1815 1881).

Wilhelmina Louise von Anhalt-Bernburg (1799 1882), apparently childless, lived 83 years. In reality, however, she had at least two children, Alexander (1820 1896) and Georg (1826 1902).

Page 17: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Point estimates of progeny number for married aristocratic women from different birth cohorts as a function of age at death. The estimates of progeny number are adjusted for trends over calendar time using multiple regression.

Source: Westendorp, R. G. J., Kirkwood, T. B. L. Human longevity at the cost of reproductive success. Nature, 1998, 396, pp 743-746

Page 18: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Antoinette de Bourbon(1493-1583)

Lived almost 90 yearsShe was claimed to have only one child

in the dataset used by Westendorp and Kirkwood: Marie (1515-1560), who became a mother of famous Queen of Scotland, Mary Stuart.

Our data cross-checking revealed that in fact Antoinette had 12 children!

Marie 1515-1560 Francois Ier 1519-1563 Louise 1521-1542 Renee 1522-1602 Charles 1524-1574 Claude 1526-1573 Louis 1527-1579 Philippe 1529-1529 Pierre 1529 Antoinette 1531-1561 Francois 1534-1563 Rene 1536-1566

Page 19: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Characteristics of Our Data Sample for ‘Reproduction-Longevity’ Studies

3,723 married women born in 1500-1875 and belonging to the upper European nobility.

Women with two or more marriages (5%) were excluded from the analysis in order to facilitate the interpretation of results (continuity of exposure to childbearing).

•Every case of childlessness has been checked using at least two different genealogical sources.

Page 20: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Typical Mistakes in Biological Studies of Human Longevity

Using lifespan data for non-extinct birth cohorts (“cemetery effect”)

Failure to control for birth cohort – spurious correlations may be found if variables have temporal dynamics

Failure to take into account social events and factors – e.g., failure to control for age at marriage in longevity-reproduction studies

Tim e

Fertility

Longevity

Page 21: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Childlessness is better outcome than number of children for testing evolutionary theories of aging on human data

Applicable even for population practicing birth control (few couple are voluntarily childless)

Lifespan is not affected by physiological load of multiple pregnancies

Lifespan is not affected by economic hardship experienced by large families

Page 22: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of
Page 23: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Source:

Gavrilova et al. Does exceptional human longevity come with high cost of infertility? Testing the evolutionary theories of aging. Annals of the New York Academy of Sciences, 2004, 1019: 513-517.

Page 24: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Source: Gavrilova, Gavrilov. Human longevity and reproduction: An evolutionary perspective. In: Grandmotherhood - The Evolutionary Significance of the Second Half of Female Life. Rutgers University Press, 2005, 59-80.

Page 25: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Short Conclusion:

Exceptional human longevity is NOT associated with infertility or childlessness

Page 26: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

More Detailed Conclusions

We have found that previously reported high rate of childlessness among long-lived women is an artifact of data incompleteness, caused by under-reporting of children. After data cleaning, cross-checking and supplementation the association between exceptional longevity and childlessness has disappeared.

Thus, it is important now to revise a highly publicized scientific concept of heavy reproductive costs for human longevity. and to make corrections in related teaching curriculums for students.

Page 27: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

More Detailed Conclusions (2) It is also important to disavow the doubts and

concerns over further extension of human lifespan, that were recently cast in biomedical ethics because of gullible acceptance of the idea of harmful side effects of lifespan extension, including infertility (Glannon, 2002).

There is little doubt that the number of children can affect human longevity through complications of pregnancies and childbearing, as well as through changes in socioeconomic status,  etc.  However,  the concept of heavy infertility cost of human longevity is not supported by data, when these data are carefully reanalyzed.

Page 28: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Mutation Accumulation Theory of Aging (Medawar, 1946)

From the evolutionary perspective, aging is an inevitable result of the declining force of natural selection with age.

So, over successive generations, late-acting deleterious mutations will accumulate, leading to an increase in mortality rates late in life.

Page 29: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Predictions of the Mutation Accumulation Theory of Aging

Mutation accumulation theory predicts that those deleterious mutations that are expressed in later life should have higher frequencies (because mutation-selection balance is shifted to higher equilibrium frequencies due to smaller selection pressure).

Therefore, ‘expressed’ genetic variability should increase with age (Charlesworth, 1994. Evolution in Age-structured Populations).

This should result in higher heritability estimates for lifespan of offspring born to longer-lived parents.

