Predictors of Exceptional Longevity Effects of earlylife childhood

  • Slides: 104
Download presentation
Predictors of Exceptional Longevity: Effects of early-life childhood conditions, mid-life environment and parental characteristics

Predictors of Exceptional Longevity: Effects of early-life childhood conditions, mid-life environment and parental characteristics Leonid A. Gavrilov Natalia S. Gavrilova Center on Aging NORC and The University of Chicago, USA

Centenarians represent the fastest growing age group in the industrialized countries Yet, factors predicting

Centenarians represent the fastest growing age group in the industrialized countries Yet, factors predicting exceptional longevity and its time trends remain to be fully understood In this study we explored the new opportunities provided by the ongoing revolution in information technology, computer science and Internet expansion to explore early-childhood predictors of exceptional longevity Jeanne Calment (1875 -1997)

Our Approach To study “success stories” in long-term avoidance of fatal diseases (survival to

Our Approach To study “success stories” in long-term avoidance of fatal diseases (survival to 100 years) and factors correlated with this remarkable survival success

An example of incredible resilience Winnie ain’t quitting now. Smith G D Int. J.

An example of incredible resilience Winnie ain’t quitting now. Smith G D Int. J. Epidemiol. 2011; 40: 537 -562 Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2011; all rights reserved.

Exceptional longevity in a family of Iowa farmers 1. 2. 3. 4. 5. 6.

Exceptional longevity in a family of Iowa farmers 1. 2. 3. 4. 5. 6. 7. 8. 9. Father: Mike Ackerman, Farmer, 1865 -1939 lived 74 years Mother: Mary Hassebroek 1870 -1961 lived 91 years Engelke "Edward" M. Ackerman b: 28 APR 1892 in Iowa 101 Fred Ackerman b: 19 JUL 1893 in Iowa 103 Harmina "Minnie" Ackerman b: 18 SEP 1895 in Iowa 100 Lena Ackerman b: 21 APR 1897 in Iowa 105 Peter M. Ackerman b: 26 MAY 1899 in Iowa 86 Martha Ackerman b: 27 APR 1901 in IA 95 Grace Ackerman b: 2 OCT 1904 in IA 104 Anna Ackerman b: 29 JAN 1907 in IA 101 Mitchell Johannes Ackerman b: 25 FEB 1909 in IA 85

Meeting with 104 -years-old Japanese centenarian (New Orleans, 2010)

Meeting with 104 -years-old Japanese centenarian (New Orleans, 2010)

Computerized genealogies is a promising source of information about potential predictors of exceptional longevity:

Computerized genealogies is a promising source of information about potential predictors of exceptional longevity: lifecourse events, early-life conditions and family history of longevity

There are two factors of longevity 1. 2. Modifiable factors – lifestyle, nutrition, early-life

There are two factors of longevity 1. 2. Modifiable factors – lifestyle, nutrition, early-life events and conditions, etc. Non-modifiable factors – sex, race, ethnicity, genotype We are more interested in modifiable type of factors

The role of early-life conditions in shaping late-life mortality is now well recognized

The role of early-life conditions in shaping late-life mortality is now well recognized

Statement of the HIDL hypothesis: (Idea of High Initial Damage Load ) "Adult organisms

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.

Practical implications from the HIDL hypothesis: "Even a small progress in optimizing the early-developmental

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. 1991. The Biology of Life Span: A Quantitative Approach. Harwood Academic Publisher, New York.

Life Expectancy and Month of Birth Data source: Social Security Death Master File Published

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. 1 -49.

