Effect of toroidal field ripple on plasma rotation in JET

Dedicated experiments on TF ripple effects on the performance of tokamak plasmas have been carried out at JET. The TF ripple was found to have a profound effect on the plasma rotation. The central Mach number, M, defined as the ratio of the rotation velocity and the thermal velocity, was found to drop as a function of TF ripple amplitude (3) from an average value of M = 0.40-0.55 for operations at the standard JET ripple of 6 = 0.08% to M = 0.25-0.40 for 6 = 0.5% and M = 0.1-0.3 for delta = 1%. TF ripple effects should be considered when estimating the plasma rotation in ITER. With standard co-current injection of neutral beam injection (NBI), plasmas were found to rotate in the co-current direction. However, for higher TF ripple amplitudes (delta similar to 1%) an area of counter rotation developed at the edge of the plasma, while the core kept its co-rotation. The edge counter rotation was found to depend, besides on the TF ripple amplitude, on the edge temperature. The observed reduction of toroidal plasma rotation with increasing TF ripple could partly be explained by TF ripple induced losses of energetic ions, injected by NBI. However, the calculated torque due to these losses was insufficient to explain the observed counter rotation and its scaling with edge parameters. It is suggested that additional TF ripple induced losses of thermal ions contribute to this effect

Genetic Risk Reclassification for Type 2 Diabetes by Age Below or Above 50 Years Using 40 Type 2 Diabetes Risk Single Nucleotide Polymorphisms

OBJECTIVE: To test if knowledge of type 2 diabetes genetic variants improves disease prediction. RESEARCH DESIGN AND METHODS: We tested 40 single nucleotide polymorphisms (SNPs) associated with diabetes in 3,471 Framingham Offspring Study subjects followed over 34 years using pooled logistic regression models stratified by age (<50 years, diabetes cases = 144; or ≥50 years, diabetes cases = 302). Models included clinical risk factors and a 40-SNP weighted genetic risk score. RESULTS: In people <50 years of age, the clinical risk factors model C-statistic was 0.908; the 40-SNP score increased it to 0.911 (P = 0.3; net reclassification improvement (NRI): 10.2%, P = 0.001). In people ≥50 years of age, the C-statistics without and with the score were 0.883 and 0.884 (P = 0.2; NRI: 0.4%). The risk per risk allele was higher in people <50 than ≥50 years of age (24 vs. 11%; P value for age interaction = 0.02). CONCLUSIONS: Knowledge of common genetic variation appropriately reclassifies younger people for type 2 diabetes risk beyond clinical risk factors but not older people

First-Trimester Prediction of Gestational Diabetes Mellitus: Examining the Potential of Combining Maternal Characteristics and Laboratory Measures

OBJECTIVE Predictors of gestational diabetes mellitus (GDM) have been widely studied, but few studies have considered multiple measures. Our objective was to integrate several potential GDM predictors with consideration to both simple and novel measures and to determine the extent to which GDM can be predicted in the first trimester. RESEARCH DESIGN AND METHODS We identified first-trimester maternal samples from 124 women who developed GDM and 248 control subjects who did not. We gathered data on age, BMI, parity, race, smoking, prior GDM, family history of diabetes, and blood pressure. Using retrieved samples, we measured routine (lipids, high-sensitivity C-reactive protein, and gamma-glutamyltransferase) and novel (adiponectin, E-selectin, and tissue plasminogen activator [t-PA]) parameters. We determined independent predictors from stepwise regression analyses, calculated areas under the receiver-operating characteristic curves (AUC-ROC), and integrated discrimination improvement (IDI) for relevant models. RESULTS Compared with control subjects, women who subsequently developed GDM were older, had higher BMIs, were more likely to be of Asian origin, had a history of GDM or family history of type 2 diabetes, and had higher systolic blood pressure (P &lt; 0.05 for all). With regard biochemical measures, stepwise analyses identified only elevated t-PA and low HDL cholesterol levels as significant (P &lt;= 0.015) independent predictors of GDM beyond simple non-laboratory-based maternal measures. Their inclusion improved the AUC-ROC from 0.824 to 0.861 and IDI by 0.052 (0.017-0.115). CONCLUSIONS GDM can be usefully estimated from a mix of simple questions with potential for further improvement by specific blood measures (lipids and t-PA)

Effect of Homocysteine-Lowering Treatment With Folic Acid and B Vitamins on Risk of Type 2 Diabetes in Women: A Randomized, Controlled Trial

