Testing Evolutionary Theories of Aging and Longevity

1
Testing Evolutionary Theories of Aging and
Longevity

• Dr. Natalia S. Gavrilova, Ph.D.
• Dr. Leonid A. Gavrilov, Ph.D.
• Center on Aging
• NORC and The University of Chicago
• Chicago, Illinois, USA

2
What are the data and the predictions of
evolutionary theories of aging on

• Variability of age-related outcomes
• Old-age mortality trajectories
• Trade-offs between longevity and fertility

3
Part 1 Testing Predictions of Programmed vs.
Stochastic Aging

• Opponents of programmed aging often argue that
  there is a too high variation in timing of
  aging-related outcomes, compared to much smaller
  variation in timing of programmed developmental
  outcomes (such as age of sexual maturation).
• In other words, aging just does not have an
  expected clock-wise accuracy, which is
  anticipated for programmed events.

4
Part 1 Testing Predictions of Programmed vs.
Stochastic Aging

• To test the validity of this argument we compared
  relative variability (coefficient of variation)
  for parameters that are known to be determined by
  the developmental program (age at sexual
  maturity) with variability of characteristic
  related to aging (age at menopause).
• We used information on the ages at sexual
  maturation (menarche) and menopause from the
  nationally representative survey of the adult
  population of the United States (MIDUS) as well
  as published data for 14 countries.

5
Why use relative variability, coefficient of
variation?

• "The fact that elephants, for instance, may have
  a standard deviation of 50 mm for some linear
  dimension and shrews a standard deviation of 0.5
  mm for the same dimension does not necessarily
  mean that the elephants are more variable, in the
  essential zoological sense, than the shrews. The
  elephants are a hundred times the size of the
  shrews in any case, and we should expect the
  absolute variation also to be a hundred times as
  great without any essential difference in
  functional variability. The solution of this
  problem is very simple it is necessary only to
  relate the measure of absolute variation to a
  measure of absolute size. The best measures to
  use for this purpose are the standard deviation
  and the mean, and since their quotient is always
  a very small number it is convenient to multiply
  it by 100. The resulting figure is a coefficient
  of variation, or of variability"

Simpson GG, Roe A, Lewontin RG. Quantitative
Zoology Revised Edition. New York Dover
Publications, Inc. 2003.
6
Our results using the MIDUS study
7

• National survey conducted in 1994/95
• Americans aged 25-74
• core national sample (N3,485)
• city oversamples (N957)
• Additional samples twins, siblings
• Subsample used in this study women having
  natural menopause (no surgeries affecting the age
  at menopause) aged 60-74

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A 30-40 minute telephone survey
A 114 page mail survey Number of respondents
4,242 Number of respondents 3,690
9
MIDUS SAMPLE POPULATION DISTRIBUTIONS ()
Women Aged 25-74 (n2,087) Women Aged 25-74 (n2,087)
AGE
25-54 68.8
55-64 19.8
65-74 11.4
RACE/ETHNICITY
White 86.9
African-American 7.7
Other 8.9
RELATIONSHIP STATUS RELATIONSHIP STATUS
Married 54.2
Other intimate relationship 4.7



10
DISTRIBUTION OF AGE AT MENARCHE IN THE MIDUS
SAMPLE
11
DISTRIBUTION OF AGE AT MENOPAUSE IN THE MIDUS
SAMPLE
12
Variation for characteristics of human aging and
development
Characteristic Mean age (SD) years Coefficient of variation Source
Age at onset of menarche 12.9 (1.6) 12.4 MIDUS data
Age at onset of menopause 49.7 (5.2) 10.5 MIDUS data
Age at death 78.7 (16.1) 20.5 USA, women, 1995. Human mortality database
13
Variation of age at onset of menarche and age at
death (in 2005)
Country Mean age (SD) for onset of menarche CV Mean age (SD) at death CV
France 12.84 (1.40) 10.9 83.3 (13.8) 16.6
Italy 12.54 (1.46) 11.6 83.3 (13.1) 15.7
Sweden 13.59 (1.41) 10.4 82.3 (12.9) 15.7
UK 12.89 (1.54) 12.0 80.2 (14.0) 17.5
USA 12.9 (1.60) 12.4 78.7 (16.1) 20.5
14
Mean age (standard deviation, SD) at natural
menopause
Population Mean age (SD) at menopause, years Source
South Korean women 46.9 (4.9) Hong et al., MATURITAS, 2007
Viennese women aged 47 to 68 49.2 (3.6) Kirchengast et al., International Journal of Anthropology , 1999
Mexico Puebla Mexico city 46.7 (4.77) 46.5 (5.00) Sievert, Hautaniemi, Human Biology, 2003
Black women in South Africa rural urban 49.5 (4.7) 48.9 (4.2) Walker et al., International Journal of Obstetrics Gynaecology, 2005
15
Mean Values and Standard Deviations for Human
Developmental Characteristics
Comparison of mean ages at menarche (1),
menopause (2), and death (3) as well as their
standard deviations for studied human
populations. Source Gavrilova N.S., Gavrilov
L.A., Severin, F.F. and Skulachev, V.P. Testing
predictions of the programmed and stochastic
theories of aging Comparison of variation in age
at death, menopause, and sexual maturation.
Biochemistry (Moscow), 2012, 77(7), 754-760.
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Conclusions