Studies of centenarians require careful design and serious work on age validation The main

Studies of centenarians require careful design and serious work on age validation The main problem is to find an appropriate control group

An example of the most simple approach: To use population control Limitation: If centenarians

An example of the most simple approach: To use population control Limitation: If centenarians and controls are sampled differently then the results of the longevity study may be biased by the factors unrelated to differential survival (for example, genetic composition of centenarians and controls may be affected by migration)

Our earlier study: centenarians were found in computerized family histories and compared to 5%

Our earlier study: centenarians were found in computerized family histories and compared to 5% sample of the U. S. 1900 census (IPUMS dataset) Household Property Status During Childhood and Survival to Age 100 Odds for household to be in a ‘centenarian’ group A – Rented House B – Owned House C – Rented Farm D – Owned farm (reference group)

How centenarians are different from their shorter-lived sibling?

How centenarians are different from their shorter-lived sibling?

Within-Family Approach: How centenarians are different from their shorter-lived sibling? Allows researchers to eliminate

Within-Family Approach: How centenarians are different from their shorter-lived sibling? Allows researchers to eliminate between-family variation including the differences in genetic background and childhood living conditions

Design of the Study

Design of the Study

Within-family study of longevity Cases - 1, 081 centenarians survived to age 100 and

Within-family study of longevity Cases - 1, 081 centenarians survived to age 100 and born in USA in 1880 -1889 Controls – 6, 413 their shorter-lived brothers and sisters (5, 778 survived to age 50) Method: Conditional logistic regression Advantage: Allows to eliminate betweenfamily variation

Age validation is a key moment in human longevity studies Death date was validated

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

A typical image of ‘centenarian’ family in 1900 census

Maternal age and chances to live to 100 for siblings survived to age 50

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 age Odds ratio 95% CI P-value <20 1. 73 1. 05 -2. 88 0. 033 20 -24 1. 63 1. 11 -2. 40 0. 012 25 -29 30 -34 35 -39 40+ 1. 53 1. 16 1. 00 1. 10 -2. 12 0. 85 -1. 60 0. 77 -1. 46 Reference 0. 011 0. 355 0. 720

People Born to Young Mothers Have Twice Higher Chances to Live to 100 Within-family

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

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 2005 72: 1336 -1343.

Possible explanation These findings are consistent with the 'best eggs are used first' hypothesis

Possible explanation These findings are consistent with the 'best eggs are used first' hypothesis suggesting that earlier formed oocytes are of better quality, and go to fertilization cycles earlier in maternal life.

Within-Family Study of Season of Birth and Exceptional Longevity Month of birth is a

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

Siblings Born in September-November Have Higher Chances to Live to 100 Within-family study of 9, 724 centenarians born in 1880 -1895 and their siblings survived to age 50

Possible explanations These are several explanations of season-of birth effects on longevity pointing to

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.

Limitation of within-family approach Relatively small number of explanatory variables

Limitation of within-family approach Relatively small number of explanatory variables

Another approach: Centenarians and their shorter-lived peers How centenarians are different from their shorter-lived

Another approach: Centenarians and their shorter-lived peers How centenarians are different from their shorter-lived peers when compared at young adult age? Compare population-based sample of male centenarians born in 1887 to their shorter-lived peers from the same county participated in WWI civil draft registration (1917)

Physical Characteristics at Young Age and Survival to 100 A study of height and

Physical Characteristics at Young Age and Survival to 100 A study of height and build of centenarians when they were young using WWI civil draft registration cards

Small Dogs Live Longer Miller RA. Kleemeier Award Lecture: Are there genes for aging?

Small Dogs Live Longer Miller RA. Kleemeier Award Lecture: Are there genes for aging? J Gerontol Biol Sci 54 A: B 297–B 307, 1999.

