Association of Serum Retinol-Binding Protein 4 Levels and the Risk of Incident Type 2 Diabetes in Subjects With Prediabetes.

OBJECTIVE: To explore the association of serum retinol-binding protein 4 (RBP4) levels and risk for the development of type 2 diabetes in individuals with prediabetes. RESEARCH DESIGN AND METHODS: A population-based prospective study was conducted among 1,011 Chinese participants with prediabetes (average age 55.6 ± 7.2 years). Incident type 2 diabetes was diagnosed according to the American Diabetes Association 2010 criteria. Serum RBP4 levels were measured using a commercially available ELISA. We analyzed the association of serum RBP4 levels with the risk of incident type 2 diabetes using the Cox proportional hazards model. RESULTS: During a median follow-up period of 3.1 years, 153 participants developed incident type 2 diabetes. A U-shaped association was observed between serum RBP4 levels and the risk of incident type 2 diabetes, with the lowest risk in the RBP4 range of 31-55 µg/mL. Multivariate Cox regression model analysis showed that serum RBP4 levels <31 µg/mL and RBP4 levels >55 µg/mL were associated with an increased risk of incident type 2 diabetes. The adjusted hazard ratios (95% CI) were 2.01 (1.31-3.09) and 1.97 (1.32-2.93), respectively, after adjusting for age, sex, BMI, waist circumference, γ-glutamyltransferase, HOMA of insulin resistance index, fasting plasma glucose, 2-h plasma glucose, and glycated hemoglobin (HbA1c) levels. CONCLUSIONS: A U-shaped relationship exists between serum RBP4 levels and the risk of incident type 2 diabetes in subjects with prediabetes.

Small dense LDL cholesterol is associated with metabolic syndrome traits independently of obesity and inflammation.

BACKGROUND: Small dense LDL cholesterol (sdLDL-c) has been established to be highly associated with metabolic disorder. However, the relationship between circulating sdLDL-c and the presence of metabolic syndrome (MetS) has not been fully established. METHODS: A total of 1065 Chinese males (45.07 ± 11.08 years old) without diabetes and general obesity was recruited into a population-based, cross-sectional study. The MetS was defined based on the updated National Cholesterol Education Program/ Adult Treatment Panel III criteria for Asian Americans. Serum sdLDL-c concentration was measured by a homogeneous assay method and its relationship with MetS and its traits was investigated. RESULTS: Serum sdLDL-c concentrations increased gradually with increasing numbers of MetS components (p < 0.001) and the proportion of patients with MetS increased gradually with increasing sdLDL-c levels (p for trend< 0.001). For the second, third, and fourth sdLDL-c quartiles versus the first, the OR (95% CI) for MetS were 4.47(2.41,8.28), 5.47(2.97,10.07) and 8.39(4.58,15.38) (p < 0.001 for trend) after multivariate adjustment. The stratified analysis conducted according to LDL-c levels showed that the OR between serum sdLDL-c levels and MetS was greater in those LDL-c levels lower than 3.3 mmol/L (OR = 22.97; 95% CI, 7.64-69.09) than in those LDL-c levels higher than 3.3 mmol/L (OR = 17.49; 95% CI, 4.43-68.98). Mediation analysis showed sdLDL-c mediated 38.6% of the association of waist circumference with triglycerides, while the association between sdLDL-c and MetS components did not mediate by hsCRP. CONCLUSIONS: This study found that high sdLDL-c concentrations were associated with the presence of MetS independently of central obesity and inflammation.

Association between dietary protein intake and type 2 diabetes varies by dietary pattern.

BACKGROUND: Epidemiological studies have demonstrated that high total protein intake was related to type 2 diabetes mellitus (T2DM) risks. However, few studies considered the impact of dietary pattern. OBJECTIVE: We examined the associations between protein intake and T2DM in different dietary patterns. METHODS: We used the demographic and dietary information of adults aged 18-75 years from the China Health and Nutrition Survey (2009), consisting of 4113 women and 4580 men. Dietary data was collected by using 24-h recalls combined with a food inventory for 3 consecutive days. Cluster analysis was used to classify subjects into groups, as determined by major sources of protein. Logistic regression models were used to calculate odds ratios (OR) and 95% confidence interval (95% CI) of T2DM according to the energy-adjusted protein intake. RESULTS: All participants were divided into three patterns according to the dietary source of protein (legumes and seafood, red meat, refined grains). Overall, plant protein intake was significantly and inversely associated with T2DM. In the subgroup analysis by dietary patterns, extreme quartile of plant protein intake was also inversely related to T2DM in the "legumes and seafood" group [OR = 0.58, 95% CI (0.33-0.96)]. Total protein intake and animal protein intake were positively related to T2DM in the "red meat" group [OR: 3.12 (1.65-5.91) and 3.48 (1.87-6.60), respectively]. However, the association of animal protein intake was reversed in the "refined grains" group [OR = 0.55, 95% CI 0.32-0.89]. CONCLUSIONS: The association between protein intake and T2DM varies by dietary pattern. Dietary pattern may be considered into the recommendation of protein intake for diabetes prevention.