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Introduction: The relationship between artificially sweetened beverage (ASB) intake and type 2 diabetes (T2D) risk remains inconclusive. Few studies have evaluated whether circulating metabolites that reflect ASB consumption may unveil potential mechanisms underlying the association between ASB consumption and T2D risk. Hypothesis: We hypothesized that a novel metabolomic profile reflecting habitual ASB consumption would positively associate with incident T2D among US adults. Methods: We quantified 120 plasma metabolites with liquid chromatography-mass spectroscopy in N=3,424 Nurses’ Health Study (NHS), NHSII, and Health Professionals Follow-up Study (HPFS) discovery cohort participants, and N=1,870 Framingham Heart Study (FHS) replication cohort participants. Habitual ASB consumption (servings/day; low-calorie cola or other carbonated beverages) was estimated from food frequency questionnaires at the time of blood draw. We used elastic net regression with 10-fold-cross-validation to age- and body mass index- adjusted metabolite levels to identify a metabolite profile associated with higher ASB consumption (ln-transformed). We then derived the continuous ASB metabolomic score as the weighted sum of these metabolites and replicated the analysis in an internal testing set and FHS. Finally, we evaluated the association of quartiles (Q) of the ASB-derived metabolomic score in NHS/NHSII/HPFS and FHS cohorts with incident T2D risk after blood draw (baseline for analysis) using Cox regression models to estimate hazard ratios (HR) and 95% confidence interval (CI), adjusted for demographics, lifestyle, diet, and body mass index at blood draw. Results: The ASB-derived metabolomic profile included 61 metabolites, primarily lipids and amino acids (Pearson correlation coefficients [ r ] between ASB self-reported intake and metabolomic score: r=0.08 [95% CI: 0.05, 0.11; p p =0.001], for discovery and replication, respectively). Mean follow-up was 20 and 16 years from blood draw, including 359 and 241 confirmed incident T2D cases for NHS/NHSII/HPFS and FHS, respectively. Compared with participants in the lowest quartile of the ASB-derived metabolomic profile, higher quartiles were incrementally and significantly at higher T2D risk in NHS/NHSII/HPFS (HR [CI] for Q2, Q3, Q4 vs. Q1: 1.45 [1.04, 2.02], 1.83 [1.33, 2.52], 1.62 [1.18, 2.22], respectively; p-trend =0.003) and FHS (1.76 [1.24, 2.48], 1.82 [1.28, 2.58], 2.45 [1.77, 3.40], respectively; p-trend Conclusions: A robust metabolomic profile of ASB intake was not identified, yet the objective and replicable ASB-derived metabolomic profile was associated with higher incident T2D risk. The identified metabolites may reflect ASB intake, western dietary patterns, underlying metabolic health, and/or T2D-related pathophysiology.

Although less muscle mass is associated with greater diabetes prevalence in cross-sectional studies, prospective data in middle-aged Black and White adults are lacking. Middle age is a critical window as accelerated muscle loss is yet to occur, and preventing diabetes reduces lifelong morbidity and mortality. We hypothesized that lower appendicular lean mass adjusted for body mass index (ALM/BMI) is associated with higher incident diabetes in middle-aged Black and White adults in the Coronary Artery Risk Development in Young Adults (CARDIA) study. ALM/BMI was measured by dual x-ray energy absorptiometry (DXA) in 2005-06 among middle-aged US men (n=855) and women (n=1045) in CARDIA. Incident diabetes occurred if any of the following were met in 2010-11 or 2015-16 among persons without diabetes in 2005-06: fasting glucose ≥7 mmol/L (126 mg/dL), 2-hour glucose ≥11.1 mmol/L (200 mg/dL) on a 75-gram glucose tolerance test, HbA 1C ≥48 mmol/mol (6.5%), or use of glucose-lowering medications. We used logistic regression models with sex stratification given sex differences in ALM. In men, mean age was 45.0 ± 3.5 y, BMI 28.0 ± 4.3 kg/m 2 , and ALM/BMI 1.07 ± 0.14 m 2 . In women, mean age was 45.2 ± 3.6 years, BMI 28.4 ± 6.4 kg/m 2 , and ALM/BMI 0.73 ± 0.12 m 2 . Diabetes developed in 70 men (8.2%) and 72 women (6.9%). For each standard deviation increase in ALM/BMI (m 2 ), the risk of diabetes decreased by 22% in men and 29% in women (Table 1). After adjusting for age, race, smoking, education, physical activity, and waist circumference, the association of ALM/BMI with diabetes incidence was no longer significant. Associations were similar between race-ethnic groups. In conclusion, less relative skeletal muscle mass is associated with a greater risk of developing diabetes in middle-aged men and women over 10 years, which is largely explained by the relationship of ALM/BMI to other metabolic risk factors. Low skeletal muscle mass in middle age is a marker for greater diabetes risk and may be a target for preventative interventions.

Background:Cardiovascular disease (CVD) risk is elevated in HIV-infected individuals, with contributions from both traditional and nontraditional risk factors. The accuracy of established CVD risk prediction functions in HIV is uncertain. We sought to assess the performance of 3 established CVD risk prediction functions in a longitudinal cohort of HIV-infected men.Methods:The FHS (Framingham Heart Study) functions for hard coronary heart disease (FHS CHD) and atherosclerotic CVD (FHS ASCVD) and the American College of Cardiology/American Heart Association ASCVD function were applied to the Partners HIV cohort. Risk scores were calculated between January 1, 2006, and December 31, 2008. Outcomes included CHD (myocardial infarction or coronary death) for the FHS CHD function and ASCVD (myocardial infarction, stroke, or coronary death) for the FHS ASCVD and American College of Cardiology/American Heart Association ASCVD functions. We investigated the accuracy of CVD risk prediction for each function when applied to the HIV cohort using comparison of Cox regression coefficients, discrimination, and calibration.Results:The HIV cohort was comprised of 1272 men followed for a median of 4.4 years. There were 78 (6.1%) ASCVD events; the 5-year incidence rate was 16.4 per 1000 person-years. Discrimination was moderate to poor as indicated by the lowcstatistic (0.68 for FHS CHD, 0.65 for American College of Cardiology/American Heart Association ASCVD, and 0.67 for FHS ASCVD). Observed CVD risk exceeded the predicted risk for each of the functions in most deciles of predicted risk. Calibration, or goodness of fit of the models, was consistently poor, with significant χ2Pvalues for all functions. Recalibration did not significantly improve model fit.Conclusions:Cardiovascular risk prediction functions developed for use in the general population are inaccurate in HIV infection and systematically underestimate risk in a cohort of HIV-infected men. Development of tailored CVD risk prediction functions incorporating traditional CVD risk factors and HIV-specific factors is likely to result in more accurate risk estimation to guide preventative CVD care.

Background— Several biomarkers have been individually associated with vascular brain injury, but no prior study has explored the simultaneous association of a biologically plausible panel of biomarkers with the incidence of stroke/transient ischemic attack and the prevalence of subclinical brain injury. Methods and Results— In 3127 stroke-free Framingham offspring (age, 59±10 years; 54% female), we related a panel of 8 biomarkers assessing inflammation (C-reactive protein), hemostasis (D-dimer and plasminogen activator inhibitor-1), neurohormonal activity (aldosterone-to-renin ratio, B-type natriuretic peptide, and N-terminal proatrial natriuretic peptides), and endothelial function ( homocysteine and urinary albumin/creatinine ratio) measured at the sixth examination (1995–1998) to risk of incident stroke/transient ischemic attack. In a subset of 1901 participants with available brain magnetic resonance imaging (1999–2005), we further related these biomarkers to total cerebral brain volume, covert brain infarcts, and large white-matter hyperintensity volume. During a median follow-up of 9.2 years, 130 participants experienced incident stroke/transient ischemic attack. In multivariable analyses adjusted for stroke risk factors, the biomarker panel was associated with incident stroke/transient ischemic attack and with total cerebral brain volume ( P P >0.05). In backward elimination analyses, higher log–B-type natriuretic peptide (hazard ratio, 1.39 per 1-SD increment; P =0.002) and log–urinary albumin/creatinine ratio (hazard ratio, 1.31 per 1-SD increment; P =0.004) were associated with increased risk of stroke/transient ischemic attack and improved risk prediction compared with the Framingham Stroke Risk Profile alone; when the 15% 10-year risk category was used, the net reclassification index was 0.109 ( P =0.037). Higher C-reactive protein (β=−0.21 per 1-SD increment; P =0.008), D-dimer (β=−0.18 per 1-SD increment; P =0.041), total homocysteine (β=−0.21 per 1-SD increment; P =0.005), and urinary albumin/creatinine ratio (β=−0.15 per 1-SD increment; P =0.042) were associated with lower total cerebral brain volume. Conclusion— In a middle-aged community sample, we identified multiple biomarkers that were associated with clinical and subclinical vascular brain injury and could improve risk stratification.

