The Utah Study of Fertility, Longevity

1
The Utah Study of Fertility, Longevity Aging
and the Utah Population DatabaseKen R.
Smith(ken.smith_at_fcs.utah.edu)
Supported by NIA grants AG00767, AG14495, and
AG13478.
2
How Long Will You Live? in Good Health?Is
there a way to know?Is there a way to change it?
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Longevity Game Northwestern Mutual
Gender Age Family History BMI Blood
Pressure Stress Exercise Diet Seat Belt Driving
Style Alcohol Smoking Illicit Drug Use
http//www.nmfn.com/tn/learnctr--lifeevents--longe
vity_end
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Longevity Calculator - New England Centenarian
Study
Tobacco Diet Alcohol Air pollution Coffee/tea Aspi
rin Flossing History of CVD Tanning Social
Support Stress Family Hx of Longevity Exercise V
itamins Gender Age
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Traditional Method

• Life Tables
• Gender
• Age
• Place
• Year

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Years of Life RemainingUtah Males 2001
Years
Age
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Years of Life Remaining Utah Females 2001
Years
Age
8
Difference in Remaining Years of Life Female
Advantage over Males 2001, Utah
Years
Age
9
Years of Life RemainingUS and Utah Males 2001
Years
Age
10
Years of Life Remaining US and Utah Females 2001
Years
Age
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Objectives

• Why study exceptional familial longevity?
• NIA support
• Utah Longevity Study
• Measures
• Importance of the Utah Population Database
• Research Highlights
• Final thoughts

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What is Possiblefor Achieving a Healthy Life
Span?Look to the record-holders.
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LONGEST LIVED HUMAN WITH AUTHENTICATED
RECORDS Jeanne Louise Calment, Paris Died 1997
at age 122
LONGEST LIVING HUMAN WITH AUTHENTICATED
RECORDS 116-year-old Elizabeth Lizzie
Bolden. Memphis, Tenn.
14
What allowed these individuals to live so
long? Aged slowly thus avoided serious fatal
diseases
Science, 2-28-2003
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National Institute on AgingInitiative to Study
Exceptional Longevity

• Advisory Panel on Exceptional Longevity (APEL)
• Find genetic environmental factors that
  contribute to exceptional longevity (EL) and
  reduce risks across major age-related diseases
• Find homogenous subgroups that comprise the
  long-lived population (e.g., familial patterns of
  longevity)

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Utah Study of Fertility, Longevity, and Aging
(USFLAG) (National Institute on Aging)
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Resistance to chronic diseases and environmental
stressors
Rank UPDB pedigrees in terms of family pattern
of longevity past 65
Psycho-social, biological, genetic markers
associated with exceptional survival among
relatives around age 90
3
1
Do offspring of long-lived parents age more
slowly than controls
4
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Utah Study of Fertility, Longevity, and Aging
(USFLAG) (National Institute on Aging)
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Selected Measures

• Demographic Factors
• Risk/Lifestyle Factors
• Medical History
• Reproductive History
• Cognitive Functioning
• Instrumental/Activities of
• Daily Living
• Depression
• Stress Resiliency
• Blood Measures
• Serum Cholesterol
• Glycosylated Hemoglobin
• C-reactive protein
• DHEA/DHEAS
• Albumin
• Uric Acid
• Creatinine
• WBC

• DNA
• Immortal cell lines
• Genome Wide Scan
• Telomere Length
• Mutations in mtDNA
• APOE
• Clinical Measures
• Grip Strength
• Blood and Pulse Pressure
• Heart Rate
• Lung Function
• Body Mass Index
• Body Temperature
• Morbidity
• (CMS Medicare claims)
• Mortality (cause of death)

