American Journal of Epidemiology Advance Access originally published online on March 10, 2007
American Journal of Epidemiology 2007 165(9):1076-1087; doi:10.1093/aje/kwk115
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PRACTICE OF EPIDEMIOLOGY |
Age, Gene/Environment Susceptibility–Reykjavik Study: Multidisciplinary Applied Phenomics
1 Laboratory of Epidemiology, Demography, and Biometry, Intramural Research Program, National Institute on Aging, Bethesda, MD
2 Icelandic Heart Association, Kopavogur, Iceland
3 Department of Radiology, Landspitali University Hospital, Reykjavik, Iceland
4 Department of Geriatrics, Landspitali University Hospital, Reykjavik, Iceland
5 Faculty of Medicine, University of Iceland, Reykjavik, Iceland
6 Department of Endocrinology and Metabolism, Landspitali University Hospital, Reykjavik, Iceland
7 Department of Medicine, Landspitali University Hospital, Reykjavik, Iceland
8 Division of Epidemiology and Clinical Research, National Eye Institute, Bethesda, MD
9 Epidemiology and Biostatistics Program, National Institute of Deafness and Communication Disorders, Bethesda, MD
Correspondence to Dr. Tamara B. Harris or Dr. Lenore J. Launer, Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, 7201 Wisconsin Avenue, Suite 3C309, Bethesda, MD 20892-9205 (e-mail: Harris99{at}mail.nih.gov or LaunerL{at}mail.nih.gov) or Dr. Vilmundur Gudnason, Icelandic Heart Association, Holtasmara 1, 201 Kopavogur, Iceland (e-mail: v.gudnason{at}hjarta.is).
Received for publication April 14, 2006. Accepted for publication October 24, 2006.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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In anticipation of the sequencing of the human genome and description of the human proteome, the Age, Gene/Environment Susceptibility–Reykjavik Study (AGES–Reykjavik) was initiated in 2002. AGES–Reykjavik was designed to examine risk factors, including genetic susceptibility and gene/environment interaction, in relation to disease and disability in old age. The study is multidisciplinary, providing detailed phenotypes related to the cardiovascular, neurocognitive (including sensory), and musculoskeletal systems, and to body composition and metabolic regulation. Relevant quantitative traits, subclinical indicators of disease, and medical diagnoses are identified by using biomarkers, imaging, and other physiologic indicators. The AGES–Reykjavik sample is drawn from an established population-based cohort, the Reykjavik Study. This cohort of men and women born between 1907 and 1935 has been followed in Iceland since 1967 by the Icelandic Heart Association. The AGES–Reykjavik cohort, with cardiovascular risk factor assessments earlier in life and detailed late-life phenotypes of quantitative traits, will create a comprehensive study of aging nested in a relatively genetically homogeneous older population. This approach should facilitate identification of genetic factors that contribute to healthy aging as well as the chronic conditions common in old age.
aging; body composition; cardiovascular diseases; cognition; genetics, population; osteoporosis; phenotype
Abbreviations: AGES–Reykjavik, Age, Gene/Environment Susceptibility–Reykjavik Study
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Aging is a complex process that reflects a person's social and biologic history. Aging may be accompanied by multiple pathologic conditions that increase the occurrence of disease, reduce cognitive and physical function, and impair quality of life. To better understand the determinants of aging, identify potential therapeutic interventions, and design effective prevention programs, a multidisciplinary approach to study well-defined older populations is needed. This approach also lends itself well to the study of genetics since the effects of genes often extend well beyond the single organ system to which a gene was thought to contribute. The rationale for establishing comprehensively evaluated phenotypes across organ systems was described by Freimer and Sabatti in what they term the "The Human Phenome Project" (1). The Age, Gene/Environment Susceptibility–Reykjavik Study (AGES–Reykjavik) was conceived and designed to provide an approach to study, among other risk factors, the genetic contribution to conditions of old age. This paper describes the rationale and design of AGES–Reykjavik and the measurements included in the study, and it provides select descriptive data on the first 2,300 participants.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Study rationale
AGES–Reykjavik is based on three general hypotheses: first, that genetic variation contributes to disease occurring in old age; second, that selected diseases common in old age share genetic, behavioral, and environmental risk factors; and third, that better classification of phenotypes based on multiple streams of data, including midlife history and subclinical disease, will further the exploration of how these risk factors are associated with complex traits and diseases manifest late in life.
