Transcript 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

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
Paradox of low heritability of lifespan
vs high familial clustering of longevity
“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
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 shorterlived 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)
Daughter's Lifespan (deviation), years
as a Function of Paternal Lifespan
6
4
2
0
-2
40
50
60
70
80
90
Paternal Lifespan, years
100
• 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
4
2
p=0.006
p=0.05
0
p=0.001
4
Lifespan difference, years
Lifespan difference, years
p=0.0003
p<0.0001
p=0.001
2
0
-2
-2
40
50
60
70
80
90
Paternal Lifespan, years
Daughters, 8,284 cases
100
40
50
60
70
80
90
Paternal Lifespan, years
Sons, 8,322 cases
100
Offspring Lifespan at Age 60
as a Function of Paternal Lifespan
Data are adjusted for other predictor variables
4
p=0.0001
2
p=0.04
p=0.04
0
Lifespan difference, years
Lifespan difference, years
4
p=0.0003
2
p=0.004
p=0.006
0
-2
-2
40
50
60
70
80
90
Paternal Lifespan, years
Daughters, 6,517 cases
100
40
50
60
70
80
90
Paternal Lifespan, years
Sons, 5,419 cases
100
Offspring Lifespan at Age 30
as a Function of Maternal Lifespan
Data are adjusted for other predictor variables
4
p=0.0004
p=0.02
Lifespan difference, years
Lifespan difference, years
4
2
p=0.01
p=0.05
0
2
0
-2
-2
40
50
60
70
80
90
100
Maternal Lifespan, years
Daughters, 8,284 cases
40
50
60
70
80
90
Maternal Lifespan, years
Sons, 8,322 cases
100
Offspring Lifespan at Age 60
as a Function of Maternal Lifespan
Data are adjusted for other predictor variables
4
Lifespan difference, years
Lifespan difference, years
p<0.0001
4
2
p=0.01
p=0.01
0
p=0.04
2
0
-2
-2
40
50
60
70
80
90
100
Maternal Lifespan, years
Daughters, 6,517 cases
40
50
60
70
80
90
Maternal Lifespan, years
Sons, 5,419 cases
100
Person’s Lifespan as a Function
of Spouse Lifespan
4
4
3
3
Lifespan difference, years
Lifespan difference, years
Data are adjusted for other predictor variables
2
1
0
-1
-2
2
1
0
-1
-2
-3
-3
-4
-4
40
50
60
70
80
90
Husband Lifespan, years
Married Women, 4,530 cases
40
50
60
70
80
90
Wife Lifespan, years
Married Men, 5,102 cases
Person’s Lifespan as a Function
of Sisters Lifespan
Data are adjusted for other predictor variables
5
4
Lifespan difference, years
Lifespan difference, years
5.0
2.5
0.0
-2.5
3
2
1
0
-1
-2
-3
-5.0
-4
-5
40
50
60
70
80
Sisters Lifespan, years
Females, 5,421 cases
90
40
50
60
70
80
Sisters Lifespan, years
Males, 7,378 cases
90
Person’s Lifespan as a Function
of Sisters-In-Law Lifespan
Data are adjusted for other predictor variables
4
3
Lifespan difference, years
Lifespan difference, years
3.0
1.5
0.0
-1.5
-3.0
2
1
0
-1
-2
-3
-4
40
50
60
70
80
Sisters-In-Law Lifespan, years
Females, 4,789 cases
90
40
50
60
70
80
90
Sisters-In-Law Lifespan, years
Males, 4,707 cases
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
1
Log(Hazard Rate)
Log(Hazard Rate)
1
0.1
0.01
0.1
0.01
short-lived parents
long-lived parents
short-lived parents
long-lived parents
Linear Regression Line
Linear Regression Line
0.001
0.001
40
50
60
70
Age
Sons
80
90
100
40
50
60
70
80
Age
Daughters
90
100
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 Function
of Paternal Age at Daughter's Birth
6,032 daughters from European aristocratic families
born in 1800-1880
1
•
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.
