Maternal Age, Fertility, and Longevity Leonid A. Gavrilov Natalia S. Gavrilova Center on Aging NORC and The University of Chicago Chicago, USA
New Vision of Aging-Related Diseases
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 The Biology of Life Span: A Quantitative Approach. Harwood Academic Publisher, New York.
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." Source: Gavrilov, L.A. & Gavrilova, N.S The Biology of Life Span: A Quantitative Approach. Harwood Academic Publisher, New York.
Hypothesis: Ovarian aging (decline in egg quality) may have long-term effects on offspring quality, health and longevity. Down syndrome is just a tip of the iceberg of numerous less visible defects. Testable prediction: Odds of longevity decrease with maternal age Negative impact of maternal aging on offspring longevity
Within-Family Approach: How centenarians are different from their shorter-lived siblings? Allows researchers to eliminate between-family variation including the differences in genetic background and childhood living conditions
Design of the Study
Within-family study of longevity Cases - 1,081 centenarians survived to age 100 and born in USA in Controls – 6,413 their shorter-lived brothers and sisters (5,778 survived to age 50) Method: Conditional logistic regression Advantage: Allows to eliminate between- family variation
Age validation is a key moment in human longevity studies Death date was validated using the U.S. Social Security Death Index Birth date was validated through linkage of centenarian records to early U.S. censuses (when centenarians were children)
A typical image of ‘centenarian’ family in 1900 census
Maternal age and chances to live to 100 for siblings survived to age 50 Conditional (fixed-effects) logistic regression N=5,778. Controlled for month of birth, paternal age and gender. Paternal and maternal lifespan >50 years Maternal ageOdds ratio95% CIP-value < Reference
People Born to Young Mothers Have Twice Higher Chances to Live to 100 Within-family study of 2,153 centenarians and their siblings survived to age 50. Family size <9 children. p=0.020 p=0.013 p=0.043
Being born to Young Mother Helps Laboratory Mice to Live Longer Source: Tarin et al., Delayed Motherhood Decreases Life Expectancy of Mouse Offspring. Biology of Reproduction :
Hypothesis: Egg Quality could be modulated by living conditions (e.g. diet), which may have seasonal variation Testable prediction: Odds of longevity should depend on month of birth
Within-Family Study of Season of Birth and Exceptional Longevity Month of birth is a useful proxy characteristic for environmental effects acting during in-utero and early infancy development
Siblings Born in September-November Have Higher Chances to Live to 100 Within-family study of 9,724 centenarians born in and their siblings survived to age 50
Possible explanations These are several explanations of season-of birth effects on longevity pointing to the effects of early-life events and conditions: seasonal exposure to infections, nutritional deficiencies, environmental temperature and sun exposure. All these factors were shown to play role in later-life health and longevity.
Life Expectancy and Month of Birth Data source: Social Security Death Master File Published in: Gavrilova, N.S., Gavrilov, L.A. Search for Predictors of Exceptional Human Longevity. In: “Living to 100 and Beyond” Monograph. The Society of Actuaries, Schaumburg, Illinois, USA, 2005, pp
Fertility and Longevity How are they related?
Founding Fathers Beeton, M., Yule, G.U., Pearson, K 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: 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 post-reproductive 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 Bettie Freeman (1935): Weak positive correlations between the duration of postreproductive life in women and the number of offspring borne. Human Biology, 7: Bideau A. (1986): Duration of life in women after age 45 was longer for those women who borne 12 or more children. Population 41: Telephone inventor Alexander Graham Bell (1918): “The longer lived parents were the most fertile.”
Studies that Found no Relationship Between Postreproductive Longevity and Reproduction Henry L Travaux et Documents. Gauter, E. and Henry L Travaux et Documents, 26. Knodel, J Demographic Behavior in the Past. Le Bourg et al., Experimental Gerontology, 28:
Study that Found a Trade-Off Between Reproductive Success and Postreproductive Longevity Westendorp RGJ, Kirkwood TBL Human longevity at the cost of reproductive success. Nature 396: Extensive media coverage including BBC and over 100 citations in the scientific literature as an established scientific fact. Previous studies were not quoted and discussed in this article.
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
“… 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
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
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).
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
Educational Significance Do we teach our students right? Impaired fertility of longevous women is often presented in the 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 ?
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 cross-checking of the data revealed that in fact she had as many as five children: Charlotte (18031886), Henry (18061889), Charles (18071882), Arabella ( ), and William (18151881).