AGES–Reykjavik is an epidemiologic study focusing on four biologic systems: vascular, neurocognitive (including sensory), musculoskeletal, and body composition/metabolism. These four systems were chosen because similar risk factors contribute to physiologic changes and disease in these systems. For instance, inflammation is associated with atherosclerosis (2, 3), diabetes (4), obesity (5), smoking-related illnesses (6), dementia (7), osteoporosis (8), and macular degeneration (9).
AGES–Reykjavik originates from the Reykjavik Study, a cohort established in 1967 to prospectively study cardiovascular disease in Iceland. Combining midlife data from the Reykjavik Study and old-age data from AGES–Reykjavik allows a life course approach to better characterize phenotypes. This combination of data can be used to identify patterns of risk factors and evaluate whether these patterns have remained stable or changed with age. For instance, previous studies demonstrate convincingly that risk factors such as blood pressure, weight, and cholesterol measured in late life are influenced by prevalent old-age morbidities and no longer reflect the exposures that initiated these pathologies (10, 11). Furthermore, midlife data are unbiased with regard to health history and are more accurate than retrospective recall.
Apart from improved phenotypic description, the availability of the midlife data allows for a complete assessment of nonresponse, particularly how death and refusals might contribute to bias. This assessment will be enhanced by additional information from hospital records, a national mortality index with authentication of all death certificates, a Minimum Data Set for Nursing Home patients (12) and Minimum Data Set for Home-Care patients (13, 14), and archival information from birth records, all available for linkage with the cohort.
To define quantitative traits as well as subclinical and clinical disease, AGES–Reykjavik includes extensive state-of-the-art imaging techniques, biochemical measurements, and diagnostic evaluations. These measures should provide insights into preclinical disease states, identify patterns of concomitant traits, and increase our ability to understand prognostic indicators underlying pathophysiologic changes. Imaging techniques yield standardized information on morphometry of organs and tissues in vivo. Use of imaging in epidemiologic studies has been an effective way to understand subclinical disease, particularly in the fields of osteoporosis (15), atherosclerosis (16), brain structure (17), and body composition (18). Because the imaging protocols used in AGES–Reykjavik are similar to protocols in other studies (19, 20), data can directly be compared with these studies. This multimeasurement strategy of phenotypic definition offers important advantages, and it has been successfully used elsewhere (21).
Some characteristics of Iceland and the Icelandic population should enhance the power to examine genetic and gene-environment interactions that modulate expression of genes in old age. The Icelandic population is relatively genetically homogeneous (22), which reduces the problem of population stratification. Thus, a greater proportion of persons at the phenotypic extremes may share the same genetic susceptibility. Genealogic databases in Iceland allow identification of relationships in the cohort. The relative isolation and hardship due to deadly infectious epidemics, few major roads, and foreign rule, coupled with volcanic soil and a cold climate, lead to restricted diet and high physical activity levels, until the mid-20th century. Nonetheless, Iceland has had high literacy rates and, across the last century, relatively low neonatal mortality. Lastly, Iceland is freer of air and water pollution than many other countries because most electrical energy is generated by a geothermal process (23), minimizing several environmental factors affecting health.
Study design: the Reykjavik Study and AGES–Reykjavik protocols
The Reykjavik Study originally comprised a random sample of 30,795 men and women born in 1907–1935 and living in Reykjavik in 1967 (24–33). The study sample was divided into six groups (B, C, A, D, E, and F) by birth year and birth date within month (table 1). Each group was invited to participate in specific stages of the study. The B group was designated for longitudinal follow-up and was examined in all stages. The F group was designated a control group and was not included in examinations until 1991. Men and women were examined in separate years for more efficient clinic operation. Table 1 shows the number from each group sampled at each stage and the number examined in each stage. Since a standard examination was performed in each stage (refer to tables 2 and 3 for measures), longitudinal and cross-sectional data could be used to study secular and individual changes over the 30-year follow-up period. The stage VI examination (1991–1996) focused on persons aged 70 years or older from the F and B groups. It included the core examination components, plus measures of cognitive and physical function, social support, and other topics particularly relevant to aging. Surveillance for vital events and cardiovascular disease events has been continual in the cohort since 1967. Some of the major published research findings from the Reykjavik Study are summarized in table 4.
