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Biosocial Surveys 5 An Overview of Biomarker Research from Community and Population-Based Studies on Aging Jennifer R. Harris, Tara L. Gruenewald, and Teresa Seeman The goal of this chapter is to provide an overview of findings from community-based studies that have profitably incorporated biomarkers along with more traditional interview data to address important questions regarding factors that affect health risks at older ages. The focus on older age stems from a series of activities (National Research Council, 1997, 2001a) funded by the National Institute on Aging that promoted a new era of population aging research predicated on the importance of integrating biomarkers into survey research. Prior efforts to include biomarkers in community and population studies have yielded a wealth of knowledge regarding the role of biological systems and processes in cognitive and physical functioning, mental and physical disease development, and mortality outcomes. The biomarker initiative under the National Institute on Aging was particularly concerned with identifying biomarkers of physiological age (Butler et al., 2004). In contrast, biomarker research in the social and behavioral sciences has emphasized elucidating the interplay of biological systems with sociodemographic, behavioral, psychosocial, pharmacological, and genetic factors in health outcomes (Institute of Medicine, 2006; National Research Council, 2001b). This chapter highlights research contributions from a selected group of community and population studies that include various biomarker measurements in study assessments. It is not intended to review completely, catalogue, or present in-depth results, but rather to provide an overview of the range of biomarker research conducted in these studies
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Biosocial Surveys and highlight some key findings and research directions stemming from this integrative research. For the purposes of this review, we have limited our consideration of biomarkers mainly to DNA and physiological biomarkers collected from blood (e.g., glycosylated hemoglobin, cholesterol), saliva (cortisol) or urine (cortisol and catecholamines); not included are more functional parameters that are also considered to be biomarkers, such as hand grip strength, measures of vision or hearing, and assessments of cognitive or physical functioning. This chapter highlights the types of questions that can be addressed when social and behavioral studies are supplemented with biomarker data, and therefore we also exclude descriptions of biomarker research derived from postmortem studies of brain tissue. It is important to note, however, the value of postmortem biomarkers for elucidating biological pathways and mechanisms, including those linking social and behavioral measures with disease outcomes. This is illustrated by research from, for example, the Nun’s Study (Snowdon, Kemper, Mortimer, Wekstein, and Markesbery, 1996), the Religious Order Study (Wilson, Bienias, and Evans, 2004), and the Memory and Aging Project (Bennett et al., 2005). Our decision to focus on physiological parameters and genes was driven in large part by a central goal of the current volume, namely, to help inform social scientists about the potential value of incorporating biomarkers into their projects through exposition of prior research in which analyses of such biomarkers or candidate genes have provided insights into processes and mechanisms affecting healthy aging. In that context, the focus taken herein seems warranted, as such biomarkers have generally not been included in social surveys (whereas assessments of functioning have), so that evidence based on physiological biomarkers is much less well known to the social science community. Much of the information presented in this chapter derives from the studies summarized in Table 5-1. These were selected to represent a sampling of ongoing community or population-based studies on aging that have collected DNA or other biological or physiological biomarkers and that are not reviewed elsewhere in this volume. We have organized the wide range of findings generated from the studies reviewed according to a number of critical thematic areas that emerged during our review. These include biomarkers and aging, genetic and environmental influences on risk factors for cardiovascular disease (CVD), social and psychological factors, behavior genetics, biomarkers of cognitive aging, biomarkers of physical function and aging, indices of cumulative biological risk, and the relationship between biomarkers and genetic pleiotropy.
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Biosocial Surveys TABLE 5-1 Description of Selected Community and Population-Based Studies Conducting Biomarker Research Study Name (year started) Sample Design and General Purpose Specimen Type and How Collected Biomarkers Ascertained MacArthur Study of Successful Aging (Berkman et al., 1993) 1,189 men and women, ages 70-79 at baseline. Selection criteria employed to enroll a cohort representing the top third of this age group in terms of physical and cognitive functioning. Longitudinal cohort study to elucidate the factors (sociodemographic, behavior, psychosocial, biological) associated with more successful aging (i.e., maintenance of higher cognitive and physical functioning). Data collected at baseline and 3- and 7-year follow-up exams via in-person and phone assessments. Biomarker data were collected at baseline and three-year follow-up. Home specimen collections—phlebotomist collected blood and 12-hour (overnight) urine sample. Total/HDL cholesterol, glycosylated hemoglobin, albumin, IL-6, C-reactive protein, fibrinogen, complete blood count (CBC), blood chemistry tests (SMAC-24), DHEAS, antioxidants, homocysteine, vitamin B and folate, urinary norepinephrine, epinephrine, cortisol and dopamine, resting and postural blood pressure, waist/hip ratio, peak flow rate (Mini-Wright meter), ApoE genotyping.
