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7/28/2019 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
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Is There Any Link Between
Longevity and Fertility?
What are the data and the predictions of
the evolutionary theory on this issue?
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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.
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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.”
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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.
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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.
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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.
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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 ?
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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
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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
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“… 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
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Source: Westendorp, R. G. J., Kirkwood, T. B. L. Human
longevity at the cost of reproductive success. Nature,
1998, 396, pp 743-746
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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
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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).
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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
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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
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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.
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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.
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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%
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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
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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
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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.
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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
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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
Lifespandifference,ye
ars
-2
2
4
0
p=0.05
p=0.0003
p=0.006
Paternal Lifespan, years
40 50 60 70 80 90 100
Lifespandifference,years
-2
2
4
0
p<0.0001p=0.001
p=0.001
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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
Lifespandifference,ye
ars
-2
2
4
0
p=0.04
p=0.0001
p=0.04
Paternal Lifespan, years
40 50 60 70 80 90 100
Lifespandifference,y
ears
-2
2
4
0
p=0.006
p=0.004
p=0.0003
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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
Lifespandifference,years
-2
2
4
0
p=0.01
p=0.0004
p=0.05
Maternal Lifespan, years
40 50 60 70 80 90 100
Lifespandifference,y
ears
-2
2
4
0
p=0.02
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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
Lifespandifference,years
-2
2
4
0
p=0.01
p<0.0001
p=0.01
Maternal Lifespan, years
40 50 60 70 80 90 100
Lifespandifference,y
ears
-2
2
4
0
p=0.04
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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
Lifespandifference,ye
ars
-4
-2
2
4
6
0
Spouse Lifespan, years
40 50 60 70 80 90 100
Lifespandifference,years
-2
2
4
6
0
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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
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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
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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
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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.
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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
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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%
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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
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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
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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
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• 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
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S
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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)?
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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
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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
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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.
Source: Gavrilov, Gavrilova,
“The Biology of L ife Span ”1991
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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
Source: Gavrilov, Gavrilova,
“The Biology of L ife Span ” 1991
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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.
Source: Gavrilov, Gavrilova,
“The Biology of L ife Span ” 1991
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Mortality at Advanced Ages
Source: Gavrilov L.A., Gavrilova N.S. The Biology of Life Span:
A Quantitative Approach, NY: Harwood Academic Publisher, 1991
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M. Greenwood, J. O. Irwin. BIOSTATISTICS OF SENILITY
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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/
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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.
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Non-Gompertzian Mortality Kinetics
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.
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Non-Gompertzian Mortality Kinetics
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.
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Redundancy Creates Both Damage Tolerance
and Damage Accumulation (Aging)
No redundancy
Damage
Death
Damage
RedundancyDamage accumulation
(aging)
Defect
Defect
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Differences in reliability structure between
(a) technical devices and (b) biological systems
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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
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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
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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
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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
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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
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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.
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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.
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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.
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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