Each year, the American Heart Association, in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together the most up-to-date statistics on heart disease, stroke, other vascular diseases, and their risk factors and presents them in its Heart Disease and Stroke Statistical Update. The Statistical Update is a valuable resource for researchers, clinicians, healthcare policy makers, media professionals, the lay public, and many others who seek the best national data available on disease morbidity and mortality and the risks, quality of care, medical procedures and operations, and costs associated with the management of these diseases in a single document. This year’s edition includes several areas not covered in previous editions. The 2009 Update Expands Data Coverage of Congenital Cardiovascular Defects and Nutritional/Dietary Intake and Adds a New Chapter on Epidemiology and Statistics of Subclinical Atherosclerosis and a Subsection on Family History of CVD. Chapters are dedicated to: Cardiovascular Diseases; Subclinical Atherosclerosis; Coronary Heart Disease, Acute Coronary Syndrome, and Angina Pectoris; Stroke (Cerebrovascular Disease); High Blood Pressure; Congenital Cardiovascular Defects; Heart Failure; Other Cardiovascular Diseases; Arrhythmias (Disorders of Heart Rhythm); Diseases of Arteries, (Including Peripheral Arterial Disease); Bacterial Endocarditis; Cardiomyopathy; Rheumatic Fever/Rheumatic Heart Disease; Valvular Heart Disease; Venous Thromboembolism; Risk Factor: Smoking/Tobacco Use; Risk Factor: High Blood Cholesterol and Other Lipids; Risk Factor: Physical Inactivity; Risk Factor: Overweight and Obesity; Risk Factor: Diabetes Mellitus; End-Stage Renal Disease and Chronic Kidney Disease; Metabolic Syndrome; Nutrition; Quality of Care; Medical Procedures; Economic Cost of Cardiovascular Diseases; At-a-Glance Summary Tables; Men and Cardiovascular Diseases; Women and Cardiovascular Diseases; Ethnic Groups and Cardiovascular Diseases; Children, Youth, and Cardiovascular Diseases.

Background— The metabolic syndrome (MetS) is associated with increased cardiovascular risk. We evaluated the relative contributions of circulating biomarkers representing distinct biological pathways to the incidence of MetS and to longitudinal changes of its constituent risk factors. Methods and Results— We measured 8 circulating biomarkers reflecting inflammation (C-reactive protein), hemostasis (plasminogen activator inhibitor-1, fibrinogen), neurohormonal activity (aldosterone, renin, B-type natriuretic peptide, N-terminal proatrial natriuretic peptide), and endothelial dysfunction (homocysteine) in 2292 Framingham Offspring Study participants (mean age, 57 years; 56% women). We related the biomarker panel to incidence of MetS on follow-up initially and then related biomarkers associated with incident MetS to longitudinal change in its components. On follow-up (mean, 2.9 years), 282 participants (of 1473 participants without prevalent MetS at baseline) developed MetS. After adjustment for clinical risk factors, the biomarker panel was associated with incident MetS ( P =0.008). On backward elimination, plasminogen activator inhibitor-1 and aldosterone remained associated with incident MetS (multivariable-adjusted odds ratio per 1-SD increment log marker, 1.31 [ P =0.004] and 1.21 [ P =0.015], respectively). In multivariable analyses evaluating longitudinal change in MetS components (analyzed as continuous variables), plasminogen activator inhibitor-1 was significantly and positively associated with changes in fasting glucose, systolic blood pressure, and triglycerides (all P P =0.023) and inversely with change in high-density lipoprotein cholesterol ( P =0.001). Conclusions— Higher circulating plasminogen activator inhibitor-1 and aldosterone levels are associated with the development of MetS and with longitudinal change of its components, suggesting that these biomarkers and related pathways play a key role in mediating metabolic risk.

Background— The burden and prognostic importance of subclinical cardiovascular disease (CVD) in obesity has not been investigated systematically. Methods and Results— We examined prevalence of subclinical disease in 1938 Framingham Study participants (mean age, 57 years; 59% women) by use of 5 tests (electrocardiography, echocardiography, carotid ultrasound, ankle-brachial pressure, and urinary albumin excretion) and stratified by body mass index (BMI) (normal, 2 ) and waist circumference (WC) (increased, ≥88 cm for women or ≥102 cm for men). We investigated risk of overt CVD associated with adiposity according to presence versus absence of subclinical disease on any of the 5 tests. Prevalence of subclinical disease was higher in overweight (40.0%; adjusted odds ratio, 1.68) and obese individuals (49.7%; odds ratio, 2.82) compared with individuals with normal BMI (29.3%) and in individuals with increased WC (44.9%; odds ratio, 1.67) compared with normal WC (31.9%). On follow-up (mean 7.2 years), 139 participants had developed overt CVD. Presence of subclinical disease was associated with >2-fold risk of overt CVD in all BMI and WC strata, with no evidence of an interaction between BMI and subclinical disease. CVD risk was attenuated in participants with obesity or increased WC but without subclinical disease (adjusted hazard ratio for obesity, 1.57; 95% confidence interval, 0.74 to 3.33; adjusted hazard ratio for increased WC, 1.22; 95% confidence interval, 0.69 to 2.15), compared with individuals with normal BMI or WC and no subclinical disease, respectively. Conclusions— In our community-based sample, overweight and obesity were associated with high prevalence of subclinical disease, which partly contributed to the increased risk of overt CVD in these strata.

Background: Previous studies have suggested an association between severe hypoglycemic events (i.e. requiring external assistance) and coronary artery disease (CAD). However, whether this association varies across patients with different vascular risk remains unclear. We tested the hypothesis that prior severe hypoglycemia was independently associated with incident CAD in diabetes patients cared for in primary care, and that this association was increased for those with elevated vascular risk. Methods: We identified a diabetes cohort within a primary care practice network using electronic health records (EHR, N=9,173). Patients with severe hypoglycemic events at baseline (before 1/1/2006) were identified via ICD-9 codes, including 251.0-251.2, from emergency department, inpatient and outpatient visits. Incident CAD, including myocardial infarction, acute coronary syndrome, percutaneous transluminal coronary angiography and coronary artery bypass grafting, occurring after the hypoglycemic event, were determined through problem and procedure lists. Patients were followed until incident CAD, death, their last visit within the network, or 6/30/2012. We used Cox regression with time interaction to determine the association between hypoglycemia and CAD, with significance set at P Results: A total of 285 (3%) patients had a history of severe hypoglycemia at baseline and 1,098 (12%) developed CAD during follow-up. Severe hypoglycemia was associated with a twofold increase in CAD risk [HR 1.90 (95%CI 1.09-3.31)], adjusting for time interaction, vascular risk factors, diabetes duration and HbA1c; the hypoglycemia-CAD association weakened over time (time interaction P=0.06). Further adjustment for microvascular complications and medication exposures attenuated the association [HR 1.67 (0.96-2.92)]. Among 1,823 (20% of the cohort) high vascular risk patients aged ≥55 years, CAD risk was elevated threefold [HR 3.01 (1.15-7.91)], and in those aged ≥65 years (N=996, 11%), fourfold [HR 4.62 (1.65-12.90)]. The association was not significant among lower vascular risk patients [HR 1.61 (0.97-3.75), N=7,350]. Conclusions: Prior severe hypoglycemic episodes were strongly associated with incident CAD, especially among high vascular risk patients who were similar to those studied in the ACCORD trial. Whereas severe hypoglycemia was not as strongly associated with CAD in patients with fewer vascular risk factors. Results of studies like ACCORD only apply to a fifth of diabetes patients in this primary care cohort. Careful evaluation of diabetes treatment and close monitoring for CAD are especially needed in older high vascular risk patients with severe hypoglycemia.

Background— Recent studies indicate that the prevalence of type 2 diabetes mellitus is increasing in the United States; less is known about trends in the incidence of type 2 diabetes mellitus. Methods and Results— Participants free of diabetes mellitus (n=3104; mean age 47 years; 1587 women) from the Framingham Offspring Study who attended a routine examination in the 1970s, 1980s, or 1990s were followed up for the 8-year incidence of diabetes mellitus. Diabetes was defined as a fasting plasma glucose ≥7.0 mmol/L or treatment with either insulin or a hypoglycemic agent. Pooled logistic regression was used to compare diabetes incidence across decades for participants between 40 and 55 years of age in each decade. The age-adjusted 8-year incidence rate of diabetes was 2.0%, 3.0%, and 3.7% among women and 2.7%, 3.6%, and 5.8% among men in the 1970s, 1980s, and 1990s, respectively. Compared with the 1970s, the age- and sex-adjusted odds ratio (OR) for diabetes was 1.40 (95% confidence interval [CI], 0.89 to 2.22) in the 1980s and 2.05 (95% CI, 1.33 to 3.14) in the 1990s ( P for trend=0.0006). Among women, the OR was 1.50 (95% CI, 0.75 to 2.98) in the 1980s and 1.84 (95% CI, 0.95 to 3.55) in the 1990s ( P for trend=0.07) compared with the 1970s, whereas among men, the OR was 1.33 (95% CI, 0.72 to 2.47) in the 1980s and 2.21 (95% CI, 1.25 to 3.90) in the 1990s ( P for trend=0.003). Most of the increase in absolute incidence of diabetes occurred in individuals with body mass index ≥30 kg/m 2 ( P for trend=0.03). Conclusions— In the present community-based sample of middle-aged adults, we observed a doubling in the incidence of type 2 diabetes over the last 30 years. Careful surveillance of changes in diabetes incidence may be necessary if current trends of excess adiposity continue.

