Gavrilov Genetics

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Testing Biological Ideas on

Evolution, Ageing and Longevitywith 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 thenumber of offspring. Proc. R. Soc. London, 67:

159-179.

• Data used: English Quaker records and Whitney

Family of Connectucut records for females andAmerican Whitney family and Burke‟s „Landed

Gentry‟ for males.



Findings and Conclusions

by Beeton et al., 1900

• They tested predictions of the Darwinianevolutionary theory that the fittest individualsshould leave more offspring.

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

• Conclusion: “ fertility is correlated with longevityeven after the fecund period is passed ” and“ selective mortality reduces the numbers of theoffspring 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 anestablished scientific fact. Previous studies werenot 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 longevityat 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

Source: Westendorp, R. G. J., Kirkwood, T. B. L. 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 1.3221-30 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



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 otherpossible explanations, including potential flaws indata quality and analysis

• Previous analysis by Westendorp and Kirkwood wasmade on the assumption of data completeness:Number of chi ldren born = Number of chi ldren recorded

• Potential concerns: data incompleteness, under-reportingof short-lived children, women (because of patrilinealstructure of genealogical records), persons who did notmarry or did not have children.Number of chi ldren born >> Number of chi ldren recorded



Test for Data Completeness

Direct Test: Cross-checking of the initial dataset with other data sources

We examined 335 claims of childlessness in the dataset used by Westendorp andKirkwood. 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 (16531741) 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 (17761864) 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 (18031886), Henry (1806-

1889), Charles (18071882), Arabella (1809-1884), and William (18151881).

Wilhelmina Louise von Anhalt-Bernburg (17991882), apparently childless, lived 83 years. In reality, however, she

had at least two children, Alexander (18201896) and Georg (18261902).



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



Antoinette de Bourbon

(1493-1583)

Lived almost 90 years

She was claimed to have only one child in thedataset 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 thosedeleterious mutations that are expressed in later

life should have higher frequencies (becausemutation-selection balance is shifted to higher equilibrium frequencies due to smaller selection

pressure).

• Therefore, „expressed‟ genetic variability shouldincrease with age.

• This should result in higher heritability estimatesfor 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 moremarriages (5%) wereexcluded from the analysisin order to facilitate theinterpretation of results(continuity of exposure tochildbearing).

•Every case of

childlessness has beenchecked using at least twodifferent genealogicalsources.



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+

PercentofChildlessness

0

10

20

30

40

50

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



Childlessness Odds Ratio Estimatesas a Function of Wife's Lifespan

Multivariate logistic regression analysis of

3,723 European aristocratic families

Wife's Lifespan

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

ChildlessnessOddsRati

o(NetEffect)

0

2

4

6

8

10

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

123

572

872

628483359

355

294

37



Childlessness Odds Ratio Estimatesas a Function of Husband's Lifespan

Multivariate logistic regression analysis of 3,723 European aristocratic families

Husband's Lifespan

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

Child

lessnessOddsRatio(NetEffect)

0

1

2

3

4

5

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

51

61



Characteristic of our Dataset• Over 16,000 persons

belonging to the Europeanaristocracy

• 1800-1880 extinct birth

cohorts• Adult persons aged 30+

• Data extracted from the

professional genealogicaldata sources includingGenealogisches Handbook des Adels, Almanac deGotha, 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

Daughter's

Lifespan(deviation),years

-2

2

4

6

0

• Offspring datafor adult lifespan(30+ years) are

smoothed by5-year runningaverage.

• Extinct birthcohorts (born in

1800-1880)• European

aristocraticfamilies.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

Lifespandifference,ye

ars

-2

2

4

0

p=0.05

p=0.0003

p=0.006


40 50 60 70 80 90 100

Lifespandifference,years

-2

2

4

0

p<0.0001p=0.001

p=0.001




as a Function of Paternal Lifespan Data are adjusted for other predictor variables



40 50 60 70 80 90 100


ars

-2

2

4

0

p=0.04

p=0.0001

p=0.04


40 50 60 70 80 90 100

Lifespandifference,y

ears

-2

2

4

0

p=0.006

p=0.004

p=0.0003




as a Function of Maternal Lifespan Data are adjusted for other predictor variables


Maternal Lifespan, years

40 50 60 70 80 90 100


-2

2

4

0

p=0.01

p=0.0004

p=0.05


40 50 60 70 80 90 100


ears

-2

2

4

0

p=0.02




as a Function of Maternal Lifespan Data are adjusted for other predictor variables



40 50 60 70 80 90 100


-2

2

4

0

p=0.01

p<0.0001

p=0.01


40 50 60 70 80 90 100


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

Spouse Lifespan, years

40 50 60 70 80 90 100


ars

-4

-2

2

4

6

0

Spouse Lifespan, years

40 50 60 70 80 90 100


-2

2

4

6

0



Daughters' Lifespan (30+) as a Function

of 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'sage when these women wereborn (expressed as a difference

from the reference level forthose born to fathers of 40-44years).

