Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 96
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
OCR for page 97
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.
OCR for page 98
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.
OCR for page 99
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).
OCR for page 100
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.
OCR for page 101
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.
OCR for page 102
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.
OCR for page 103
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.
OCR for page 104
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
OCR for page 105
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
OCR for page 106
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
OCR for page 125
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.
OCR for page 126
Biosocial Surveys
Bennett, D.A., Schneider, J.A., Buchman, A.S., et al. (2005). The rush memory and aging project: Study design and baseline characteristics of the study cohort. Neuroepidemiology, 25, 163-175.
Berkman, L.F., and Kawachi, I. (Eds.) (2000). Social epidemiology. New York: Oxford Press.
Berkman, L.F., Seeman, T.E., Albert, M.A., et al. (1993). High, usual and impaired functioning in community-dwelling older men and women: Findings from the MacArthur Foundation Research Network on Successful Aging. Journal of Clinical Epidemiology, 46, 1129-1140.
Blaum, C.S., Volpato, S., Cappola, A.R., Chaves, P., Xue, Q.L., Guralnik, J.M., and Fried, L.P. (2005a). Diabetes, hyperglycaemia, and mortality in disabled older women: The Women’s Health and Ageing Study I. Diabetic Medicine, 22(5), 543-550.
Blaum, C.S., Xue, Q.L., Michelon, E., Semba, R.D., and Fried, L.P. (2005b). The association between obesity and the frailty syndrome in older women: The Women’s Health and Aging Studies. Journal of the American Geriatrics Society, 53(6), 1069-1070.
Ble, A., Cherubini, A., Volpato, S., Bartali, B., Walston, J.D., Windham, B.G., Bandinelli, S., Lauretani, F., Guralnik, J.M., and Ferrucci, L. (2006). Lower plasma vitamin E levels are associated with the frailty syndrome: The InCHIANTI study. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 61(3), 278-283.
Boomsma, D.I., Vink, J.M., van Beijsterveldt, T.C., de Geus, E.J., Beem, A.L., Mulder, E.J., Derks, E.M., Riese, H., Willemsen, G.A., Bartels, M., van den, Berg, M., Kupper, N.H., Polderman, T.J., Posthuma, D., Rietveld, M.J., Stubbe, J.H., Knol, L.I., Stroet, T., and van Baal, G.C. (2002). Netherlands Twin Register: A focus on longitudinal research. Twin Research and Human Genetics, 5(5), 401-406.
Boomsma, D.I., Willemsen, G., and Dolan, C.V., Hawkley, L.C., and Cacioppo, J.T. (2005). Genetic and environmental contributions to loneliness in adults: The Netherlands twin register study. Behavioral Genetics, 35(6), 745-752.
Boomsma, D.I., Cacioppo, J.T., Slagboom, P.E., and Posthuma, D. (2006). Genetic linkage and association analysis for loneliness in Dutch twin and sibling pairs points to a region on chromosome 12q23-24. Behavioral Genetics, 36(1), 137-146. Epub 2005 Dec 24.
Bretsky, P., Guralnik, J., Launer, L., Albert, M.A., and Seeman, T.E. (2003). The role of APOE-ε4 in longitudinal cognitive decline in an elderly cohort: MacArthur studies of successful aging. Neurology, 60, 1077-1081.
Burg, M.M., and Seeman, T.E. (1994). Families and health: The negative side of social ties. Annals of Behavioral Medicine, 16, 109-115.
Butler, J., Rodondi, N., Zhu, Y., Figaro, K., Fazio, S., Vaughan, D.E., Satterfield, S., Newman, A.B., Goodpaster, B., Bauer, D.C., Holvoet, P., Harris, T.B., de Rekeneire, N., Rubin, S., Ding, J., Kritchevsky, S.B, and Health ABC Study. (2006). Metabolic syndrome and the risk of cardiovascular disease in older adults. Journal of the American College of Cardiology, 47(8), 1595-1602.
Butler, R.N., Sprott, R., Warner, H., Bland, J., Feuers, R., Forster, M., Fillit, H., Harman, M., Hewitt, M., Hyman, M., Johnson, K., Kligman, E., McClearn, G., Nelson, J., Richardson, A., Sonntag, W., Weindruch, R., and Wolf, N. (2004). Biomarkers of aging: From primitive organisms to humans. Journal of Gerontology: Biological Sciences, 59A, 560-567.
Cacioppo, J.T., Hughes, M.E., Waite, L.J., Hawkley, L.C., and Thisted, R.A. (2006). Loneliness as a specific risk factor for depressive symptoms: Cross-sectional and longitudinal analyses. Psychology and Aging, 21(1), 140-151.
Cappola, A.R., Bandeen-Roche, K., Wand, G.S., Volpato, S., and Fried, L.P. (2001). Association of IGF-I levels with muscle strength and mobility in older women. Journal of Clinical Endocrinology & Metabolism, 86(9), 4139-4146.
Caspi, A., Sugden, K., Moffitt, T.E., et al. (2003). Influence of life stress on depression: Moderation by a polymorphism in the 5-HTT gene. Science, 301, 386-389.
OCR for page 127
Biosocial Surveys
Cesari, M., Penninx, B.W., Newman, A.B., Kritchevsky, S.B., Nicklas, B.J., Sutton-Tyrrell, K., Tracy, R.P., Rubin, S.M., Harris, T.B., and Pahor, M. (2003a). Inflammatory markers and cardiovascular disease (The Health, Aging, and Body Composition [Health ABC] Study). The American Journal of Cardiology, 92(5), 522-528.
Cesari, M., Penninx, B.W., Newman, A.B., Kritchevsky, S.B., Nicklas, B.J., Sutton-Tyrrell, K., Rubin, S.M., Ding, J., Simonsick, E.M., Harris, T.B., and Pahor, M. (2003b). Inflammatory markers and onset of cardiovascular events: Results from the Health ABC study. Circulation, 108(19), 2317-2322.
Cesari, M., Penninx, B.W., Pahor, M., Lauretani, F., Corsi, A.M., Rhys Williams, G., Guralnik, J.M., and Ferrucci, L. (2004a). Inflammatory markers and physical performance in older persons: The InCHIANTI study. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 59(3), 242-248.
Cesari, M., Penninx, B.W., Lauretani, F., Russo, C.R., Carter, C., Bandinelli, S., Atkinson, H., Onder, G., Pahor, M., and Ferrucci, L. (2004b). Hemoglobin levels and skeletal muscle: Results from the InCHIANTI study. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 59(3), 249-254.
Cesari, M., Pahor, M., Lauretani, F., Penninx, B.W., Bartali, B., Russo, R., Cherubini, A., Woodman, R., Bandinelli, S., Guralnik, J.M., and Ferrucci, L. (2005). Bone density and hemoglobin levels in older persons: Results from the InCHIANTI study. Osteoporosis International, 16(6), 691-699.
Chaves, P.H., Semba, R.D., Leng, S.X., Woodman, R.C., Ferrucci, L., Guralnik, J.M., and Fried, L.P. (2005). Impact of anemia and cardiovascular disease on frailty status of community-dwelling older women: The Women’s Health and Aging Studies I and II. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 60(6), 729-735.
Cherubini, A., Martin, A., Andres-Lacueva, C., Di Iorio, A., Lamponi, M., Mecocci, P., Bartali, B., Corsi, A., Senin, U., and Ferrucci, L. (2005). Vitamin E levels, cognitive impairment, and dementia on older persons: The InCHIANTI study. Neurobiology of Aging, 26(7), 987-994.
Crimmins, E.M., Alley, D., Reynolds, S.L., Johnston, M., Karlamangla, A., and Seeman, T. (2005). Changes in biological markers of health: Older Americans in the 1990s. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 60(11), 1409-1413.
de Quervain, D.J.F., Henke, K., Aerni, A., Coluccia, D., Wollmer, M.A., Hock, C., et al. (2003). A functional genetic variation of the 5-HT2A receptor affects human memory. Nature Neuroscience, 6(11), 1141-1142.
Di Iorio, A., Cherubini, A., Volpato, S., Sparvieri, E., Lauretani, F., Franceschi, C., Senin, U., Abate, G., Paganelli, R., Martin, A., Andres-Lacueva, C., and Ferrucci, L. (2006). Markers of inflammation, vitamin E and peripheral nervous system function: The InCHIANTI study. Neurobiology of Aging, 27(9), 1280-1288.
Ding, J., Nieto, F.J., Beauchamp, N.J., Longstreth, W.T., Jr, Manolio, T.A., Hetmanski, J.B., and Fried, L.P. (2003). A prospective analysis of risk factors for white matter disease in the brain stem: The Cardiovascular Health Study. Neuroepidemiology, 22(5), 275-282.
Elosua, R., Bartali, B., Ordovas, J.M., Corsi, A.M., Lauretani, F., Ferrucci, L., and InCHIANTI Investigators. (2005). Association between physical activity, physical performance, and inflammatory biomarkers in an elderly population: The InCHIANTI study. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 60(6), 760-767.
Eley, T.C., Sugden, K., Corsico, A., Gregary, A.M., Sham, P., McGuffin, P., Plomin, R., and Craig, I.W. (2004). Gene-environment interaction analysis of serotonin system markers with adolescent depression. Molecular Psychiatry, 9, 908-915.
Epel, E.S., Blackburn, E.H., Lin, J., Dhabhar, F.S., Adler, N.E., Morrow, J.D., and Cawthon, R.M. (2004). Accelerated telomere shortening in response to life stress. Proceedings of the National Academy of Sciences, USA, 101(49), 17312-17315. Epub 2004 Dec 1.
OCR for page 128
Biosocial Surveys
Ewbank, D.C. (2002). Mortality differences by APOE genotype estimated from demographic synthesis. Genetic Epidemiology, (22), 146-155.
Ferrucci, L., Penninx, B.W., Volpato, S., Harris, T.B., Bandeen-Roche, K., Balfour, J., Leveille, S.G., Fried, L.P., and Guralnik, J.M. (2002). Change in muscle strength explains accelerated decline of physical function in older women with high interleukin-6 serum levels. Journal of the American Geriatrics Society, 50(12), 1947-1954.
Fratiglioni, L., Wang, H.X., Ericsson, K., Maytan, M., and Winblad, B. (2000). Influence of social network on occurrence of dementia: A community-based longitudinal study. Lancet, 355, 1315-1319.
Fried, L.P., Kronmal, R.A., Newman, A.B., Bild, D.E., Mittelmark, M.B., Polak, J.F., Robbins, J.A., and Gardin, J.M. (1998). Risk factors for 5-year mortality in older adults: The Cardiovascular Health Study. Journal of the American Medical Association, 279(8), 585-592.
Fried, L.P., Bandeen-Roche, K., Chaves, P.H.M., and Johnson, B.A. (2000). Preclinical mobility disability predicts incident mobility disability in older women. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 55A(1), M43-M52.
Fried, L.P., Tangen, C.M., Walston, J., Newman, A.B., Hirsch, C., Gottdiener, J., Seeman, T., Tracy, R., Kop, W.J., Burke, G., McBurnie, M.A., and Cardiovascular Health Study Collaborative Research Group. (2001). Frailty in older adults: Evidence for a phenotype. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 56(3), M146-M156.
Friedman, E.M., Hayney, M.S., Love, G.D., Urry, H.L., Rosenkranz, M.A., Davidson, R.J., Singer, B.H., and Ryff, C.D. (2005). Social relationships, sleep quality, and interleukin-6 in aging women. Proceedings of the National Academy of Sciences, USA, 102(51), 18757-18762.
Gatz, M., Pedersen, N.L., Berg, S., Johansson, B., et al. (1997). Heritability for Alzheimer’s disease: The study of dementia in Swedish twins. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, (52A), M117-M125.
Gillespie, N.A., Whitfield, J.B., Williams, B., Heath, A.C., and Martin, N. (2005). The relationship between stressful life events, the serotonin transporter (5-HTTLPR) genotype, and major depression. Psychological Medicine, 35, 101-111.
Gottesman, I.I., and Gould, T.D. (2003). The endophenotype concept in psychiatry: Etymology and strategic intentions. American Journal of Psychiatry, 160(4), 636-645.
Grabe, H.J., Lange, M., Wolff, B., Volzke, H., Lucht, M., Freyberger, H.J., John, U., and Cascorbi, I. (2005). Mental and physical distress is modulated by a polymorphism in the 5-HT transporter gene interacting with social stressors and chronic disease burden. Molecular Psychiatry, 10, 220-224.
Gruenewald, T.L., Seeman, T.E., Ryff, C.D., Karlamangla, A.S., and Singer, B.H. (2006). Early warning biomarkers: Combinations predictive of later life mortality. Proceedings of the National Academy of Sciences, USA, 103(3i8), 14158-14163.
Harris, J.R. (2005). Research on environmental effects in genetic studies of aging: Introduction. The Journal of Gerontology B: Psychological and Social Sciences, 60, Spec No. 1:5-6.
Harris, J.R. (2007). Genetics, social behaviors, social environments, and aging. Twin Research and Human Genetics, 10(2), 235-240.
Hawkley, L.C., and Cacioppo, J.T. (2003). Loneliness and pathways to disease. Brain, Behavior, and Immunity, 17, S98-S105.
Hawkley, L.C., Masi, C.M., Berry, J.D., and Cacioppo, J.T. (2006). Loneliness is a unique predictor of age-related differences in systolic blood pressure. Psychology and Aging, 21, 152-164.
Heller, D.A., de Faire, U., Pedersen, N.L., Dahlen, G., and McClearn, G.E. (1993). Genetic and environmental influences on serum lipid levels in twins. New England Journal of Medicine, 22:328(16), 1150-1156.
OCR for page 129
Biosocial Surveys
Heller, D.A., Pedersen, N.L., de Faire, U., and McClearn, G.E. (1994). Genetic and environmental correlations among serum lipids and apolipoproteins in elderly twins reared together and apart. American Journal of Human Genetics, (6), 1255-1267.
Hong, Y., Dahlen, G., Pedersen, N., Heller, D., McClearn, G.E., and deFaire, U. (1995). Potential environmental effects on adult lipoprotein(a) levels: Results from Swedish twins. Atherosclerosis, 17, 295-304.
Hong, Y., Pedersen, N.L., Brismar, K., and de Faire, U. (1997). Genetic and environmental architecture of the features of the insulin-resistance syndrome. American Journal of Human Genetics, 60(1), 143-152.
Hong, Y., Pedersen, N.L., Egberg, N., and de Faire, U. (1999). Genetic effects for plasma factor VII levels independent of and in common with triglycerides. Journal of Thrombosis and Haemostasis, 81(3), 382-386.
House, J.S., Landis, K.R., and Umberson, D. (1988). Social relationships and health. Science, 241, 540-545.
House, J.S., Lepkowski, J.M., Kinney, A.M., Mero, R.P., Kessler, R.C., and Herzog, A.R. (1994). The social stratification of aging and health. Journal of Health and Social Behavior, 35, 213-234.
Hu, P., Reuben, D.B., Crimmins, E., Harris, T.B., Huang, M.H., and Seeman, T.E. (2005). The effects of serum beta-carotene level and burden of inflammation on all-cause mortality in high-functioning older persons: MacArthur Studies of Successful Aging. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 59, 849-854.
Hu, P., Wagle, N., Goldman, N., Weinstein, M., and Seeman, T.E. (2006). The associations between socioeconomic status, allostatic load and measures of health in older Taiwanese persons: Taiwan social environment and biomarkers of aging study. Journal of Biosocial Science, 20, 1-12.
Huang, T.L., Zandi, P.P., Tucker, K.L., Fitzpatrick, A.L., Kuller, L.H., Fried, L.P., Burke, G.L., and Carlson, M.C. (2005). Benefits of fatty fish on dementia risk are stronger for those without APOE epsilon4. Neurology, 65(9), 1409-1414.
Iliadou, A., Lichtenstein, P., de Faire, U., and Pedersen, N.L. (2001). Variation in genetic and environmental influences in serum lipid and apolipoprotein levels across the lifespan in Swedish male and female twins. American Journal of Medical Genetics, 22:102(1), 48-58.
Iliadou, A., and Sneider, H. (2004). Genetic epidemiological approaches in the study of risk factors for cardiovascular disease. European Journal of Epidemiology, (19), 209-217.
Institute of Medicine. (2006). Genes, behavior, and the social environment. Washington, DC: The National Academies Press.
Jansson, M., Gatz, M., Berg, S., Johansson, B., Malmberg, B., McClearn, G.E., Schalling, M., and Pedersen, N.L. (2003). Association between depressed mood in the elderly and a 5-HTR2A gene variant. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 120(1), 79-84.
Jenny, N.S., Arnold, A.M., Kuller, L.H., Sharrett, A.R., Fried, L.P., Psaty, B.M., and Tracy, R.P. (2006). Soluble intracellular adhesion molecule-1 is associated with cardiovascular disease risk and mortality in older adults. Journal of Thrombosis and Haemostasis, 4(1), 107-113.
Kado, D.M., Karlamangla, A.S., Huang, M.H., Troen, A., Rowe, J.W., Selhub, J., and Seeman, T.E. (2005). Homocysteine versus the vitamins folate, B6 and B12 as predictors of cognitive function and decline in older high functioning adults: MacArthur Studies of Successful Aging. American Journal of Medicine, 118, 161-167.
Karlamangla, A.S., Singer, B.H., McEwen, B.S., Rowe, J.W., and Seeman, T.E. (2002). Allostatic lead as a predictor of functional decline: MacArthur Studies of Successful Aging. Journal of Clinical Epidemiology, 55(7), 696-710.
OCR for page 130
Biosocial Surveys
Karlamangla, A.S., Singer, B.S., Reuben, D., and Seeman, T.E. (2004). Increase in serum non-high-density lipoprotein cholesterol may be beneficial in some high-functioning older adults: MacArthur Studies of Successful Aging. Journal of the American Geriatrics Society, 52, 487-494.
Karlamangla, A.S., Singer, B., Greendale, G., and Seeman, T. (2005a). Increase in epinephrine excretion is associated with cognitive decline in elderly men: MacArthur Studies of Successful Aging. Psychoneuroendocrinology, 30(5), 453-460.
Karlamangla, A.S., Singer, B.H., Chodosh, J., McEwen, B.S., and Seeman, T.E. (2005b). Urinary cortisol excretion as a predictor of incident cognitive impairment. Neurobiology of Aging, 26S, S80-S84.
Karlamangla, A.S., Singer, B.H., and Seeman, T.E. (2006). Reduction in allostatic load in older adults is associated with lower all-cause mortality risk: MacArthur Studies of Successful Aging. Psychosomatic Medicine, 68, 500-507.
Kaufman, J., Yang, B.Z., Douglas-Palumberi, H., Houshyar, S., Lipshitz, D., Krystal, J., and Gelernter, J. (2004). Social supports and serotonin transporter gene moderate depression in maltreated children. Proceedings of the National Academy of Sciences, USA, 101, 17316-17321.
Kendler, K.S., Kuhn, J.W., Vittum, J., Prescott, C.A., and Riley, B. (2005). The interaction of stressful life events and a serotonin transporter polymorphism in the prediction of episodes of major depression: A replication. Archives of General Psychiatry, 62, 529-535.
Kiecolt-Glaser, J.K., Loving, T.J., Stowell, J.R., Malarkey, W.B., Lemeshow, S., Dickinson, S.L., and Glaser, R. (2005). Hostile marital interactions, proinflammatory cytokine production, and wound healing. Archives of General Psychiatry, 62, 1377-1384.
Koster, A., Penninx, B.W., Bosma, H., Kempen, G.I., Harris, T.B., Newman, A.B., Rooks, R.N., Rubin, S.M., Simonsick, E.M., van Eijk, J.T., and Kritchevsky, S.B. (2005a). Is there a biomedical explanation for socioeconomic differences in incident mobility limitation? Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 60(8), 1022-1027.
Koster, A., Penninx, B.W., Bosma, H., Kempen, G.I., Newman, A.B., Rubin, S.M., Satterfield, S., Atkinson, H.H., Ayonayon, H.N., Rosano, C., Yaffe, K., Harris, T.B., Rooks, R.N., Van Eijk, J.T., and Kritchevsky, S.B. (2005b). Socioeconomic differences in cognitive decline and the role of biomedical factors. Annals of Epidemiology, 15(8), 564-571.
Koster, A., Bosma, H., Penninx, B.W., Newman, A.B., Harris, T.B., van Eijk, J.T., Kempen, G.I., Simonsick, E.M., Johnson, K.C., Rooks, R.N., Ayonayon, H.N., Rubin, S.M., Kritchevsky, S.B., and Health ABC Study. (2006). Association of inflammatory markers with socioeconomic status. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 61(3), 284-290.
Kubzansky, L.D., Kawachi, I., and Sparrow, D. (1999). Socioeconomic status, hostility, and risk factor clustering in the Normative Aging Study: Any help from the concept of allostatic load? Annals of Behavioral Medicine, 21(4), 330-338.
Lichtenstein, P., de Faire, U., Floderus, B., Svartenren, M., Svedberg, P., and Pedersen, N.L. (2002). The Swedish Twin Registry: A unique resource for clinical, epidemiological, and genetic studies. Journal of International Medical Research, (252), 184-205.
Loucks, E.B., Berkman, L.F., Gruenewald, T.L., and Seeman, T.E. (2006). Relation of social integration to inflammatory marker concentrations in men and women 70-79 years. American Journal of Cardiology, 97(7), 1010-1016.
McClearn, G.E., Johansson, B., Berg, S., Pedersen, N.L., Ahern, F., Petrill, S.A., and Plomin, R. (1997). Substantial genetic influence on cognitive abilities in twins 80 or more years old. Science, 276(5318), 1560-1563.
McDermott, M.M., Guralnik, J.M., Corsi, A., Albay, M., Macchi, C., Bandinelli, S., and Ferrucci, L. (2005). Patterns of inflammation associated with peripheral arterial disease: The InCHIANTI study. American Heart Journal, 150(2), 276-281.
OCR for page 131
Biosocial Surveys
McGuire, S., and Clifford, J. (2000). Genetic and environmental contributions to loneliness in children. Psychological Sciences, 11, 487-491.
Moceri, V.M., Kukull, W.A., Emanual, I., van Belle, G., Starr, J.R., Schellenberg, G.D., McCormick, W.C., Bowen, J.D., Teri, L., and Larson, E.B. (2001). Using census data and birth certificates to reconstruct the early-life socioeconomic environment and the relation to the development of Alzheimer’s disease. Epidemiology, (12), 383-389.
National Research Council (2001a). Cells and surveys: Should biological measures be included in social science research? Committee on Population, C.E. Finch, J.W. Vaupel, and K. Kinsella, Eds. Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.
National Research Council. (1997). Between Zeus and the salmon: The biodemography of longevity. Committee on Population. K.W. Wachter and C.E. Finch, Eds. Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.
National Research Council (2001b). New horizons in health: An integrative approach. Committee on Future Directions for Behavioral and Social Sciences Research at the National Institutes of Health, B.H. Singer and C.D. Ryff, Eds. Board on Behavioral, Cognitive, and Sensory Sciences. Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.
Niaura, R., Banks, S.M., Ward, K.D., Stoney, C.M, Spiro, A., III, Aldwin, C.M., Landsberg, L., and Weiss, S.T. (2000). Hostility and the metabolic syndrome in older males: The normative aging study. Psychosomatic Medicine, 62(1), 7-16.
Nilsson, S.E., Evrin, P.E., Tryding, N., Berg, S., McClearn, G., and Johansson, B. (2003a). Biochemical values in persons older than 82 years of age: Report from a population-based study of twins. Scandinavian Journal of Clinical and Laboratory Investigation, 63(1), 1-13.
Nilsson, S.E., Takkinen, S., Tryding, N., Evrin, P.E., Berg, S., McClearn, G., and Johansson, B. (2003b). Association of biochemical values with morbidity in the elderly: A population-based Swedish study of persons aged 82 or more years. Scandinavian Journal of Clinical and Laboratory Investigation, 63(7-8), 457-466.
Onder, G., Penninx, B.W., Cesari, M., Bandinelli, S., Lauretani, F., Bartali, B., Gori, A.M., Pahor, M., and Ferrucci, L. (2005). Anemia is associated with depression in older adults: Results from the InCHIANTI study. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 60(9), 1168-1172.
Payton, M., Riggs, K.M., Spiro, A., III, Weiss, S.T., and Hu, H. (1998). Relations of bone and blood lead to cognitive function: The VA Normative Aging Study. Neurotoxicology and Teratology, 20(1), 19-27.
Pedersen, N.L., McClearn, G.E., Plomin, R., Nesselroade, J.R., Berg, S., DeFaire, U., Mortimer, J.A., et al. (1991). The Swedish Adoption Twin Study of Aging: An update. Acta Geneticae Medicae et Gemellologiae (40), 7-20.