• Relative variability, coefficients of variation,
  for ages at onset of menarche and ages at death
  for contemporary populations are of the same
  order of magnitude
• Theories of programmed aging are fruitful in
  suggesting new testable predictions.
• "Although any claim that humans are
  programmed to age and die would be highly
  speculative, we believe that as a hypothesis it
  suggests fruitful avenues for biological and even
  medical research." Longo VD, Mitteldorf J,
  Skulachev VP. Programmed and altruistic ageing.
  Nature Review Genetics. 2005 Nov6(11) 866-72.

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To read more about this part of our study see

• Gavrilova NS, Gavrilov LA, Severin FF, Skulachev
  VP. Testing predictions of the programmed and
  stochastic theories of aging comparison of
  variation in age at death, menopause, and sexual
  maturation. Biochemistry (Moscow). 2012
  Jul77(7)754-60. http//www.ncbi.nlm.nih.gov/pubm
  ed/22817539

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Part 2 Testing the Prediction of Late-Life
Mortality Plateau

• Many evolutionary biologists believe that aging
  can be readily understood in terms of the
  declining force of selection pressure with age.
• At extremely old postreproductive ages when the
  force of natural selection reaches a zero
  plateau, some evolutionary biologists (i.e.
  Michael Rose) believe that the mortality plateau
  should also be observed (no further increase in
  mortality rates with age).
• To test the validity of this argument we analyzed
  mortality data for humans, rats and mice.

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Some evolutionary theories predict late-life
mortality plateau
Source Presentation by Michael Rose
20
When the force of natural selection reaches a
zero plateau, the mortality plateau is also
expected
21
Problems with Hazard Rate Estimation At
Extremely Old Ages

1. Mortality deceleration in humans may be an
   artifact of mixing different birth cohorts with
   different mortality (heterogeneity effect)
2. Standard assumptions of hazard rate estimates may
   be invalid when risk of death is extremely high
3. Ages of very old people may be highly exaggerated

22
Monthly Estimates of Mortality are More
AccurateSimulation assuming Gompertz law for
hazard rate
Stata package uses the Nelson-Aalen estimate of
hazard rate H(x) is a cumulative hazard
function, dx is the number of deaths occurring at
time x and nx is the number at risk at
time x before the occurrence of the deaths. This
method is equivalent to calculation of
probabilities of death
23
Social Security Administrations Death Master
File (SSAs DMF) Helps to Alleviate the First Two
Problems

• Allows to study mortality in large, more
  homogeneous single-year or even single-month
  birth cohorts
• Allows to estimate mortality in one-month age
  intervals narrowing the interval of hazard rates
  estimation

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What Is SSAs DMF ?

• As a result of a court case under the Freedom of
  Information Act, SSA is required to release its
  death information to the public. SSAs DMF
  contains the complete and official SSA database
  extract, as well as updates to the full file of
  persons reported to SSA as being deceased.
• SSA DMF is no longer a publicly available data
  resource (now is available from Ancestry.com for
  fee)
• We used DMF full file obtained from the National
  Technical Information Service (NTIS). Last deaths
  occurred in September 2011.

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SSA DMF birth cohort mortality
Nelson-Aalen monthly estimates of hazard rates
using Stata 11
26
Conclusions from our earlier study of SSA DMF

• Mortality deceleration at advanced ages among DMF
  cohorts is more expressed for data of lower
  quality
• Mortality data beyond ages 106-107 years have
  unacceptably poor quality (as shown using
  female-to-male ratio test). The study by other
  authors also showed that beyond age 110 years the
  age of individuals in DMF cohorts can be
  validated for less than 30 cases (Young et al.,
  2010)
• Source Gavrilov, Gavrilova, North American
  Actuarial Journal, 2011, 15(3)432-447

27
Selection of competing mortality models using DMF
data

• Data with reasonably good quality were used
  non-Southern states and 85-106 years age interval
• Gompertz and logistic (Kannisto) models were
  compared
• Nonlinear regression model for parameter
  estimates (Stata 11)
• Model goodness-of-fit was estimated using AIC and
  BIC

