Non-fasting lipids and risk of cardiovascular disease in patients with diabetes mellitus

AIMS/HYPOTHESIS

The aim of this study was to examine the effect of postprandial time on the associations and predictive value of non-fasting lipid levels and cardiovascular disease risk in participants with diabetes.

METHODS

This study was conducted among 1,337 participants with diabetes from the Dutch and German (Potsdam) contributions to the European Prospective Investigation into Cancer and Nutrition. At baseline, total cholesterol, LDL- and HDL-cholesterol and triacylglycerol concentrations were measured and the ratio of total cholesterol/HDL-cholesterol was calculated. Participants were followed for incidence of cardiovascular disease.

RESULTS

Lipid concentrations changed minimally with increasing postprandial time, except for triacylglycerol which was elevated just after a meal and declined over time (1.86 at 0.1 h to 1.33 at >6 h, p for trend <0.001). During a mean follow-up of 8 years, 116 cardiovascular events were documented. After adjustment for potential confounders, triacylglycerol (HR for third tertile compared with first tertile (HR(t)₃(to)₁), 1.73 [95% CI 1.04, 2.87]), HDL-cholesterol (HR(t)₃(to)₁, 0.41 [95% CI 0.23, 0.72]) and total cholesterol/HDL-cholesterol ratio (HR(t)₃(to)₁, 1.65 [95% CI 0.95, 2.85]) were associated with cardiovascular disease, independent of postprandial time. Cardiovascular disease risk prediction using the UK Prospective Diabetes Study risk engine was not affected by postprandial time.

CONCLUSIONS/INTERPRETATION

Postprandial time did not affect associations between lipid concentrations and cardiovascular disease risk in patients with diabetes, nor did it influence prediction of cardiovascular disease. Therefore, it may not be necessary to use fasting blood samples to determine lipid concentrations for cardiovascular disease risk prediction in patients with diabetes.

Fasting plasma glucose and Type 2 diabetes risk: a non-linear relationship

AIMS

The current definition of impaired fasting glucose (IFG, >or=100 mg/dl) has been criticized as being too low for selective identification of individuals at risk for Type 2 diabetes. Furthermore, it is unclear whether any cut-off is justifiable from the shape of association between fasting plasma glucose (FPG) and diabetes. We therefore evaluated the association between FPG and incidence of Type 2 diabetes in a prospective, population-based study.

METHODS

A case-cohort study within the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort study involved 589 fasted participants in a randomly selected subcohort and 153 incident cases. Restricted cubic spline regression was used to examine non-linearity of associations and we calculated pairs of sensitivities and specificities for different cut-offs of FPG.

RESULTS

Spline regression with adjustment for age, sex, body mass index, waist circumference, education, physical activity, alcohol consumption, and plasma levels of triglycerides, high-density lipoprotein cholesterol and gamma-glutamyltransferase indicated that FPG was associated with risk in a non-linear fashion. Risk with higher FPG increased only above approximately 84 mg/dl. FPG>or=84 mg/dl yielded a sensitivity of 95.4% at a false-positive rate of 86.8%. In comparison, FPG>or=100 and>or=110 mg/dl yielded sensitivities of 78.4 and 42.5% and false-positive rates of 27.8 and 6.8%, respectively. The optimal cut-off of FPG was at approximately 102 mg/dl (sensitivity: 75.8%, false-positive rate: 20.7%).

CONCLUSIONS

Although our study suggests a threshold for increasing diabetes risk at 84 mg/dl, this cut-off would classify the vast majority of the population as at risk. The statistically optimal cut-off supports the current definition of IFG (>or=100 mg/dl).

Diet-induced gene expression of isolated pancreatic islets from a polygenic mouse model of the metabolic syndrome

AIMS/HYPOTHESIS

Numerous new genes have recently been identified in genome-wide association studies for type 2 diabetes. Most are highly expressed in beta cells and presumably play important roles in their function. However, these genes account for only a small proportion of total risk and there are likely to be additional candidate genes not detected by current methodology. We therefore investigated islets from the polygenic New Zealand mouse (NZL) model of diet-induced beta cell dysfunction to identify novel genes and pathways that may play a role in the pathogenesis of diabetes.

METHODS

NZL mice were fed a diabetogenic high-fat diet (HF) or a diabetes-protective carbohydrate-free HF diet (CHF). Pancreatic islets were isolated by laser capture microdissection (LCM) and subjected to genome-wide transcriptome analyses.

RESULTS

In the prediabetic state, 2,109 islet transcripts were differentially regulated (>1.5-fold) between HF and CHF diets. Of the genes identified, 39 (e.g. Cacna1d, Chd2, Clip2, Igf2bp2, Dach1, Tspan8) correlated with data from the Diabetes Genetics Initiative and Wellcome Trust Case Control Consortium genome-wide scans for type 2 diabetes, thus validating our approach. HF diet induced early changes in gene expression associated with increased cell-cycle progression, proliferation and differentiation of islet cells, and oxidative stress (e.g. Cdkn1b, Tmem27, Pax6, Cat, Prdx4 and Txnip). In addition, pathway analysis identified oxidative phosphorylation as the predominant gene-set that was significantly upregulated in response to the diabetogenic HF diet.

CONCLUSIONS/INTERPRETATION

We demonstrated that LCM of pancreatic islet cells in combination with transcriptional profiling can be successfully used to identify novel candidate genes for diabetes. Our data strongly implicate glucose-induced oxidative stress in disease progression.