OBJECTIVE: Homocysteinemia may play an etiologic role in the pathogenesis of type 2 diabetes by promoting oxidative stress, systemic inflammation, and endothelial dysfunction. We investigated whether homocysteine-lowering treatment by B vitamin supplementation prevents the risk of type 2 diabetes. RESEARCH DESIGN AND METHODS: The Women's Antioxidant and Folic Acid Cardiovascular Study (WAFACS), a randomized, double-blind, placebo-controlled trial of 5,442 female health professionals aged ≥40 years with a history of cardiovascular disease (CVD) or three or more CVD risk factors, included 4,252 women free of diabetes at baseline. Participants were randomly assigned to either an active treatment group (daily intake of a combination pill of 2.5 mg folic acid, 50 mg vitamin B6, and 1 mg vitamin B12) or to the placebo group. RESULTS: During a median follow-up of 7.3 years, 504 women had an incident diagnosis of type 2 diabetes. Overall, there was no significant difference between the active treatment group and the placebo group in diabetes risk (relative risk 0.94 [95% CI 0.79–1.11]; P = 0.46), despite significant lowering of homocysteine levels. Also, there was no evidence for effect modifications by baseline intakes of dietary folate, vitamin B6, and vitamin B12. In a sensitivity analysis, the null result remained for women compliant with their study pills (0.92 [0.76–1.10]; P = 0.36). CONCLUSIONS: Lowering homocysteine levels by daily supplementation with folic acid and vitamins B6 and B12 did not reduce the risk of developing type 2 diabetes among women at high risk for CVD

Multi-Tissue Epigenetic analysis Identifies Distinct associations Underlying insulin Resistance and alzheimer\u27s Disease at Cpt1A Locus

BACKGROUND: Insulin resistance (IR) is a major risk factor for Alzheimer\u27s disease (AD) dementia. The mechanisms by which IR predisposes to AD are not well-understood. Epigenetic studies may help identify molecular signatures of IR associated with AD, thus improving our understanding of the biological and regulatory mechanisms linking IR and AD. METHODS: We conducted an epigenome-wide association study of IR, quantified using the homeostatic model assessment of IR (HOMA-IR) and adjusted for body mass index, in 3,167 participants from the Framingham Heart Study (FHS) without type 2 diabetes at the time of blood draw used for methylation measurement. We identified DNA methylation markers associated with IR at the genome-wide level accounting for multiple testing (P \u3c 1.1 × 10 RESULTS: We confirmed the strong association of blood DNA methylation with IR at three loci (cg17901584-DHCR24, cg17058475-CPT1A, cg00574958-CPT1A, and cg06500161-ABCG1). In FHS, higher levels of blood DNA methylation at cg00574958 and cg17058475 were both associated with lower IR (P = 2.4 × 10 CONCLUSIONS: Our results suggest potentially distinct epigenetic regulatory mechanisms between peripheral blood and dorsolateral prefrontal cortex tissues underlying IR and AD at CPT1A locus

A better coefficient of determination for genetic profile analysis

Genome-wide association studies have facilitated the construction of risk predictors for disease from multiple Single Nucleotide Polymorphism markers. The ability of such "genetic profiles" to predict outcome is usually quantified in an independent data set. Coefficients of determination (R-2) have been a useful measure to quantify the goodness-of-fit of the genetic profile. Various pseudo-R-2 measures for binary responses have been proposed. However, there is no standard or consensus measure because the concept of residual variance is not easily defined on the observed probability scale. Unlike other nongenetic predictors such as environmental exposure, there is prior information on genetic predictors because for most traits there are estimates of the proportion of variation in risk in the population due to all genetic factors, the heritability. It is this useful ability to benchmark that makes the choice of a measure of goodness-of-fit in genetic profiling different from that of nongenetic predictors. In this study, we use a liability threshold model to establish the relationship between the observed probability scale and underlying liability scale in measuring R-2 for binary responses. We show that currently used R-2 measures are difficult to interpret, biased by ascertainment, and not comparable to heritability. We suggest a novel and globally standard measure of R-2 that is interpretable on the liability scale. Furthermore, even when using ascertained case-control studies that are typical in human disease studies, we can obtain an R-2 measure on the liability scale that can be compared directly to heritability. Genet. Epidemiol. 36:214-224, 2012. (C) 2012 Wiley Periodicals, Inc

Common Variants at 10 Genomic Loci Influence Hemoglobin A(1C) Levels via Glycemic and Nonglycemic Pathways

OBJECTIVE Glycated hemoglobin (HbA1c), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA1c. We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA1c levels. RESEARCH DESIGN AND METHODS We studied associations with HbA1c in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA1c loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening. RESULTS Ten loci reached genome-wide significant association with HbA1c, including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10−26), HFE (rs1800562/P = 2.6 × 10−20), TMPRSS6 (rs855791/P = 2.7 × 10−14), ANK1 (rs4737009/P = 6.1 × 10−12), SPTA1 (rs2779116/P = 2.8 × 10−9) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10−9), and four known HbA1c loci: HK1 (rs16926246/P = 3.1 × 10−54), MTNR1B (rs1387153/P = 4.0 × 10−11), GCK (rs1799884/P = 1.5 × 10−20) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10−18). We show that associations with HbA1c are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA1c) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA1c. CONCLUSIONS GWAS identified 10 genetic loci reproducibly associated with HbA1c. Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA1c levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA1c