Cardiovascular Risk Factors and Dehydroepiandrosterone Sulfate Among Latinos in the Boston Puerto Rican Health Study.

Low blood dehydroepiandrosterone sulfate (DHEAS) levels have strong positive associations with stroke and coronary heart disease. However, it is unclear whether DHEAS is independently associated with cardiovascular risk factors. Therefore, we examined the association between cardiovascular risk factors and DHEAS concentration among a high-risk population of Latinos (Puerto Ricans aged 45 to 75 years at baseline) in a cross-sectional analysis of the Boston Puerto Rican Health Study. Of eligible participants, 72% completed baseline interviews and provided blood samples. Complete data were available for 1355 participants. Associations between cardiovascular risk factors (age, sex, total cholesterol, high-density lipid cholesterol, triglycerides, and glucose) and log-transformed DHEAS (μg/dL) were assessed. In robust multivariable regression analyses, DHEAS was significantly inversely associated with age (β = -12.4; 95% CI: -15.2, -9.7; per 5 years), being female (vs. male) (β = -46; 95% CI: -55.3, -36.6), and plasma triglyceride concentration (β = -0.2; 95% CI: -0.3, -0.1; per 10 mg/dL) and was positively associated with total cholesterol and plasma glucose levels (β = 1.8; 95% CI: 0.6, 3 and β = 0.2; 95% CI: 0.04, 0.3, respectively, per 10 mg/dL) after adjustment for smoking, alcohol, and physical activity and for postmenopausal hormone use in women. Estimates were unchanged after adjustment for measures of chronic disease and inflammation. Women exhibited a stronger age-related decline in DHEAS and a positive association with glucose in contrast to findings among men (P interaction < 0.05). In conclusion, in this large study of Latinos with a heavy cardiovascular risk factor burden, we observed significant associations between cardiovascular disease (CVD) risk factors and DHEAS, with variations by sex. These findings improve our understanding of the role DHEAS may play in CVD etiology

The power of numbers

The technical and methodological advancements, as well as the knowledge accrued over the past decade on the haplotype block structure of the human genome, have enabled investigators to tackle the complexity of the genetic architecture of type 2 diabetes in populations of European and non-European descent by performing large-scale genome-wide association studies (GWAS) for both common and rare genetic variants. Interestingly, while interpreting the GWAS results one may observe that as the number of identified type 2 diabetes risk variants has increased over time, and the loci uncovered by earlier GWAS have been further replicated in larger association studies, the individual (per-allele) effect estimate has become smaller than the one originally detected in the discovery GWAS. This may be due to the non-mutually exclusive occurrence of two statistical phenomena, usually dubbed as "winner's curse" and "spectrum bias" effects. The present commentary discusses the work of the China Kadoorie Biobank Collaborative Group, which sought to provide a demonstration of the calculation of (relatively) unbiased allelic effect sizes for a set of 56 established type 2 diabetes risk variants in a large population-based cohort study of Chinese adult individuals. In particular we critically discuss whether theGWAS approach should remain a matter of statistical constraints only, or whether its integration with functional maps may highlight some sub-threshold loci as informative as those that reach genome-wide significance. The complementary information that could arise from the full integration of the genetic and functional maps holds the promise of potentially uncovering clinically relevant mechanistic insights and might expand the regulatory framework in which to interpret the functional follow-up and fine-mapping currently ongoing at established type 2 diabetes risk loci

Genome-Wide Association with Diabetes-Related Traits in the Framingham Heart Study

