On the non-linear association between serum uric acid levels and all-cause mortality rate in patients with type 2 diabetes mellitus.

BACKGROUND AND AIMS: High levels of serum uric acid (SUA) are associated with increased mortality risk in the general population. Contrasting results are available in people with diabetes. The aim of our study was to investigate the association and its functional form between SUA and all cause-mortality in patients with type 2 diabetes mellitus (T2DM). METHODS: We studied three cohorts of patients with T2DM: Gargano Mortality Study, Foggia Mortality Study, Pisa Mortality Study. All-cause mortality rate was the end point of this study. RESULTS: The most reliable relationship between SUA levels and all-cause mortality rate was quadratic, with such model being well approximated by SUA tertiles. Both tertiles 1 and 3 were at higher risk of mortality as compared to tertile 2: Hazard Ratio (HR) [95% Confidence Interval (CI)] = 1.34 (1.07-1.68) and 1.61 (1.29-1.99), respectively. In the pseudo-sample, created from the real pooled sample, the best relationship between SUA and all-cause mortality rate was quadratic. In a tree-based Recursive Partitioning and Regression Tree analysis two subgroups at increased risk of mortality were identified, namely those with SUA levels ≥7.28 mg/dl and with SUA levels <4.16 mg/dl as compared to patients with intermediate SUA levels (i.e. 4.16-7.28), thus providing further evidence on the J-shaped relationship between SUA levels and mortality rate. CONCLUSIONS: SUA was not linearly associated with all-cause mortality rate in patients with T2DM. For clinical and public health purposes such association is J-shaped.

IRS1 G972R missense polymorphism is associated with failure to oral antidiabetes drugs in white patients with type 2 diabetes from Italy.

This study tried to replicate in a large sample of white patients with type 2 diabetes (T2D) from Italy a previously reported association of the IRS1 G972R polymorphism with failure to oral antidiabetes drugs (OAD). A total of 2,409 patients from four independent studies were investigated. Case subjects (n = 1,193) were patients in whom, because of uncontrolled diabetes (i.e., HbA1c >8%), insulin therapy had been added either on, or instead of, maximal or near-maximal doses of OAD, mostly metformin and sulfonylureas; control subjects (n = 1,216) were patients with HbA1c <8% in the absence of insulin therapy. The IRS1 G972R polymorphism was typed by TaqMan allele discrimination. In all samples, individuals carrying the IRS1 R972 risk variant tended to be more frequent among case than control subjects, though reaching statistical significance only in one case. As no IRS1 G972R-by-study sample interaction was observed, data from the four samples were analyzed together; a significant association was observed (allelic odds ratio [OR] 1.30, 95% CI 1.03-1.63). When our present data were meta-analyzed with those obtained in a previous study, an overall R972 allelic OR of 1.37 (1.12-1.69) was observed. This study confirms in a large and ethnically homogeneous sample that IRS1 G972R polymorphism is associated with failure to OAD among patients with T2D.

Development and validation of a predicting model of all-cause mortality in patients with type 2 diabetes.

OBJECTIVE: To develop and validate a parsimonious model for predicting short-term all-cause mortality in patients with type 2 diabetes mellitus (T2DM). RESEARCH DESIGN AND METHODS: Two cohorts of patients with T2DM were investigated. The Gargano Mortality Study (GMS, n = 679 patients) was the training set and the Foggia Mortality Study (FMS, n = 936 patients) represented the validation sample. GMS and FMS cohorts were prospectively followed up for 7.40 ± 2.15 and 4.51 ± 1.69 years, respectively, and all-cause mortality was registered. A new forward variable selection within a multivariate Cox regression was implemented. Starting from the empty model, each step selected the predictor that, once included into the multivariate Cox model, yielded the maximum continuous net reclassification improvement (cNRI). The selection procedure stopped when no further statistically significant cNRI increase was detected. RESULTS: Nine variables (age, BMI, diastolic blood pressure, LDL cholesterol, triglycerides, HDL cholesterol, urine albumin-to-creatinine ratio, and antihypertensive and insulin therapy) were included in the final predictive model with a C statistic of 0.88 (95% CI 0.82-0.94) in the GMS and 0.82 (0.76-0.87) in the FMS. Finally, we used a recursive partition and amalgamation algorithm to identify patients at intermediate and high mortality risk (hazard ratio 7.0 and 24.4, respectively, as compared with those at low risk). A web-based risk calculator was also developed. CONCLUSIONS: We developed and validated a parsimonious all-cause mortality equation in T2DM, providing also a user-friendly web-based risk calculator. Our model may help prioritize the use of available resources for targeting aggressive preventive and treatment strategies in a subset of very high-risk individuals.