Comparison of the 1997 and 2003 American Diabetes Association Classification of Impaired Fasting Glucose Impact on Prevalence of Impaired Fasting Glucose, Coronary Heart Disease Risk Factors, and Coronary Heart Disease in a Community-Based Medical Practice

ObjectivesThe goals of this study were to assess the effect of the 2003 American Diabetes Association definition of impaired fasting glucose (IFG) on prevalence of IFG, coronary heart disease (CHD) risk factors, and CHD compared with the 1997 IFG definition.BackgroundAlthough IFG is viewed as increasing CHD risk, this association is unclear and has not been well studied after changing the IFG criterion, especially in a clinical practice setting.MethodsThis was a cross-sectional evaluation of 8,295 members (3,763 men and 4,532 women) of a community medical center who were between the ages of 30 and 69 years, without a history of diabetes mellitus, and who had available measurements of fasting plasma glucose and lipid concentrations within the past 2 years. The prevalence of IFG, CHD risk factors, and CHD with the 1997 and 2003 IFG definition was compared.ResultsThe prevalence of IFG increased from 8% to 35% with the 2003 criterion. Individuals with glucose of 100 to 109 mg/dl had lower prevalence of most CHD risk factors (hypertension, triglyceride ≥150 mg/dl, high-density lipoprotein cholesterol <40 mg/dl, meeting 2 components of the metabolic syndrome criteria, CHD risk ≥10% by Framingham score) compared with individuals with glucose 110 to 125 mg/dl. Individuals identified with the 2003 IFG definition did not have an increase in known CHD when adjusted for covariates (odds ratio 1.4 [95% confidence interval (CI) 0.7 to 2.3] vs. 3.2 [95% CI 1.8 to 5.9]).ConclusionsOne-third of the population has IFG with the 2003 definition, yet many of these individuals do not have increased prevalence of CHD risk factors or CHD

First Trimester Plasma Glucose Values in Women without Diabetes are Associated with Risk for Congenital Heart Disease in Offspring

In a retrospective study of 19 171 mother-child dyads, elevated random plasma glucose values during early pregnancy were directly correlated with increased risk for congenital heart disease in offspring. Plasma glucose levels proximal to the period of cardiac development may represent a modifiable risk factor for congenital heart disease in expectant mothers without diabetes.Peer reviewe

Identification and validation of N-acetyltransferase 2 as an insulin sensitivity gene

Journal ArticleDecreased insulin sensitivity, also referred to as insulin resistance (IR), is a fundamental abnormality in patients with type 2 diabetes and a risk factor for cardiovascular disease. While IR predisposition is heritable, the genetic basis remains largely unknown. The GENEticS of Insulin Sensitivity consortium conducted a genome-wide association study (GWAS) for direct measures of insulin sensitivity, such as euglycemic clamp or insulin suppression test, in 2,764 European individuals, with replication in an additional 2,860 individuals. The presence of a nonsynonymous variant of N-acetyltransferase 2 (NAT2) [rs1208 (803A>G, K268R)] was strongly associated with decreased insulin sensitivity that was independent of BMI. The rs1208 "A" allele was nominally associated with IR-related traits, including increased fasting glucose, hemoglobin A1C, total and LDL cholesterol, triglycerides, and coronary artery disease. NAT2 acetylates arylamine and hydrazine drugs and carcinogens, but predicted acetylator NAT2 phenotypes were not associated with insulin sensitivity. In a murine adipocyte cell line, silencing of NAT2 ortholog Nat1 decreased insulin-mediated glucose uptake, increased basal and isoproterenol- stimulated lipolysis, and decreased adipocyte differentiation, while Nat1 overexpression produced opposite effects. Nat1-deficient mice had elevations in fasting blood glucose, insulin, and triglycerides and decreased insulin sensitivity, as measured by glucose and insulin tolerance tests, with intermediate effects in Nat1 heterozygote mice. Our results support a role for NAT2 in insulin sensitivity

Using Pre-existing Microarray Datasets to Increase Experimental Power: Application to Insulin Resistance

Although they have become a widely used experimental technique for identifying differentially expressed (DE) genes, DNA microarrays are notorious for generating noisy data. A common strategy for mitigating the effects of noise is to perform many experimental replicates. This approach is often costly and sometimes impossible given limited resources; thus, analytical methods are needed which increase accuracy at no additional cost. One inexpensive source of microarray replicates comes from prior work: to date, data from hundreds of thousands of microarray experiments are in the public domain. Although these data assay a wide range of conditions, they cannot be used directly to inform any particular experiment and are thus ignored by most DE gene methods. We present the SVD Augmented Gene expression Analysis Tool (SAGAT), a mathematically principled, data-driven approach for identifying DE genes. SAGAT increases the power of a microarray experiment by using observed coexpression relationships from publicly available microarray datasets to reduce uncertainty in individual genes' expression measurements. We tested the method on three well-replicated human microarray datasets and demonstrate that use of SAGAT increased effective sample sizes by as many as 2.72 arrays. We applied SAGAT to unpublished data from a microarray study investigating transcriptional responses to insulin resistance, resulting in a 50% increase in the number of significant genes detected. We evaluated 11 (58%) of these genes experimentally using qPCR, confirming the directions of expression change for all 11 and statistical significance for three. Use of SAGAT revealed coherent biological changes in three pathways: inflammation, differentiation, and fatty acid synthesis, furthering our molecular understanding of a type 2 diabetes risk factor. We envision SAGAT as a means to maximize the potential for biological discovery from subtle transcriptional responses, and we provide it as a freely available software package that is immediately applicable to any human microarray study