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UPDB and FLAG
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Aging Research Advances ThroughNovel Linkages to
UPDB
Centers for Medicare Medicaid
Services 1991-2002 400,000 Utahns age 65
Cache County Study on Memory, Health Aging
Ron Munger, JoAnn Tschanz, Maria
Norton USU Kathleen Welsh-Bohmer Duke
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Research HighlightsA Sampler
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Does Exceptional Survival Run in Families?Does
a Family History of Extreme Longevity Protect
Against Major Age-Related Diseases?
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Recurrence Risks of Extreme Longevity (Lived
Past 99th Percentile of Excess Longevity) in
Various Classes of Relatives (UPDB)
Extreme Longevity Runs in Families
Parent, Sibling
Gparent, Gchild, Aunt, Uncle, Niece, Nephews
1st Cousin Once Removed
1st Cousins
Kerber, OBrien, Smith and Cawthon, 2001, J of
Gerontology
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Relative Risk of MortalityTop 25 vs. Bottom
25 of Familial Longevity (UPDB)
Extreme Familial Longevity Reduces Risk at Most
Ages
Worse
Cancer
Cerebrovascular
Diabetes
Better
Heart
Age
OBrien, Kerber, Smith, Mineau, submitted, 2006
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Are there factors that we can observe in midlife
(age 50) in UPDB that predict lower mortality in
later years?The case of late female
fertility(or if you can measure it, late
natural menopause)
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Relative Risk of Living to 100 Vs. Dying Before
Age 85 Among Women By Age at Last Birth (Among
Women Surviving to Age 60 Born Late Fertile Women More Likely to be Centenarians
Relative Risk
Age at Last Birth
Controls for Mormon/Non-Mormon status, birth
year, parity, age at first birth Reference group
Women whose Age at Last Birth is 37 to 42 years
of age
Smith, Mineau, Bean, Social Biology, 2002
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Reduced Risk of Mortality from Leading Causes of
Death Among Females (60) with Very Late
Fertility (age 45) vs. Controls (UPDB)
Late Fertile Women Less Prone to Major Causes
of Death
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Relative Risk for Late vs. Average Age (Last Birth Between Female Members 55 or Older of
Lineages Selected for Exceptional Longevity
Control Lineages
3 Probands per Lineage
8.9
Women from LL Pedigrees are More Prone to be
Late Fertile
Adjusts for birth date, age at first birth, LDS
affiliation Bars represent 95 CI N440 All
subjects lived to age 55.
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Relative Risks for Mortality for Brothers Living
to Age 50 byMaximum Age at Last Birth of Sisters
Who Survived to Age 50(Reference Group Sisters
with Max Age at Last Birth below 50th Pct)
Brothers of Late Fertile Women Live Longer
Relative Risk
UT Utah Population Database (n29,445 males)
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Telomeres and Longevity
As cells divide, telomeres shorten with
increasing age due to a decline in the enzyme
telomerase Short telomeres trigger cell death
cellular senescence (possibly as a protection
against cancer) and are associated with aging and
death UGRP (Utah Genetic Reference Project)
families linked to UPDB
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Those with longer telomeres in blood DNA live
longer among the elderly, controlling for age
Years Since Blood Draw
Cawthon, Smith, OBrien, Sivatchenko, Kerber,
Lancet, 2003
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Women
Men
Years Since Blood Draw
Cawthon, Smith, OBrien, Sivatchenko, Kerber,
Lancet, 2003
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Age 60-74
Age 75
Cawthon, Smith, OBrien, Sivatchenko, Kerber,
Lancet, 2003
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How are the Middle-agedand Young Elderly of the
Exceptional Survivors Doing?
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Relative Risk of Disease Offspring of Proband
vs. Controls
Odds Ratio
69 offspring 1569 NHANES controls
Controls for age, gender, education, marital
status
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Cache County Study on Memory, Health Aging
Cache Co.
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Genetic Markers Associated with Aging and
Survival that are Linked to the UPDB

• e4 allele
• Risk of cardiovascular disease
• Alzheimers Disease
• e2 allele
• More frequent in centenarians
• Association between APOE genotype and
  cause-specific mortality
• Modified by indicators of rates of aging
• Possible because of record linkage to UPDB

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Mortality Rate Ratios for APOE Genotypes (e33 is
reference)
All-Cause
Cardiovascular (633)
e22 e23 e24 e34 e44
e22 e23 e24 e34 e44
Respiratory (176)
Cancer (249)
e22 e23 e24 e34 e44
e22 e23 e24 e34 e44
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Mortality Rate Ratios for APOE Genotypes (e33 is
reference)
Nervous (AD, Parkinson) (84)
Endocrine (Diabetes) (62)
e22 e23 e24 e34 e44
e22 e23 e24 e34 e44
Digestive System (57)
Mental Disorders (42)
e22 e23 e24 e34 e44
e22 e23 e24 e34 e44
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Effects of APOE Genotype on All-Cause Mortality
by Familial Longevity
Pedigrees of Average Longevity
Long-Lived Pedigrees
e22 e23 e24 e34 e44
e22 e23 e24 e34 e44
Interaction between FEL and e34 e44, p 41
Medicare Claims

• 65-69 year olds (n28,000)
• 1998-2002
• Medicare Claims in Utah
• Compare top 10 for familial longevity to the
  rest
• 11.2 fewer claims
• 8 fewer claims for heart disease, stroke, cancer,
  and diabetes combined
• 40 more likely to have made no claims for these
  four leading conditions

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Final Thoughts

• Study not designed with personalized medicine in
  mind
• Potential for identifying measures that capture
  rates of aging that predict risks from multiple
  diseases
• Long-lived as a source of information about low
  disease risk
• Value of deep family history of longevity to
  supplement family medical history
• Value of UPDB

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USFLAG Team

• Project Coordinators
• Heather Anderson
• Jahn Barlow
• Database Linkage/Programmers
• Richard Pimentel
• Heidi Hanson
• Study Coordinator
• Michelle Robinson
• Phlebotomists
• Kellie Littlefield
• Jayci Bash
• Layah Steinberg-Moss
• Informatics
• Dinny Berry
• Research Administration
• Camille Phillips
• CRC
• Sayed Ahmed

• Genetics and Genetic Epidemiology
• Richard Cawthon
• Richard Kerber
• Elizabeth OBrien
• Demography
• Geri Mineau
• Ken Smith
• Biostatistics
• Ken Boucher
• Gilda Garibotti
• Psychology
• Cindy Berg
• Project Manager
• Diana Lane Reed
• Database Manager
• Alison Fraser