Background— Cardiovascular disease (CVD) reflects a highly coordinated complex of traits. Although genome-wide association studies have reported numerous single nucleotide polymorphisms (SNPs) to be associated with CVD, the role of most of these variants in disease processes remains unknown. Methods and Results— We built a CVD network using 1512 SNPs associated with 21 CVD traits in genome-wide association studies (at P ≤5×10 −8 ) and cross-linked different traits by virtue of their shared SNP associations. We then explored whole blood gene expression in relation to these SNPs in 5257 participants in the Framingham Heart Study. At a false discovery rate cis –expression quantitative trait loci (eQTLs; SNPs associated with altered expression of nearby genes) and 44 trans -eQTLs (SNPs associated with altered expression of remote genes). The eQTL network revealed 13 CVD-related modules. Searching for association of eQTL genes with CVD risk factors (lipids, blood pressure, fasting blood glucose, and body mass index) in the same individuals, we found examples in which the expression of eQTL genes was significantly associated with these CVD phenotypes. In addition, mediation tests suggested that a subset of SNPs previously associated with CVD phenotypes in genome-wide association studies may exert their function by altering expression of eQTL genes (eg, LDLR and PCSK7 ), which in turn may promote interindividual variation in phenotypes. Conclusions— Using a network approach to analyze CVD traits, we identified complex networks of SNP-phenotype and SNP-transcript connections. Integrating the CVD network with phenotypic data, we identified biological pathways that may provide insights into potential drug targets for treatment or prevention of CVD.

Background— Data are limited with regard to the relations of low-grade albuminuria (below the microalbuminuria threshold) and incidence of cardiovascular disease (CVD) events in nondiabetic, nonhypertensive individuals. Methods and Results— We examined the association of urinary albumin excretion (spot urine albumin indexed to creatinine [UACR]) and the incidence of CVD events and all-cause mortality in 1568 nonhypertensive, nondiabetic Framingham Offspring Study participants (mean age, 55 years; 58% women) free of CVD. On follow-up (median, 6 years), 54 participants (20 women) developed a first CVD event, and 49 (19 women) died. After adjustment for established risk factors, increasing UACR was associated with greater risk of CVD (hazards ratio [HR] per SD increment in log UACR, 1.36; 95% CI, 1.00 to 1.87) and death (HR per SD increment in log UACR, 1.55; 95% CI, 1.10 to 2.20). Participants with UACR greater than or equal to the sex-specific median (≥3.9 μg/mg for men, ≥7.5 μg/mg for women) experienced a nearly 3-fold risk of CVD (adjusted HR, 2.92; 95% CI, 1.57 to 5.44; P P =0.08) compared with those with UACR below the median. The increased CVD risk associated with UACR at or above the median remained robust in analyses restricted to individuals without microalbuminuria (n=1470) and in subgroups with intermediate (n=1469) and low (n=1186) pretest probabilities of CVD. Conclusions— In our community-based sample of middle-aged nonhypertensive, nondiabetic individuals, low levels of urinary albumin excretion well below the current microalbuminuria threshold predicted the development of CVD. Our observations add to the growing body of evidence that challenges the notion that UACR

Background— It has been postulated that glomerular hyperfiltration and endothelial dysfunction are early features of essential hypertension that may antedate blood pressure elevation. Microalbuminuria, a marker of glomerular hyperfiltration and endothelial dysfunction, has been described in individuals with established hypertension, but its role as a biomarker of preclinical stages of this disease has not been investigated prospectively. Methods and Results— We examined the association between urinary albumin excretion and the risks of hypertension and blood pressure progression in 1499 nonhypertensive individuals (58% women) without diabetes. During a mean follow-up of 2.9 years, 230 participants (15%) developed hypertension and 499 (33%) progressed to a higher blood pressure category (defined by the sixth report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure). In multivariable logistic regressions that adjusted for known risk factors, the urine albumin-creatinine ratio (UACR) was a significant predictor of incident hypertension (adjusted OR 1.20, 95% CI 1.01 to 1.44, per 1-SD increment in log UACR). Compared with those in the lowest UACR quartile, participants in the highest quartile (men: >6.66 mg/g; women: >15.24 mg/g) had an ≈2-fold risk of developing hypertension (adjusted OR 1.93, P =0.006) and 1.5-fold risk of blood pressure progression (adjusted OR 1.45, P =0.03). Conclusions— Urinary albumin excretion predicts blood pressure progression in nondiabetic, nonhypertensive individuals incrementally over established risk factors and at levels well below the conventional threshold for microalbuminuria. UACR may be a useful biomarker for identifying individuals most likely to develop hypertension.

Background: The majority of Chinese adults with fasting blood glucose (FBG) diagnostic of diabetes (≥126 mg/dl) are not diagnosed or treated, placing them at high risk for cardiovascular disease-related complications. While demographic and economic factors might differentially predict each step in the continuum from having FBG diagnostic of diabetes to diagnosed and treated diabetes, most of the literature uses separate models to examine these three outcomes. Furthermore, most strategies ignore underlying, observable and unobservable individual and household factors that influence healthcare-seeking behavior and demand for treatment. We address these gaps, using a three-step sequential model to estimate FBG diagnostic of diabetes, diagnosed, and treated diabetes, correcting for observable and unobservable factors. Methods: Data came from 7,653 adults aged 18-75y in the 2009 wave of the China Health and Nutrition Survey, a prospective cohort study of economically diverse Chinese households in 9 provinces (228 communities). We used a Heckman-type system of equations to predict three steps: 1) FBG diagnostic of diabetes; 2) diagnosed diabetes (reported doctor diagnosis); and 3) treated diabetes (reported insulin or diabetes medication use), correcting for unobservable factors. We hypothesized differential prediction of each of the three steps by: age, gender, income (low, medium, or high tertiles), community health infrastructure (number/type of health facilities and pharmacies in or nearby (12 km) each community), and health insurance (none, low, medium, or high quality). Results: Eight percent (301 of 3,544) of men and 6% (248 of 4,059) of women had FBG diagnostic of diabetes. Using our three-step system of equations adjusting for observable and unobservable factors, income and health insurance were not statistically associated with FBG diagnostic of diabetes. Among individuals with FBG diagnostic of diabetes, 47% (95% confidence interval (CI): 44-50%) of high income versus 39% (CI: 36-42%) of low income individuals had diagnosed diabetes. Conditional on FBG≥126 mg/dl and diabetes diagnosis, treatment differed by health insurance quality: 47% (CI: 43-50%) of individuals with high quality insurance [relative to medium: 43% (CI: 39-47%), low: 34% (CI: 30-38%), and none: 29% (CI: 25-33%)] received diabetes treatment. Discussion: Using a three-step model and correcting for observable and unobservable factors, our findings suggest that, in China, income and health insurance play a stronger role in diabetes diagnosis and treatment than in having FBG diagnostic of diabetes. Our results from this unique modeling strategy highlight the stage in the diabetes continuum during which investment in diabetes treatment and screening is likely to have large impact.

Background: Acculturation is the process by which immigrant groups adopt the cultural practices and values of the host country. Research has produced conflicting results in the association between acculturation and cardiovascular disease (CVD) risk among Hispanics. The National Health and Nutrition Examination Survey (NHANES) has collected multiple acculturation measures. We evaluated the hypothesis that the acculturation measure utilized impacts the association with CVD risk. Methods: The following measures of acculturation were available: Nativity, Language spoken at home (‘mostly English,’ ‘Equal English/Spanish,’ ‘Mostly Spanish’), Years in the US (0-5, 6-10, 11-15, 16-20, >20), and US citizenship. The validated Short Acculturation Scale (SAS) was only used between 1999 and 2004. Chi-squared analyses and logistic regression models were used to examine the independent association of acculturation with CVD risk factors: hypertension, BMI, diabetes (HgbA1c ≥6.5), total cholesterol ≥ 240 mg/dl, HDL (male: Results: Of 8707 Hispanics, less acculturated adults, as measured through language, were slightly older (mean age 41 vs. 39), were more likely to have less than a high school education (66 vs. 25%), less likely to have a usual source of care (61 vs.79%), and more likely to be uninsured (57 vs. 27%). Only 8% were born in the US, 31% were US citizens, and 58% reported 15 yrs or less in the US. In fully adjusted models, the SAS and other language based acculturation measures showed null association with CVD risk while increasing years in the US was consistently associated with increasing risk over time of hypertension (ORs range: 1.1-2.3), obesity (ORs range: 1.2-2.4) and diabetes (ORs range: 1.0-2.6). Only obesity had similar point estimates and confidence intervals across acculturation measures (ORs range: 1.0 - 2.4). Moderate positive correlation was highest between language usually spoken at home and country of birth (UC=0.4) followed by US citizenship (UC=0.3). Conclusions: NHANES is widely used in the study of Hispanic health. Different acculturation measures demonstrate differing associations with CVD risk factors. In contrast to language-based measures, only length of residence in the US showed consist associations across CVD risk factors. Future research is needed to further characterize acculturation metrics among heterogeneous Hispanic populations.