Small Mice Live Longer Source: Miller et al. , 2000. The Journals of Gerontology

Small Mice Live Longer Source: Miller et al. , 2000. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 55: B 455 -B 461

Study Design Cases: male centenarians born in 1887 (randomly selected from the SSA Death

Study Design Cases: male centenarians born in 1887 (randomly selected from the SSA Death Master File) and linked to the WWI civil draft records. Out of 240 selected men, 15 were not eligible for draft. The linkage success for remaining records was 77. 5% (174 records) Controls: men matched on birth year, race and county of WWI civil draft registration

Data Sources 1. Social Security Administration Death Master File 2. WWI civil draft registration

Data Sources 1. Social Security Administration Death Master File 2. WWI civil draft registration cards (completed for almost 100 percent men born between 1873 and 1900)

WWI Civilian Draft Registration In 1917 and 1918, approximately 24 million men born between

WWI Civilian Draft Registration In 1917 and 1918, approximately 24 million men born between 1873 and 1900 completed draft registration cards. President Wilson proposed the American draft and characterized it as necessary to make "shirkers" play their part in the war. This argument won over key swing votes in Congress.

WWI Draft Registration was done in three parts, each designed to form a pool

WWI Draft Registration was done in three parts, each designed to form a pool of men for three different military draft lotteries. During each registration, church bells, horns, or other noise makers sounded to signal the 7: 00 or 7: 30 opening of registration, while businesses, schools, and saloons closed to accommodate the event.

Registration Day Parade

Registration Day Parade

Information Available in the Draft Registration Card age, date of birth, race, citizenship permanent

Information Available in the Draft Registration Card age, date of birth, race, citizenship permanent home address occupation, employer's name height (3 categories), build (3 categories), eye color, hair color, disability

Draft Registration Card: An Example

Draft Registration Card: An Example

Height and survival to age 100

Height and survival to age 100

Body build and survival to age 100

Body build and survival to age 100

Multivariate Analysis Conditional multiple logistic regression model for matched case-control studies to investigate the

Multivariate Analysis Conditional multiple logistic regression model for matched case-control studies to investigate the relationship between an outcome of being a case (extreme longevity) and a set of prognostic factors (height, build, occupation, marital status, number of children, immigration status) Statistical package Stata-10, command clogit

Results of multivariate study Variable Odds Ratio P-value Medium height vs short and tall

Results of multivariate study Variable Odds Ratio P-value Medium height vs short and tall height Slender and medium build vs stout build Farming 1. 35 0. 260 2. 63* 0. 025 2. 20* 0. 016 Married vs unmarried 0. 68 0. 268 Native born vs foreign b. 1. 13 0. 682

Having children by age 30 and survival to age 100 Conditional (fixed-effects) logistic regression

Having children by age 30 and survival to age 100 Conditional (fixed-effects) logistic regression N=171. Reference level: no children Variable Odds ratio 95% CI P-value 1 -3 children 1. 62 0. 89 -2. 95 0. 127 4+ children 2. 71 0. 99 -7. 39 0. 051

Conclusion The study of height and build among men born in 1887 suggests that

Conclusion The study of height and build among men born in 1887 suggests that rapid growth and overweight at young adult age (30 years) might be harmful for attaining longevity

Other Conclusions Both farming and having large number of children (4+) at age 30

Other Conclusions Both farming and having large number of children (4+) at age 30 significantly increased the chances of exceptional longevity by 100 -200%. The effects of immigration status, marital status, and body height on longevity were less important, and they were statistically insignificant in the studied data set.

One more approach Compare centenarians with their peers born in the same year but

One more approach Compare centenarians with their peers born in the same year but died at age 65 years Both centenarians and shorter-lived controls are randomly sampled from the same data universe: computerized genealogies It is assumed that the majority of deaths at age 65 occur due to chronic diseases related to aging rather than injuries or infectious diseases

Case-control study of longevity Cases - 765 centenarians survived to age 100 and born

Case-control study of longevity Cases - 765 centenarians survived to age 100 and born in USA in 1890 -91 Controls – 783 their shorter-lived peers born in USA in 1890 -91 and died at age 65 years Method: Multivariate logistic regression Genealogical records were linked to 1900 and 1930 US censuses providing a rich set of variables

Age validation is a key moment in human longevity studies Death dates of centenarians

