Bio-Actuarial Studies of Human Longevity

Leonid A. Gavrilov

Natalia S. Gavrilova Center on Aging, NORC/University of Chicago,

1155 East 60th Street, Chicago, IL 60637

Three scientific problems:• Mechanisms of familial transmission of human

longevity Paradox of low heritability of lifespan vs high familial

clustering of longevity

• Parental-age effects (accumulation of mutation load in parental germ cells)

Does progeny conceived to older parents live shorter lives?

• Does exceptional human longevity come with high cost of infertility?

Testing the evolutionary theories of aging

“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

Gavrilova et al., 1998 0.18 European aristocracy

Cournil et al., 2000 0.27 French village

Mitchell et al., 2001 0.25 Old Order Amish

Early Study on Familial Longevity

• This study found that the relatives of nonagenarians and centenarians live longer than relatives of shorter-lived individuals

• These findings were confirmed in later studies (Gudmundsson et al., 2000; Perls et al., 2002 and others )

Characteristics 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.

Unusual Non-linear Pattern of Lifespan Inheritance

It is theoretically predicted (by quantitative genetics) and experimentally confirmed that the dependence of most offspring quantitative traits (body weight for example) on parental traits is linear.However, if some parents are damaged during early development and therefore have shorter lifespan (despite having normal germ cell DNA), the dependence for lifespan inheritance should become non-linear. This is because the offspring born to these short-lived parents with normal germ cell DNA should have normal rather than shorter lifespan

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

Offspring Lifespan at Age 30 as a Function of Paternal Lifespan

Data 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

Offspring Lifespan at Age 60 as a Function of Paternal Lifespan

Data are adjusted for other predictor variables

Daughters, 6,517 cases Sons, 5,419 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=0.04

p=0.0001

p=0.04

Paternal Lifespan, years

40 50 60 70 80 90 100

Lif

esp

an d

iffe

ren

ce, y

ears

-2

2

4

0

p=0.006

p=0.004

p=0.0003

Offspring Lifespan at Age 30 as a Function of Maternal Lifespan

Data 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

Offspring Lifespan at Age 60 as a Function of Maternal Lifespan

Data are adjusted for other predictor variables

Daughters, 6,517 cases Sons, 5,419 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<0.0001

p=0.01

Maternal Lifespan, years

40 50 60 70 80 90 100

Lif

esp

an d

iffe

ren

ce, y

ears

-2

2

4

0

p=0.04

Person’s Lifespan as a Function of Spouse Lifespan

Data 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

Person’s Lifespan as a Function of Sisters Lifespan

Data are adjusted for other predictor variables

Females, 5,421 cases Males, 7,378 cases

Sisters Lifespan, years

40 50 60 70 80 90

Lif

es

pa

n d

iffe

ren

ce

, y

ea

rs

-5.0

-2.5

2.5

5.0

0.0

Sisters Lifespan, years

40 50 60 70 80 90

Lif

es

pa

n d

iffe

ren

ce

, y

ea

rs-4

-3

-2

-1

1

2

3

4

-5

0

5

Person’s Lifespan as a Function of Sisters-In-Law Lifespan

Data are adjusted for other predictor variables

Females, 4,789 cases Males, 4,707 cases

Sisters-In-Law 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

Sisters-In-Law Lifespan, years

40 50 60 70 80 90

Lif

esp

an d

iffe

ren

ce, y

ears

-3.0

-1.5

1.5

3.0

0.0

Mortality Kinetics Long-Lived Mutants of Mouse and Drosophila

Mouse Snell dwarf mutant. Flurkey et al., PNAS, 2001.

Drosophila mutant methuselah. Lin et al., Science, 1998.

Mortality Kinetics for Progeny Born to Long-Lived (80+) vs Short-Lived Parents

Data are adjusted for historical changes in lifespan

Sons Daughters

Age

40 50 60 70 80 90 100

Lo

g(H

aza

rd R

ate)

0.001

0.01

0.1

1

short-lived parentslong-lived parents

Linear Regression Line

Age

40 50 60 70 80 90 100

Lo

g(H

aza

rd R

ate)

0.001

0.01

0.1

1

short-lived parentslong-lived parents

Linear Regression Line

Parental-Age Effects (accumulation of mutation load in

parental germ cells)

Does progeny conceived to older parents live shorter lives?

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

6,032 daughters from European aristocratic familiesborn 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

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

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.

Statement of the HIDL hypothesis:(Idea of High Initial Damage Load )

"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.

Why should we expect high initial damage load ?

• General argument:--  In contrast to technical devices, which are built from pre-tested high-quality components, biological systems are formed by self-assembly without helpful external quality control.

• Specific arguments: 1. Cell cycle checkpoints are disabled in early development

    (Handyside, Delhanty,1997. Trends Genet. 13, 270-275 )

2. extensive copy-errors in DNA, because most cell divisions   responsible for  DNA copy-errors occur in early-life   (loss of telomeres is also particularly high in early-life)

3. ischemia-reperfusion injury and asphyxia-reventilation injury   during traumatic process of 'normal' birth

Birth Process is a Potential Source of High Initial Damage

• During birth, the future child is deprived of oxygen by compression of the umbilical cord and suffers severe hypoxia and asphyxia. Then, just after birth, a newborn child is exposed to oxidative stress because of acute reoxygenation while starting to breathe. It is known that acute reoxygenation after hypoxia may produce extensive oxidative damage through the same mechanisms that produce ischemia-reperfusion injury and the related phenomenon, asphyxia-reventilation injury. Asphyxia is a common occurrence in the perinatal period, and asphyxial brain injury is the most common neurologic abnormality in the neonatal period that may manifest in neurologic disorders in later life.