Aim: Common variation at the polygenic 11p11.2 locus has been associated with fasting glucose (FG) and insulin (FI) in genome-wide association studies. Further insights into the genetic pathways involved in glucose homeostasis and type 2 diabetes pathogenesis might rely on discovery of functional variants in genes or regulatory regions. Hypothesis: We hypothesized that high-throughput next-generation deep sequencing at the polygenic 11p11.2 locus might identify additional rare, potentially functional variants influencing FG and/or FI levels. Methods: We deeply sequenced (mean depth 38X) 16.1kb across the 11p11.2 locus in 3,566 non-diabetic individuals enrolled in the CHARGE Consortium (http://web.chargeconsortium.com/). We analyzed rare variants (minor allele frequency [MAF] MADD , ACP2 , NR1H3 , MYBPC3 and SPI1 , with FI or FG using Sequence Kernel Association Test (SKAT). Predicted regulatory variants were then analyzed by conditioning in SKAT on two previously known variants at MADD locus (rs7944584 and rs10838687 associated, respectively, with FG and FI). All analyses were adjusted for age, sex and study design variables. FI (adjusted for BMI) was naturally log-transformed to improve normality. Further functional studies were performed in human HepG2 hepatoma cells to unravel possible mechanistic pathways linked to functional variants. Results: We identified 653 allelic variants (including the known rs7944584 and rs10838687), 79.9% of which were rare and novel. At NR1H3, 53 rare variants were jointly associated with FI ( p =2.7 x 10 -3 ); of these, seven were predicted to have regulatory function. Conditional analysis suggested more than two independent signals at 11p11.2- MADD locus. One predicted regulatory variant, chr11:47227430 (hg18; MAF=0.0007), contributed 20.6% to the overall SKAT score at NR1H3, and lies in intron 2 of NR1H3 , a predicted binding site of the FOXA1 enhancer, a transcription factor associated with insulin regulation. Functional studies in HepG2 cells showed that the chr11:47227430 variant disrupts FOXA1 binding and significantly reduces FOXA1-dependent transcriptional activity. Conclusions/interpretation: We confirmed known common FI-associated variants near MADD gene and identified rare variation in an intron of NR1H3 associated with FI. Functional in vitro studies showed that the rare A allele of the chr11:47227430 variant at the NR1H3 locus might theoretically affect insulin regulation by interfering with transcription factor FOXA1 binding and, consequently, FOXA1-dependent transcriptional activity. Our targeted deep resequencing approach proved valuable in identifying new rare functional variants; quantitation of their actual impact on glucose homeostasis needs further confirmation.

Introduction: Low blood levels of dehydroepiandrosterone sulfate (DHEAS) have been shown to have strong positive associations with diabetes, cardiovascular disease (CVD) mortality and stroke. However, the underlying pathways remain unclear, given limited data to systematically examine associations of DHEAS with CVD risk. In exploratory analyses we tested the association between CVD risk factors and DHEAS levels in a large population of Latinos. Methods: Among 1,450 participants in the Boston Puerto Rican Health Study between the ages of 45-75 years at baseline, socio-demographic, behavior, medical history, anthropometric and blood pressure data were collected at in-home interviews conducted by trained staff. A certified phlebotomist collected fasting blood samples. All samples were assayed for DHEAS, lipids, and C-reactive protein (CRP), HbA1c, insulin and glucose (GL). Spearman correlations were estimated between DHEAS and continuous CVD risk factors (lipids, systolic blood pressure [SBP] and diastolic blood pressure [DBP], CRP, GL, HbA1c, insulin, body mass index [BMI], waist circumference, physical activity and alcohol consumptions). We used robust multivariable linear regression models adjusted for potential confounders and intermediates with α=0.05 to estimate the association selected CVD risk factors and DHEAS levels. CVD risk factors were identified from a set of potential candidate predictors (age, female gender, history of heart disease, diabetes status, SBP, DBP, total and high density cholesterol [TC, HDL], triglycerides [TG], GL, CRP) using stepwise linear regression with an entry criterion of α=0.20 and exit criterion of α=0.10. Results: The mean DHEAS concentration among women was 70.7 μg/dL (s.d. 53.9; median=70.7) and among men was 119 μg/dL (s.d. 87.7; median=100). In age and sex adjusted Spearman correlations, TC, low density lipoprotein cholesterol, physical activity and alcohol were positively significantly correlated with DHEAS, while BMI and waist circumference were inversely correlated. In robust multivariable linear regression adjusted for potential confounders, age (-8.3; 95%CI:-10.0,-6.5; per 5 yrs), sex (β=-32.5; 95%CI:-38.4,-26.6) and TG (β=-0.5; 95%CI:-0.7,-0.2; per 10 mg/dl) were significantly inversely associated with DHEAS concentration, while TC (β=0.9; 95%CI:0.2,1.6;per 10 mg/dL) and GL (β=0.7; 95%CI:0.2, 1.2;per 10 mg/dL) were positively associated, albeit non-statistically significant. Adjustment for history of CVD, diabetes and BMI, only marginally attenuated these associations. Conclusions: Our data provide support for a significant association between TG levels and DHEAS concentrations even after adjustment for potential confounders and intermediates, which has been previously untested. These results suggest that DHEAS may work through lipid pathways.

Background: The American Heart Association has identified a set of 7 ideal health metrics that are associated with reduced cardiovascular disease mortality. Higher acculturation has been linked to increased risk of cardiovascular disease risk factors among Hispanics. We tested whether acculturation is associated with these ideal metrics in a large contemporary sample of Hispanic adults. Methods: We examined the association between acculturation and 7 ideal cardiovascular health metrics in the National Health and Nutrition Examination Survey (NHANES) from 1999-2010. Acculturation was measured by language use, with higher acculturation defined as the use of at least equal English and Spanish. The 7 ideal health metrics included not smoking, a body mass index (BMI) Results: The study sample consisted of 8,707 Hispanic adults. As compared with more acculturated adults, less acculturated adults were slightly older (mean age 41.1 vs. 39.2, p=0.004), were more likely to have less than a high school education (67.1 vs. 25.1%, p Conclusion: Contrary to prior reports of the positive health effects of low acculturation, we found that lower acculturation is associated with poor cardiovascular health metrics in a national sample of Hispanic adults. However, this group is also heterogeneous and future studies should explore the complex role of acculturation in cardiovascular disease risk profiles.

Introduction: Common variants in the gene encoding insulin receptor substrate 1 ( IRS1 ) and nearby on 2q36.3 have been associated with levels of fasting insulin (FI). We hypothesized that a greater burden of rare variants in these regions is associated with higher FI. Methods: CHARGE-S sequenced (average coverage >60x) the IRS1 and 2q36.6 regions (totaling 185 kb) in 3,539 individuals on the SOLiD platform. FI information among non-diabetics was available in 3 studies: Framingham Heart Study ( N =811), Cardiovascular Heart Study ( N =967) and Atherosclerosis Risk in Communities Study ( N =1761). We analyzed rare variants (MAF < 1%) using a weighted sum test, similar to Madsen-Browning (powerful to detect an association if effects of casual rare variants are in the same direction), and the SKAT test (preferred method if variant effects are in opposite directions). Meta-analyses of weighted rare variants results used the inverse-variance method while SKAT results used a similar approach. For multi-variant tests, the threshold for significance was considered to be α = 0.05. Coding annotation predictions were obtained from the dbNSFP database which includes functional predictions from SIFT, MutationTaster, Polyphen-2, Phylo-P and LRT. Non-coding annotation information (protein binding regions, transcription factor binding sites, DNase hypersensitivity sites, conservation scores) was obtained from ENCODE and ORegAnno databases. From these annotations, we grouped different types of variants together (possible loss of function; possibly regulatory) in order to determine specific variants contributing most to the effect. Results: Sequencing found 4,534 variants in two regions, 86.7% of which were rare and novel, not seen in 1000 genomes or dbSNP. Approximately 20% of variants had annotation information available; of these, 34 variants were possibly damaging. We found suggestive association with FI ( p =0.03) for all rare variants in the meta-analysis of weighted-sum tests at 2q36.3 but not at IRS1 . At IRS1 (but not at 2q36.3), SKAT meta-analysis tests showed evidence for all rare variants associated with FI ( p =0.03). SKAT tests restricted to N =365 possibly damaging variants at IRS1 suggested an association with FI in coding ( p =0.06) and in non-coding ( p =0.02) variants. Conclusion: Large scale deep sequencing in the IRS1 and 2q36.3 regions found very large numbers of new, rare variants. Multi-variant tests suggest that rare variation in these regions influence FI levels, with individuals with more and rarer variants having higher FI. Further investigation is warranted to address why weighted sum and SKAT tests provide different levels of evidence for association in the two regions. Also, conditional analyses will test whether new rare variants at IRS1 or 2q36 explain observed GWAS associations.