• The data are point estimates(with standard errors) of thedifferential interceptcoefficients adjusted for otherexplanatory variables usingmultiple regression withnominal variables.

• Daughters of parents whosurvived to 50 years.

Paternal Age at Reproduction15-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59

Lifes

panDifference(yr)

-4

-3

-2

-1

1

0

p = 0.04



Daughters' Lifespan (60+) as a Function

of Paternal Age at Daughter's Birth

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

• Life expectancy of olderwomen (60+) as a function of father's age when these womenwere born (expressed as a

difference from the referencelevel for those born to fathers of 40-44 years).

• The data are point estimates(with standard errors) of thedifferential interceptcoefficients adjusted for otherexplanatory variables usingmultiple regression withnominal variables.

• Daughters of parents whosurvived to 50 years.

Paternal Age at Reproduction15-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59

Lifes

panDifference(yr)

-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

P

arentalage

atchildbirth

(years)

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 andpercentage of offspringestimated to have anonset of schizophrenia

by age 34 years, forcategories of paternalage. The numbersabove the bars showthe proportion of offspring who wereestimated to have an

onset of schizophreniaby 34 years of age.

• Source: Malaspina et al., Arch GenPsychiatry.2001.



Molecular Effects on Ageing

New 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 modernwealthy 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 unintendedretardation of the aging process, caused by decreased damage accumulation,

because of improving the micronutrient status in recent decades

Mi t i t U d t iti i A i



Micronutrient Undernutrition in Americans

25%50%90; 75 mgMen; WomenC

5; ~10-25%10-20; 25-50 %2.4 mcgMen; WomenB12

25%; 50%75%400 mcgMen; WomenFolate**

10%50%1.7; 1.5 mgMen; WomenB6

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 mechanismsincluding production of Hypochlorous acid (HOCl), which producesDNA damage (through incorporation of chlorinated nucleosides).

Chronic inflammation may contribute to many age-related degenerativediseases including cancer

Hypothesis:

Remarkable improvement in the oldest-old survival may reflect an unintendedretardation of the aging process, caused by decreased damage accumulation,

because of partial PREVENTION of INFLAMMATION through better controlover infectious diseases in recent decades



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

born in 1800-1880

Seasonal Differences in Adult Lifespan at Age 30

• Life expectancy of adultwomen (30+) as a function of month of birth (expressed as

a difference from thereference level for thoseborn in February).

• The data are point estimates(with standard errors) of thedifferential interceptcoefficients adjusted forother explanatory variablesusing multivariateregression with categorizednominal variables.

Month of Birth

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

Lifesp

anDifference(yr)

1

2

0

3

p=0.02

p=0.006



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

born in 1800-1880

Seasonal Differences in Adult Lifespan at Age 60

• Life expectancy of adultwomen (60+) as a function of month of birth (expressed as

a difference from thereference level for thoseborn in February).

• The data are point estimates(with standard errors) of thedifferential interceptcoefficients adjusted forother explanatory variablesusing multivariateregression with categorizednominal variables.

Month of Birth

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

Lifes

panDifference(yr)

1

2

0

p=0.04

p=0.008



• Lifeexpectancy of

adult women

(30+) as a

function of year of birth

Mean Lifespan of Females

Born in December and February

as a Function of Birth Year

Year of Birth

1800 1820 1840 1860 1880

M

eanLifespan,years

60

65

70

75

80

Born in February

Born in December Linear Regression Fit



S



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 theage of Drosophila .

Based on the life tablefor 2400 females of Drosophi la melanogaster published by Hall (1969).

Mortality force wascalculated for 3-day ageintervals.

Source: Gavrilov, Gavrilova,

“The Biology of L ife 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 mortalityforce (2) on the age of flour

beetles (Tr ibolium confusum Duval).

Based on the life tablefor 400 female flour beetlespublished by Pearl andMiner (1941). Mortality

force was calculated for 30-day age intervals.

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



Age-Trajectory of Mortality in Italian Women

(Gompertz-Makeham Law of Mortality)

Dependence of thelogarithm of mortalityforce (1) and logarithm of increment of mortality

force (2) on the age of Italian women.

Based on the officialItalian period life table for1964-1967. Mortality forcewas calculated for 1-year

age intervals.