28
Fitting mortality with Kannisto and Gompertz
models
Gompertz model
Kannisto model
29
Akaike information criterion (AIC) to compare
Kannisto and Gompertz models, men, by birth
cohort (non-Southern states)
Conclusion In all ten cases Gompertz model
demonstrates better fit than Kannisto model for
men in age interval 85-106 years
30
Akaike information criterion (AIC) to compare
Kannisto and Gompertz models, women, by birth
cohort (non-Southern states)
Conclusion In all ten cases Gompertz model
demonstrates better fit than Kannisto model for
men in age interval 85-106 years
31
The second studied datasetU.S. cohort death
rates taken from the Human Mortality Database
32
Selection of competing mortality models using HMD
data

• Data with reasonably good quality were used
  80-106 years age interval
• Gompertz and logistic (Kannisto) models were
  compared
• Nonlinear weighted regression model for parameter
  estimates (Stata 11)
• Age-specific exposure values were used as weights
  (Muller at al., Biometrika, 1997)
• Model goodness-of-fit was estimated using AIC and
  BIC

33
Fitting mortality with Kannisto and Gompertz
models, HMD U.S. data
34
Fitting mortality with Kannisto and Gompertz
models, HMD U.S. data
35
Akaike information criterion (AIC) to compare
Kannisto and Gompertz models, men, by birth
cohort (HMD U.S. data)
Conclusion In all ten cases Gompertz model
demonstrates better fit than Kannisto model for
men in age interval 80-106 years
36
Akaike information criterion (AIC) to compare
Kannisto and Gompertz models, women, by birth
cohort (HMD U.S. data)
Conclusion In all ten cases Gompertz model
demonstrates better fit than Kannisto model for
men in age interval 80-106 years
37
Compare DMF and HMD data Females, 1898 birth
cohort
Hypothesis about two-stage Gompertz model is not
supported by real data
38
What about other mammals?

• Mortality data for mice
• Data from the NIH Interventions Testing Program,
  courtesy of Richard Miller (U of Michigan)
• Argonne National Laboratory data,
  courtesy of Bruce Carnes (U of Oklahoma)

39
Mortality of mice (log scale) Data by Richard
Miller
males
females

• Actuarial estimate of hazard rate with 10-day age
  intervals

40
Bayesian information criterion (BIC) to compare
the Gompertz and Kannisto models, mice data
Dataset Miller data Controls Miller data Controls Miller data Exp., no life extension Miller data Exp., no life extension Carnes data Early controls Carnes data Early controls Carnes data Late controls Carnes data Late controls
Sex M F M F M F M F
Cohort size at age one year 1281 1104 2181 1911 364 431 487 510
Gompertz -597.5 -496.4 -660.4 -580.6 -585.0 -566.3 -639.5 -549.6
Kannisto -565.6 -495.4 -571.3 -577.2 -556.3 -558.4 -638.7 -548.0
Better fit (lower BIC) is highlighted in red
Conclusion In all cases Gompertz model
demonstrates better fit than Kannisto model for
mortality of mice after one year of age
41
Laboratory rats

• Data sources Dunning, Curtis (1946) Weisner,
  Sheard (1935), Schlettwein-Gsell (1970)

42
Mortality of Wistar rats
males
females

• Actuarial estimate of hazard rate with 50-day age
  intervals
• Data source Weisner, Sheard, 1935

43
Bayesian information criterion (BIC) to compare
Gompertz and Kannisto models, rat data
Line Wistar (1935) Wistar (1935) Wistar (1970) Wistar (1970) Copenhagen Copenhagen Fisher Fisher Backcrosses Backcrosses
Sex M F M F M F M F M F
Cohort size 1372 1407 1372 2035 1328 1474 1076 2030 585 672
Gompertz -34.3 -10.9 -34.3 -53.7 -11.8 -46.3 -17.0 -13.5 -18.4 -38.6
Kannisto 7.5 5.6 7.5 1.6 2.3 -3.7 6.9 9.4 2.48 -2.75
Better fit (lower BIC) is highlighted in red
Conclusion In all cases Gompertz model
demonstrates better fit than Kannisto model for
mortality of laboratory rats
44
Simulation study of the Gompertz mortalityKernel
smoothing of hazard rates
45
Recent developments

• none of the age-specific mortality
  relationships in our nonhuman primate analyses
  demonstrated the type of leveling off that has
  been shown in human and fly data sets
• Bronikowski et al., Science, 2011
• "

46
Conclusions for Part 2 of our Study

• We found that mortality rates increase
  exponentially with age (the Gompertz law), and no
  expected late-life mortality plateaus are
  observed in humans, mice, and rats.
• Late-life mortality deceleration and mortality
  plateau observed in some earlier studies may be
  related to problems with data quality and biased
  estimates of hazard rates at extreme old ages
• It seems unreasonable to explain aging (Gompertz
  law of mortality) by declining force of natural
  selection, because aging continues at the same
  pace at extremely old postreproductive ages when
  the force of natural selection already reaches a
  zero plateau