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Biosocial Surveys Normative Aging Study (1963) (Bell, Rose, and Damon, 1972) Sample of 2,280 initially healthy men, ages 21-80 at enrollment. Study conducted through Department of Veteran Affairs in Boston (96% of participants are veterans). Longitudinal cohort study to examine biomedical and psychosocial characteristics of aging and development of disease. Clinical examinations every 3-5 years with collection of blood and 24-hour urines. CBC, creatinine, albumin, fasting glucose, insulin, 2-hour glucose tolerance test, lipid profiles (LDL, HDL, triglycerides), serum glutamic-oxaloacetic transaminase (SGOT), calcium, blood urea nitrogen (BUN), blood lead levels, urinary catecholamines, blood pressure, heart rate, EKG reading, pulmonary function, anthropometry, homocysteine, vitamin B and folate. Cardiovascular Health Study (CHS, 1989; Fried et al., 2001) Representative sample of 5,000 adults, ages 65+, sampled from Medicare listings for 4 communities (N = 1,250 each; Forsyth County, ND; Sacramento, CA; Washington County, MD; Pittsburgh, PA). Longitudinal cohort study of risk factors for coronary heart disease and stroke in older adults. Assessments at baseline and one-, two-, and three-year follow-ups. Clinic-based collection of blood for wide array of biomarkers, EBCT, ultrasound, and MRI. Resting and postural blood pressure, ankle-arm index, fasting glucose, insulin, 2-hour oral glucose tolerance test, lipid profile (LDL, HDL, triclycerides), albumin, CBC, left ventricular ejection fraction, markers of inflammation and coagulation, ApoE, body fat (bioelectric impedance), height, weight, waist / hip ratio, 12-lead ECGs (24-hour ambulatory ECGs on subset of 600), forced vital capacity, forced expiratory volume, grip strength, ultrasonography of carotid arteries, m-mode, Doppler echocardiography (for left ventricular mass, ejection fraction, stroke volume and end-systolic stress, regional and segmental wall motion, % fractional shortening).
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Biosocial Surveys Study Name (year started) Sample Design and General Purpose Specimen Type and How Collected Biomarkers Ascertained Later Life Resilience Study (Ryff, Singer, and Dienberg Love, 2004) 135 older women, ages 61-91, recruited from a prior longitudinal investigation of move to an assisted living facility from personal residence in Madison or Milwaukee, WI (approximately half of the larger sample participated in biomarker substudy). All participants took part in a prior 4-wave longitudinal study of community relocation. Participants in the biomarker study participated in a fifth wave of data collection, including assessment of biomarkers. Biological data collected through overnight visit to General Clinical Research Center, including blood, 12 hour (overnight) urines and saliva. Blood pressure, heart rate, urinary norepinephrine, epinephrine, and cortisol, glycosylated hemoglobin, total/HDL cholesterol, DHEAS, salivary cortisol, waist/hip ratio, BMI.
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Biosocial Surveys Women’s Health and Aging Studies (WHAS) I and II. WHAS I (Simonsick et al., 1997); WHAS II (Fried et al., 2000) WHAS I: cohort of women (n = 1,002) ages 65+ sampled to represent the one-third most disabled Medicare enrollees from the Baltimore, MD, area (basedon 12 zip code areas in eastern Baltimore and parts of Baltimore County). WHAS II = cohort of women, age s65+, sampled to represent the remaining two-thirds least disabled women in that age range. Longitudinal cohorts, followed to elucidate factors associated with trajectories of functional decline or lack thereof. Home-based specimen collection. Fasted glucose, markers of inflammation, growth factors (IGF-1), genetic information (e.g., IL-6 haplotype), antioxidants, ambulatory ECG, resting 12-lead ECG, BP (resting, ankle-arm), height, weight, grip strength, spirometry.