Introduction: Myocardial infarction (MI) risk is increased among HIV populations, but the use of aspirin (ASA) for primary prevention of MI has not been investigated in this group. Hypothesis: We hypothesized that ASA use would be associated with a decreased risk of incident MI in both HIV and matched control patients. Methods: Patients who received care from the Partners HealthCare System in Boston between 2000 and 2009 without baseline coronary heart disease (CHD) were included. Non-HIV patients were matched to HIV patients by demographic factors, with 33,348 control and 3,698 HIV patients. To ascertain non-episodic ASA use, we developed an algorithm combining medication data and electronic health record free text search and validated it using medical record review. We used a definition of at least two ASA prescriptions, two text strings, or one of each, all occurring more than 30 days prior to MI (sensitivity 73%, specificity 83%, AUC 78%). We used Cox proportional hazard modeling to assess the association between ASA use and first MI within the HIV and control groups. We further assessed the effect of ASA in models stratified by cardiovascular risk (low CHD risk = 0-1 traditional risk factors, high CHD risk = 2 or more risk factors), to minimize potential confounding by indication. Results: ASA use was significantly lower in HIV compared to non-HIV patients in the overall groups (12.4% vs. 15.3%, p Conclusions: ASA use was relatively lower in HIV compared to matched control patients, particularly among those with high underlying CHD risk. In contrast to non-HIV patients for whom ASA was significantly associated with decreased MI risk, ASA did not show this effect among HIV patients. Further studies will help to establish whether patients with HIV disease may represent a subgroup in which ASA is less effective for primary prevention of MI.

Background A recent genome-wide association study (GWAS) meta-analysis for type 2 diabetes (T2D) increased by 50% the number of common T2D-associated single-nucleotide polymorphisms (SNPs). We hypothesized that a genotype risk score (GRS) consisting of more SNPs improves T2D prediction achieved by published 17-SNP and 40-SNP scores. Methods We used data from four 8-year periods of the Framingham Offspring Study to test the association between effect-weighted 17-SNP, 40-SNP, and 62-SNP GRSs and incident T2D, defined by medication use and fasting glucose criteria. We used pooled logistic regression models with generalized estimating equations adjusted for 1) age and sex and 2) age, sex, parental T2D, BMI, systolic blood pressure, fasting glucose, HDL cholesterol, and triglyceride levels. Model improvement by the addition of each GRS was assessed with C statistics, integrated discrimination improvement (IDI) indices, and net reclassification improvement (NRI) indices with category thresholds at 2% and 8% cumulative incidence. Results Mean baseline age was 46, and 46.5% were men. There were 446 cases of incident T2D among 11,358 person-exams. In all models, the inclusion of a GRS resulted in significant T2D risk reclassification (NRI range 22.3-29.8%, see Table). The inclusion of a greater number of SNPs in the GRS increased the C statistics and relative IDI of the age- and sex-adjusted model, but the 62-SNP GRS did not result in a higher NRI than the 40-SNP GRS (28.6% vs. 29.8%). Similarly, the 62-SNP score did not result in a higher NRI for the fully adjusted clinical model than the 40-SNP score (25.6% vs. 29.0%). Conclusions A GRS of common susceptibility SNPs from GWAS improves T2D risk reclassification in a prospective study of adults, but incorporating a greater number of SNPs with smaller individual effect sizes may not result in additional reclassification improvement. Further advances in genotype prediction of T2D may require the identification and incorporation of functional risk variants putatively tagged by GWAS SNPs.

Background: Type 2 diabetes mellitus (DM) results from insulin resistance (IR), pancreatic β-cell dysfunction, or both. We hypothesized that risk factors could differ for DM preceded predominantly by IR, β-cell dysfunction, or both. This hypothesis is particularly important for older adults, in whom β-cell dysfunction may be relatively common. Methods: During 18 years of follow-up among 3,899 older adults free of DM (mean±sd age =73.0±5.8), we identified 274 incident DM cases by DM medication use, fasting glucose (≥126 mg/dL), or 2-hour post-challenge glucose (≥200 mg/dL), for whom homeostatic model assessments for IR (HOMA-IR) and β-cell function (HOMA-B) were assessed after baseline and before DM diagnosis. Using median cutoffs of the follow-up HOMA-IR and HOMA-B, we subclassified incident DM into DM preceded by IR only (n=112), β-cell dysfunction only (n=70), or both (n=77). Using multivariate competing-risk Cox models, we tested whether DM risk factors were differentially associated with risk of each DM subclass. Results: Elevated triglyceride levels (≥150 mg/dL) and impaired fasting glucose (100-125 mg/dL) were each positively associated with DM, irrespective of the DM subclass. Other DM risk factors of older age, overweight, obesity, low HDL cholesterol, and hypertension had substantially varying relationships with risk of different DM subclasses (p Conclusions: Among older adults, some DM risk factors differ substantially depending on HOMA-IR or HOMA-B subclassification. These findings support our hypothesis of heterogeneity in incident DM, especially among older adults.

Background Prediction of type 2 diabetes (T2D) early in the life course, before metabolic derangements occur, might facilitate prevention. In middle-aged adults, genotype risk scores (GRS) predict incident T2D but do not outperform prediction models consisting of clinical variables such as family history, body-mass index (BMI), and routine laboratory tests. Unlike clinical predictors and even family history, however, genotype does not change over the life course. We hypothesized that a diabetes GRS predicts incident adult T2D from adolescence and that the addition of this GRS improves T2D prediction models based on clinical adolescent risk factors alone. Methods The biracial Bogalusa Heart Study is a geographically based cohort study of children aged 4 to 18 years followed serially into adulthood. We limited the present analyses to participants seen in adolescence and followed into adulthood for whom genotype information was available. The baseline examination was the first assessment for each participant after age 12 years. Participants with childhood or type 1 diabetes were excluded. Incident T2D was defined as a fasting glucose ≥126 mg/dL or diabetes treatment after age 19 years. We used Cox regression to build nested T2D prediction models based on clinical risk factors assessed in adolescence (parental history of diabetes, BMI z -score, mean arterial pressure, fasting glucose, HDL cholesterol, and triglycerides) with and without a 38-variant GRS. We used likelihood ratios tests (LRT), differences in C statistics (ΔC), and net reclassification improvement indices (NRI) to assess whether GRS improved prediction in each model. Results Mean age at baseline was 14.4 years (range 12 to 19). Of the 1056 participants, 585 (55%) were girls, and 340 (32%) were black. Ninety (8.5%) developed T2D over a mean 26.3 years of follow-up. GRS significantly predicted incident T2D in all models, with hazard ratios of 1.09 per risk allele (95% CI 1.03, 1.15) in the basic demographic model and 1.06 (95% CI 1.00, 1.13) in the full model. The addition of GRS to all models improved model fit (all LRT p p >0.05). The addition of GRS to the demographic model significantly improved the risk classification of the cohort (NRI 24.8%, 95% CI 8.3, 41.7%), but this reclassification was not significant when GRS was added to the full model (NRI 3.9%, 95% CI -5.0, 13.9%). Moreover, the GRS did not meaningfully improve prediction in a model which included only demographics and parental history of diabetes (NRI 0.8%, 95% CI -13.0, 16.0%). We found no interaction between race and GRS in any model (all p >0.05). Conclusions Knowledge of genotype predicts adult T2D in white and black adolescents. Genotype does not meaningfully improve prediction over routinely collected clinical risk factors, including parental history.