Age validation is a key moment in human longevity studies Death dates of centenarians were validated using the U. S. Social Security Death Index Birth dates were validated through linkage of centenarian records to early U. S. censuses (when centenarians were children)

A typical image of ‘centenarian’ family in 1900 census

A typical image of ‘centenarian’ family in 1900 census

Early-life characteristics • Type of parental household (farm or nonfarm, own or rented), •

Early-life characteristics • Type of parental household (farm or nonfarm, own or rented), • Parental literacy, • Parental immigration status • Paternal (or head of household) occupation • Number of children born/survived by mother • Size of parental household in 1900 • Region of birth

Midlife Characteristics from 1930 census • Type of person’s household • Availability of radio

Midlife Characteristics from 1930 census • Type of person’s household • Availability of radio in household • Person’s age at first marriage • Person’s occupation (husband’s occupation in the case of women) • Industry of occupation • Number of children in household • Veteran status, Marital status

Family Characteristics from genealogy • Information on paternal and maternal lifespan • Paternal and

Family Characteristics from genealogy • Information on paternal and maternal lifespan • Paternal and maternal age at person’s birth, • Number of spouses and siblings • Birth order • Season of birth

Example of images from 1930 census (controls)

Example of images from 1930 census (controls)

Parental longevity, early-life and midlife conditions and survival to age 100. Males Multivariate logistic

Parental longevity, early-life and midlife conditions and survival to age 100. Males Multivariate logistic regression, N=714 Odds ratio 95% CI P-value Father lived 80+ 1. 82 1. 33 -2. 50 <0. 001 Mother lived 80+ Farmer in 1930 Age at first marriage Born in North-East 1. 97 1. 80 1. 01 1. 89 1. 44 -2. 70 1. 30 -2. 49 0. 99 -1. 03 1. 16 -3. 10 <0. 001 0. 204 0. 011 1. 43 1. 05 -1. 96 0. 022 0. 92 0. 67 -1. 28 0. 620 Variable Born in the second half of year Radio in household, 1930

Parental longevity, early-life and midlife conditions and survival to age 100 Women Multivariate logistic

Parental longevity, early-life and midlife conditions and survival to age 100 Women Multivariate logistic regression, N=750 Odds ratio 95% CI P-value Father lived 80+ 2. 04 1. 48 -2. 81 <0. 001 Mother lived 80+ Husband farmer in 1930 Age at first marriage Radio in hh, 1930 Born in the second half of year 2. 33 1. 23 1. 02 1. 60 1. 71 -3. 17 0. 89 -1. 70 1. 001 -1. 04 1. 16 -2. 23 <0. 001 0. 210 0. 013 0. 005 0. 99 0. 69 -1. 43 0. 966 Born in North-East 1. 02 0. 62 -1. 65 0. 950 Variable

Variables found to be non-significant in multivariate analyses Parental literacy and immigration status, farm

Variables found to be non-significant in multivariate analyses Parental literacy and immigration status, farm childhood, size of household in 1900, percentage of survived children (for mother), sibship size, father-farmer in 1900 Marital status, veteran status, childlessness Paternal and maternal age at birth, loss of parent before 1910

Season of birth and survival to 100 Birth in the first half and the

Season of birth and survival to 100 Birth in the first half and the second half of the year among centenarians and controls died at age 65 Significant difference: P=0. 008 p=0. 008

Within-Family Study of Season of Birth and Exceptional Longevity Month of birth is a

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

Siblings Born in September-November Have Higher Chances to Live to 100 Within-family study of 9, 724 centenarians born in 1880 -1895 and their siblings survived to age 50

Possible explanations These are several explanations of season-of birth effects on longevity pointing to

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.