Parental Lifespan0 20 40 60 80

Off

sp

rin

g L

ifesp

an

0

10

20

30

40

Page 30: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Linearity Principle of Inheritance in Quantitative Genetics

Dependence between parental traits and offspring traits is linear

Page 31: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

The Best Possible Source on Familial Longevity Genealogies of European Royal and Noble Families

Charles IX d’Anguleme (1550-1574)

Henry VIII Tudor (1491-1547)

Marie-Antoinette von Habsburg-Lothringen

(1765-1793)

Page 32: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Characteristic of our Dataset Over 16,000 persons

belonging to the European aristocracy

1800-1880 extinct birth cohorts

Adult persons aged 30+

Data extracted from the professional genealogical data sources including Genealogisches Handbook des Adels, Almanac de Gotha, Burke Peerage and Baronetage.

Page 33: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

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

as a Function of Paternal Lifespan

Paternal Lifespan, years

40 50 60 70 80 90 100

Da

ug

hte

r's

Lif

es

pa

n (

de

via

tio

n),

ye

ars

-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

Page 34: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

“The Heritability of Life-Spans Is

Small”C.E. Finch, R.E. Tanzi, Science, 1997, p.407

“… long life runs in families”A. Cournil, T.B.L. Kirkwood, Trends in Genetics, 2001, p.233

Paradox of low heritability of lifespan vs high familial clustering of longevity

Page 35: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Heritability Estimates of Human LifespanAuthor(s) Heritability

estimatePopulation

McGue et al., 1993 0.22 Danish twins

Ljungquist et al., 1998

<0.33 Swedish twins

Bocquet-Appel, Jacobi, 1990

0.10-0.30 French village

Mayer, 1991 0.10-0.33 New England families

Cournil et al., 2000

0.27 French village

Mitchell et al., 2001

0.25 Old Order Amish

Page 36: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Is the effect of non-linear inheritance remain valid after controlling for other explanatory variables?

Lifespan of other parent Parental ages at child’s conception Ethnicity Month of birth

Page 37: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Offspring Lifespan at Age 30 as a Function of Paternal LifespanData are adjusted for other predictor variables

Daughters, 8,284 cases Sons, 8,322 cases

Paternal Lifespan, years

40 50 60 70 80 90 100

Lif

esp

an d

iffe

ren

ce, y

ears

-2

2

4

0

p=0.05

p=0.0003

p=0.006

Paternal Lifespan, years

40 50 60 70 80 90 100

Lif

esp

an d

iffe

ren

ce, y

ears

-2

2

4

0

p<0.0001p=0.001

p=0.001

Page 38: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Offspring Lifespan at Age 30 as a Function of Maternal LifespanData are adjusted for other predictor variables

Daughters, 8,284 cases Sons, 8,322 cases

Maternal Lifespan, years

40 50 60 70 80 90 100

Lif

esp

an d

iffe

ren

ce, y

ears

-2

2

4

0

p=0.01

p=0.0004

p=0.05

Maternal Lifespan, years

40 50 60 70 80 90 100

Lif

esp

an d

iffe

ren

ce, y

ears

-2

2

4

0

p=0.02

Page 39: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Is the effect of non-linear inheritance observed for non-biological relatives?

We need to test an alternative hypothesis that positive effects of long-lived parents on the offspring survival may be non-biological and caused by common environment and life style

What about lifespan of spouses?

Page 40: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Person’s Lifespan as a Function of Spouse LifespanData are adjusted for other predictor variables

Married Women, 4,530 cases Married Men, 5,102 cases

Husband Lifespan, years

40 50 60 70 80 90

Lif

es

pan

dif

fere

nc

e, ye

ars

-3

-2

-1

1

2

3

-4

0

4

Wife Lifespan, years

40 50 60 70 80 90

Lif

esp

an

dif

fere

nc

e, ye

ars

-4

-3

-2

-1

1

2

3

4

0

Page 41: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Parental-Age Effects in Humans (accumulation of mutation load in parental germ cells)

What are the Data and the Predictions of Evolutionary

Theory on the Quality of Offspring Conceived to Older

Parents?

Does progeny conceived to older parents live shorter

lives?

Page 42: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Evolutionary Justification for Parental-Age Effects

"The evolutionary explanation of senescence proposes that selection against alleles with deleterious effects manifested only late in life is weak because most individuals die earlier for extrinsic reasons.

