Low levels of ApoA1 improve risk prediction of type 2 diabetes mellitus.

BACKGROUND: Type 2 diabetes mellitus (T2DM) has reported to be a major public health crisis in China.

OBJECTIVE: We examined the incidence of new T2DM over 4 years for association of clinical factors and lipids with development of T2DM in a community-based population.

METHODS: We included 923 Chinese subjects who participated in community-organized health checkout in both 2009 and 2013. Health history was collected; physical examination was performed; biochemistry, lipids, and glucose were measured. Of 923, 819 were confirmed without T2DM in 2009 and included in the analysis. Unadjusted and adjusted logistic regression models were used to estimate the effects of clinical factors and biomarkers on the risk of new T2DM.

RESULTS: Of 819 subjects without T2DM in 2009, 65 were identified as T2DM in 2013, 8% over 4 years. These 65 subjects, compared with those 754 without new T2DM, were older, more likely to be male and smokers. They had higher body mass index (BMI), fasting glucose, blood pressure and triglycerides, and lower levels of high-density lipoprotein-cholesterol and apolipoprotein A1 (ApoA1). Multivariate logistic regression identified larger BMI (odds ratio [OR] = 1.7; 95% confidence interval [CI], 1.22-2.39, P = .002), higher fasting glucose levels (OR = 4.2, 95% CI, 2.90-6.19, P < .001), and low levels of ApoA1 (OR = 0.51, 95% CI 0.33-0.76, P = .002) were independently associated with new T2DM. Furthermore, receiver operating characteristics curves for multivariate models for new T2DM showed that area under the curve improved from 0.87 to 0.89 when adding ApoA1 to the Framingham Diabetes Risk Scoring Model and from 0.85 to 0.89 when adding ApoA1 to a 4-variable (age, BMI, glucose, and triglycerides) Chinese model.

CONCLUSIONS: There is a high incidence of new T2DM at 8% over 4 years among Chinese. Larger BMI, higher glucose levels, and lower levels of ApoA1 are significantly and independently associated with new T2DM. Lower ApoA1 improves the risk prediction of new type 2 diabetes when it was added to the existing risk models.