Conclusions Both midlife and early-life conditions affect survival to age 100 Parental longevity turned

Conclusions Both midlife and early-life conditions affect survival to age 100 Parental longevity turned out to be the strongest predictor of survival to age 100 Information about such an important predictor as parental longevity should be collected in contemporary longitudinal studies

Study of biological and nonbiological relatives of centenarians Numerous studies showed that biological relatives

Study of biological and nonbiological relatives of centenarians Numerous studies showed that biological relatives of centenarians have substantial survival advantage compared to biological relatives of shorter-lived individuals

Who lives longer in centenarian families? Siblings > Spouses > Siblings-in-law Relatives of 1,

Who lives longer in centenarian families? Siblings > Spouses > Siblings-in-law Relatives of 1, 711 centenarians born in 1880 -1895 Relatives: Men N Women LS 50* N LS 50* Parents 1590 76. 2 1557 77. 2 Spouses Siblings in law 877 75. 4 283 81. 4 5324 77. 6 4877 82. 4 2363 75. 1 2410 79. 5 1900 US birth cohort 73. 3 *Mean lifespan conditional on survival to age 50 79. 4

Little is known about effects of centenarian’s sex on longevity of relatives In this

Little is known about effects of centenarian’s sex on longevity of relatives In this study effects of centenarian’s sex were used to explore genetic and environmental effects on longevity

Dataset We have developed analyzed a new computerized database on 1, 711 validated centenarians

Dataset We have developed analyzed a new computerized database on 1, 711 validated centenarians born in 1880 -1895 in the United States, their parents and 13, 185 shorter-lived siblings.

Having centenarian brother is ‘better’ than centenarian sister (for males only) Siblings of centenarians

Having centenarian brother is ‘better’ than centenarian sister (for males only) Siblings of centenarians Brothers Male centenarians 1268 29. 25 4056 27. 09 <0. 001 Sisters 1071 3806 0. 328 N Female P-value centenarians LE 50 N 32. 06 LE 50 32. 45 Life expectancy of siblings at age 50 depending on the sex of centenarian

Survival of male siblings of centenarians, by sex of centenarian

Survival of male siblings of centenarians, by sex of centenarian

Having centenarian son is ‘better’ than centenarian daughter (for fathers only) Male centenarians Fathers

Having centenarian son is ‘better’ than centenarian daughter (for fathers only) Male centenarians Fathers Mothers Female P-value centenarians N LE 50 374 27. 22 1216 25. 93 0. 023 362 27. 97 1195 0. 176 27. 03 Life expectancy of parents at age 50 depending on the sex of centenarian

Using siblings-in-law as a control group Siblings-in-law do not share genetic background and living

Using siblings-in-law as a control group Siblings-in-law do not share genetic background and living conditions with centenarians On the other hand, they usually come from a similar socio-economic background, so may be a good control group

Sex of centenarian is important for siblings but not for siblings-in-law Married relatives: Brothers

Sex of centenarian is important for siblings but not for siblings-in-law Married relatives: Brothers Sisters Brothers in law Sisters in law Male Females centenarians P-value N LE 50 784 29. 53 2437 27. 12 <0. 001 650 31. 36 2378 32. 40 0. 045 492 24. 95 1857 25. 06 0. 846 611 29. 22 1796 29. 55 0. 539 Life expectancy of relatives at age 50 depending on the sex of centenarian

Only women benefit from having centenarian spouse Centenarian spouses Sibling spouses P-value Sex of

Only women benefit from having centenarian spouse Centenarian spouses Sibling spouses P-value Sex of spouse Men N LE 50 875 25. 40 2349 25. 04 0. 411 Men 214 25. 36 2349 25. 04 NS Women 283 31. 40 2407 29. 46 (married to 103+ centenarians) 0. 007 Life expectancy of spouses at age 50 depending on the sex of centenarian

Conclusion Familial factors in human longevity are likely to be sex-specific. Exploring complex environmental

Conclusion Familial factors in human longevity are likely to be sex-specific. Exploring complex environmental and genetic effects in longevity could be facilitated by further analysis of sex-specific effects

Final Conclusion The shortest conclusion was suggested in the title of the New York