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Biosocial Surveys Study Name (year started) Sample Design and General Purpose Specimen Type and How Collected Biomarkers Ascertained Swedish Adoption Twin Study of Aging (SATSA) 1984-present (Pedersen et al., 1991) Population-based subset of 958 twin pairs from the Swedish Twin Registry. The sample includes twins reared apart and a matched sample of twins reared together. Longitudinal, currently in 7th wave, questionnaires on all and in-person testing on a subsample. Combines twin and adoption design to study genetic and environmental influences affecting variation in normal aging. Blood samples were drawn during in-person testing (12-hour fasting) drawn on subsample of twins at central testing sites or in home. Serum lipoproteins: total cholesterol, total triglycerides, HDL cholesterol, LDL cholesterol, apolipoproteins A-1 and B, MaoB H pylori, metals, creatinine, gamma-glutamyltransferase (gamma-GT), potassium, sodium, urea and uric acid, creatinine, electrolytes, telomere length. Whole blood for DNA bank. Plasma at in-person testing occasion 3 for evaluation of a variety of coagulation factors. Origins of Variance in the Oldest-Old: Octogenarian Twins (OCTO-Twin) 1991-2002 (McClearn et al., 1997) Swedish twins ages 80 and older. Original sample identified 351 pairs for the first wave of in-person testing. 5-wave longitudinal study, measurements conducted at 2-year intervals, to explore the origins of individuality in the oldest-old. Blood samples for clinical assessment and DNA extraction for molecular genetic analyses. Collected in home. Albumin, calcium, total cholesterol, HDL cholester ol, creatinine, gamma-glutamyltransferase (gamma-GT), potassium, sodium, urea and uric acid, creatinine, electrolytes, cobalamin, free thyroxin, folic acid, prostate-specific antigen (PSA), and thyroid-stimulating hormone (TSH), homocysteine.
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Biosocial Surveys Screening Across the Lifespan Study (SALT) 1998-2002 Pilot included a random sample of 2,000 twins ages 5-85. Followed by full-scale screening ofall twins born 1958 or earlier, population-based. Computer-assisted telephone interview. Multidimensional health, demography, health behaviors, disease identification diagnostic items. Blood samples collected at local clinics. Biological samples only collected in pilot project include clinical biochemistries, zygosity plus blood stored for future analyses. Through recent efforts, DNA, serum, TG, HDL, LDL, apoliproteins, glucose, HbA1C, and DNA collected on all pairs alive (goal 16,000 individuals, current at 12,000). Men and Women’s Aging (GENDER) 486 unlike-sexed twins born 1906-1925, population-based. 3 wave longitudinal study with measures 1995-1997, 1999-2001, and 2001-2005. Collected during in-person testing in home. Whole blood, serum, DNA, lipids, clinical panel. Study of Dementia in Swedish Twins (HARMONY) (Gatz et al., 1997) Twins born in or before 1935, population-based. Cross-sectional, 1998-2001. Genetic and environmental factors for Alzheimer disease and other dementias. Diagnostic assessments for dementia, cognitive testing, and risk factor information. Three-year follow up for small subsample. Collected in home. Whole blood, serum, plasma, DNA, lipids, clinical panel. NOTES: HDL = high-density lipoprotein; IL-6 = interleukin 6; DHEAS = dehydroepiandrosterone sulfate; ApoE = apolipoprotein E; LDL = low-density lipoprotein; ECG or EKG = electrocardiogram; EBCT = electron beam computed tomography; MRI = magnetic resonance imaging.
