contemporary methods of mortality analysis: determinants of exceptional longevity

Contemporary methods of mortality analysis:

Determinants of Exceptional Longevity

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

Center on Aging

NORC and The University of Chicago Chicago, Illinois, USA

Approach

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

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)

Revolution in Information Technology

What does it mean for longevity studies?

Over 75 millions of computerized genealogical records are available online now!

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

Computerized Genealogies as a Resource for Longevity

Studies Pros: provide important information

about family and life-course events, which otherwise is difficult to collect (including information about lifespan of parents and other relatives)

Cons: Uncertain data quality Uncertain validity and generalizability

For longevity studies the genealogies with detailed birth dates and death

dates for long-lived individuals (centenarians) and their relatives are of

particular interest

In this study 1,001 genealogy records for centenarians born in 1875-1899 were collected and used for further age validation

Steps of Centenarian Age Verification

1. Internal consistency checks of dates

2. Verification of death dates – linkage to the Social Security Administration Death Master File (DMF)

3. Verification of birth dates – linkage to early Federal censuses (1900, 1910, 1920, 1930)

Results of Centenarian Age Verification

1001 records consistency checks

990 records used for further verification

990 records were linked to the SSA Death Master File

Linkage success rate 77% (80% for centenarians born after 1890) In 3% of cases centenarian status was not confirmed

548 records found in DMF for persons born in 1890-1899 were then linked to early US censuses

Linkage success rate 80% when using Genealogy.com and 91% after supplementation with Ancestry.com. In 8% of cases a 1-year disagreement between genealogy and census record was observed

Conclusions of the Age Verification Study

Death dates of centenarians recorded in genealogies always require verification because of strong outliers (1.3%, misprints)

Birth dates of centenarians recorded in genealogies are sufficiently accurate - 92% are correct; for the remaining 8% only one-year disagreements

Quality of genealogical data is good enough if these data are pre-selected for high data quality

Predictors of Exceptional Longevity

Study 1:

Compare centenarians with their siblings

(within-family study)

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

Design of the Study

A typical image of ‘centenarian’ family in 1900

census

First-born siblings are more likely to become centenarians

(odds = 1.8)

Conditional (fixed-effects) logistic regressionN=950, Prob > chi2=0.0000

VariableOdds ratio

95% CIP-

value

First-born status

1.771.18-2.66

0.006

Male sex 0.400.28-0.58

<0.001

Birth Order and Odds to Become a Centenarian

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)

First-born adult siblings (20+years) are more likely

to become centenarians (odds = 1.95)


VariableOdds ratio

95% CIP-

value

First-born status

1.951.26-3.01

0.003

Male sex 0.460.32-0.66

<0.001

Are young fathers responsible for birth order effect?


VariableOdds ratio

95% CIP-

value

Born to young father

1.860.99-3.50

0.056

Male sex 0.420.29-0.59

<0.001

Birth order is more important than paternal age for chances

to become a centenarianConditional (fixed-effects) logistic regressionN=950, Prob > chi2=0.0000

VariableOdds ratio

95% CIP-

value

First-born status

1.641.03-2.61

0.039

Born to young father

1.290.63-2.67

0.484

Male sex 0.410.29-0.58

<0.001

Are young mothers responsible for the birth order

effect?


VariableOdds ratio

95% CIP-

value

Born to young mother

2.031.33-3.11

0.001

Male sex 0.410.29-0.59

<0.001

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

Centenarian

Birth order effect explained:Being born to young mother!


VariableOdds ratio

95% CIP-

value

First-born status

1.360.86-2.15

0.189


1.761.09-2.85

0.021

Male sex 0.410.29-0.58

<0.001

Even at age 75 it still helps to be born to young mother (age

<25)(odds = 1.9)


VariableOdds ratio

95% CIP-

value


1.861.15-3.05

0.012

Male sex 0.460.31-0.69

<0.001

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.

Results In smaller families (less than 9 children) the

effect of young mother is even larger:Odds ratio = 2.23, P=0.004; 95%CI = 1.30 -

3.98 Compare to larger families (more than 9

children):Odds ratio = 1.72, P=0.11; 95%CI = 0.88 - 3.34

Conclusion:"Young mother effect" is not confined to

extremely large family size

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.

Study 2:

Compare centenarians when they were young adults

to their peersusing WWI Civil Draft

Registration Cards

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

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

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.

Small Dogs Live Longer

Miller RA. Kleemeier Award Lecture: Are there genes for aging? J Gerontol Biol Sci 54A:B297–B307, 1999.