“The Biology of L ife Span ”1991



Compensation Law of MortalityConvergence of Mortality Rates with Age

1 – India, 1941-1950, males

2 – Turkey, 1950-1951, males

3 – Kenya, 1969, males

4 - Northern Ireland, 1950-1952,males

5 - 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 EaglePoint strains (1,000 virginfemales)

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 Swedishwomen for calendar years 1900, 1980,and 1999 (cross-sectional data). Notethat after age 100, the logarithm of

survival fraction is decreasing withoutmuch further acceleration (aging) inalmost a linear fashion. Also note anincreasing pace of survival improvementin history: it took less than 20 years(from year 1980 to year 1999) to repeatessentially the same survivalimprovement that initially took 80 years(from year 1900 to year 1980).

Source: cross-sectional (period) lifetables at the Berkeley MortalityDatabase (BMD):

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



Non-Gompertzian Mortality Kinetics

of Four Invertebrate Species

Non-Gompertzian mortalitykinetics of four invertebratespecies: nematodes,

Campanularia f lexuosa ,rotifers and shrimp.

Source: A. Economos.A non-Gompertzianparadigm for mortality

kinetics of metazoananimals and failure kineticsof manufactured products.AGE, 1979, 2: 74-76.




of Three Rodent Species

Non-Gompertzianmortality kinetics of three rodent species:guinea pigs, rats and

mice.Source: A. Economos.A non-Gompertzianparadigm for mortalitykinetics of metazoan

animals and failurekinetics of manufacturedproducts. AGE, 1979, 2:74-76.




of Three Industrial Materials

Non-Gompertzianmortality kinetics of threeindustrial materials: steel,

industrial relays andmotor heat insulators.

Source: A. Economos.A non-Gompertzianparadigm for mortality

kinetics of metazoananimals and failurekinetics of manufacturedproducts. AGE, 1979, 2:74-76.



Redundancy Creates Both Damage Tolerance

and Damage Accumulation (Aging)

No redundancy

Damage

Death

Damage

RedundancyDamage accumulation

(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 )" Adul t organisms already have an

exceptional ly 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



y p g

damage load ?

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

• Specific arguments:

1. Cell cycle checkpoints are disabled in earlydevelopment (Handyside, Delhanty,1997. TrendsGenet. 13, 270-275 )

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

3. ischemia-reperfusion injury and asphyxia-reventilation

injury during traumatic process of 'normal' birth

Spontaneous mutant frequencies with



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 35

Age (months)

M u t a n t f r e q u e n c y

( x 1 0 - 5 )

Small Intestine

Heart

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

Bi th P i P t ti l



Birth Process is a Potential

Source of High Initial Damage• During birth, the future child is deprived

of oxygen by compression of theumbilical cord and suffers severehypoxia and asphyxia. Then, just afterbirth, a newborn child is exposed tooxidative stress because of acute

reoxygenation while starting to breathe.It is known that acute reoxygenationafter hypoxia may produce extensiveoxidative damage through the samemechanisms that produce ischemia-reperfusion injury and the relatedphenomenon, asphyxia-reventilation

injury. Asphyxia is a commonoccurrence in the perinatal period, andasphyxial brain injury is the mostcommon neurologic abnormality in theneonatal period that may manifest inneurologic disorders in later life.

P ti l i li ti f



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 l i fe, postponement of aging-related

morbidity and mortali ty, and signif icant extension of healthy

lifespan."

" Thus, the idea of early-li fe programming of aging and longevity

may have important practical implications for developing earl y-

l i fe 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.

F il Ki ti i Mi t f S t ith



Failure Kinetics in Mixtures of Systems with

Different Redundancy Levels

Initial PeriodThe dependence of logarithm of mortality force(failure rate) as afunction of age inmixtures of parallelredundant systemshaving Poissondistribution byinitial numbers of functional elements(mean number of elements, = 1, 5,

10, 15, and 20.



Conclusions (I)

• Redundancy is a key notion for understandingaging and the systemic nature of aging inparticular. Systems, which are redundant innumbers 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 withhigher redundancy levels.



Conclusions (II)

• Redundancy exhaustion over the life course explains theobserved „compensation law of mortality‟ (mortalityconvergence at later life) as well as the observed late-lifemortality deceleration, leveling-off, and mortality plateaus.

• Living organisms seem to be formed with a high load of

initial damage, and therefore their lifespans and agingpatterns may be sensitive to early-life conditions thatdetermine this initial damage load during early

development. The idea of early-life programming of agingand longevity may have important practical implicationsfor developing early-life interventions promoting healthand longevity.



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

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