BACKGROUND: Susceptibility to type 2 diabetes may be conferred by genetic variants having modest effects on risk. Genome-wide fixed marker arrays offer a novel approach to detect these variants. METHODS: We used the Affymetrix 100K SNP array in 1,087 Framingham Offspring Study family members to examine genetic associations with three diabetes-related quantitative glucose traits (fasting plasma glucose (FPG), hemoglobin A1c, 28-yr time-averaged FPG (tFPG)), three insulin traits (fasting insulin, HOMA-insulin resistance, and 0–120 min insulin sensitivity index); and with risk for diabetes. We used additive generalized estimating equations (GEE) and family-based association test (FBAT) models to test associations of SNP genotypes with sex-age-age2-adjusted residual trait values, and Cox survival models to test incident diabetes. RESULTS: We found 415 SNPs associated (at p 1%) 100K SNPs in LD (r2 > 0.05) with ABCC8 A1369S (rs757110), KCNJ11 E23K (rs5219), or SNPs in CAPN10 or HNFa. PPARG P12A (rs1801282) was not significantly associated with diabetes or related traits. CONCLUSION: Framingham 100K SNP data is a resource for association tests of known and novel genes with diabetes and related traits posted at. Framingham 100K data replicate the TCF7L2 association with diabetes.National Heart, Lung, and Blood Institute's Framingham Heart Study (N01-HC-25195); National Institutes of Health National Center for Research Resources Shared Instrumentation grant (1S10RR163736-01A1); National Center for Research Resources General Clinical Research Center (M01-RR-01066); American Diabetes Association Career Developement Award; GlaxoSmithKline; Merck; Lilly; National Institutes of Health Research Career Award (K23 DK659678-03

A genome-wide association for kidney function and endocrine-related traits in the NHLBI's Framingham Heart Study

<p>Abstract</p> <p>Background</p> <p>Glomerular filtration rate (GFR) and urinary albumin excretion (UAE) are markers of kidney function that are known to be heritable. Many endocrine conditions have strong familial components. We tested for association between the Affymetrix GeneChip Human Mapping 100K single nucleotide polymorphism (SNP) set and measures of kidney function and endocrine traits.</p> <p>Methods</p> <p>Genotype information on the Affymetrix GeneChip Human Mapping 100K SNP set was available on 1345 participants. Serum creatinine and cystatin-C (cysC; n = 981) were measured at the seventh examination cycle (1998–2001); GFR (n = 1010) was estimated via the Modification of Diet in Renal Disease (MDRD) equation; UAE was measured on spot urine samples during the sixth examination cycle (1995–1998) and was indexed to urinary creatinine (n = 822). Thyroid stimulating hormone (TSH) was measured at the third and fourth examination cycles (1981–1984; 1984–1987) and mean value of the measurements were used (n = 810). Age-sex-adjusted and multivariable-adjusted residuals for these measurements were used in association with genotype data using generalized estimating equations (GEE) and family-based association tests (FBAT) models. We presented the results for association tests using additive allele model. We evaluated associations with 70,987 SNPs on autosomes with minor allele frequencies of at least 0.10, Hardy-Weinberg Equilibrium p-value ≥ 0.001, and call rates of at least 80%.</p> <p>Results</p> <p>The top SNPs associated with these traits using the GEE method were rs2839235 with GFR (p-value 1.6*10^-05), rs1158167 with cysC (p-value 8.5*10^-09), rs1712790 with UAE (p-value 1.9*10^-06), and rs6977660 with TSH (p-value 3.7*10^-06), respectively. The top SNPs associated with these traits using the FBAT method were rs6434804 with GFR(p-value 2.4*10^-5), rs563754 with cysC (p-value 4.7*10^-5), rs1243400 with UAE (p-value 4.8*10^-6), and rs4128956 with TSH (p-value 3.6*10^-5), respectively. Detailed association test results can be found at <url>http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007</url>. Four SNPs in or near the <it>CST</it>3 gene were highly associated with cysC levels (p-value 8.5*10^-09to 0.007).</p> <p>Conclusion</p> <p>Kidney function traits and TSH are associated with SNPs on the Affymetrix GeneChip Human Mapping 100K SNP set. These data will serve as a valuable resource for replication as more SNPs associated with kidney function and endocrine traits are identified.</p

Pretreatment, Psychological, and Behavioral Predictors of Weight Outcomes Among Lifestyle Intervention Participants in the Diabetes Prevention Program (DPP)

OBJECTIVE To identify the most important pretreatment characteristics and changes in psychological and behavioral factors that predict weight outcomes in the Diabetes Prevention Program (DPP). RESEARCH DESIGN AND METHODS Approximately 25% of DPP lifestyle intervention participants (n = 274) completed questionnaires to assess weight history and psychological and behavioral factors at baseline and 6 months after completion of the 16-session core curriculum. The change in variables from baseline to 6 months was assessed with t tests. Multivariate models using hierarchical logistic regression assessed the association of weight outcomes at end of study with each demographic, weight loss history, psychological, and behavioral factor. RESULTS At end of study, 40.5% had achieved the DPP 7% weight loss goal. Several baseline measures (older age, race, older age when first overweight, fewer self-implemented weight loss attempts, greater exercise self-efficacy, greater dietary restraint, fewer fat-related dietary behaviors, more sedentary activity level) were independent predictors of successful end-of-study weight loss with the DPP lifestyle program. The DPP core curriculum resulted in significant improvements in many psychological and behavioral targets. Changes in low-fat diet self-efficacy and dietary restraint skills predicted better long-term weight loss, and the association of low-fat diet self-efficacy with weight outcomes was explained by dietary behaviors. CONCLUSIONS Health care providers who translate the DPP lifestyle intervention should be aware of pretreatment characteristics that may hamper or enhance weight loss, consider prioritizing strategies to improve low-fat diet self-efficacy and dietary restraint skills, and examine whether taking these actions improves weight loss outcomes

Multilevel examination of diabetes in modernising China: what elements of urbanisation are most associated with diabetes?

Aims/hypothesis: The purpose of this study was to examine the association between urbanisation-related factors and diabetes prevalence in China. Methods: Anthropometry, fasting blood glucose (FBG) and community-level data were collected for 7,741 adults (18–90 years) across 217 communities and nine provinces in the 2009 China Health and Nutrition Survey to examine diabetes (FBG ≥7.0 mmol/l or doctor diagnosis). Sex-stratified multilevel models, clustered at the community and province levels and controlling for individual-level age and household income were used to examine the association between diabetes and: (1) a multicomponent urbanisation measure reflecting overall modernisation and (2) 12 separate components of urbanisation (e.g., population density, employment, markets, infrastructure and social factors). Results: Prevalent diabetes was higher in more-urbanised (men 12%; women 9%) vs less-urbanised (men 6%; women 5%) areas. In sex-stratified multilevel models adjusting for residential community and province, age and household income, there was a twofold higher diabetes prevalence in urban vs rural areas (men OR 2.02, 95% CI 1.47, 2.78; women, OR 1.94, 95% CI 1.35, 2.79). All urbanisation components were positively associated with diabetes, with variation across components (e.g. men, economic and income diversity, OR 1.42, 95% CI 1.20, 1.66; women, transportation infrastructure, OR 1.18, 95% CI 1.06, 1.32). Community-level variation in diabetes was comparatively greater for women (intraclass correlation [ICC] 0.03–0.05) vs men (ICC ≤0.01); province-level variation was greater for men (men 0.03–0.04; women 0.02). Conclusions/interpretation: Diabetes prevention and treatment efforts are needed particularly in urbanised areas of China. Community economic factors, modern markets, communications and transportation infrastructure might present opportunities for such efforts. Electronic supplementary material The online version of this article (doi:10.1007/s00125-012-2697-8) contains peer-reviewed but unedited supplementary material, which is available to authorised users

Electronic Medical Record Cancer Incidence over Six Years Comparing New Users of Glargine with New Users of NPH Insulin

Background: Recent studies suggested that insulin glargine use could be associated with increased risk of cancer. We compared the incidence of cancer in new users of glargine versus new users of NPH in a longitudinal clinical cohort with diabetes for up to 6 years. Methods and Findings: From all patients who had been regularly followed at Massachusetts General Hospital from 1/01/2005 to 12/31/2010, 3,680 patients who had a medication record for glargine or NPH usage were obtained from the electronic medical record (EMR). From those we selected 539 new glargine users (age: 60.1±13.6 years, BMI: 32.7±7.5 kg/m2) and 343 new NPH users (61.5±14.1 years, 32.7±8.3 kg/m2) who had no prevalent cancer during 19 months prior to glargine or NPH initiation. All incident cancer cases were ascertained from the EMR requiring at least 2 ICD-9 codes within a 2 month period. Insulin exposure time and cumulative dose were validated. The statistical analysis compared the rates of cancer in new glargine vs. new NPH users while on treatment, adjusted for the propensity to receive one or the other insulin. There were 26 and 28 new cancer cases in new glargine and new NPH users for 1559 and 1126 person-years follow-up, respectively. There were no differences in the propensity-adjusted clinical characteristics between groups. The adjusted hazard ratio for the cancer incidence comparing glargine vs. NPH use was 0.65 (95% CI: 0.36–1.19). Conclusions: Insulin glargine is not associated with development of cancers when compared with NPH in this longitudinal and carefully retrieved EMR data

Pericardial Fat is Associated With Atrial Conduction: The Framingham Heart Study

Background: Obesity is associated with altered atrial electrophysiology and a prominent risk factor for atrial fibrillation. Body mass index, the most widely used adiposity measure, has been related to atrial electrical remodeling. We tested the hypothesis that pericardial fat is independently associated with electrocardiographic measures of atrial conduction. Methods and Results: We performed a cross‐sectional analysis of 1946 Framingham Heart Study participants (45% women) to determine the relation between pericardial fat and atrial conduction as measured by P wave indices (PWI): PR interval, P wave duration (P‐duration), P wave amplitude (P‐amplitude), P wave area (P‐area), and P wave terminal force (P‐terminal). We performed sex‐stratified linear regression analyses adjusted for relevant clinical variables and ectopic fat depots. Each 1‐SD increase in pericardial fat was significantly associated with PR interval (β=1.7 ms, P=0.049), P‐duration (β=2.3 ms, P<0.001), and P‐terminal (β=297 μV·ms, P<0.001) among women; and P‐duration (β=1.2 ms, P=0.002), P‐amplitude (β=−2.5 μV, P<0. 001), and P‐terminal (β=160 μV·ms, P=0.002) among men. Among both sexes, pericardial fat was significantly associated with P‐duration in analyses additionally adjusting for visceral fat or intrathoracic fat; a similar but non‐significant trend existed with P‐terminal. Among women, pericardial fat was significantly associated with P wave area after adjustment for visceral and intrathoracic fat. Conclusions: Pericardial fat is associated with atrial conduction as quantified by PWI, even with adjustment for extracardiac fat depots. Further studies are warranted to identify the mechanisms through which pericardial fat may modify atrial electrophysiology and promote subsequent risk for arrhythmogenesis

Determinants of Smoking and Quitting in HIV-Infected Individuals

Background: Cigarette smoking is widespread among HIV-infected patients, who confront increased risk of smoking-related co-morbidities. The effects of HIV infection and HIV-related variables on smoking and smoking cessation are incompletely understood. We investigated the correlates of smoking and quitting in an HIV-infected cohort using a validated natural language processor to determine smoking status. Method We developed and validated an algorithm using natural language processing (NLP) to ascertain smoking status from electronic health record data. The algorithm was applied to records for a cohort of 3487 HIV-infected from a large health care system in Boston, USA, and 9446 uninfected control patients matched 3:1 on age, gender, race and clinical encounters. NLP was used to identify and classify smoking-related portions of free-text notes. These classifications were combined into patient-year smoking status and used to classify patients as ever versus never smokers and current smokers versus non-smokers. Generalized linear models were used to assess associations of HIV with 3 outcomes, ever smoking, current smoking, and current smoking in analyses limited to ever smokers (persistent smoking), while adjusting for demographics, cardiovascular risk factors, and psychiatric illness. Analyses were repeated within the HIV cohort, with the addition of CD4 cell count and HIV viral load to assess associations of these HIV-related factors with the smoking outcomes. Results: Using the natural language processing algorithm to assign annual smoking status yielded sensitivity of 92.4, specificity of 86.2, and AUC of 0.89 (95% confidence interval [CI] 0.88–0.91). Ever and current smoking were more common in HIV-infected patients than controls (54% vs. 44% and 42% vs. 30%, respectively, both P<0.001). In multivariate models HIV was independently associated with ever smoking (adjusted rate ratio [ARR] 1.18, 95% CI 1.13–1.24, P <0.001), current smoking (ARR 1.33, 95% CI 1.25–1.40, P<0.001), and persistent smoking (ARR 1.11, 95% CI 1.07–1.15, P<0.001). Within the HIV cohort, having a detectable HIV RNA was significantly associated with all three smoking outcomes. Conclusions: HIV was independently associated with both smoking and not quitting smoking, using a novel algorithm to ascertain smoking status from electronic health record data and accounting for multiple confounding clinical factors. Further research is needed to identify HIV-related barriers to smoking cessation and develop aggressive interventions specific to HIV-infected patients