Small Mice Live Longer

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

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)

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

Design of the Study

De athw in d o w fo r

ce n te n arian s

Birthyear

Death window for controls

Controls: M ales m atched on birth year, race andcounty of registration

M ale Centenarians born in 1887(from the Social Security Adm inistration Death M aster F ile)

1887 1917-18 1987

W W I draftregistration

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.

WWI Draft RegistrationRegistration 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 7:00 or 7:30 opening of registration, while businesses, schools, and saloons closed to accommodate the event.

Registration Day Parade

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

SAMPLE CHARACTERISTICS (%)

Centenarians

Controls

Foreign born

20.5 22.2

Married 68.4 63.7

Had children 52.6 42.1

Farmers 31.6 23.4

African Am. 5.3 5.3

171 pairs of men born in 1887

Centenarians Controls

Body Height short 6.4 8.8 medium 65.5 56.7 tall 28.1 34.5Body Build slender 25.2 25.2 medium 67.8 60.2 stout 7.0 14.6

BODY HEIGHT AND BODY BUILD DISTRIBUTIONS (%)

Height Distribution Among Centenarians and Controls

Body Build Distribution Among Centenarians and

Controls

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

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

Results of multivariate study

Significant 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

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

Having children by age 30 and survival to age 100

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

VariableOdds ratio

95% CIP-

value

1-3 children 1.620.89-2.95

0.127

4+ children 2.710.99-7.39

0.051

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

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.

Study 3:

Compare centenarians found in computerized

genealogies with general population

Case-Control Study of Early-Life Conditions and Exceptional

LongevityCases - 382 ‘white’ households where centenarians (born in 1890-1899) were raised (from centenarian records linked to 1900 census)

Controls – 1% random sample of ‘white’ households with children below age 10 enumerated by 1900 census (from Integrated Public Use Microdata Sample, IPUMS: http://www.ipums.umn.edu/usa/index.html)

Statistical Approach:

Logistic regressionDependent variable: Households with child-future centenarian (y=1) vs control households (y=0)Predictor variables: childhood residence, household property status, paternal immigration status, etc.

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 Central0

0.5

1

1.5

2

2.5

3

3.5

A B C D

MalesFemales

Household Property Status During Childhood and Survival to Age 100


A – Rented House

B – Owned House

C – Rented Farm

D – Owned farm(reference group)

00.10.20.30.40.50.60.70.80.9

1

A B C D

MalesFemales

Paternal Immigration Status and Survival to Age 100


A – Father immigrated

B – Father native-born

(reference group)

00.10.20.30.4

0.50.60.70.80.9

1

A B

MalesFemales

No Association was Found (so far) Between Chances to

Become a Centenarian and

Paternal literacy

Child mortality of siblings

Limitations

Reporting bias in genealogies

People mentioned in genealogies may be not representative to the whole population: more fertile, longer-living (?), wealthier (?), more educated (?)

General Conclusionof Centenarian Studies

The shortest conclusion was suggested in the title of the New York Times article about our previous related study

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/

What are the Links Between Human Longevity

and Fertility?

Testing the evolutionary theory of aging

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.

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.”

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.”

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.

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.

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

“… 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

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

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).

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.

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 ?

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

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).

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.


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

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.

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

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.

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.

Short Conclusion:

Exceptional human longevity is NOT associated with infertility or childlessness

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.

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.

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?

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.

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, geneticist

Professor 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.

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.

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

Daughters' Lifespan (30+) as a Functionof Paternal Age at Daughter's Birth

6,032 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

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

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

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

Heritability of Longevity

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.

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

Linearity Principle of Inheritance in Quantitative Genetics

Dependence between parental traits and offspring traits is linear

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)

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.

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

as a Function of Paternal Lifespan


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

“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

Heritability Estimates of Human Lifespan

Author(s) Heritability

estimate

Population

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

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

Offspring Lifespan at Age 30 as a Function of Paternal Lifespan


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


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


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

Offspring Lifespan at Age 30 as a Function of Maternal Lifespan


Daughters, 8,284 cases Sons, 8,322 casesMaternal Lifespan, years

40 50 60 70 80 90 100

Life

span

diff

eren

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

Life

span

diff

eren

ce, y

ears

-2

2

4

0

p=0.02

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?

Person’s Lifespan as a Function of Spouse Lifespan


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

Husband Lifespan, years

40 50 60 70 80 90

Lif

esp

an d

iffe

ren

ce, y

ears

-3

-2

-1

1

2

3

-4

0

4

Wife Lifespan, years

40 50 60 70 80 90

Lif

esp

an d

iffe

ren

ce, y

ears

-4

-3

-2

-1

1

2

3

4

0

AcknowledgmentsThis study was made possible thanks to:

generous support from the National Institute on Aging and

the Society of Actuaries

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