Final Conclusion The shortest conclusion was suggested in the title of the New York Times article about this study

Acknowledgment This study was made possible thanks to: generous support from the National Institute

Acknowledgment This study was made possible thanks to: generous support from the National Institute on Aging grant #R 01 AG 028620 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

For More Information and Updates Please Visit Our Scientific and Educational Website on Human Longevity: http: //longevity-science. org And Please Post Your Comments at our Scientific Discussion Blog: http: //longevity-science. blogspot. com/

Population surveys Provide more detailed information on specific topics compared to censuses Cover relatively

Population surveys Provide more detailed information on specific topics compared to censuses Cover relatively small proportion of population (usually several thousand) Population-based survey – random sample of the total population; represents existing groups of population

New trends in health surveys Harmonization of surveys at world scale Biomarker collection

New trends in health surveys Harmonization of surveys at world scale Biomarker collection

 Large-scale study of health and retirement of older Americans Survey of more that

Large-scale study of health and retirement of older Americans Survey of more that 22000 Americans older than 55 years every 2 years. Started in 1992

HRS-harmonizing studies UK English Longitudinal Study of Ageing (ELSA) Study on Health, Ageing and

HRS-harmonizing studies UK English Longitudinal Study of Ageing (ELSA) Study on Health, Ageing and Retirement in Europe (SHARE) WHO Study on global AGEing and adult health (SAGE) including Russia Other studies in Mexico, China, India, Japan, Korea, Ireland

Introduction to:

Introduction to:

Public Dataset http: //www. icpsr. umich. edu/NACDA/

Public Dataset http: //www. icpsr. umich. edu/NACDA/

NSHAP Collaborators Co-Investigators Linda Waite, PI Ed Laumann Wendy Levinson Martha Mc. Clintock Stacy

NSHAP Collaborators Co-Investigators Linda Waite, PI Ed Laumann Wendy Levinson Martha Mc. Clintock Stacy Tessler Lindau Colm O’Muircheartaigh Phil Schumm NORC Team Stephen Smith and many others Collaborators David Friedman Thomas Hummel Jeanne Jordan Johan Lundstrom Thomas Mc. Dade Ethics Consultant John Lantos Outstanding Research Associates and Staff

Study Timeline Funding: NIH / October, 2003 Pretest: September – December, 2004 Wave I

Study Timeline Funding: NIH / October, 2003 Pretest: September – December, 2004 Wave I Field Period: June 2005 – March 2006 Wave I Analysis: Began October, 2006

NSHAP Design Overview Interview 3, 005 community-residing adults ages 57 -85 Population-based sample, minority

NSHAP Design Overview Interview 3, 005 community-residing adults ages 57 -85 Population-based sample, minority over-sampling 75. 5% weighted response rate 120 -minute in-home interview Questionnaire Biomarker collection Leave-behind questionnaire

Domains of Inquiry Demographics Basic Background Information Marriage Employment and Finances Religion Social Networks

Domains of Inquiry Demographics Basic Background Information Marriage Employment and Finances Religion Social Networks Social Support Activities, Engagement Intimate relationships, sexual partnerships Physical Contact Medical Physical Health Medications, vitamins, nutritional supplements Mental Health Caregiving HIV Women’s Health Ob/gyn history, care Hysterectomy, oophorectomy Vaginitis, STDs Incontinence

NSHAP Biomeasures Blood: hgb, Hgb. A 1 c, CRP, EBV Saliva: estradiol, testosterone, progesterone,

NSHAP Biomeasures Blood: hgb, Hgb. A 1 c, CRP, EBV Saliva: estradiol, testosterone, progesterone, DHEA, cotinine Vaginal Swabs: BV, yeast, HPV, cytology Anthropometrics: ht, waist Physiological: BP, HR and regularity Sensory: olfaction, taste, vision, touch Physical: gait, balance