Penninx, B.W., Guralnik, J.M., Ferrucci, L., Fried, L.P., Allen, R.H., and Stabler, S.P. (2000). Vitamin B(12) deficiency and depression in physically disabled older women: Epidemiologic evidence from the Women’s Health and Aging Study. American Journal of Psychiatry, 157(5), 715-721.
Penninx, B.W., Pahor, M., Cesari, M., Corsi, A.M., Woodman, R.C., Bandinelli, S., Guralnik, J.M., and Ferrucci, L. (2005). Anemia is associated with disability and decreased physical performance and muscle strength in the elderly. Journal of the American Geriatric Society, 52(5), 719-724.
OCR for page 132
Biosocial Surveys
Psaty, B.M., Anderson, M., Kronmal, R.A., Tracy, R.P., Orchard, T., Fried, L.P., Lumley, T., Robbins, J., Burke, G., Newman, A.B., and Furberg, C.D. (2004). The association between lipid levels and the risk of incident myocardial infarction, stroke, and total mortality: The Cardiovascular Health Study. Journal of the American Geriatric Society, 52(10), 1639-1647.
Rantanen, T., Guralnik, J.M., Sakari-Rantala, R., Leveille, S., Simonsick, E.M., Ling, S., and Fried, L.P. (1999). Disability, physical activity, and muscle strength in older women: The Women’s Health and Aging Study. Archives of Physical Medicine and Rehabilitation, 80(2), 130-135.
Ray, A.L., Semba, R.D., Walston, J., Ferrucci, L., Cappola, A.R., Ricks, M.O., Xue, Q.L., and Fried, L.P. (2006). Low serum selenium and total carotenoids predict mortality among older women living in the community: The Women’s Health and Aging Studies. Journal of Nutrition, 136(1), 172-176.
Reuben, D.B., Cheh, A.I., Harris, T.B., Ferrucci, L., Rowe, J.W., Tracy, R.P., and Seeman, T.E. (2002). Peripheral blood markers of inflammation predict mortality and functional decline among high functioning community-dwelling older persons. Journal of the American Geriatric Society, 50(4), 638-644.
Reuben, D.B., Judd-Hamilton, L., Harris, T.B., and Seeman, T.E. (2003, August). The associations between physical activity and inflammatory markers in high functioning older persons: MacArthur Studies of Successful Aging. Journal of the American Geriatrics Society, 51(8), 1125-1130.
Reynolds, C.A., Jansson, M., Gatz, M., and Pedersen, N.L. (2006a). Longitudinal change in memory performance associated with HTR2A polymorphism. Neurobiology of Aging, 27(1), 150-154.
Reynolds, C.A., Prince, J.A., Feuk, L., Brookes, A.J., Gatz, M., and Pedersen, N.L. (2006b). Longitudinal memory performance during normal aging: Twin association models of APOE and other Alzheimer candidate genes. Behavioral Genetics, 36(2), 185-194. Epub 2006 Jan 10.
Reynolds, C.A., Gatz, M., Berg, S., and Pedersen, N.L. (2007). Genotype-environment interactions: Cognitive aging and social factors. Twin Research and Human Genetics, 10(2), 241-254.
Rutter, M., Moffitt, T.E., and Caspi, A. (2006). Gene-environment interplay and psychopathology: Multiple varieties but real effects. Journal of Child Psychology and Psychiatry, 47(3-4), 226-261.
Ryff, C.D., and Singer, B.H. (Eds.) (2001). Emotion, social relationships, and health. New York: Oxford University Press.
Ryff, C., and Singer, B.H. (2005). Social environments and the genetics of aging: Advancing knowledge of protective health mechanisms. The Journal of Gerontology B: Psychological and Social Sciences, 60, 12-23.
Ryff, C.D., Singer, B.H., and Dienberg Love, G. (2004). Positive health: Connecting well-being with biology. Philosophical Transactions of the Royal Society B: Biological Sciences, 359, 1383-1394.
Ryff, C.D., Dienberg Love, G., Urry, H.L., Muller, D., Rosenkranz, M.A., Friedman, E.M., Davidson, R.J., and Singer, B. (2006). Psychological well-being and ill-being: Do they have distinct or mirrored biological correlates? Psychotherapy and Psychosomatics, 75(2), 85-95.
Saxton, J., Lopez, O.L., Ratcliff, G., Dulberg, C., Fried, L.P., Carlson, M.C., Newman, A.B., and Kuller, L. (2004). Preclinical Alzheimer disease: Neuropsychological test performance 1.5 to 8 years prior to onset. Neurology, 63(12), 2341-2347.
Seeman, T.E. (1996). Social ties and health. Annals of Epidemiology, 6, 442-451.
OCR for page 133
Biosocial Surveys
Seeman, T.E., Berkman, L.F., Blazer, D., and Rowe, J. (1994). Social ties and support and neuroendocrine function: MacArthur Studies of Successful Aging. Annals of Behavioral Medicine, 16, 95-106.
Seeman, T.E., Berkman, L.F., Kohout, F., LaCroix, A., Glynn, R., and Blazer, D. (1993). Intercommunity variations in the association between social ties and mortality in the elderly: A comparative analysis of three communities. Annals of Epidemiology, 3, 325-335.