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Biosocial Surveys BIOMARKERS IN COMMUNITY- OR POPULATION-BASED STUDIES Scientists have long been interested in obtaining measures of biomarkers to understand the role of biological systems in functioning and disease processes. Much of this work has been conducted via well-controlled experiments in the laboratory with nonhuman animals or in small-scale human studies focused on specific physiological processes in the lab (e.g., physiological responses to a specific challenge or interaction of specific physiological systems). More recently, the value of collecting biomarker measurements in large-scale community and population studies has been recognized (National Research Council, 2001a) and greater emphasis is being placed, by researchers and by funding agencies, on banking biological samples. The impetus for the collection of biomarkers is to gain a better understanding of the role of specific biological systems in health conditions, including an understanding of the role of biological systems in association with other sociodemographic, behavioral, pharmacological, psychosocial, and genetic contributions to health outcomes. There is a growing literature in the behavioral sciences literature linking social and behavioral factors (ranging from sociocultural and neighborhood influences to interpersonal relations) to biomarkers and health (Berkman and Kawachi, 2000; Cacioppo, Hughes, Waite, Hawkley, and Thisted, 2006; Hawkley, Masi, Berry, and Cacioppo, 2006; House, Landis, and Umberson, 1988; Kiecolt-Glaser et al., 2005; Ryff and Singer, 2001; Uchino, Cacioppo, and Kiecolt-Glaser, 1996; Wen, Hawkley, and Cacioppo, 2006). These findings, in conjunction with methodological advances, are fostering integrative lines of research to study health and pathways to disease. For example, the recent Institute of Medicine report Genes, Behavior, and the Social Environment (2006) focuses on social environments in the study of gene by environment interactions and health. A wide variety of biomarkers have been assessed in community- or population-based studies. Biomarkers of cardiovascular, metabolic, endocrine, and immune systems or processes are most commonly assessed. However, other types of biomarker measurements have also been obtained, including exposure indices (e.g., bone and blood lead levels or pesticide blood levels to assess environmental exposure), measurements of vitamin or antioxidant levels, anthropometric measures (e.g., bone length, height, weight), bone density scans, measurements of brain activity (e.g., functional magnetic resonance imaging, fMRI, or electroencephalogram, EEG), as well as markers of the functional status of a bodily system (e.g., forced expiratory volume to assess lung function). Biomarker values are typically assessed for biological systems at a
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Biosocial Surveys resting state (e.g., resting blood pressure, blood levels of glucose), but values are also sometimes assessed under conditions of challenge to a system (e.g., blood pressure levels after standing, levels of glucose after a glucose challenge test). Blood, urine, and saliva samples are typical sources for biomarker assessments, although for some of the biomarkers described above, mechanical or electrical devices (e.g., MRI machine) are also used to obtain measurements. The measurement of biomarkers in community- or population-based studies presents a methodological challenge for researchers, as they must determine how and where to obtain biomarker measurements on a large number of people. Most studies have used home-based or clinic-based protocols for the collection of biological specimens, with some investigations utilizing both approaches. The primary advantage of clinic-based specimen collection is the ability to implement protocols requiring greater temperature control (e.g., samples must be kept cold or iced) as well as meeting more restricted processing requirements (e.g., within minutes or several hours at most). However, sample representativeness can sometimes suffer as certain subgroups are less able or willing to come to a clinic for reasons related to health, transportation, or unfamiliarity with the location. Examples of studies that have used clinic-based protocols successfully include the Women’s Health and Aging Studies I and II, the Cardiovascular Health Study, and the Health, Aging and Body Composition Study. Studies using home-based protocols include the MacArthur Study of Successful Aging (Berkman et al., 1993), the Later Life Resilience Study (Ryff, Singer, and Dienberg Love, 2004), and the Swedish Twin Studies (Lichtenstein et al., 2002). BIOMARKERS AND AGING Most established biomarkers indices reflect age norms in disease-free samples from which individuals with known risks and diseases are excluded from study. This poses a challenge for aging studies because these criteria make it difficult to define “normal” values among groups of elderly individuals for whom morbidity is common. Furthermore, this approach could mask age changes in many biomarkers and values of routine biochemical blood tests. A study of clinical biochemical values in a population-based sample of twins ages 82 and older from the Swedish study of Origins of Variance in the Oldest-Old: Octogenarian Twins (OCTO-Twin) found few participants without clinical diagnoses; therefore, subsequent survival for six years was used as a marker of overall health in late life. Results revealed an association between mortality and higher serum levels of urea, urate, gamma-glutamyltransferase (gamma-GT), free thyroxin, and plasma homocysteine. In women, increased mortality was associated with
