Randomized trials demonstrated a lower risk of cardiovascular (CV) events with sodium-glucose cotransporter-2 inhibitors (SGLT-2i) in patients with type 2 diabetes (T2D) at high CV risk. Prior real-world data suggested similar SGLT-2i effects in T2D patients with a broader risk profile, but these studies focused on heart failure and death and were limited to the United States and Europe. The purpose of this study was to examine a broad range of CV outcomes in patients initiated on SGLT-2i versus other glucose-lowering drugs (oGLDs) across 6 countries in the Asia Pacific, the Middle East, and North American regions. New users of SGLT-2i and oGLDs were identified via claims, medical records, and national registries in South Korea, Japan, Singapore, Israel, Australia, and Canada. Propensity scores for SGLT-2i initiation were developed in each country, with 1:1 matching. Hazard ratios (HRs) for death, hospitalization for heart failure (HHF), death or HHF, MI, and stroke were assessed by country and pooled using weighted meta-analysis. After propensity-matching, there were 235,064 episodes of treatment initiation in each group; ∼27% had established CV disease. Patient characteristics were well-balanced between groups. Dapagliflozin, empagliflozin, ipragliflozin, canagliflozin, tofogliflozin, and luseogliflozin accounted for 75%, 9%, 8%, 4%, 3%, and 1% of exposure time in the SGLT-2i group, respectively. Use of SGLT-2i versus oGLDs was associated with a lower risk of death (HR: 0.51; 95% confidence interval [CI]: 0.37 to 0.70; p < 0.001), HHF (HR: 0.64; 95% CI: 0.50 to 0.82; p = 0.001), death or HHF (HR: 0.60; 95% CI: 0.47 to 0.76; p < 0.001), MI (HR: 0.81; 95% CI: 0.74 to 0.88; p < 0.001), and stroke (HR: 0.68; 95% CI: 0.55 to 0.84; p < 0.001). Results were directionally consistent across both countries and patient subgroups, including those with and without CV disease. In this large, international study of patients with T2D from the Asia Pacific, the Middle East, and North America, initiation of SGLT-2i was associated with a lower risk of CV events across a broad range of outcomes and patient characteristics. (Comparative Effectiveness of Cardiovascular Outcomes in New Users of SGLT-2 Inhibitors [CVD-REAL]; NCT02993614).

OBJECTIVE To investigate the long-term associations of magnesium intake with incidence of diabetes, systemic inflammation, and insulin resistance among young American adults. RESEARCH DESIGN AND METHODS A total of 4,497 Americans, aged 18–30 years, who had no diabetes at baseline, were prospectively examined for incident diabetes based on quintiles of magnesium intake. We also investigated the associations between magnesium intake and inflammatory markers, i.e., high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and fibrinogen, and the homeostasis model assessment of insulin resistance (HOMA-IR). RESULTS During the 20-year follow-up, 330 incident cases of diabetes were identified. Magnesium intake was inversely associated with incidence of diabetes after adjustment for potential confounders. The multivariable-adjusted hazard ratio of diabetes for participants in the highest quintile of magnesium intake was 0.53 (95% CI, 0.32–0.86; P_trend < 0.01) compared with those in the lowest quintile. Consistently, magnesium intake was significantly inversely associated with hs-CRP, IL-6, fibrinogen, and HOMA-IR, and serum magnesium levels were inversely correlated with hs-CRP and HOMA-IR. CONCLUSIONS Magnesium intake was inversely longitudinally associated with incidence of diabetes in young American adults. This inverse association may be explained, at least in part, by the inverse correlations of magnesium intake with systemic inflammation and insulin resistance.

Kim DJ. Diabetes Metab J. 2011 Aug;35(4):303-308. https://doi.org/10.4093/dmj.2011.35.4.303

A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice.