Prediction of type 2 diabetes using alternate anthropometric measures in a multi-ethnic cohort: the insulin resistance atherosclerosis study.
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SUBMITTER: MacKay MF
PROVIDER: S-EPMC2671128 | biostudies-literature | 2009 May
REPOSITORIES: biostudies-literature
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Prediction of type 2 diabetes using alternate anthropometric measures in a multi-ethnic cohort: the insulin resistance atherosclerosis study.
MacKay Meredith F MF Haffner Steven M SM Wagenknecht Lynne E LE D'Agostino Ralph B RB Hanley Anthony J G AJ
Diabetes care 20090205 5
<h4>Objective</h4>To compare different anthropometric measures in terms of their ability to predict type 2 diabetes and to determine whether predictive ability was modified by ethnicity.<h4>Research design and methods</h4>Anthropometry was measured at baseline for 1,073 non-Hispanic white (nHW), African American (AA), and Hispanic (HA) subjects, of whom 146 developed type 2 diabetes after 5.2 years. Logistic regression models were used with areas under the receiver operator characteristic curve ...[more]
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Project description:This study compares a previously developed Diabetes Risk Score to commonly used clinical tools for type 2 diabetes risk evaluation in the Insulin Resistance Atherosclerosis Study (IRAS) cohort, a multi-ethnic US cohort. Available as a clinical test, the PreDx® Diabetes Risk Score uses fasting concentrations of adiponectin, C-reactive protein, ferritin, interleukin-2 receptor alpha, HbA(1c) , glucose and insulin, plus age and gender to predict 5-year risk of diabetes. It was developed in a Northern European population.The Diabetes Risk Score was measured using archived fasting plasma specimens from 722 non-diabetic IRAS participants, 17.6% of whom developed diabetes during 5.2?years median follow-up (inter-quartile range: 5.1-5.4?years). The study included non-Hispanic whites (41.8%), Hispanics (34.5%) and African Americans (23.7%). Performance of the algorithm was evaluated by area under the receiver operating characteristic curve (AROC) and risk reclassification against other tools.The Diabetes Risk Score discriminates participants who developed diabetes from those who did not significantly better than fasting glucose (AROC?=?0.763 versus 0.710, p?=?0.003). The Diabetes Risk Score performed equally well in subpopulations defined by race/ethnicity or gender. The Diabetes Risk Score provided a significant net reclassification improvement of 0.24 (p?=?0.01) when comparing predefined low/moderate/high Diabetes Risk Score categories to metabolic syndrome risk factor counting. The Diabetes Risk Score complemented the use of the oral glucose tolerance test by identifying high risk patients with impaired fasting glucose but normal glucose tolerance, 33% of whom converted.Measuring the Diabetes Risk Score of elevated-risk US patients could help physicians decide which patients warrant more intensive intervention. The Diabetes Risk Score performed equally well across the ethnic subpopulations present in this cohort.
| S-EPMC5931211 | biostudies-literature
Project description:ObjectiveThe study objective was to examine the impact of race/ethnicity on associations between anthropometric measures and diabetes risk.Research design and methodsA total of 136,112 postmenopausal women aged 50-79 years participating in the Women's Health Initiative without baseline cancer or diabetes were followed for 14.6 years. BMI, waist circumference (WC), and waist-to-hip ratio (WHR) were measured in all participants, and a subset of 9,695 had assessment of whole-body fat mass, whole-body percent fat, trunk fat mass, and leg fat mass by DXA. Incident diabetes was assessed via self-report. Multivariate Cox proportional hazards regression models were used to assess associations between anthropometrics and diabetes incidence.ResultsDuring follow-up, 18,706 cases of incident diabetes were identified. BMI, WC, and WHR were all positively associated with diabetes risk in each racial and ethnic group. WC had the strongest association with risk of diabetes across all racial and ethnic groups. Compared with non-Hispanic whites, associations with WC were weaker in black women (P < 0.0001) and stronger in Asian women (P < 0.0001). Among women with DXA determinations, black women had a weaker association with whole-body fat (P = 0.02) but a stronger association with trunk-to-leg fat ratio (P = 0.03) compared with white women.ConclusionsIn postmenopausal women across all racial/ethnic groups, WC was a better predictor of diabetes risk, especially for Asian women. Better anthropometric measures that reflect trunk-to-leg fat ratio may improve diabetes risk assessment for black women.
| S-EPMC6463546 | biostudies-literature
Project description:IntroductionThe purpose of this study was to evaluate if adding SES to risk prediction models based upon traditional risk factors improves the prediction of diabetes.MethodsRisk prediction models without and with individual- and area-level SES predictors were compared using the prospective Multi-Ethnic Study of Atherosclerosis. Cox proportional hazards models were utilized to estimate hazard ratios for SES predictors and to generate 10-year predicted risks for 5,021 individuals without diabetes at baseline followed from 2000 to 2012. C-statistics were used to compare model discrimination, and the proportion of individuals reclassified into higher or lower risk categories with the addition of SES predictors was calculated. The accuracy of risk prediction by SES was assessed by comparing observed and predicted risks across tertiles of the SES variables. Statistical analyses were performed in 2015-2016.ResultsOver a median of 9.2 years of follow-up, 615 individuals developed diabetes. Individual- and area-level SES variables did not significantly improve model discrimination or reclassify substantial numbers of individuals across risk categories. Models without SES predictors generally underestimated risk for low-SES individuals or individuals residing in low-SES areas (underestimates ranging from 0.31% to 1.07%) and overestimated risk for high-SES individuals or individuals residing in high-SES areas (overestimates ranging from 0.70% to 1.30%), and the addition of SES variables largely mitigated these differences.ConclusionsStandard diabetes risk models may underestimate risk for low-SES individuals and overestimate risk for those of high SES. Adding SES predictors helps correct this systematic misestimation, but may not improve model discrimination.
| S-EPMC5584566 | biostudies-literature
Project description:BackgroundRecent studies have found low-normal potassium (K) to be associated with increased diabetes risk. We sought to verify these associations in a multi-ethnic US cohort; and to determine if these associations extend to US Hispanics and Asian-Americans.MethodsWe analyzed data from Multi-Ethnic Study of Atherosclerosis (MESA) participants who were free-of-diabetes at baseline. We examined cross-sectional associations between measures of K-serum, dietary, and urine-with fasting glucose and HOMA-IR. We examined longitudinal associations between K and diabetes risk over 8 years.FindingsIn multivariable models, compared to those with higher serum K (?4.5mmol/L), those with lower serum K (<4.0mmol/L) had significantly higher fasting glucose [1.3 mg/dL (95%CI 0.2, 2.4), P-value = 0.03]. Incident diabetes developed in 1281 of 5415 at-risk participants. In minimally-adjusted models, we found inverse associations between serum and dietary K and diabetes risk. Compared to those with higher serum K, those with lower serum K had an HR (95% CI) of incident diabetes of 1.23 (1.04, 1.47), P-value = 0.02. However, these associations were attenuated in fully-adjusted models. We found no significant interaction between potassium and ethnicity.ConclusionsIn this multi-ethnic cohort, we found a significant inverse association between serum K and fasting glucose but no significant association with longer-term diabetes risk. This inverse association between potassium and glucose must be studied further to understand the physiology and its potential impact on chronic health.
| S-EPMC4900670 | biostudies-literature
Project description:BackgroundExisting equations for prediction of atrial fibrillation (AF) have been developed and validated in white and African-American populations. Whether these models adequately predict AF in more racially and ethnically diverse populations is unknown.Methods and resultsWe studied 6663 men and women 45 to 84 years of age without AF at baseline (2000-2002) enrolled in the Multi-Ethnic Study of Atherosclerosis (MESA). Of these, 38% were non-Hispanic whites, 28% non-Hispanic African Americans, 22% Hispanics, and 12% Chinese Americans. AF during follow-up was ascertained from hospitalization discharge codes through 2012. Information collected at baseline was used to calculate predicted 5-year risk of AF using the previously published simple CHARGE-AF model, which only includes clinical variables, and a biomarker-enriched CHARGE-AF model, which also considers levels of circulating N-terminal of the prohormone B-type natriuretic peptide and C-reactive protein. For comparison purposes, we also assessed performance of the 10-year Framingham AF model. During a mean follow-up of 10.2 years, 351 cases of AF were identified. The C-statistic of the CHARGE-AF models were 0.779 (95% CI, 0.744-0.814) for the simple model and 0.825 (95% CI, 0.791-0.860) for the biomarker-enriched model. Calibration was adequate in the biomarker-enriched model (χ(2)=7.9; P=0.55), but suboptimal in the simple model (χ(2)=25.6; P=0.002). In contrast, the 10-year Framingham score had a C-statistic (95% CI) of 0.746 (0.720-0.771) and showed poor calibration (χ(2)=57.4; P<0.0001).ConclusionThe CHARGE-AF risk models adequately predicted 5-year AF risk in a large multiethnic cohort. These models could be useful to select high-risk individuals for AF screening programs or for primary prevention trials in diverse populations.
| S-EPMC4802458 | biostudies-literature
Project description:IntroductionTobacco use is associated with insulin resistance and incident diabetes. Given the racial/ethnic differences in smoking patterns and incident type 2 diabetes our objective was to evaluate the association between tobacco use and insulin resistance (IR) as well as incident type 2 diabetes mellitus in a contemporary multiethnic cohort.Methods and resultsWe studied 5,931 Multi- Ethnic Study of Atherosclerosis (MESA) participants who at baseline were free of type 2 diabetes (fasting glucose ≥7.0 mmol/l (126 mg/dl) and/or use of insulin or oral hypoglycemic medications) categorized by self-reported tobacco status and reclassified by urinary cotinine (available in 58% of participants) as never, current or former tobacco users. The association between tobacco use, IR (fasting plasma glucose, insulin, and the homeostatic model assessment of insulin resistance (HOMA-IR)) and incident diabetes over 10 years was evaluated using multivariable linear regression and Cox proportional hazards models, respectively. Mean age of the participants was 62 (±10) years, 46% were male, 41% Caucasian, 12% Chinese, 26% African American and 21% Hispanic/Latino. IR biomarkers did not significantly differ between current, former, and never cigarette users (P >0.10) but showed limited unadjusted differences for users of cigar, pipe and smokeless tobacco (All P <0.05). Fully adjusted models showed no association between dose or intensity of tobacco exposure and any index of IR. When stratified into participants that quit smoking vs. those who continued smoking during the 10-year study there was no difference in serum glucose levels or frequency of diabetes. In fully adjusted models, there was no significant difference in diabetes risk between former or current cigarette smokers compared to never smokers [HR (95% CI) 1.02 (0.77,1.37) and 0.81 (0.52,1.26) respectively].ConclusionIn a contemporary multi-ethnic cohort, there was no independent association between tobacco use and IR or incident type 2 diabetes. The role smoking plays in causing diabetes may be more complicated than originally thought and warrants more in-depth large contemporary multi-ethnic studies.
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Project description:OBJECTIVE:Heart failure (HF) is a leading cause of mortality especially in older populations. Early detection of high-risk individuals is imperative for primary prevention. The purpose of this study was to develop a HF risk model from a population without clinical cardiac disease. METHODS:The Multi-Ethnic Study of Atherosclerosis is a multicentre observational cohort study following 6814 subjects (mean age 62±10?years; 47% men) who were free of clinical cardiovascular disease at baseline. Median follow-up was 4.7?years. HF events developed in 176 participants. Cox proportional hazards models and regression coefficients were used to determine independent risk factors and generate a 5-year risk score for incident HF. Bootstrapping with bias correction was used for internal validation. RESULTS:Independent predictors for HF (HR, p value) were age (1.30 (1.10 to 1.50) per 10?years), male gender (2.27 (1.53 to 3.36)), current smoking (1.97 (1.15 to 3.36)), body mass index (1.40 (1.10 to 1.80) per 5?kg/m(2)), systolic blood pressure (1.10 (1.00 to 1.10) per 10?mm?Hg), heart rate (1.30) (1.10 to 1.40) per 10?bpm), diabetes (2.27 (1.48 to 3.47)), N-terminal pro-B-type natriuretic peptide (NT proBNP) (2.48 (2.16 to 2.84) per unit log increment) and left ventricular mass index (1.40 (1.30 to 1.40) per 10?g/m(2)). A parsimonious model based on age, gender, body mass index, smoking status, systolic blood pressure, heart rate, diabetes and NT proBNP natriuretic peptide predicted incident HF risk with a c-statistic of 0.87. CONCLUSIONS:A clinical algorithm based on risk factors readily available in the primary care setting can used to identify individuals with high likelihood of developing HF without pre-existing cardiac disease.
| S-EPMC4685458 | biostudies-literature
Project description:<p><b>MESA</b><br/> The Multi-Ethnic Study of Atherosclerosis (MESA) is a study of the characteristics of subclinical cardiovascular disease (disease detected non-invasively before it has produced clinical signs and symptoms) and the risk factors that predict progression to clinically overt cardiovascular disease or progression of the subclinical disease. MESA researchers study a diverse, population-based sample of 6,814 asymptomatic men and women aged 45-84. Thirty-eight percent of the recruited participants are white, 28 percent African-American, 22 percent Hispanic, and 12 percent Asian, predominantly of Chinese descent.</p> <p>Participants were recruited from six field centers across the United States: Wake Forest University, Columbia University, Johns Hopkins University, University of Minnesota, Northwestern University and University of California - Los Angeles. Each participant received an extensive physical exam to determine coronary calcification, ventricular mass and function, flow-mediated endothelial vasodilation, carotid intimal-medial wall thickness and presence of echogenic lucencies in the carotid artery, lower extremity vascular insufficiency, arterial wave forms, electrocardiographic (ECG) measures, standard coronary risk factors, sociodemographic factors, lifestyle factors, and psychosocial factors. Selected repetition of subclinical disease measures and risk factors at follow-up visits allows study of the progression of disease. Blood samples have been assayed for putative biochemical risk factors and stored for case-control studies. DNA has been extracted and lymphocytes cryopreserved (for possible immortalization) for study of candidate genes and possibly, genome-wide scanning, expression, and other genetic techniques. Participants are being followed for identification and characterization of cardiovascular disease events, including acute myocardial infarction and other forms of coronary heart disease (CHD), stroke, and congestive heart failure; for cardiovascular disease interventions; and for mortality.</p> <p>In addition to the six Field Centers, MESA involves a Coordinating Center, a Central Laboratory, and Central Reading Centers for Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, and Electrocardiography (ECG). Protocol development, staff training, and pilot testing were performed in the first 18 months of the study. The first examination took place over two years, from July 2000 - July 2002. It was followed by four examination periods that were 17-20 months in length. Participants have been contacted every 9 to 12 months throughout the study to assess clinical morbidity and mortality.</p> <p><b>MESA Family</b><br/> The general goal of the MESA Family Study, an ancillary study to MESA funded by a grant from NHLBI, is to apply modern genetic analysis and genotyping methodologies to delineate the genetic determinants of early atherosclerosis. This is being accomplished by utilizing all the current organizational structures of the <b>Multi-Ethnic Study of Atherosclerosis (MESA)</b> and Genetic Centers at Cedars-Sinai Medical Center and University of Virginia.</p> <p>In the MESA Family Study, the goal is to locate and identify genes contributing to the genetic risk for cardiovascular disease (CVD), by looking at the early changes of atherosclerosis within families (mainly siblings). 2128 individuals from 594 families, yielding 3,026 sibpairs divided between African Americans and Hispanic-Americans, were recruited by utilizing the existing framework of MESA. MESA Family studied siblings of index subjects from the MESA study and from new sibpair families (with the same demographic characteristics) and is determining the extent of genetic contribution to the variation in coronary calcium (obtained via CT Scan) and carotid artery wall thickness (B-mode ultrasound) in the two largest non-majority U.S. populations. In a small proportion of subjects, parents of MESA index subjects participating in MESA Family were studied but only to have blood drawn for genotyping.</p> <p>The MESA Family cohort was recruited from the six MESA Field Centers. MESA Family participants underwent the same examination as MESA participants during May 2004 - May 2007. DNA was extracted and lymphocytes immortalized for study of candidate genes, genome-wide linkage scanning, and analyzed for linkage with these subclinical cardiovascular traits. While linkage analysis is the primary approach being used, an additional aspect of the MESA Family Study takes advantage of the existing MESA study population for testing a variety of candidate genes for association with the same subclinical traits. Genotyping and data analysis will occur throughout the study.</p> <p><b>MESA Air</b><br/> The general goal of the Multi-Ethnic Study of Atherosclerosis and Air Pollution ('MESA Air') is to prospectively examine the relation between an individual level assessment of long-term ambient air pollution exposures (including PM<sub>2.5</sub> and the progression of subclinical cardiovascular disease in a multi-city, multi-ethnic cohort. MESA Air will also prospectively examine the relationship between an individual level assessment of long-term ambient air pollution exposures and the incidence of cardiovascular disease, including myocardial infarction and cardiovascular death. MESA AIR is funded by a grant from the United States Environmental Protection Agency to the University of Washington and subcontracts from the UW to other participating institutions.</p> <p>MESA Air will assess if ambient air pollution is associated with changes over time in subclinical measures of atherosclerosis and plasma markers of inflammation, oxidative damage, and endothelial activation in a longitudinal data model, adjusting for age, race/ethnicity, socioeconomic status, and specific cardiovascular risk factors (such as diabetes, hypertension, smoking, and diet). The study will similarly assess if the incidence of cardiovascular events is associated with long-term exposure to ambient air pollution, using a proportional hazards model. The study includes refinement of statistical tools, and explores joint/independent effects of acute and long-term pollutant exposure in the occurrence of cardiovascular disease.</p> <p>The MESA Air study is built on the foundation of the ongoing MESA study. The parent MESA Study cohort is located in six geographic areas ('Field Centers') that capture tremendous exposure heterogeneity, comparable to or greater than the variability in locations of prior U.S. cohort studies. In addition to the six Field Centers, the study involves a Coordinating Center, a Central Laboratory, and Reading Centers for Computed Tomography (CT), ultrasound and air pollution data.</p> <p>The cohort for the MESA Air study currently includes 6226 subjects: 5479 enrolled in the parent MESA study; 257 recruited specifically for this study, and 490 recruited from the MESA Family study. The entire MESA Air cohort will be followed over a 10-year project period for the occurrence of cardiovascular disease events.</p> <p>On two occasions over the ten-year study period, 3600 subjects from the MESA Air cohort, residing in nine locales, will undergo computed tomography scanning to assess presence and extent of coronary artery calcification (CAC), and ultrasound of the carotid artery to determine intima-media thickness (IMT). We will also repeatedly assess plasma markers of inflammation, oxidative damage, and endothelial function in 720 subjects.</p> <p>MESA Air adds state-of-the-art air pollution exposure assessment information to the MESA cohort study, and introduces new subjects and outcome measures to achieve our aims. The study will assess long-term individual-level exposure to ambient air pollutants for each subject using community-scale monitoring, outdoor spatial variation, subject proximity to pollution sources, pollutants' infiltration efficiency, and personal time-activity information. The exposure models will be validated using detailed monitoring in a subset of subjects.</p> <p><b>The MESA Cohort is utilized in the following dbGaP substudies.</b> To view genotypes, analysis, expression data, other molecular data, and derived variables collected in these substudies, please click on the following substudies below or in the "Substudies" box located on the right hand side of this top-level study page phs000209 MESA Cohort. <ul> <li><a href="./study.cgi?study_id=phs000420">phs000420</a> MESA SHARe</li> <li><a href="./study.cgi?study_id=phs000283">phs000283</a> MESA CARe</li> </ul> </p>
| phs000209.v10.p2 | EGA
Project description:RATIONALE:Machine learning may be useful to characterize cardiovascular risk, predict outcomes, and identify biomarkers in population studies. OBJECTIVE:To test the ability of random survival forests, a machine learning technique, to predict 6 cardiovascular outcomes in comparison to standard cardiovascular risk scores. METHODS AND RESULTS:We included participants from the MESA (Multi-Ethnic Study of Atherosclerosis). Baseline measurements were used to predict cardiovascular outcomes over 12 years of follow-up. MESA was designed to study progression of subclinical disease to cardiovascular events where participants were initially free of cardiovascular disease. All 6814 participants from MESA, aged 45 to 84 years, from 4 ethnicities, and 6 centers across the United States were included. Seven-hundred thirty-five variables from imaging and noninvasive tests, questionnaires, and biomarker panels were obtained. We used the random survival forests technique to identify the top-20 predictors of each outcome. Imaging, electrocardiography, and serum biomarkers featured heavily on the top-20 lists as opposed to traditional cardiovascular risk factors. Age was the most important predictor for all-cause mortality. Fasting glucose levels and carotid ultrasonography measures were important predictors of stroke. Coronary Artery Calcium score was the most important predictor of coronary heart disease and all atherosclerotic cardiovascular disease combined outcomes. Left ventricular structure and function and cardiac troponin-T were among the top predictors for incident heart failure. Creatinine, age, and ankle-brachial index were among the top predictors of atrial fibrillation. TNF-? (tissue necrosis factor-?) and IL (interleukin)-2 soluble receptors and NT-proBNP (N-Terminal Pro-B-Type Natriuretic Peptide) levels were important across all outcomes. The random survival forests technique performed better than established risk scores with increased prediction accuracy (decreased Brier score by 10%-25%). CONCLUSIONS:Machine learning in conjunction with deep phenotyping improves prediction accuracy in cardiovascular event prediction in an initially asymptomatic population. These methods may lead to greater insights on subclinical disease markers without apriori assumptions of causality. CLINICAL TRIAL REGISTRATION:URL: http://www.clinicaltrials.gov. Unique identifier: NCT00005487.
| S-EPMC5640485 | biostudies-literature
Project description:Hypercortisolism is associated with insulin resistance (IR) and diabetes mellitus (DM); however, to our knowledge prior studies have not examined the association of diurnal cortisol curve features with measures of glycemia or IR in a population-based setting. Using log-transformed salivary cortisol data on 850 ethnically diverse men and women from the Multi-Ethnic Study of Atherosclerosis, we investigated the cross-sectional association of cortisol curve features with (1) glycemia in those with and without DM and (2) IR, in non-diabetic subjects. The log-transformed salivary cortisol curve features included wake-up cortisol, cortisol awakening response (CAR), early decline slope (30min to 2h post-awakening), late decline slope (2h post-awakening to bedtime), overall decline slope (0min to bedtime, excluding 30min cortisol), bedtime cortisol and total area under the curve (AUC). Overall, following multivariable adjustment, among those with diabetes mellitus (DM), early decline slope, overall decline slope, bedtime cortisol, and AUC were significantly and positively associated with a 5.4% (95% CI: 1.3, 9.7), 54.7% (95% CI: 12.4, 112.9), 4.0% (95% CI: 1.6,6.4), and 6.8% (95% CI: 3.3,10.4) higher HbA1c per 1 unit increase in log cortisol feature, respectively. Cortisol curve features were not associated with HbA1c among non-diabetic participants; however, wake-up cortisol and AUC were associated with a 8.2% lower (95% CI: -13.3,-2.7) and 7.9% lower (95% CI: -14.6, -0.6) log HOMA-IR, respectively. This was attenuated by adjustment for waist circumference. Among participants with DM, cortisol curve parameters suggestive of higher hypothalamic-pituitary-adrenal (HPA) axis activity and dysfunction were associated with higher HbA1c. In non-diabetic participants, greater HPA activity was paradoxically associated with lower insulin resistance.
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