NSHAP Biomeasures Cooperation

NSHAP Biomeasures Cooperation

Principles of Minimal Invasiveness Compelling rationale: high value to individual health, population health or

Principles of Minimal Invasiveness Compelling rationale: high value to individual health, population health or scientific discovery In-home collection is feasible Cognitively simple Can be self-administered or implemented by single data collector during a single visit Affordable Low risk to participant and data collector Low physical and psychological burden Minimal interference with participant’s daily routine Logistically simple process for transport from home to laboratory Validity with acceptable reliability, precision and accuracy Lindau ST and Mc. Dade TW. 2006. Minimally-Invasive and Innovative Methods for Biomeasure Collection in Population-Based Research. National Academies and Committee on Population Workshop. Under Review.

NSHAP Biomeasures Mc. Clintock Laboratory (Cytology) UC Cytopathology (Cytology) “Laboratory Without Walls” Jordan Clinical

NSHAP Biomeasures Mc. Clintock Laboratory (Cytology) UC Cytopathology (Cytology) “Laboratory Without Walls” Jordan Clinical Lab Magee Women’s Hospital (Bacterial, HPV Analysis) Salimetrics (Saliva Analysis) Mc. Dade Lab Northwestern (Blood Spot Analysis)

Salivary Biomeasures Sex hormone assays Estradiol Progesterone DHEA Testosterone Cotinine

Salivary Biomeasures Sex hormone assays Estradiol Progesterone DHEA Testosterone Cotinine

Frequency Salivary Sex Hormones (preliminary analysis) log(estradiol) Units: pg/ml log(progesterone) log(testosterone)

Frequency Salivary Sex Hormones (preliminary analysis) log(estradiol) Units: pg/ml log(progesterone) log(testosterone)

Salivary Cotinine Nicotine metabolite Objective marker of tobacco exposure, including second-hand Non-invasive collection method

Salivary Cotinine Nicotine metabolite Objective marker of tobacco exposure, including second-hand Non-invasive collection method (vs. serum cotinine)

Distribution of Salivary Cotinine Classification of Smoking Status by Cotinine Level in Females Cut-points

Distribution of Salivary Cotinine Classification of Smoking Status by Cotinine Level in Females Cut-points based on distribution among smokers. 2 Occasional Fraction . 15 Nonsmoker Passive Regular . 1 10 ng 15 ng 34 ng 10% M 103 ng 30% M 344 ng M . 05 0 -5 0 log(Cotinine) M = mean cotinine among female who report current smoking Bar on left corresponds to cotinine below level of detection 5 10

Dried Blood Spots C-Reactive Protein (CRP) Epstein-Barr Virus (EBV) Antibody Titers Thanks, Thom and

Dried Blood Spots C-Reactive Protein (CRP) Epstein-Barr Virus (EBV) Antibody Titers Thanks, Thom and Mc. Dade Lab Staff!

 Chicago Core on Biomarkers in Population-Based Aging Research An information and educational resource

Chicago Core on Biomarkers in Population-Based Aging Research An information and educational resource on biomarkers Biomeasures conferences at a regular basis Monthly electronic CCBAR Newsletter CCBAR website

More detailed information can be found at the site: http: //biomarkers. uchicago. edu/

More detailed information can be found at the site: http: //biomarkers. uchicago. edu/

Publication on sexuality Lindau, Gavrilova, British Medical Journal, 2010, 340, c 810

Publication on sexuality Lindau, Gavrilova, British Medical Journal, 2010, 340, c 810

Prevalence of Sexual Activity by Age and Gender (MIDUS 1) Men and women having

Prevalence of Sexual Activity by Age and Gender (MIDUS 1) Men and women having intimate partner

Life expectancy and sexually active life expectancy (SALE) Based on the MIDUS study

Life expectancy and sexually active life expectancy (SALE) Based on the MIDUS study

Sexually active life expectancy and self-rated health Based on the MIDUS study

Sexually active life expectancy and self-rated health Based on the MIDUS study