Seeman, T.E., Bruce, M.L., and McAvay, G. (1996). Social network characteristics and onset of ADL disability: MacArthur Studies of Successful Aging. Journal of Gerontology: Psychological Sciences, 51B, S191-S200.
Seeman, T.E., Crimmins, E., Bucur, A., Huang, M.H., Singer, B., Gruenewald, T., Berkman, L.F., and Reuben, D.B. (2004a). Cumulative biological risk and socioeconomic differences in mortality: MacArthur Studies of Successful Aging. Social Science and Medicine, 58, 1985-1997.
Seeman, T.E., Glei, D., Goldman, N., Weinstein, M., Singer, B., and Lin, Y-H. (2004b). Social relationships and allostatic load in Taiwanese elderly and near elderly. Social Science and Medicine, 59, 2245-2257.
Seeman, T.E., Huang, M.H., Bretsky, P., Crimmins, E., Launer, L., and Guralnik, J.M. (2005). Education and APOE-ε4 in longitudinal cognitive decline: MacArthur Studies of Successful Aging. Journal of Gerontology: Psychological Sciences, 60(2), P74-P83.
Seeman, T.E., Kaplan, G.A., Knudsen, L., Cohen, R., and Guralnik, J. (1987). Social ties and mortality in the elderly: A comparative analysis of age-dependent patterns of association. American Journal of Epidemiology, 126, 714-723.
Seeman, T.E., Lusignolo, T., Berkman, L., and Albert, M. (2001a). Social environment characteristics and patterns of cognitive aging: MacArthur Studies of Successful Aging. Health Psychology, 20, 243-255.
Seeman, T.E., and McEwen, B.S. (1996). Impact of social environment characteristics on neuroendocrine regulation. Psychosomatic Medicine, 58(5), 459-471.
Seeman, T.E., McEwen, B.S., Singer, B., Albert, M., and Rowe, J.W. (1997a). Increase in urinary cortisol excretion and declines in memory: MacArthur Studies of Successful Aging. Journal of Clinical Endocrinology & Metabolism, 82, 2458-2465.
Seeman, T.E, Merkin, S., Crimmins, E., Koretz, B., and Karlamangla, A. (no date). Socioeconomic status, ethnicity, and cumulative biological risk profiles in a national sample of U.S. adults: NHANES III. (manuscript in preparation).
Seeman, T.E., Singer, B., Horwitz, R., and McEwen, B.S. (1997b). The price of adaptation—allostatic load and its health consequences: MacArthur Studies of Successful Aging. Archives of Internal Medicine, 157, 2259-2268.
Seeman, T.E., Singer, B., Rowe, J., and McEwen, B. (2001b). Exploring a new concept of cumulative biological risk—allostatic load and its health consequences: MacArthur Studies of Successful Aging. Proceedings of the National Academy of Sciences, USA, 98(8), 4770-4775.
Seeman, T.E., Singer, B., Ryff, C., and Levy-Storms, L. (2002, May/June). Psychosocial factors and the development of allostatic load. Psychosomatic Medicine, 64, 395-406.
Shadlen, M.F., Larson, E.B., Wang, L., Phelan, E.A., McCormick, W.C., Jolley, L., Teri, L., and van Belle, G. (2005). Education modifies the effect of apolipoprotein epsilon 4 on cognitive decline. Neurobiology of Aging, 26(1), 17-24.
Simonsick, E.M., Maffeo, C.E., Rogers, S.K., Skinner, E.A., Davis, D., Guralnik, J.M., and Fried, L.P. (1997). Methodology and feasibility of a home-based examination in disabled older women: The Women’s Health and Aging Study. Journals of Gerontology, 52A(5), M264-M274.
Singer, B., and Ryff, C. (1999). Hierarchies of life histories and associated health risks. Annals of New York Academy of Sciences, 896, 96-115.
OCR for page 134
Biosocial Surveys
Singer, B., Ryff, C., and Seeman, T.E. (2004). Allostasis, homeostasis, and the cost of physiological adaptation. In J. Schulkin (Ed.), Operationalizing allostatic load (pp. 113-149). Cambridge, England: Cambridge University Press.
Sneider, H., van Doornen, L.J., and Boomsma, D.I. (1997). The age dependency of gene expression for plasma lipids, lipoproteins, and apolipoproteins. American Journal of Human Genetics, (60), 638-650.
Snowdon, D.A., Kemper, S.J., Mortimer, J.A, Wekstein, D.R., and Markesbery, W.R. (1996). Linguistic ability in early life and cognitive function and Alzheimer’s disease in late life: Findings from the Nun study. Journal of the American Medical Association, 275(7), 528-532.
Surtees, P.G., Wainwright, N.W., Willis-Owen, S.A., Luben, R., Day, N.E., and Flint, J. (2006). Social adversity, the serotonin transporter (5-HTTLPR) polymorphism, and major depressive disorder. Biological Psychology, 59, 224-229.