47
To read more about this part of our study see

• Gavrilov L.A., Gavrilova N.S. Mortality
  measurement at advanced ages A study of the
  Social Security Administration Death Master File.
  North American Actuarial Journal, 2011, 15(3)
  432-447.
• http//www.ncbi.nlm.nih.gov/pmc/articles/PMC326991
  2/

48
Part 3 Testing the Prediction of a
Trade-off between Longevity and Fertility

• One of the predictions of the disposable soma
  theory and the antagonistic pleiotropy theory is
  that exceptional longevity should come with the
  price of impaired fertility (longevity-fertility
  trade-off ).
• This prediction seems to be confirmed by a high
  profile study published by Nature, which claimed
  that almost half of long lived women were
  childless.
• Here we re-evaluate this study with more complete
  data

49
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
  100 citations in scientific literature as an
  established scientific fact. Previous studies
  were not quoted and discussed in this article.

50
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, Kirkwood, Human longevity at
  the cost of reproductive success. Nature, 1998,
  396, pp 743-746

51
it is not a matter of reduced fertility, but a
case of 'to have or have not'.
Source Toon Ligtenberg Henk Brand. Longevity
does family size matter? Nature, 1998, 396, pp
743-746
52
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

53

• Source Westendorp, R. G. J., Kirkwood, T. B. L.
  Human longevity at the cost of reproductive
  success. Nature, 1998, 396, pp 743-746

54
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 (Kirkwood)

"The disposable soma theory on the evolution of
ageing states that longevity requires investments
in somatic maintenance that reduce the resources
available for reproduction (Westendorp,
Kirkwood, Nature, 1998).
55
Do longevous women have impaired fertility ?

• Practical Importance.
• Do we really wish to live a long life at the
  cost of infertility?
• the next generations of Homo sapiens will
  have even longer life spans but at the cost of
  impaired fertility
• Rudi Westendorp Are we becoming less
  disposable? EMBO Reports, 2004, 5 2-6.

"... 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.
56

• 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 (Brandt et al.,
  2005 Fessler et al., 2005 Schrempf et al.,
  2005 Tavecchia et al., 2005 Kirkwood, 2002
  Westendorp, 2002, 2004 Glennon, 2002 Perls et
  al., 2002, etc.).
• This "fact" is now included in teaching
  curriculums in biology, ecology and anthropology
  world-wide (USA, UK, Denmark).
• Is it a fact or artifact ?

57
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 completenessNumbe
  r 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   gtgt
  Number of children recorded

58
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 examplesHenrietta 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
  (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).

59
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 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

61
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.

62
Childlessness is better outcome than number of
children for testing evolutionary theories of
aging on human data

• Applicable even for population practicing birth
  control (few couple are voluntarily childless)
• Lifespan is not affected by physiological load of
  multiple pregnancies
• Lifespan is not affected by economic hardship
  experienced by large families

63
(No Transcript)
64
Source Gavrilova et al. Does exceptional human
longevity come with high cost of infertility?
Testing the evolutionary theories of aging.
Annals of the New York Academy of Sciences, 2004,
1019 513-517.
65
Source Gavrilova, Gavrilov. Human longevity and
reproduction An evolutionary perspective. In
Grandmotherhood - The Evolutionary Significance
of the Second Half of Female Life. Rutgers
University Press, 2005, 59-80.
66
Short Conclusion

• Exceptional human longevity is NOT associated
  with infertility or childlessness

67
More Detailed Conclusions

• We have found that previously reported high rate
  of childlessness among long-lived women is an
  artifact of data incompleteness, caused by
  under-reporting of children. After data cleaning,
  cross-checking and supplementation the
  association between exceptional longevity and
  childlessness has disappeared.
• Thus, it is important now to revise a highly
  publicized scientific concept of heavy
  reproductive costs for human longevity. and to
  make corrections in related teaching curriculums
  for students.

68
More Detailed Conclusions (2)

• It is also important to disavow the doubts and
  concerns over further extension of human
  lifespan, that were recently cast in biomedical
  ethics because of gullible acceptance of the idea
  of harmful side effects of lifespan extension,
  including infertility (Glannon, 2002).
• There is little doubt that the number of children
  can affect human longevity through complications
  of pregnancies and childbearing, as well as
  through changes in socioeconomic status,  etc. 
  However,  the concept of heavy infertility cost
  of human longevity is not supported by data, when
  these data are carefully reanalyzed.

69
Acknowledgments

• This study was made possible thanks to
• generous support from the
• National Institute on Aging (R01 AG028620)
• Stimulating working environment at the Center
  on Aging, NORC/University of Chicago

70
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