Background: Tobacco smoking is widespread among HIV patients and is likely to contribute to the development of chronic diseases, yet accurate information on smoking is often difficult to obtain from observational data sources. We sought to validate a natural language processing (NLP) classifier to identify smokers among HIV and control cohorts and investigate whether HIV is an independent predictor of smoking and failure to quit smoking. Methods: We applied an NLP classifier developed within the Partners HealthCare System to electronic medical records for a cohort of HIV patients and a control cohort matched on age, gender, race, and number of clinical encounters. The NLP classifier searches free text notes for “tokens”, phrases containing smoking-related words, and assigns a smoking status (current, former, or non-smoker) to each token. We developed an algorithm for combining token classifications from a 12 month period into a single smoking status (current vs. non-smoker). We validated the yearly smoking status on a random sample of 500 patients (250 from each cohort) using as a gold standard a trained nurse medical record reviewer who assigned a tobacco smoking status to each patient for each calendar year observed. We calculated sensitivity, specificity and area under the receiver operating characteristic curve (AUC). Using NLP, we classified the full cohorts as ever versus never smokers and current versus non-smokers at last observation. We used logistic regression to assess HIV as a predictor of smoking and failure to quit (current smoking among ever smokers). Results: Smoking-related tokens were found in the records of 2926/3554 HIV and 7039/9601 non-HIV patients, providing 34,956 patient years of data. Using NLP to assign smoking status by year yielded sensitivity of 92.4, specificity of 86.2, and AUC of 0.89 (95% confidence interval [CI] 0.88-0.91). NLP assignment of ever versus never smoking status yielded sensitivity of 94.3, specificity of 73.4 and AUC of 0.84 (95% CI 0.81-0.87). Performance of the classifier did not vary by HIV status, gender, age, calendar year, or number of tokens/year. Ever and current smoking were more common in HIV patients than controls (54% vs. 44% and 42% vs. 30%, respectively, both p Conclusions: We validated a novel tool to ascertain smoking status from HIV observational cohort data. HIV was independently associated with both smoking and failure to quit smoking. These data underscore the need for aggressive smoking cessation strategies specific to HIV patients.

Introduction: Coronary heart disease rates are increased among HIV-infected patients compared to control groups, with a relatively greater increase in women compared to men. Whether a similar pattern with respect to gender is true for ischemic stroke is not known. We assessed the hypothesis that the adjusted hazard ratio for ischemic stroke given HIV infection is greater among women compared to men. Methods: The study was conducted using data from an HIV observational clinical care cohort at a large Boston-based health care system. A control cohort of non-HIV-infected patients was matched in a 10:1 ratio to the HIV cohort based on age, gender, and race. The observation period was between 1996 and 2009, with right censoring at the initial stroke event or last encounter if prior to 2009. Ischemic stroke events were identified by pre-specified and validated ICD-9-CM codes. Gender-specific stroke incidence rates were calculated. To assess the association of HIV and ischemic stroke within each gender, Cox proportional hazard modeling was employed. Results: The cohorts consisted of 4,308 HIV-infected patients (31% women) and 32,423 non-HIV-infected patients (35% women). Among women, ischemic stroke event rates were 5.02 per 1000 person years (PY) in HIV-infected versus 2.31 per 1000 PY in non-HIV-infected patients (40 events in HIV and 177 events in non-HIV) with an unadjusted hazard ratio (HR) of 2.16 (95% confidence interval [CI] 1.53–3.04, P P =0.14). Comparing HIV-infected to non-HIV-infected patients, incidence rates were significantly increased among women in the 18–29, 30–39, and 40–49 age groups and among men in the 30–39 age group. In a gender-stratified, multivariate regression model adjusting for age, race, hypertension, diabetes, dyslipidemia, smoking, structural heart disease, atrial fibrillation, aspirin use, and warfarin use, the adjusted HR for stroke associated with HIV infection was 1.76 (95% CI 1.24–2.52, P =0.002) among women compared with 1.05 (95% CI 0.84–1.32, P =0.639) among men. Conclusions: Ischemic stroke incidence rates were significantly increased in HIV-infected patients compared to non-HIV-infected patients among young women (< 50) and a subset of young men. HIV infection remained independently associated with stroke among women after adjusting for demographic and traditional stroke risk factors. While further studies are merited investigating causality and potential mechanisms for stroke among HIV-infected women, this group may represent a uniquely at-risk population for accelerated cerebrovascular aging.

Background: Genome-wide association studies (GWAS) have identified several genetic loci that influence fasting glucose (FG) and insulin (FI). Whether the favorable relation between eating a healthy diet and FG or FI is the same regardless of genetic risk is unknown. Objective: We studied 15 well-characterized U.S., Northern European and Mediterranean cohorts that had dietary and genetic data (maximum N = 51,289) to test whether genotype and healthy diet interact to influence FG or FI concentrations. Design: Within each cohort, we constructed a healthy diet score comprising foods with previous evidence of associations with metabolic risk: whole grains, fish, fruits, vegetables, nuts/seeds (favorable food groups) and red meat, sweets, sugared beverages, fried potatoes (unfavorable food groups). Intakes of each food group were categorized in quartiles and assigned ascending values (0, 1, 2, 3) for favorable foods and descending values (3, 2, 1, 0) for unfavorable foods. These values were summed to generate an overall diet score (range: 0 to 27 points), with higher scores representing healthier diets. We used multivariable linear regression including an additive genetic model within cohorts followed by inverse weighted meta-analysis of all cohorts to quantify 1) associations between healthy diet and FG and FI and 2) interactions of healthy diet with 16 established FG- or two FI-associated loci on FG and FI concentrations. Results: Healthier diets (per additional diet score unit) were associated with lower FG (β: -0.004; 95% CI: -0.005, -0.003 mmol/L, p: Conclusions: A healthy diet score allowing summarization of dietary intake as an environmental exposure across diverse cohorts is favorably associated with FG and FI concentrations regardless of genotype at FG or FI-associated loci. Modest dietary differences are far larger than an individual’s apparent genetic risk at these loci.

Background: Hypertension (HTN) and some anti-HTN drugs raise fasting glucose (FG) levels and type 2 diabetes (T2D) risk. We hypothesized that common single nucleotide polymorphisms (SNPs) associated with FG or T2D that influence beta-cell function will interact with hypertension (HTN) or anti-HTN drugs in their positive associations with change over time in FG (ΔFG) or incident T2D. Methods: We studied 3,471 Framingham Offspring Study participants comprising 18,786 person-exams (mean age 49 yr; 54% women), in pooled (7 ∼4-yr periods) age-, sex-adjusted logistic regression models predicting T2D incidence, or age-, sex-, baseline FG-adjusted mixed linear models testing ΔFG (FG end minus beginning of period). T2D cases were excluded at baseline of each period, and people who started T2D treatment within a given period were removed for ΔFG analyses. We defined three HTN exposures: 1) HTN (SBP≥140/DBP≥90 mmHg) vs. no-HTN; 2) treated vs. untreated HTN; 3) five mutually-exclusive anti-HTN drug categories (beta-blockers, thiazides, renin-angiotensin system agents, combination, other) vs. untreated HTN, and two genetic exposures reflecting total beta-cell genetic risk burden: 16 FG-SNP and 33 T2D-SNP (only 8 overlap) additive genetic risk scores. We tested ∼4-year mean ΔFG and odds of T2D by HTN category and per-risk allele change in SNP scores, seeking first-order HTNxSNP significant interaction (P Results: Versus no HTN, HTN (n=5,372 p-e) was associated with higher ∼4-year ΔFG (0.14 vs. 0.03 mmol/l; P−16 ) and odds of T2D increased 17% (P=3.1x10 −08 ). In joint models with interaction terms, all HTN-treatment category-by-SNP score interaction terms were non-significant. In joint models without interaction, HTN (P0.02), and per FG-SNP or T2D-SNP risk allele (highest P value=0.0005), all independently predicted ΔFG or incident T2D. Conclusion: HTN, HTN treatment and common FG- and T2D-SNP genetic scores were independently associated with ΔFG and T2D incidence, but did not modify each other's association with ΔFG or T2D risk. Next, larger samples and individual SNP-by-specific drug category interaction tests might disclose some biological basis for T2D risk associated with anti-HTN therapy.

Background— Several biological pathways are activated in ventricular remodeling and in overt heart failure (HF). There are no data, however, on the incremental utility of a parsimonious set of biomarkers (reflecting pathways implicated in HF) for predicting HF risk in the community. Methods and Results— We related a multibiomarker panel to the incidence of a first HF event in 2754 Framingham Heart Study participants (mean age, 58 years; 54 women) who were free of HF and underwent routine assays for 6 biomarkers (C-reactive protein, plasminogen activator inhibitor-1, homocysteine, aldosterone-to-renin ratio, B-type natriuretic peptide, and urinary albumin-to-creatinine ratio). We estimated model c statistic, calibration, and net reclassification improvement to assess the incremental predictive usefulness of biomarkers. We also related biomarkers to the incidence of nonischemic HF in participants without prevalent coronary heart disease. On follow-up (mean, 9.4 years), 95 first HF events occurred (54 in men). In multivariable-adjusted models, the biomarker panel was significantly related to HF risk ( P =0.00005). On backward elimination, B-type natriuretic peptide and urinary albumin-to-creatinine ratio emerged as key biomarkers predicting HF risk; hazards ratios per 1-SD increment in log marker were 1.52 (95 confidence interval, 1.24 to 1.87) and 1.35 (95 confidence interval, 1.11 to 1.66), respectively. B-type natriuretic peptide and urinary albumin-to-creatinine ratio significantly improved the model c statistic from 0.84 (95 confidence interval, 0.80 to 0.88) in standard models to 0.86 (95 confidence interval, 0.83 to 0.90), enhanced risk reclassification (net reclassification improvement=0.13; P =0.002), and were independently associated with nonischemic HF risk. Conclusion— Using a multimarker strategy, we identified B-type natriuretic peptide and urinary albumin-to-creatinine ratio as key risk factors for new-onset HF with incremental predictive utility over standard risk factors.

