Associations Between the Metabolic Score for Insulin Resistance Index and the Risk of Type 2 Diabetes Mellitus Among Non-Obese Adults: Insights from a Population-Based Cohort Study.

PURPOSE: The metabolic score for insulin resistance (METS-IR) index is an emerging surrogate predictor of type 2 diabetes mellitus (T2DM). This study aimed to evaluate the association between the METS-IR index and the risk of T2DM in non-obese Japanese adults. METHODS: A total of 12,290 non-obese participants were selected from the NAGALA prospective cohort study conducted from 2004 to 2015. Cox proportional hazards models were used to assess the association between the baseline METS-IR index and risk of T2DM. Generalized additive models were used to identify nonlinear relationships. In addition, we performed subgroup analyses and interaction tests. Results were expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: During a median follow-up of 2050 days, 176 (1.43%) incident T2DM occurred. The fully adjusted HR (95% CI) for the incidence of T2DM in non-obese adults was 1.17 (HR=1.17, 95% CI: 1.09-1.27, P<0.001) for every 1-unit increase in the METS-IR index. The risk of developing T2DM increased with the quartile of change in the METS-IR index, after adjustment for multiple potential confounding, the HRs for the Q4 group versus the Q1 group was 4.01 (95% CI, 1.39-11.57). Generalized additive models also showed a cumulative increase in the risk of T2DM with increasing the METS-IR index. Time-dependent receiver operating curve suggested helpful discriminative power of the METS-IR index for T2DM. The C-statistics by the clinical risk factors significantly improve with the addition of the METS-IR index (from 0.862 to 0.875, P = 0.035); the discriminatory power and risk reclassification also appeared to be substantially better, with the category-free NRI of 0.216, and the IDI of 0.011. CONCLUSION: The METS-IR index was a significant and independent predictor for future T2DM development in non-obese adults. The METS-IR index may have clinical significance in identifying groups at high risk of T2DM.

Increased Urinary CD163 Levels in Systemic Vasculitis with Renal Involvement.

OBJECTIVES: Systemic vasculitis includes a group of disorders characterized by inflammation of the vessel wall, involving multiple systems, and can cause malignant hypertension. CD163 is a specific marker of anti-inflammatory macrophages. This study is aimed at evaluating the CD163 levels in relation to systemic vasculitis and renal involvements. METHODS: Urinary CD163 levels were retrospectively measured by enzyme-linked immunosorbent assay (ELISA) in 51 patients with systemic vasculitis, 42 essential hypertensions, and 36 healthy volunteers. The associations between urinary CD163 levels and clinical indicators were analyzed. RESULTS: Urinary CD163 levels were significantly higher in patients with systemic vasculitis [68.20 (38.25~158.78) (pg/ml)] compared to essential hypertension [43.86 (23.30-60.71) (pg/ml)] (p = 0.003) and the healthy volunteers [30.76 (9.30-54.16) (pg/ml)] (p < 0.001). Furthermore, systemic vasculitis patients with renal involvement had significantly higher urinary CD163 levels relative to patients without renal involvement [86.95 (47.61 and 192.38) pg/ml] vs. [41.99 (17.70 and 71.95) pg/ml, p = 0.005]. After control factors age, sex, and BMI, urinary CD163 levels in systemic vasculitis patients were positively correlated with serum creatinine, blood urea nitrogen, and ß-2 microglobulin (r = 0.45, 0.48, and 0.46; p = 0.001, 0.001, and 0.002, respectively). In addition, we found the level of urinary CD163 in granulomatous vasculitis (including TA, GPA, and EGPA) was significantly higher than that in necrotizing vasculitis (including PAN) [86.95 (41.99 and 184.82) pg/ml] vs. [45.73 (21.43 and 74.43) pg/ml, p = 0.016]. CONCLUSION: Urinary CD163 levels were significantly higher in patients with systemic vasculitis, especially in patients with renal involvement. Thus, urinary CD163 has the potential to be a biomarker for systemic vasculitis with renal involvement.

Derivation and Validation of a Prediction Model for Predicting the 5-Year Incidence of Type 2 Diabetes in Non-Obese Adults: A Population-Based Cohort Study.

PURPOSE: The aim of this study was to derivate and validate a nomogram based on independent predictors to better evaluate the 5-year risk of T2D in non-obese adults. PATIENTS AND METHODS: This is a historical cohort study from a collection of databases that included 12,940 non-obese participants without diabetes at baseline. All participants were randomised to a derivation cohort (n = 9651) and a validation cohort (n = 3289). In the derivation cohort, the least absolute shrinkage and selection operator (LASSO) regression model was used to determine the optimal risk factors for T2D. Multivariate Cox regression analysis was used to establish the nomogram of T2D prediction. The receiver operating characteristic (ROC) curve, C-index, calibration curve, and decision curve analysis were performed by 1000 bootstrap resamplings to evaluate the discrimination ability, calibration, and clinical practicability of the nomogram. RESULTS: After LASSO regression analysis of the derivation cohort, it was found that age, fatty liver, γ-glutamyltranspeptidase, triglycerides, glycosylated hemoglobin A1c and fasting plasma glucose were risk predictors, which were integrated into the nomogram. The C-index of derivation cohort and validation cohort were 0.906 [95% confidence interval (CI), 0.878-0.934] and 0.837 (95% CI, 0.760-0.914), respectively. The AUC of 5-year T2D risk in the derivation cohort and validation cohort was 0.916 (95% CI, 0.889-0.943) and 0.829 (95% CI, 0.753-0.905), respectively. The calibration curve indicated that the predicted probability of nomogram is in good agreement with the actual probability. The decision curve analysis demonstrated that the predicted nomogram was clinically useful. CONCLUSION: Our nomogram can be used as a reasonable, affordable, simple, and widely implemented tool to predict the 5-year risk of T2D in non-obese adults. With this model, early identification of high-risk individuals is helpful to timely intervene and reduce the risk of T2D in non-obese adults.