This argument also applies to alleles whose deleterious effects are nongenetically transmitted from mother to progeny, that is, that affect the performance of progeny produced at late ages rather than of the aging individuals themselves.

… a decline of offspring quality with parental age should receive more attention in the context of the evolution of aging.”

Stearns et al. "Decline in offspring viability as a manifestation of aging in Drosophila melianogaster." Evolution, 2001, Vol. 55, No. 9, pp. 1822–1831.

Page 43: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Genetic Justification for Paternal Age Effects

Advanced paternal age at child conception is the main source of new mutations in human populations.

James F. Crow, geneticistProfessor Crow (University of Wisconsin-Madison) is recognized as a leader and statesman of science. He is a member of the National Academy of Sciences, the National Academy of Medicine, The American Philosophical Society, the American Academy of Arts and Sciences, the World Academy of Art and Science.

Page 44: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Paternal Age and Risk of Schizophrenia

Estimated cumulative incidence and percentage of offspring estimated to have an onset of schizophrenia by age 34 years, for categories of paternal age. The numbers above the bars show the proportion of offspring who were estimated to have an onset of schizophrenia by 34 years of age.

Source: Malaspina et al., Arch Gen Psychiatry.2001.

Page 45: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Paternal Age as a Risk Factor for Alzheimer Disease

MGAD - major gene for Alzheimer Disease

Source: L. Bertram et al. Neurogenetics, 1998, 1: 277-280.

Paternal age Maternal age

Pa

ren

tal a

ge

at

ch

ild

bir

th (

ye

ars

)

25

30

35

40

Sporadic Alzheimer Disease (low likelihood of MGAD) Familial Alzheimer Disease (high likelihood of MGAD) Controls

p = 0.04

p=0.04

NS

NSNS

NS

Page 46: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Daughters' Lifespan (30+) as a Functionof Paternal Age at Daughter's Birth6,032 daughters from European aristocratic families born in 1800-1880

Life expectancy of adult women (30+) as a function of father's age when these women were born (expressed as a difference from the reference level for those born to fathers of 40-44 years).

The data are point estimates (with standard errors) of the differential intercept coefficients adjusted for other explanatory variables using multiple regression with nominal variables.

Daughters of parents who survived to 50 years.

Paternal Age at Reproduction

15-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59

Lif

es

pa

n D

iffe

ren

ce

(y

r)

-4

-3

-2

-1

1

0

p = 0.04

Page 47: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Contour plot for daughters’ lifespan (deviation from cohort mean) as a function of paternal lifespan (X axis) and paternal age at daughters’ birth (Y axis)

7984 cases

1800-1880 birth cohorts

European aristocratic families

Distance weighted least squares smooth

40 50 60 70 80 90

Paternal Lifespan, years

20

25

30

35

40

45

50

55

60

65

Pat

erna

l Age

at

Per

son'

s B

irth

, yea

rs

3 2 1 0 -1 -2 -3

Page 48: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Daughters’ Lifespan as a Function of Paternal Age at Daughters’ Birth Data are adjusted for other predictor variables

Daughters of shorter-lived fathers (<80), 6727 cases

Daughters of longer-lived fathers (80+), 1349 cases

Paternal Age at Person's Birth

15-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59

Lif

esp

an D

iffe

ren

ce (

yr)

-4

-3

-2

-1

1

0

Paternal Age at Person's Birth

15-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59

Lif

esp

an D

iffe

ren

ce (

yr)

-4

-2

2

4

0

Page 49: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Conclusions

Being conceived to old fathers is a risk factor, but it is moderated by paternal longevity

It is OK to be conceived to old father if he lives more than 80 years

Methodological implications: Paternal lifespan should be taken into account in the studies of paternal-age effects

Page 50: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

The Most Recent and Interesting Developments:

Young Mother and Exceptional Longevity

Page 51: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

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

Page 52: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Design of the Study

Page 53: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

A typical image of ‘centenarian’ family in 1900 census

Page 54: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Birth Order and Odds to Become a Centenarian

Page 55: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

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)

Page 56: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

First-born adult siblings (20+years) are more likely to become centenarians (odds ratio= 1.95)

Conditional (fixed-effects) logistic regression Number of obs = 797 LR chi2(2) = 27.54 Prob > chi2 = 0.0000Log likelihood = -247.93753 Pseudo R2 = 0.0526