Background— Biomarkers of multiple pathophysiological pathways have been related to incident atrial fibrillation (AF), but their predictive ability remains controversial. Methods and Results— In 3120 Framingham cohort participants (mean age 58.4±9.7 years, 54% women), we related 10 biomarkers that represented inflammation (C-reactive protein and fibrinogen), neurohormonal activation (B-type natriuretic peptide [BNP] and N-terminal proatrial natriuretic peptide), oxidative stress (homocysteine), the renin-angiotensin-aldosterone system (renin and aldosterone), thrombosis and endothelial function (D-dimer and plasminogen activator inhibitor type 1), and microvascular damage (urinary albumin excretion; n=2673) to incident AF (n=209, 40% women) over a median follow-up of 9.7 years (range 0.05 to 12.8 years). In multivariable-adjusted analyses, the biomarker panel was associated with incident AF ( P P P P =0.004) were chosen. The addition of BNP to variables recently combined in a risk score for AF increased the C-statistic from 0.78 (95% confidence interval 0.75 to 0.81) to 0.80 (95% confidence interval 0.78 to 0.83) and showed an integrated discrimination improvement of 0.03 (95% confidence interval 0.02 to 0.04, P Conclusions— BNP is a predictor of incident AF and improves risk stratification based on well-established clinical risk factors. Whether knowledge of BNP concentrations may be used to target individuals at risk of AF for more intensive monitoring or primary prevention requires further investigation.

Background— Von Willebrand factor (vWF) is inconsistently associated with cardiovascular disease (CVD). This might be explained by associations of vWF with type 2 diabetes mellitus and insulin resistance. Methods and Results— We tested whether vWF predicted incident CVD in 3799 Framingham Offspring Study participants, and in particular, among those with type 2 diabetes mellitus or insulin resistance. During 11 years of follow-up, 351 participants developed CVD. In proportional hazards models (with adjustment for age, sex, blood pressure, smoking, body mass index, total and high-density lipoprotein cholesterol, and treatment with aspirin, insulin, antihypertensives, and lipid-lowering medications) with the lowest quartile of the vWF distribution as the referent, the hazard ratio (HR) for CVD was 0.94 in the second quartile, 0.98 in the third, and 1.32 in the highest ( P =0.04 for trend). Additional adjustment for type 2 diabetes mellitus or insulin resistance (homeostasis model) partially attenuated the association (multivariable HRs for top quartile 1.28 and 1.21, respectively). We then stratified the models by diabetes status or the homeostasis model of insulin resistance distribution (top quartile versus lower 3 quartiles). vWF was associated with CVD among participants with diabetes mellitus (HR for top quartile relative to bottom 1.47, P =0.04 for trend) but not among nondiabetic participants (HR 1.15, P =0.5) and similarly among insulin-resistant (HR 1.50, P =0.01) but not insulin-sensitive (HR 1.02, P =0.9) participants. Conclusions— Higher levels of vWF were associated with risk of CVD in people with type 2 diabetes mellitus or insulin resistance, which suggests that vWF may be a risk factor unique to these populations.

Bell and O’Keefe question the clinical utility of the triglyceride:high-density lipoprotein (TG:HDL) cholesterol ratio compared with fasting insulin–based surrogate makers for diabetes or cardiovascular disease prediction that were used in our study.1 This issue was addressed in a recent study by several of our Framingham colleagues.2 They found that the performance of the TG:HDL ratio was modest for predicting insulin resistance, and that in regression models, the homeostasis model assessment of insulin resistance was superior to the …

Background— The performance characteristics of surrogate insulin resistance (IR) measures, commonly defined as the top 25% of the measure’s distribution, used to predict incident type 2 diabetes mellitus (DM) and cardiovascular disease (CVD) have not been critically assessed in community samples. Methods and Results— Baseline IR was assessed among 2720 Framingham Offspring Study subjects by use of fasting insulin, the homeostasis model assessment of IR (HOMA-IR), and the reciprocal of the Gutt insulin sensitivity index, with 7- to 11-year follow-up for incident DM (130 cases) or CVD (235). Area under the receiver operating characteristic curve, sensitivity, specificity, and positive likelihood ratio were estimated at 12 diagnostic thresholds (quantiles) of IR measures. Positive likelihood ratios for DM or CVD increased in relation to IR quantiles; risk gradients were greater for DM than for CVD, with no 9th to 10th quantile (76th centile) threshold effects. IR had better discrimination for incident DM than for CVD (HOMA-IR area under the receiver operating characteristic curve: DM 0.80 versus CVD 0.63). The HOMA-IR ≥76th centile threshold was associated with these test-performance values: sensitivity (DM 68%, CVD 40%), specificity (DM 77%, CVD 76%), and positive likelihood ratio (DM 3.0, CVD 1.7). The HOMA-IR threshold that yielded >90% sensitivity was the 6th quantile for DM prediction and the 3rd quantile for CVD. Compared with the ≥76th centile threshold, these alternative thresholds yielded lower specificity (DM 43%, CVD 17%) and positive likelihood ratios (DM 1.6, CVD 1.1). Conclusions— Surrogate IR measures have modest performance at the 76th centile, with no threshold effects. Different centile thresholds might be selected to optimize sensitivity versus specificity for DM versus CVD prediction if surrogate IR measures are used for risk prediction.

Background : Chronic subclinical inflammation is a prominent feature of atherosclerotic disease. The genetic background for this pro-inflammatory state is not well-established. Circulating biomarker concentrations have become attractive candidates to measure disease activity and prognosis. Methods : We examined 2356 single-nucleotide polymorphisms (SNPs) in 235 inflammatory pathway genes in association with 11 circulating inflammatory biomarkers in about 1800 Framingham Offspring cohort participants [CD40 ligand, CRP, intercellular adhesion molecule-1, interleukin-6 (IL6), urinary isoprostanes, monocyte chemoattractant protein-1 (MCP1), myeloperoxidase, P-selectin, tumor necrosis factor alpha, tumor necrosis factor receptor-2, fibrinogen]. We created residuals of log transformed biomarker concentrations adjusting for 16 potential confounders. Only SNPs with call rate ≥0.98 and HWE p>0.01, which had at least 5 minor allele carriers entered analyses. False discovery rate (FDR) and q-value methods were applied. Results : We observed similar results with FDR and q-value methods. A total of 45 associations were significant at a cutoff q value of 0.25. The top SNPs were observed in the SELP gene in association with P-selectin concentrations (rs6136 [nonsynonymous], p= 5.17*10 −39 , rs3753305 [intronic], p= 6.17*10 −9 ) and the ICAM1 gene in relation to ICAM-1 concentrations (rs1799969 [coding-nonsynonymous], p= 1.32*10 −8 ). Lowest p-values for trans-acting SNPs were observed for APCS (rs1374486 [function unknown], p= 1.01*10 −7 , and rs6695377 [function unknown], p= 1.85*10 −7 ) with MCP-1 concentrations and for IL6R (rs8192284 [coding-nonsynonimous,intronic], p= 3.36*10 −5 ) with IL6 concentrations. In addition, we could replicate reported findings for rs1799969, and rs5498 in the ICAM-1 gene in relation to ICAM-1 concentrations as well as associations of SNPs rs2857654, rs1024611, and rs2857657 in the CCL-2 gene with MCP-1 concentrations. Conclusions : The results of this candidate gene approach support the relevance of genetic variation for circulating inflammatory biomarker traits. Some former findings were confirmed and novel potential candidates are reported. Our findings merit replication in other cohorts.