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(2002–2006) through February 2004
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AGES–Reykjavik examinations began in 2002. At that time, 11,549 previously examined Reykjavik Study cohort members were still alive. From these persons, recruitment order was randomly assigned within the six Reykjavik Study groups. First, the A, B, and C groups were sampled, since the largest amount of past examination data was available for these persons. Then the rest of the formerly examined participants (D and E groups) were sampled. AGES–Reykjavik was not sampled within gender to preserve the fact that the Reykjavik Study was initiated with a random sample of the population of Reykjavik in these birth cohorts. The AGES–Reykjavik examinations concluded in February 2006, with a total sample size of 5,764 survivors of the Reykjavik Study cohort (42 percent are male). The single-wave AGES–Reykjavik examination was completed in three clinic visits, with a participant's full examination finished within a 4- to 6-week time window.
Phenotypic data in AGES–Reykjavik are collected by using standardized protocols (table 3). The first clinic visit includes a blood draw, blood pressure measurement, electrocardiography, anthropometry, and measures of different domains of physical and cognitive function. The questionnaire, based on the original Reykjavik Study questions, includes health history, lifestyle practices, a medication survey, and a food history including early-life diet and social aspects of daily life (table 2). Serum, plasma, salivary swabs, and urine are obtained for metabolic, hormonal, and inflammatory markers. White blood cells for DNA are obtained, processed, and stored. Chemical measurements are carried out in the laboratory of the Icelandic Heart Association with independent external standards. Additional white blood cells have been saved for transformation for more than half the cohort.
The second examination day includes imaging protocols using magnetic resonance imaging, computed tomography, and ultrasound instrumentation (table 3). The third examination includes vision screening, assessment of intraocular pressure, digital retinal photographs through dilated pupils, a hearing test, a dementia assessment (if indicated), and the exit interview with a physician or nurse. The clinic, laboratory, and imaging suite are all housed in the same building. For those unable or unwilling to come to the clinic, a home examination has been available but was used sparingly.
Dementia case ascertainment is a three-step process. The Mini-Mental State Examination (34) and the Digit Symbol Substitution Test (35) are administered to all participants. Persons who are screen positive based on a combination of these tests are administered a second, more diagnostic test battery, and a subset of them are selected for a neurologic examination. Proxies for this latter group are interviewed about medical history and social, cognitive, and daily functioning relevant to the diagnosis. A consensus diagnosis based on international guidelines is made by a panel that includes a geriatrician, neurologist, neuropsychologist, and neuroradiologist. Screening for depression is done at the first clinic visit, with follow-up testing for screen positives with the Mini-International Neuropsychiatric Interview, which gives more detailed diagnostic information about psychiatric morbidity (36).
The image acquisition and reading protocols were designed in conjunction with expert consultants. Image acquisition is performed by a team of radiographers trained and certified in each of the protocols. This group, augmented by trained lay readers, also analyzes all images except the retinal photographs, which are read by an independent reading center. Scans are first reviewed by a radiologist for major clinical abnormalities. Image analysis is generally semiautomated. All information, including images, are deidentified prior to transfer into the permanent study database.
Phenotypic data will be combined with supplemental data on clinical outcomes. Sources of supplemental data include registries of vital status, cardiovascular disease and procedures, and fractures; hospital records with International Classification of Diseases, Ninth Revision, and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes; the Minimum Data Set for Nursing Home patients (12); and the Minimum Data Set for Home-Care patients (13, 14). Registries are based on medical record data using predetermined algorithmic criteria.
Standardized quality control protocols have been established for the clinical and laboratory measures, image acquisition, and image analysis. For all image modalities, a 5–10 percent random sample is reread by consulting experts. In addition, a standard set of scans for each core measure is reread over the year by the image analysis team to monitor drift in the readings. For the laboratory, all analyses are controlled with a set of daily internal quality control samples, and quality assurance samples are measured monthly in accordance with the organization External Quality Assurance in Laboratory Medicine in Sweden (EQUALIS) (37). Imaging machines are also monitored with daily, weekly, and monthly measures.
Genotyping will be carried out at both the Icelandic Heart Association and other laboratories. With high-throughput genotyping becoming more available, collaborations with other studies with similar phenotypic data are planned for initial gene discovery and for replication.