---------------------------------------------------------------------------------- Variable | Odds Ratio P>|z| [95% Conf. Interval]-------------+--------------------------------------------------------------------

First-born status | 1.949 0.003 1.261 3.010

Male sex | .458 0.000 .318 .658

----------------------------------------------------------------------------------

Page 57: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Even at age 75 it still helps to be a first-born child (odds ratio= 1.7)

Conditional (fixed-effects) logistic regression Number of obs = 557 LR chi2(2) = 19.03 Prob > chi2 = 0.0001Log likelihood = -186.22869 Pseudo R2 = 0.0486

---------------------------------------------------------------- Variable Odds Ratio P>|z| [95% Conf. Interval]-------------+--------------------------------------------------

First-born status 1.659 0.040 1.022 2.693

Male sex .459 0.000 .306 .687

----------------------------------------------------------------

Page 58: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Birth order is more important than paternal age for chances to become a centenarianConditional (fixed-effects) logistic regression Number of obs = 950 LR chi2(3) = 34.24 Prob > chi2 = 0.0000Log likelihood = -281.97993 Pseudo R2 = 0.0572

---------------------------------------------------------------- Variable Odds Ratio P>|z| [95% Conf. Interval]-------------+--------------------------------------------------

First-born status 1.635 0.039 1.025 2.607

Born to young father 1.294 0.484 .628 2.668

Male sex .407 0.000 .285 .580

--------------------------------------------------------------------------------------

Page 59: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Are young mothers responsible for the birth order effect?

Conditional (fixed-effects) logistic regression Number of obs = 950 LR chi2(2) = 37.35 Prob > chi2 = 0.0000Log likelihood = -280.42473 Pseudo R2 = 0.0624

------------------------------------------------------------------------------------- Variable Odds Ratio P>|z| [95% Conf. Interval]-------------+-----------------------------------------------------------------------

Born to young mother 2.031 0.001 1.326 3.110

Male sex .412 0.000 .289 .586

-------------------------------------------------------------------------------------

Page 60: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Maternal Age at Person’s Birth and Odds to Become a Centenarian

Page 61: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Birth order effect explained:Being born to young mother!Conditional (fixed-effects) logistic regression Number of obs = 950 LR chi2(3) = 39.05 Prob > chi2 = 0.0000Log likelihood = -279.57165 Pseudo R2 = 0.0653

------------------------------------------------------------------------------------- Variable Odds Ratio P>|z| [95% Conf. Interval]-------------+-----------------------------------------------------------------------

First-born status 1.360 0.189 .859 2.153

Born to young mother 1.760 0.021 1.089 2.846

Male sex .407 0.000 .285 .580

--------------------------------------------------------------------------------------

Page 62: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Even at age 75 it still helps to be born to young mother (age <25)(odds ratio= 1.9)

Conditional (fixed-effects) logistic regression Number of obs = 557 LR chi2(2) = 21.31 Prob > chi2 = 0.0000Log likelihood = -185.08639 Pseudo R2 = 0.0544

---------------------------------------------------------------------------------- Variable Odds Ratio P>|z| [95% Conf. Interval]-------------+--------------------------------------------------------------------

Born to young mother 1.869 0.012 1.145 3.051

Male sex .461 0.000 .307 .690

----------------------------------------------------------------------------------

Page 63: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Younger Moms' Kids Get Longevity Edge

Children of women under 25 twice as likely to live to 100, study finds

HealthDayMonday, April 17, 2006

MONDAY, April 17 (HealthDay News) -- Society's oldest members are most likely to be born to its youngest mothers, new research suggests.

The odds of living to 100 and beyond double when a person is born to a woman under 25 years of age, compared to those people born to older mothers, according to one of the most rigorous studies on the subject yet conducted.

The finding may also help clear up a statistical mystery -- three years ago, the same husband-and-wife team of researchers found that being the first-born child in a family also boosted longevity, although no one knew why.

Page 64: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

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.

Page 65: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Conclusions

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

Page 66: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of
Page 67: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

Acknowledgments

This study was made possible thanks to:

generous support from the National Institute on Aging, and

stimulating working environment at the Center on Aging,

NORC/University of Chicago

Page 68: Testing Biological Theories of Aging with Demographic and Genealogical Data Natalia S. Gavrilova Leonid A. Gavrilov Center on Aging, NORC/University of

For More Information and Updates Please Visit Our Scientific and Educational Website on Human Longevity:

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