Lifespan Difference (yr)
0
-1
-2
-3
p = 0.04
-4
15-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59
Paternal Age at Reproduction
Paternal Age as a Risk Factor
for Alzheimer Disease
Parental age at childbirth (years)
40
• MGAD - major
gene for Alzheimer
Disease
p = 0.04
35
NS
p=0.04
NS
NS
30
NS
25
Paternal age
Maternal age
Sporadic Alzheimer Disease (low likelihood of MGAD)
Familial Alzheimer Disease (high likelihood of MGAD)
Controls
• Source: L. Bertram
et al.
Neurogenetics,
1998, 1: 277-280.
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 pretested 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 ischemiareperfusion 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
40
Small Intestine
Heart
-5
Mutant frequency (x10 )
35
30
25
20
15
10
5
0
0
5
10
15
20
Age (months)
25
30
35
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 earlylife 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 Lifespan
8,284 females from European aristocratic families
born in 1800-1880
Seasonal Differences in Adult Lifespan at Age 30
3
•
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.
p=0.006
Lifespan Difference (yr)
p=0.02
2
1
0
FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB.
Month of Birth
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 743746
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 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 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 (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 crosschecking of the data revealed that in fact she had as many as five children: Charlotte (18031886), Henry (18061889), 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).
Antoinette de Bourbon
(1493-1583)
Lived almost 90 years
She 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 Estimates
as a Function of Wife's Lifespan
Multivariate logistic regression analysis of
3,723 European aristocratic families
Childlessness Odds Ratio (Net Effect)
10
Net effects, adjusted for calendar year of birth,
maternal age at marriage, husband's lifespan
and husband's age at marriage
8
37
6
4
2
294
572
359
483
123
628
872
355
0
<20
20-29 30-39 40-49 50-59 60-69 70-79 80-89
Wife's Lifespan
90+
Childlessness Odds Ratio Estimates
as a Function of Husband's Lifespan
Multivariate logistic regression analysis of
3,723 European aristocratic families
Childlessness Odds Ratio (Net Effect)
5
Net effects, adjusted for calendar year of birth,
wife's age at marriage, wife's lifespan
and husband's age at marriage
4
61
3
2
1
51
0
<30
30-39 40-49 50-59 60-69 70-79 80-89
Husband's Lifespan
90+
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.
2.
3.
4.
Norway, total mortality
Denmark, total
mortality
Norway, age-dependent
mortality
Denmark, agedependent mortality
Source: Gavrilov, Gavrilova,
“The Biology of Life Span”
1991
Historical Changes in Mortality for
40-year-old Italian Women and Men
1.
2.
3.
4.
Women, total
mortality
Men, total mortality
Women, agedependent mortality
Men, age-dependent
mortality
Source: Gavrilov, Gavrilova,
“The Biology of Life
Span” 1991
Historical Changes in Mortality
Swedish Females
1
1925
1960
1980
1999
Log (Hazard Rate)
0.1
0.01
0.001
0.0001
0
20
40
60
Age
80
100
Historical Changes in Survival
from Age 90 to 100 years. France
Percent Surviving from Age 90 to 100
6
5
Females
Males
4
3
2
1
0
1900
1920
1940
1960
Calendar Year
1980
2000
Historical Changes in Survival
from Age 90 to 100 years. Japan
Percent Surviving from Age 90 to 100
10
Females
Males
8
6
4
2
0
1950
1960
1970
1980
Calendar Year
1990
2000
Extension of the GompertzMakeham 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
4
Factor 1 ('young ages')
Factor 2 ('old ages')
3
Factor score
2
1
0
-1
-2
1900
1920
1940
Year
1960
1980
2000
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.
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
For More Information and
Updates Please Visit Our
Scientific and Educational Website
on Human Longevity:
• http://longevity-science.org