AGES–Reykjavik was approved by the National Bioethics Committee in Iceland that acts as the institutional review board for the Icelandic Heart Association (approval number VSN-00-063) and by the National Institute on Aging Intramural Institutional Review Board. A multistage consent is obtained for AGES–Reykjavik to cover participation, use of specimens and DNA, and access to administrative records. All requests to merge AGES–Reykjavik data with administrative, genealogic, hospital, or nationally maintained databases are reviewed by the Icelandic Data Protection Authority. Release of data for analysis is governed by rules created by these bodies to protect the privacy of Icelandic participants.
Starting in 2007, all surviving AGES–Reykjavik participants will be recruited for a second examination that is restricted to components central to testing hypotheses related to the four study areas and will show change over time. The planned measurements are shown in tables 2 and 3.
Statistical methods
Selected cardiovascular risk factors are compared for all Reykjavik Study participants eligible for AGES–Reykjavik, for the first 1,310 men and 1,933 women invited to participate in AGES–Reykjavik, and for the first 976 men and 1,324 women enrolled. Not described are the additional 3,464 participants enrolled in AGES–Reykjavik. Those eligible are compared with those invited, and nonresponding invited persons are compared with those enrolled. The following factors are compared: total cholesterol, triglycerides (log-transformed and then back-transformed), fasting glucose, systolic blood pressure, and body mass index (weight in kilograms divided by height in meters squared)) (25). In AGES–Reykjavik, lipids and glucose were assessed by using a Hitachi 912 clinical chemistry analyzer (Roche Diagnostics, Basel, Switzerland, 1999) with quality assessment standards comparable to those used in the Reykjavik Study.
All age-adjusted regression models were created separately for men and women by using the SAS PROC GENMOD procedure (38) (tables 5 and 6). Midlife data were adjusted to age = 50 years and AGES–Reykjavik data to age = 76 years. Age-adjusted linear regression was used to compare groups regarding continuously distributed data; logistic regression models were used to study smoking.
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Among the first 2,300 enrolled participants, we compared measures of cardiovascular risk factors from midlife with their current measurements (table 7). Repeated-measures generalized estimation models were used, with age at entry and time between visits as covariates.
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To illustrate the power of obtaining detailed measures on several biologic systems, we identified a key measurement from each of the four focus areas of the study and assessed their joint prevalence in the first 2,300 of the total 5,764 persons enrolled in the cohort. We examined trabecular bone mass, performance on two cognitive tests, fasting insulin, and arterial calcification (table 8). Trabecular bone mass was measured from the quantitative computed tomography scans of the femoral neck and spine (39). For insulin, cognition, and trabecular bone density, scores below gender-specific medians were considered low (table 8). Higher arterial calcification, imaged with helical computed tomography and calculated as an Agatston score (40), was defined as calcification in four of the five sites examined, including the ascending and descending aorta, the combined coronary arteries, and the thoracic and abdominal aorta. For persons missing data on one site, if calcium was present at all other sites analyzed, they were considered at high risk. For this illustrative example, we selected cutpoints that would provide overlap between traits; if other cutpoints had been defined, the overlap proportions would have changed.
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RESULTS |
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Total eligible Reykjavik Study cohort versus randomly selected AGES–Reykjavik invitees
As of March 2002, 11,549 Reykjavik Study participants were alive, including 4,800 men (41.6 percent of those alive). From this group, a random sample of 1,310 men was invited to the AGES–Reykjavik clinic through February 2004. We first compared mean midlife values of cardiovascular risk factors for the 4,800 living, eligible men with those for the 1,310 invited to the AGES–Reykjavik examination (table 5). Those invited had higher total cholesterol, lower triglycerides, higher systolic blood pressure, and lower body mass index in midlife than the average midlife values for the pool of men alive. A similar analysis for women also showed differences between women who participated in the Reykjavik Study and those invited to participate in AGES–Reykjavik, but the factors that differed were not the same as those for men. Of the 6,749 living, eligible women, a random sample of 1,933 women was invited to attend the AGES–Reykjavik examination. Compared with all living Reykjavik Study women, the 1,933 invited had significantly lower triglycerides, lower fasting blood glucose, and lower body mass index and included a smaller percentage of smokers (table 6).
Responders versus nonresponders through February 2004
Among the 1,310 men invited, 976 (response rate of 75 percent) agreed to participate in the study. Compared with those who refused, participants had significantly lower midlife triglycerides, fasting blood glucose, and systolic blood pressure (table 5). The percentage of men who smoked in midlife was similar in the two groups, as was midlife total cholesterol level and body mass index. Of the 1,933 women invited, 1,324 participated in the examination (response rate of 68 percent). Women who participated in AGES–Reykjavik had significantly lower midlife glucose and systolic blood pressure levels and were less likely than nonresponders to have been a smoker (table 6). Body mass index, total cholesterol, and triglycerides did not differ between these groups. For both men and women, nonresponse was greater among persons with a previously poor cardiovascular risk profile, particularly for systolic blood pressure and blood glucose.
Midlife versus late-life characteristics of the first 2,300 participants recruited for the AGES–Reykjavik Study
Among the first 2,300 participants, all measures differed significantly between midlife and late life, with the exception of triglyceride levels in men (table 7). Interestingly, other than body mass index, midlife and older-age measurements were only moderately correlated, with the lowest correlations for systolic blood pressure and fasting glucose. Body mass index, glucose, and systolic blood pressure all increased into old age, as did triglyceride levels in women; only total cholesterol levels decreased.
Joint prevalence of health measures
In this older population, overlap between measures representing the four focus areas of the study (trabecular bone mass, cognitive test performance, fasting insulin, and arterial calcification) was more common than the occurrence of a single characteristic (figure 1): the prevalence of each alone was less than 3 percent, except for arterial calcification, which was 9 percent. Forty percent of the participants had three of the four defined characteristics, with the most common combination being lower trabecular bone mass, more arterial calcification, and lower cognitive score (18 percent); the least common combination involved lower trabecular bone mass, poorer cognition, and higher insulin level (1 percent). Variation among these characteristics can be used to study successful aging, with few diseases, or to study the extreme of frailty, often accompanied by multiple health conditions.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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A major goal of AGES–Reykjavik is intensive, quantitative trait identification, within and across biologic systems, for studying the genetic contribution to diseases of old age. Because of the in-depth characterization within and between multiple physiologic systems, this study should also create a valuable resource for a comprehensive study of aging.
Many system-specific studies of the contribution of genetics to complex disorders have been undertaken. To our knowledge, this is one of the few designed a priori to comprehensively phenotype a cohort for multiple diseases, where the target conditions were selected based on the potential of genetic factors that contribute either to the discrete disease state or to quantitative traits that might underlie these conditions. The comprehensive phenotyping in AGES–Reykjavik should allow for broader exploration of contributing genes and should be particularly valuable for analyzing markers of whole genome single nucleotide polymorphisms. The range of phenotypic characterization of the cohort, from clinically recognized conditions defined by criteria-based diagnoses to novel intermediate endophenotypes based on noninvasive technologies integrated with genetic, biochemical, physiologic, and performance-based measures of health and function, should provide a rich basis for newly proposed analytic approaches, such as reverse phenotyping (41).
As the world's population ages, a major challenge is to unravel the pathways to disease and disability in older persons. Iceland and other industrialized countries share the same major chronic diseases, with similar rates of cognitive and physical impairment. Focusing on this population will allow development of innovative approaches to studying how people reach old age and what factors enable older persons to enjoy a healthy old age. Practically, studies such as this one, which require extensive long-term data, can be achieved only by leveraging longitudinal studies onto existing cohorts that have already accrued data, thereby facilitating a life-course approach to understanding the trajectories of disease and disability. Studies such as this one complement the "organ-specific" studies of health in old age and provide an opportunity for extending the findings in a context that can identify homologies between and among conditions that may better reveal factors that affect multiple conditions. From this perspective, measurements in the study were selected on the basis of well-designed population studies contemporary with AGES–Reykjavik, and collaborations with investigators outside of the study will continue to be sought to augment these measurements.
Studies such as AGES–Reykjavik that take advantage of existing data resources can also address methodological problems. The question of selective survival or selective participation often arises in studies of older populations, although it has been argued that the associations of risk factors within the survivors are unaffected by the bias. Because data from earlier life exist from the original study, it will be possible to model the effect that both survival and nonparticipation might have on the direction and strength of associations observed between risk factors and outcomes. This might be particularly important in estimating risks for older women, who tend to live longer but to be frailer and therefore have lower rates of study participation. Selective participation of healthier older persons in this cohort is reflected in at least two ways. First, the response rate for older women is lower than for older men because older women are frailer and more likely to be institutionalized. Second, the midlife profile of the nonresponders shows higher blood pressure and higher glucose, both major contributors to health in old age. Again, because the study was nested within the Reykjavik Study, these potential biases are known (unlike most studies of aging, where older persons are sampled de novo), and we hope to use the earlier data to model sensitivity of our results to these factors.
The design of the AGES–Reykjavik Study represents an integrative approach to methodological problems that may affect studies of genetics and studies of aging. As with many of the ongoing major cohort studies, it is hoped that this one will serve as the basis for ancillary studies that utilize the biologic specimens and the image database for studies consistent with the original consent obtained from the participants.
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ACKNOWLEDGMENTS |
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This study was funded by National Institutes of Health contract N01-AG-12100, the National Institute on Aging Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). Components of the study were also supported by the National Eye Institute, the National Institute on Deafness and Other Communication Disorders, and the National Heart, Lung, and Blood Institute.
Collaborators: Dr. Uggi Agnarsson (Icelandic Heart Association, Kopavagur, Iceland, and Landspitali University Hospital, Reykjavik, Iceland); Drs. Elin Olafsdottir, Nikulas Sigfusson, and Birna Jonsdottir; Kristin Siggeirsdottir and Sigurdur Sigurdsson (Icelandic Heart Association, Kopavagur, Iceland); Drs. Rafn Benediktsson, Fridbert Jonasson, Helgi Jonsson, and Hannes Petersen (Landspitali University Hospital and University of Iceland, Reykjavik, Iceland); Drs. Halldora Bjornsdottir, Bjorn Einarsson, Jon H. Eliasson, Adalsteinn Gudmundsson, Maria Jonsdottir, Thordur Sigmundsson, Albert P. Sigurdsson, and Sigurlaug Sveinbjornsdottir (Landspitali University Hospital, Reykjavik, Iceland); Dr. Thorvaldur Ingvarsson (FSA University Hospital, Akureyri, Iceland); Dr. Johannes Kari Kristinsson (Sjónlag, Reykjavik, Iceland); Smari Kristinsson (Raförninn, Reykjavik, Iceland); Dr. Stefan Kristjansson (Fornix, Reykjavik, Iceland); Dr. Laufey Steingrimsdottir (Agricultural University of Iceland, Hvanneyri, Iceland); Dr. Andrew E. Arai (National Heart, Lung, and Blood Institute, Bethesda, Maryland); and Dr. Gary Mitchell (Cardiovascular Engineering, Inc., Waltham, Massachusetts).
Consultants: Dr. Michiel Bots (Utrecht University, Utrecht, the Netherlands); Dr. Robert Detrano (University of California, Los Angeles, California); Dr. John Hardy (National Institute on Aging, Bethesda, Maryland); Ron Klein (University of Wisconsin, Madison, Wisconsin); Barbara Klein (University of Wisconsin, Madison, Wisconsin); Dr. Thomas F. Lang (University of California, San Francisco, California); Dr. Oscar Lopez (University of Pittsburgh, Pittsburgh, Pennsylvania); Rudy Meijer (Utrecht University, Utrecht, the Netherlands); Dr. David Owens (University of Washington, Seattle, Washington); Dr. Jonathan Plehn (George Washington University School of Medicine, Washington, DC); Dr. Ilmari Pyykko (University of Tampere, Tampere, Finland); Dr. Mark A. van Buchem (Leiden University Medical Center, Leiden, the Netherlands); and Dr. Alex Zijdenbos (Neuralyse, Montreal, Quebec, Canada).
Advisory Committee: Dr. Vladimir Hachinski (University of Western Ontario, London, Ontario, Canada); Dr. Nick Bryan (University of Pennsylvania, Philadelphia, Pennsylvania); Dr. Steven Cummings (California Pacific Medical Center Research Institute, San Francisco, California); Dr. Karl Tryggvason (Karolinska Institute, Stockholm, Sweden); and Dr. Robert Ferrell (University of Pittsburgh, Pittsburgh, Pennsylvania).
Conflict of interest: none declared.
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