testing biological ideas on evolution, ageing and longevity with demographic and genealogical data...

Testing Biological Ideas on Evolution, Ageing and

Longevity with Demographic and Genealogical Data

Leonid A. Gavrilov

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

1155 East 60th Street, Chicago, IL 60637

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.

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

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


“… 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

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

Testing Evolutionary Theories of Ageing and Mutation Accumulation

Theory in Particular• 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.

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

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.

Proportion of Childless Womenas a Function of Their Lifespan

Univariate data for 3,723 European aristocratic women born in 1500-1875

Women's Lifespan

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

Pe

rce

nt

of

Ch

ild

les

sn

es

s

0

10

20

30

40

50

Compare these results with the Knodel's (1988) estimates for German villages in the 18th and 19th centuries: 6.7-16.2%

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

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

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


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 60 as a Function of Paternal Lifespan




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


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



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


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




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


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


Married Women, 6,442 cases Married Men, 6,596 cases

Spouse Lifespan, years

40 50 60 70 80 90 100

Lif

esp

an d

iffe

ren

ce, y

ears

-4

-2

2

4

6

0

Spouse Lifespan, years

40 50 60 70 80 90 100

Lif

esp

an d

iffe

ren

ce, y

ears

-2

2

4

6

0

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

Daughters' Lifespan (60+) as a Functionof Paternal Age at Daughter's Birth

4,832 daughters from European aristocratic familiesborn in 1800-1880

• Life expectancy of older women (60+) 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)

-3

-2

-1

1

0

p = 0.004

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.

Molecular Effects on AgeingNew Ideas and Findings by Bruce Ames:

• The rate of mutation damage is NOT immutable, but it can be dramatically decreased by very simple measures:

-- Through elimination of deficiencies in vitamins and other micronutrients (iron, zinc, magnesium, etc).

• Micronutrient deficiencies are very common even in the modern wealthy populations

• These deficiencies are much more important than radiation, industrial pollution and most other hazards

Our hypothesis:

Remarkable improvement in the oldest-old survival may reflect an unintended retardation of the aging process, caused by decreased damage accumulation, because of improving the micronutrient status in recent decades

Micronutrient Undernutrition in Americans

25%50%90; 75 mgMen; Women C

5; ~10-25%10-20; 25-50 %2.4 mcgMen; Women B12

25%; 50%75%400 mcgMen; Women Folate**

10% 50%1.7; 1.5 mgMen; Women B6

Vitamins

5-10% 25%8 mgWomen 50+ years

25% 75%18 mgWomen 20-30 years Iron

Minerals

<50% RDA

% ingesting

< RDA Population GroupNutrient

•Wakimoto and Block (2001) J Gerontol A Biol Sci Med Sci. Oct; 56 Spec No 2(2):65-80.

** Before U.S. Food Fortification Source: Presentation by Bruce Ames at the IABG Congress

RDA % ingesting < 50% RDA

Zinc Men; Women 50+ years 11; 8 mg 50% 10%

Molecular Effects on Ageing (2)Ideas and Findings by Bruce Ames:

• The rate of damage accumulation is NOT immutable, but it can be dramatically decreased by PREVENTING INFLAMMATION:

Inflammation causes tissue damage through many mechanisms including production of Hypochlorous acid (HOCl), which produces DNA damage (through incorporation of chlorinated nucleosides).

Chronic inflammation may contribute to many age-related degenerative diseases including cancer

Hypothesis:

Remarkable improvement in the oldest-old survival may reflect an unintended retardation of the aging process, caused by decreased damage accumulation, because of partial PREVENTION of INFLAMMATION through better control over infectious diseases in recent decades

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

Season of Birth and Female Lifespan6,517 females from European aristocratic families

born in 1800-1880Seasonal Differences in Adult Lifespan at Age 60

• Life expectancy of adult women (60+) 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

p=0.04

p=0.008

• Life expectancy of adult women (30+) as a function of year of birth

Mean Lifespan of FemalesBorn in December and February

as a Function of Birth Year

Year of Birth

1800 1820 1840 1860 1880

Mea

n L

ifes

pan

, yea

rs

60

65

70

75

80

Born in FebruaryBorn in December

Linear Regression Fit

Aging is a Very General Phenomenon!

What Should the Aging Theory Explain:

• Why do most biological species deteriorate with age?

• Specifically, why do mortality rates increase exponentially with age in many adult species (Gompertz law)?

• Why does the age-related increase in mortality rates vanish at older ages (mortality deceleration)?

• How do we explain the so-called compensation law of mortality (Gavrilov & Gavrilova, 1991)?

Exponential Increase of Death Rate with Age in Fruit Flies

(Gompertz Law of Mortality) Linear dependence of

the logarithm of mortality force on the age of Drosophila.