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Biosocial Surveys low serum values for albumin and total cholesterol. The authors propose that these results could provide guidelines for clinical practice and general health examinations (Nilsson et al., 2003a, 2003b). Further study of the association of biochemical values with morbidity, drug therapy, and anthropometry was examined. In addition to expected findings showing that biochemical values deviate under disease states common among the elderly, a number of biological risk factors exhibit patterns of increasing risk with age, including blood pressure, glucose, and markers of inflammation and homocysteine, each of which is associated with risks for one or more common diseases of aging, such as cardiovascular disease, osteoporosis, hip fracture, depression, and dementia (Nilsson et al., 2003a, 2003b). These findings indicate that morbidity and health-related factors common in aging populations substantially influence routine biochemical values. Analyses of community-based studies, such as the MacArthur Study of Successful Aging, also point to potentially important age-related reductions in risks associated with some biological factors (e.g., a reduction in the apparent risks associated with elevated total cholesterol—Karlamangla, Singer, Reuben, and Seeman, 2004), although other major risk factors continue to exhibit strong effects with respect to risks for major outcomes, such as physical function (Reuben et al., 2002), cognitive function (Weaver et al., 2002), and longevity (Hu et al., 2005). Analyses of biomarker data from the Cardiovascular Health Study (CHS) have confirmed that lipid profiles (specifically total and low-density lipoprotein [LDL], cholesterol) are not significant predictors of myocardial infarction (MI), stroke, or mortality among older adults (Psaty et al., 2004). CHS data also point to the continued importance of such biomarkers as high blood pressure, fasted glucose, low albumin, elevated creatinine, and low forced vital capacity as significant, independent risk factors for mortality, along with additional measures of subclinical disease, including aortic stenosis, abnormal left ventricular ejection fraction, major electrocardiographic abnormalities, and stenosis of the internal carotid artery (Fried et al., 1998) and markers of inflammation (Jenny et al., 2006). Analyses from the Health, Aging and Body Composition Study (Health ABC) (another cohort study of adults ages 70 and older) suggest that the presence of metabolic syndrome (based on a complex of risk factors, including cholesterol, blood pressure, and glucose) in older adults does continue to predict subsequent coronary events, heart failure, myocardial infarctions, and cardiovascular-related mortality (Butler et al., 2006). Like the MacArthur and other studies of aging, Health ABC data indicate that inflammatory markers such as interleukin-6 (IL-6) and tumor necrosis factor (TNF)-α are associated cross-sectionally with the presence of subclinical or clinical cardiovascular disease (Cesari et al., 2003a), and the
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Biosocial Surveys the logistical constraints imposed by time and handling requirements for obtaining many biological measurements. Examples include (1) the need for phlebotomy to collect venous blood (e.g., when dried blood spots cannot be used), (2) the need for fasted blood samples, which can constrain collection to morning hours, (3) the need for sample collection at specific times due to diurnal rhythms of parameters such as cortisol, necessitating collection at the same time of day for everyone and collection of multiple samples over time, (4) the need for blood or urine samples to be processed within a limited time frame (usually within a couple of hours). The selection of biomarkers for inclusion in a given study will thus necessarily be heavily influenced by what is possible in the context of specific study designs and logistical parameters. For example, the national scope of the HRS study precludes collection of venous blood so dried blood spots are being collected. Selection of biomarkers is thus restricted to those for which assays are available that can use dried blood spots rather than venous blood. By contrast, the MIDUS study is collecting a wide array of biomarkers because participants are being brought to regional clinical research centers (each in a hospital setting) where venous blood can be drawn first thing in the morning (allowing for fasted samples) and where these samples can be processed immediately. Thus, the final set of biomarkers included in any studies will reflect both the underlying scientific questions investigators seek to address and what is possible given their logistical and financial constraints. Despite the demands and challenges inherent in incorporating biomarkers into social science surveys, continued research development and efforts in the directions presented herein are critical to understanding better the factors that affect patterns of biological aging and trajectories of health at older ages. REFERENCES Antonelli-Incalzi, R., Pedone, C., McDermott, M.M., Bandinelli, S., Miniati, B., Lova, R.M., Lauretani, F., and Ferrucci, L. (2006). Association between nutrient intake and peripheral artery disease: Results from the InCHIANTI study. Atherosclerosis, 186(1), 200-206. Bandeen-Roche, K., Xue, Q.L., Ferrucci, L., Walston, J., Guralnik, J.M., Chaves, P., Zeger, S.L., and Fried, L.P. (2006). Phenotype of frailty: Characterization in the Women’s Health and Aging Studies. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 61(3), 262-266. Bassuk, S.S., Glass, T.A., and Berkman, L.F. (1999). Social disengagement and incident cognitive decline in community-dwelling elderly persons. Annals of Internal Medicine, 131, 165-173. Bell, B., Rose, C.L., and Damon, A. (1972). The normative aging study: An interdisciplinary and longitudinal study of health and aging. Aging and Human Development, 3(1), 5-17.
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