Background— Excess adiposity is associated with greater systemic inflammation. Whether visceral adiposity is more proinflammatory than subcutaneous abdominal adiposity is unclear. Methods and Results— We examined the relations of abdominal subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT), assessed by multidetector computerized tomography, to circulating inflammatory and oxidative stress biomarkers in 1250 Framingham Heart Study participants (52% women; age 60±9 years). Biomarkers were examined in relation to increments of SAT and VAT after adjustment for age, sex, smoking, physical activity, menopause, hormone replacement therapy, alcohol, and aspirin use; additional models included body mass index and waist circumference. SAT and VAT were positively and similarly (with respect to strength of association) related to C-reactive protein, fibrinogen, intercellular adhesion molecule-1, interleukin-6, P-selectin, and tumor necrosis factor receptor-2 (multivariable model R 2 0.06 to 0.28 [SAT] and 0.07 to 0.29 [VAT]). However, compared with SAT, VAT was more highly associated with urinary isoprostanes and monocyte chemoattractant protein-1 (SAT versus VAT comparison: isoprostanes, R 2 0.07 versus 0.10, P =0.002; monocyte chemoattractant protein-1, R 2 0.07 versus 0.08, P =0.04). When body mass index and waist circumference were added to the models, VAT remained significantly associated with only C-reactive protein ( P =0.0003 for women; P =0.006 for men), interleukin-6 ( P =0.01), isoprostanes ( P =0.0002), and monocyte chemoattractant protein-1 ( P =0.008); SAT only remained associated with fibrinogen ( P =0.01). Conclusions— The present cross-sectional data support an association between both SAT and VAT with inflammation and oxidative stress. The data suggest that the contribution of visceral fat to inflammation may not be completely accounted for by clinical measures of obesity (body mass index and waist circumference).

Background— Visceral adipose tissue (VAT) compartments may confer increased metabolic risk. The incremental utility of measuring both visceral and subcutaneous abdominal adipose tissue (SAT) in association with metabolic risk factors and underlying heritability has not been well described in a population-based setting. Methods and Results— Participants (n=3001) were drawn from the Framingham Heart Study (48% women; mean age, 50 years), were free of clinical cardiovascular disease, and underwent multidetector computed tomography assessment of SAT and VAT volumes between 2002 and 2005. Metabolic risk factors were examined in relation to increments of SAT and VAT after multivariable adjustment. Heritability was calculated using variance-components analysis. Among both women and men, SAT and VAT were significantly associated with blood pressure, fasting plasma glucose, triglycerides, and high-density lipoprotein cholesterol and with increased odds of hypertension, impaired fasting glucose, diabetes mellitus, and metabolic syndrome ( P range P for difference between SAT and VAT P ≤0.01). Among overweight and obese individuals, the prevalence of hypertension, impaired fasting glucose, and metabolic syndrome increased linearly and significantly across increasing VAT quartiles. Heritability values for SAT and VAT were 57% and 36%, respectively. Conclusions— Although both SAT and VAT are correlated with metabolic risk factors, VAT remains more strongly associated with an adverse metabolic risk profile even after accounting for standard anthropometric indexes. Our findings are consistent with the hypothesized role of visceral fat as a unique, pathogenic fat depot. Measurement of VAT may provide a more complete understanding of metabolic risk associated with variation in fat distribution.

Background— Marked reductions in cardiovascular disease (CVD) morbidity and mortality have occurred in the United States over the last 50 years. We tested the hypothesis that the relative burden of CVD attributable to diabetes mellitus (DM) has increased over the past 5 decades. Methods and Results— Participants aged 45 to 64 years from the Framingham Heart Study, who attended examinations in an “early” time period (1952 to 1974), were compared with those who attended examinations in a later time period (1975 to 1998). The risk of CVD events (n=133 among those with and 1093 among those without DM) attributable to DM in the 2 time periods was assessed with Cox proportional hazards models; population attributable risk of DM as a CVD risk factor was calculated for each time period. The age- and sex-adjusted hazard ratio for DM as a CVD risk factor was 3.0 (95% CI, 2.3 to 3.9) in the earlier time period and 2.5 (95% CI, 1.9 to 3.2) in the later time period. The population attributable risk for DM as a CVD risk factor increased from 5.4% (95% CI, 3.8% to 6.9%) in the earlier time period to 8.7% (95% CI, 5.9% to 11.4%) in the later time period ( P for attributable risk ratio=0.04), although multivariable adjustment resulted in attenuation of these findings ( P =0.12); most of these observations were found among men. Conclusions— The proportion of CVD attributable to DM has increased over the past 50 years in Framingham. These findings emphasize the need for increased efforts to prevent DM and to aggressively treat and control CVD risk factors among those with DM.

1. About These Statistics 2. Cardiovascular Diseases 3. Coronary Heart Disease, Acute Coronary Syndrome and Angina Pectoris 4. Stroke and Stroke in Children 5. High Blood Pressure (and End-Stage Renal Disease) 6. Congenital Cardiovascular Defects 7. Heart Failure 8. Other Cardiovascular Diseases 9. Risk Factors 10. Metabolic Syndrome 11. Nutrition 12. Quality of Care 13. Medical Procedures 14. Economic Cost of Cardiovascular Diseases 15. At-a-Glance Summary Tables 16. Glossary and Abbreviation Guide 17. Acknowledgment 18. References Appendix I: List of Statistical Fact Sheets. URL: http://www.americanheart.org/presenter.jhtml?identifier=2007 The American Heart Association works with the Centers for Disease Control and Prevention’s National Center for Health Statistics (CDC/NCHS), the National Heart, Lung, and Blood Institute (NHLBI), the National Institute of Neurological Disorders and Stroke (NINDS), and other government agencies to derive the annual statistics in this update. This section describes the most important sources we use. For more details and an alphabetical list of abbreviations, see the Glossary and Abbreviation Guide. All statistics are for the most recent year available. Prevalence, mortality and hospitalizations are computed for 2003 unless otherwise noted. Mortality as an underlying or contributing cause of death is for 2002. Economic cost estimates are for 2006. Due to late release of data, some disease mortality are not updated to 2003. Mortality for 2003 are underlying preliminary data, obtained from the NCHS publication National Vital Statistics Report: Deaths: Preliminary Data for 2003 (NVSR, 2005;53:15) and from unpublished tabulations furnished by Robert Anderson of NCHS. US and state death rates and prevalence rates are age-adjusted per 100 000 population (unless otherwise specified) using the 2000 …

Background— The relations of insulin sensitivity (IS) to hypertension incidence may vary according to baseline age, body mass index (BMI), and blood pressure (BP). Methods and Results— We investigated the relations of IS (insulin sensitivity index, ISI 0,120 ) to 4-year incidence of hypertension and BP progression in 1933 nonhypertensive Framingham Study participants (median age, 51 years; 56% women). Analyses were stratified by age (less than versus greater than or equal to median), BMI (2 [obese]), and BP category (systolic BP≥130 or diastolic BP≥85, “high normal” per the sixth report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High BP [JNC-VI] versus BP0,120 quartiles were associated with lower multivariable-adjusted odds for hypertension incidence (0.27; 95% CI, 0.09 to 0.83; P P Conclusions— In our large community-based sample, reduced IS predicted BP tracking principally in younger people with normal BMI and BP

Background— Inflammation (assessed by C-reactive protein [CRP]) and the metabolic syndrome (MetS) are associated with cardiovascular disease (CVD), but population-based data are limited. Methods and Results— We assessed the cross-sectional relations of CRP to the MetS (National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults, Adult Treatment Panel III definition) in 3037 subjects (1681 women; mean age, 54 years) and the utility of CRP and the MetS to predict new CVD events (n=189) over 7 years. MetS (≥3 of 5 traits) was present in 24% of subjects; mean age-adjusted CRP levels for those with 0, 1, 2, 3, 4, or 5 MetS traits were 2.2, 3.5, 4.2, 6.0, or 6.6 mg/L, respectively ( P trend P Conclusions— Elevated CRP levels are related to insulin resistance and the presence of the MetS, especially in women. Although discrimination of subjects at risk of CVD events using both MetS and CRP is not better than using either phenotype alone, both CRP and MetS are independent predictors of new CVD events.

Background— Although insulin resistance has been implicated in the pathogenesis of left ventricular (LV) hypertrophy, previous studies have yielded inconsistent results and are limited by referral bias. Methods and Results— We examined the relations between echocardiographic LV measurements and glucose tolerance status in 2623 Framingham Study subjects (1514 women, mean age 53 years) free of myocardial infarction and heart failure. We also evaluated the relations of insulin resistance (homeostasis model, HOMA-IR) and LV and left atrial (LA) measures within the normal and abnormal glucose tolerance categories (the latter included impaired glucose tolerance, impaired fasting glucose, and newly diagnosed diabetes). LV mass (adjusted for age, height, heart rate, and systolic blood pressure) increased across categories of worsening glucose tolerance; the trend was more striking in women ( P P =0.054). In subjects with normal (n=2022) and abnormal glucose tolerance (n=327), covariate-adjusted LV mass and LV wall thickness increased across HOMA-IR quartiles in women ( P Conclusions— In our large community-based sample, LV mass and wall thickness increased with worsening glucose intolerance, an effect that was more striking in women compared with men. Insulin resistance was associated with increased LV mass in women alone, but this relation was largely accounted for by obesity.