Based on the life table for 2400 females of Drosophila melanogaster published by Hall (1969). Mortality force was calculated for 3-day age intervals.

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

Age-Trajectory of Mortality in Flour Beetles(Gompertz-Makeham Law of Mortality)

Dependence of the logarithm of mortality force (1) and logarithm of increment of mortality force (2) on the age of flour beetles (Tribolium confusum Duval).

Based on the life table for 400 female flour beetles published by Pearl and Miner (1941). Mortality force was calculated for 30-day age intervals.

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

Age-Trajectory of Mortality in Italian Women(Gompertz-Makeham Law of Mortality)

Dependence of the logarithm of mortality force (1) and logarithm of increment of mortality force (2) on the age of Italian women.

Based on the official Italian period life table for 1964-1967. Mortality force was calculated for 1-year age intervals.

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

1991

Compensation Law of MortalityConvergence of Mortality Rates with Age

1 – India, 1941-1950, males 2 – Turkey, 1950-1951, males3 – Kenya, 1969, males 4 - Northern Ireland, 1950-1952,

males5 - England and Wales, 1930-

1932, females 6 - Austria, 1959-1961, females 7 - Norway, 1956-1960, females


Compensation Law of Mortality in Laboratory Drosophila

1 – drosophila of the Old Falmouth, New Falmouth, Sepia and Eagle Point strains (1,000 virgin females)

2 – drosophila of the Canton-S strain (1,200 males)

3 – drosophila of the Canton-S strain (1,200 females)

4 - drosophila of the Canton-S strain (2,400 virgin females)

Mortality force was calculated for 6-day age intervals.


Mortality at Advanced Ages

Source: Gavrilov L.A., Gavrilova N.S. The Biology of Life Span:

A Quantitative Approach, NY: Harwood Academic Publisher, 1991

M. Greenwood, J. O. Irwin. BIOSTATISTICS OF SENILITY

Survival Patterns After Age 90

Percent surviving (in log scale) is plotted as a function of age of Swedish women for calendar years 1900, 1980, and 1999 (cross-sectional data). Note that after age 100, the logarithm of survival fraction is decreasing without much further acceleration (aging) in almost a linear fashion. Also note an increasing pace of survival improvement in history: it took less than 20 years (from year 1980 to year 1999) to repeat essentially the same survival improvement that initially took 80 years (from year 1900 to year 1980).

Source: cross-sectional (period) life tables at the Berkeley Mortality Database (BMD):

http://www.demog.berkeley.edu/~bmd/

Non-Gompertzian Mortality Kinetics of Four Invertebrate Species

Non-Gompertzian mortality kinetics of four invertebrate species: nematodes, Campanularia flexuosa, rotifers and shrimp.

Source: A. Economos. A non-Gompertzian paradigm for mortality kinetics of metazoan animals and failure kinetics of manufactured products. AGE, 1979, 2: 74-76.

Non-Gompertzian Mortality Kinetics of Three Rodent Species

Non-Gompertzian mortality kinetics of three rodent species: guinea pigs, rats and mice.


Non-Gompertzian Mortality Kinetics of Three Industrial Materials

Non-Gompertzian mortality kinetics of three industrial materials: steel, industrial relays and motor heat insulators.


Redundancy Creates Both Damage Tolerance and Damage Accumulation (Aging)

No redundancy

Dam age

Death

Dam age

RedundancyDam age accum ulation

(aging)

Defect

Defect

Differences in reliability structure between

(a) technical devices and (b) biological systems

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

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

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.

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.

Failure Kinetics in Mixtures of Systems with Different Redundancy Levels

Initial Period The dependence of

logarithm of mortality force (failure rate) as a function of age in mixtures of parallel redundant systems having Poisson distribution by initial numbers of functional elements (mean number of elements, = 1, 5, 10, 15, and 20.

Conclusions (I)• Redundancy is a key notion for understanding

aging and the systemic nature of aging in particular. Systems, which are redundant in numbers of irreplaceable elements, do deteriorate (i.e., age) over time, even if they are built of non-aging elements.

• An actuarial aging rate or expression of aging (measured as age differences in failure rates, including death rates) is higher for systems with higher redundancy levels.

Conclusions (II)• Redundancy exhaustion over the life course explains the

observed ‘compensation law of mortality’ (mortality convergence at later life) as well as the observed late-life mortality deceleration, leveling-off, and mortality plateaus.

• Living organisms seem to be formed with a high load of initial damage, and therefore their lifespans and aging patterns may be sensitive to early-life conditions that determine this initial damage load during early development. The idea of early-life programming of aging and longevity may have important practical implications for developing early-life interventions promoting health and longevity.

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

testing biological ideas on evolution, ageing and longevity with demographic and genealogical data...

Documents

human longevity

duration of life

number of offspring

postreproductive lifespan

human life span

long life

mechanisms of aging

positive relationship