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

Leveraging Family History in Genetic Association Analyses of Binary Traits

BACKGROUND: Considering relatives\u27 health history in logistic regression for case-control genome-wide association studies (CC-GWAS) may provide new information that increases accuracy and power to detect disease associated genetic variants. We conducted simulations and analyzed type 2 diabetes (T2D) data from the Framingham Heart Study (FHS) to compare two methods, liability threshold model conditional on both case-control status and family history (LT-FH) and Fam-meta, which incorporate family history into CC-GWAS. RESULTS: In our simulation scenario of trait with modest T2D heritability (h CONCLUSIONS: Overall, LT-FH and Fam-meta had higher power than CC-GWAS in simulations, especially using phenotypes that were more prevalent in older age groups, and both methods detected known genetic variants with lower P-values in real data application, highlighting the benefits of including family history in genetic association studies

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

Genetic Effect on Body Mass Index and Cardiovascular Disease Across Generations

BACKGROUND: Whether genetics contribute to the rising prevalence of obesity or its cardiovascular consequences in today\u27s obesogenic environment remains unclear. We sought to determine whether the effects of a higher aggregate genetic burden of obesity risk on body mass index (BMI) or cardiovascular disease (CVD) differed by birth year. METHODS: We split the FHS (Framingham Heart Study) into 4 equally sized birth cohorts (birth year before 1932, 1932 to 1946, 1947 to 1959, and after 1960). We modeled a genetic predisposition to obesity using an additive genetic risk score (GRS) of 941 BMI-associated variants and tested for GRS-birth year interaction on log-BMI (outcome) when participants were around 50 years old (N=7693). We repeated the analysis using a GRS of 109 BMI-associated variants that increased CVD risk factors (type 2 diabetes, blood pressure, total cholesterol, and high-density lipoprotein) in addition to BMI. We then evaluated whether the effects of the BMI GRSs on CVD risk differed by birth cohort when participants were around 60 years old (N=5493). RESULTS: Compared with participants born before 1932 (mean age, 50.8 yrs [2.4]), those born after 1960 (mean age, 43.3 years [4.5]) had higher BMI (median, 25.4 [23.3-28.0] kg/m CONCLUSIONS: The significant GRS-birth year interactions indicate that common genetic variants have larger effects on middle-age BMI and CVD risk in people born more recently. These findings suggest that the increasingly obesogenic environment may amplify the impact of genetics on the risk of obesity and possibly its cardiovascular consequences

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