Tang, M.X., Stern, Y., Marder, K., Bell, K., Gurland, B., Lantigua, R., Andrews, H., Feng, L., Tycko, B., and Mayeux, R. (1998). The APOE-epsilon4 allele and the risk of Alzheimer’s disease among African Americans, Whites, and Hispanics. Journal of the American Medical Association, 11, 751-575.
Taylor, S.E., Repetti, R.L., and Seeman, T. (1997). Health psychology: What is an unhealthy environment and how does it get under the skin? Annual Review of Psychology, 48, 411-447.
Todaro, J.F., Con, A., Niaura, R., Spiro, A., III, Ward, K.D., and Roytberg, A. (2005). Combined effect of the metabolic syndrome and hostility on the incidence of myocardial infarction (the Normative Aging Study). American Journal of Cardiology, 96(2), 221-226.
Tomaka, J., Thompson, S., and Palacios, R.J. (2005). High homocysteine and low B vitamins predict cognitive decline in aging men: The Veterans Affairs Normative Aging Study. American Journal of Clinical Nutrition, 82(3), 627-635.
Tomaka, J., Thompson, S., and Palacios, R.J. (2006, June). The relation of social isolation, loneliness, and social support to disease outcomes among the elderly. Aging Health, 18(3), 359-384.
Tucker, K.L., Qiao, N., Scott, T., Rosenbert, I., and Spiro, A., III (2005). High homocysteine and low B vitamins predict cognitive decline in aging men: The Veterans Affairs normative aging study. American Journal of Clinical Nutrition, 82(3), 627-635.
Uchino, B.N., Cacioppo, J.T., and Kiecolt-Glase, J.K. (1996). The relationship between social support and physiological processes: A review with emphasis on underlying mechanisms and implications for health. Psychological Bulletin, 119, 488-531.
Visser, M., Pahor, M., Taaffe, D.R., Goodpaster, B.H., Simonsick, E.M., Newman, A.B., Nevitt, M., and Harris, T.B. (2002). Relationship of interlukin-6 and tumor necrosis factor-alpha with muscle mass and muscle strength in elderly men and women: The Health ABC Study. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 57(5), M326-M332.
Volpato, S., Guralnik, J.M., Fried, L.P., Remaley, A.T., Cappola, A.R., and Launer, L.J. (2002). Serum thyroxine level and cognitive decline in euthyroid older women. Neurology, 58(7), 1055-1061.
von Kanel, R., Dimsdale, J.E., Ancoli-Israel, S., Mills, P.J., Patterson, T.L., McKibbin, C.L., Archuleta, C., and Grant, I. (2006). Poor sleep is associated with higher plasma proinflammatory cytokine interleukin-6 and procoagulant marker fibrin D-dimer in older caregivers of people with Alzheimer’s disease. Journal of the American Geriatrics Society, 54(3), 431-437.
OCR for page 135
Biosocial Surveys
Walston, J., McBurnie, M.A., Newman, A., Tracy, R.P., Kop, W.J., Hirsch, C.H., Gottdiener, J., and Fried, L.P. (2002). Frailty and activation of the inflammation and coagulation systems with and without clinical comorbidities: Results from the Cardiovascular Health Study. Archives of Internal Medicine, 162(20), 2333-2341.
Walston, J., Arking, D.E., Fallin, D., Li, T., Beamer, B., Xue, Q., Ferrucci, L., Fried, L.P., and Chakravarti, A. (2005). IL-6 gene variation is not associated with increased serum levels of IL-6, muscle, weakness, or frailty in older women. Experimental Gerontology, 40(4), 344-352.
Walston, J., Xue, Q., Semba, R.D., Ferrucci, L., Cappola, A.R., Ricks, M., Guralnik, J., and Fried, L.P. (2006). Serum antioxidants, inflammation, and total mortality in older women. American Journal of Epidemiology, 163(1), 18-26.
Weaver, J., Huang, M.H., Albert, M., Harris, T., Rowe, J., and Seeman, T.E. (2002). Interleukin-6 as a predictor of cognitive function and cognitive decline: MacArthur Studies of Successful Aging. Neurology, 59, 371-378.
Wen, M., Hawkley, L.C., and Cacioppo, J.T. (2006). Objective and perceived neighborhood environment, individual SES and psychosocial factors, and self-rated health: An analysis of older adults in Cook County, Illinois. Social Science Medicine, 63, 2575-2590.
Wilson, R.S., Bienias, J.L., Evans, D.A., et al. (2004). The Religious Orders Study: Overview and relation between change in cognitive and motor speed. Aging Neuropsychology and Cognition, 11, 280-303.
Yende, S., Waterer, G.W., Tolley, E.A., Newman, A.B., Bauer, D.C., Taaffe, D.R., Jensen, R., Crapo, R., Rubin, S., Nevitt, M., Simonsick, E.M., Satterfield, S., Harris, T., and Kritchevsky, S.B. (2006). Inflammatory markers are associated with ventilatory limitation and muscle dysfunction in obstructive lung disease in well functioning elderly subjects. Thorax, 61(1), 10-16.
Zhang, H.P., and Singer, B. (1999). Recursive partitioning in the health sciences. New York: Springer-Verlag.