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
Characteristics of Our Data Sample for ‘Reproduction-Longevity’ Studies 3,723 married women born in 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.
Typical Mistakes in Biological Studies of Human Longevity Using lifespan data for non- extinct birth cohorts (“cemetery effect”) Failure to control for birth cohort – spurious correlations may be found if variables have temporal dynamics Failure to take into account social events and factors – e.g., failure to control for age at marriage in longevity- reproduction studies
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
Data published by Westendorp and Kirkwood (1998) Our corrected data
Antoinette de Bourbon ( ) Lived almost 90 years She was claimed to have only one child in the dataset used by Westendorp and Kirkwood: Marie ( ), who became a mother of famous Queen of Scotland, Mary Stuart. Our data cross-checking revealed that in fact Antoinette had 12 children! Marie Francois Ier Louise Renee Charles Claude Louis Philippe Pierre 1529 Antoinette Francois Rene
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: Childlessness and lifespan in aristocratic women 31 case Our results were based on carefully checked data (genealogies for European aristocratic families)
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,
Short Conclusion: Exceptional human longevity is NOT associated with infertility or childlessness
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.
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.
Current state of research Some studies found support for disposable soma theory Lycett, Dunbar et al. 2000; Doblhammer and Oeppen 2003; Tabatabaie, Atzmon et al. 2011) Other studies found no relation between longevity and reproduction (Gavrilova, Gavrilov et al. 2004; Chereji, Gatz et al. 2013) or even higher fertility among long-lived individuals (Goegele, Pattaro et al. 2011). Conclusion: This issue is still not resolved We plan to revisit this issue using data on validated American centenarians and their shorter- lived controls
Acknowledgment This study was made possible thanks to: generous support from the National Institute on Aging grant #R01AG stimulating working environment at the Center on Aging, NORC/University of Chicago
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Testing Predictions of the Programmed and Stochastic Theories of Aging: Comparison of Variation in Age at Death, Menopause, and Sexual Maturation
One of the arguments used by the opponents of programmed aging is a too high variation in individual lifespans compared to the observed variation of programmed events (such as the age of sexual maturation). The main goal of this study was to test the validity of this argument.
Measures of variability Absolute measure – standard deviation For distribution of lifespan, demographers often calculate standard deviation at age 10 – SD10 (Edwards & Tuljapurkar 2005). Relative measure – coefficient of variation. Equals the standard deviation divided by the mean
Age at natural menopause as a marker of reproductive aging
Mean age (SD) at natural menopause PopulationMean age (SD) at menopause, years Source South Korean women46.9 (4.9)Hong et al., MATURITAS, 2007 Viennese women aged 47 to (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
Our results using the MIDUS study
National survey conducted in 1994/95 Americans aged core national sample (N=3,485) city oversamples (N=957) Additional samples: twins, siblings Subsample used in this study : women having natural menopause (no surgeries affecting the age at menopause) aged 60-74
DISTRIBUTION OF AGE AT MENARCHE IN THE MIDUS SAMPLE
DISTRIBUTION OF AGE AT MENOPAUSE IN THE MIDUS SAMPLE
DISTRIBUTION OF AGE AT DEATH, SWEDISH FEMALES, 1995 Data source: Human Mortality Database
Variation for characteristics of human aging and development CharacteristicMean 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 death78.7 (16.1)20.5%USA, women, Human mortality database
Variation of age at onset of menarche and age at death (in 2005) CountryMean age (SD) for onset of menarche CV % Mean age (SD) at death CV % France12.84 (1.40) (13.8)16.6 Italy12.54 (1.46) (13.1)15.7 Sweden13.59 (1.41) (12.9)15.7 UK12.89 (1.54) (14.0)17.5 USA12.9 (1.60) (16.1)20.5
Variation of age at onset of menarche and age at death (in 2005) after 10 years CountryMean age (SD) for onset of menarche CV % Mean age (SD10) at death after 10 CV10 % France12.84 (1.40) (12.7)15.2 Italy12.54 (1.46) (11.9)14.2 Sweden13.59 (1.41) (12.0)14.5 UK12.89 (1.54) (12.6)15.5 USA12.9 (1.60) (14.3)18.0
Standard Deviations (Y-axis) and Mean Values (X- axis) for Human Life Cycle Characteristics Mean ages at menarche (1), menopause (2), and death (3)
Conclusions Standard deviations for age at onset of menarche are about 10 times lower than standard deviations for ages at death Coefficients of variation for ages at onset of menarche and ages at death for contemporary populations are of the same order of magnitude