Spontaneous mutant frequencies with age in heart and small intestine

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30 35Age (months)

Mu

tan

t fr

eq

uen

cy (

x10

-5)

Small IntestineHeart

Source: Presentation of Jan Vijg at the IABG Congress, Cambridge, 2003

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."

"Thus, the idea of early-life programming of aging and longevity may have important practical implications for developing early-life interventions promoting health and longevity."

Source: Gavrilov, L.A. & Gavrilova, N.S. 1991. The Biology of Life Span: A Quantitative Approach. Harwood Academic Publisher, New York.

Season of Birth and Female Lifespan8,284 females from European aristocratic families

born in 1800-1880Seasonal Differences in Adult Lifespan at Age 30

• Life expectancy of adult women (30+) as a function of month of birth (expressed as a difference from the reference level for those born in February).

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

Month of Birth

FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB.

Lif

es

pa

n D

iffe

ren

ce

(y

r)

1

2

0

3

p=0.02

p=0.006

Is There Any Link Between Longevity and Fertility?

What are the data and the predictions of the evolutionary theory on this issue?

Brief Historical Note

• 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

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

• 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.

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 70 citations in scientific literature as an established scientific fact. Previous studies were not quoted and discussed in this article.

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, Westendorp, Kirkwood, 1998)

• Practical Importance. Do we really wish to live a long life at the cost of infertility? Based these concerns a suggestion was made:

"... 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 (Kirkwood, 2002; Westendorp, 2002; Glennon, 2002; Perls et al., 2002 etc.) Is it a fact or artifact ?

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

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).

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 Odds Ratio Estimatesas a Function of Wife's Lifespan

Multivariate logistic regression analysis of3,723 European aristocratic families

Wife's Lifespan

<20 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90+

Ch

ild

les

sn

ess

Od

ds

Rati

o (

Net

Eff

ec

t)

0

2

4

6

8

10

Net effects, adjusted for calendar year of birth, maternal age at marriage, husband's lifespan and husband's age at marriage

123

572

872628

483359

355

294

37

Childlessness Odds Ratio Estimatesas a Function of Husband's Lifespan

Multivariate logistic regression analysis of3,723 European aristocratic families

Husband's Lifespan

<30 30-39 40-49 50-59 60-69 70-79 80-89 90+

Ch

ild

les

sn

es

s O

dd

s R

ati

o (

Ne

t E

ffe

ct)

0

1

2

3

4

5

Net effects, adjusted for calendar year of birth, wife's age at marriage, wife's lifespan and husband's age at marriage

51

61

Questions of Scientific and Practical (Actuarial) Significance

• How far could mortality decline go?

(absolute zero seems implausible)• Are there any ‘biological’ limits to human mortality

decline, determined by ‘reliability’ of human body?

(lower limits of mortality dependent on age, sex, and population genetics)

• Were there any indications for ‘biological’ mortality limits in the past?

• Are there any indications for mortality limits now?

The Gompertz-Makeham Law

μ(x) = A + R0exp(α x)

A – Makeham term or background mortality

R0exp(α x) – age-dependent mortality

Historical Changes in Mortality for 40-year-old Swedish Males

1. Total mortality

2. Background mortality

3. Age-dependent mortality

• Source: Gavrilov, Gavrilova, “The Biology of Life Span” 1991

Historical Changes in Mortality for 40-year-old Women in Norway and

Denmark

1. Norway, total mortality2. Denmark, total

mortality3. Norway, age-dependent

mortality4. Denmark, age-

dependent mortality

Source: Gavrilov, Gavrilova, “The Biology of Life Span” 1991

Historical Changes in Mortality for 40-year-old Italian Women and Men

1. Women, total mortality

2. Men, total mortality3. Women, age-

dependent mortality4. Men, age-dependent

mortality

Source: Gavrilov, Gavrilova, “The Biology of Life Span” 1991

Historical Changes in Mortality Swedish Females

Age

0 20 40 60 80 100

Lo

g (

Ha

zard

Ra

te)

0.0001

0.001

0.01

0.1

1

1925196019801999

Historical Changes in Survival from Age 90 to 100 years. France

Calendar Year

1900 1920 1940 1960 1980 2000

Pe

rce

nt

Su

rviv

ing

fro

m A

ge

90

to

10

0

0

1

2

3

4

5

6

FemalesMales

Historical Changes in Survival from Age 90 to 100 years. Japan

Calendar Year

1950 1960 1970 1980 1990 2000

Pe

rce

nt

Su

rviv

ing

fr

om

A

ge

9

0 to

1

00

0

2

4

6

8

10

FemalesMales

Extension of the Gompertz-Makeham Model through the

Factor Analysis of Mortality Trends

Mortality force (age, time) =

= a0(age) + a1(age) x F1(time) + a2(age) x F2(time)

Factor Analysis of Mortality Swedish Females

Year

1900 1920 1940 1960 1980 2000

Fa

cto

r s

co

re

-2

-1

0

1

2

3

4 Factor 1 ('young ages')Factor 2 ('old ages')

Preliminary Conclusions

• There was some evidence for ‘ biological’ mortality limits in the past, but these ‘limits’ proved to be responsive to the recent technological and medical progress.

• Thus, there is no convincing evidence for absolute ‘biological’ mortality limits now.

• Analogy for illustration and clarification: There was a limit to the speed of airplane flight in the past (‘sound’ barrier), but it was overcome by further technological progress. Similar observations seems to be applicable to current human mortality decline.

AcknowledgmentsThis 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

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• http://longevity-science.org