Daily Endocrinology Research Analysis

OBJECTIVE: Glucocorticoids (GCs) are potent anti-inflammatory drugs, but strategies to prevent side effects are lacking. We investigated whether metformin could prevent GC-related toxicity and explored the underlying mechanisms. RESEARCH DESIGN AND METHODS: This single-center, randomized, placebo-controlled, double-blind, crossover trial compared metformin with placebo during high-dose GC treatment in 18 lean, healthy, male study participants. The trial was conducted at the University Hospital Basel, Basel, Switzerland. Participants received prednisone 30 mg/day in combination with metformin or placebo for two 7-day periods (1:1 randomization). The primary outcome, change in insulin sensitivity, was assessed using a two-sided paired t test. Before and after each study period, we conducted a mixed-meal tolerance test, blood metabolomics, and RNA sequencing of subcutaneous adipose tissue biopsy specimens. RESULTS: Metformin improved insulin sensitivity as assessed by the Matsuda index (n = 17; mean change -2.73 ± 3.55 SD for placebo, 2.21 ± 3.95 for metformin; mean difference of change -4.94 [95% CI, -7.24, -2.65]; P < 0.001). Metabolomic and transcriptomic analyses revealed that metformin altered fatty acid flux in the blood and downregulated genes involved in fatty acid synthesis in adipose tissue. Metformin reduced markers of protein breakdown and bone resorption. Furthermore, metformin downregulated genes responsible for AMPK inhibition and affected glucagon-like peptide 1 and bile acid metabolism. CONCLUSIONS: Metformin prevents GC-induced insulin resistance and reduces markers of dyslipidemia, myopathy, and, possibly, bone resorption through AMPK-dependent and -independent pathways.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a significant global public health concern that has steadily increased over the past few decades. Thus, this study aimed to predict the incidence of T2DM within 5 years and the risk of mortality following the onset of T2DM. Data from three independent cohorts worldwide were used. METHODS: We utilized data from three independent, large-scale, general population-based, and worldwide cohort studies. The Korean cohort (NHIS-NSC cohort; discovery cohort; n = 973,303), conducted between 1 January, 2002 and 31 December, 2013, was used for training and internal validation, whereas the Japanese cohort (JMDC cohort; validation cohort A; n = 12,143,715) and UK cohort (UK Biobank; validation cohort B; n = 416,656) were used for external validation. We employed various machine learning (ML)-based models, using 18 features, to predict the incidence of T2DM within five years of regular health checkups and calculated the Shapley Additive Explanation (SHAP) values. To ensure the robustness of our ML-based prediction model, we investigated the potential association between the model probability divided into tertiles and the risk of mortality following the onset of T2DM. FINDINGS: In the discovery cohort, the ensemble model using voting with logistic regression and adaptive boosting achieved a balanced accuracy of 72.6% and an area under the receiver operating characteristics curve (AUROC) of 0.792. The SHAP value analysis of our proposed model revealed that age was the most important predictor of incident T2DM, followed by fasting blood glucose, hemoglobin, γ-glutamyl transferase level, and body mass index. The model probability is associated with an increased risk of mortality (T1: adjusted hazard ratio, 2.82 [95% CI, 2.01-3.94]; T2: 3.89 [2.74-5.53]; and T3: 7.73 [5.37-11.12]). Similar patterns and trends were observed in the validation cohorts (T1: 1.74 [1.49-2.03], T2: 1.97 [1.69-2.30], and T3: 3.31 [2.82-3.38] in validation cohort A; T1: 1.33 [1.03-1.71], T2: 1.54 [1.21-1.96], and T3: 1.73 [1.36-2.20] in validation cohort B). INTERPRETATION: This study derived and validated an ML-based model to predict the incidence of T2DM within 5 years across three countries (South Korea, Japan, and the UK), showing that the model probability is associated with an increased risk of mortality. FUNDING: Institute of Information & Communications Technology Planning & Evaluation, South Korea.

AIMS: To investigate the clinical significance of the modification of the kidney protective effects of sodium-glucose cotransporter-2 (SGLT2) inhibitors by baseline body mass index (BMI). METHODS AND RESULTS: We included individuals with SGLT2 inhibitors or dipeptidyl peptidase-4 (DPP4) inhibitors newly prescribed for type 2 diabetes using a nationwide epidemiological cohort and performed propensity score matching (1:2). The primary outcome was the annual eGFR decline, assessed using a linear mixed-effects model, compared between individuals with SGLT2 inhibitors and DPP4 inhibitors. We investigated the interaction effect of BMI at the time of prescription using a three-knot restricted cubic spline model. We analysed 2165 individuals with SGLT2 inhibitor prescriptions and 4330 individuals with DPP4 inhibitor prescriptions. Overall, the annual decline in eGFR was less pronounced in the group treated with SGLT2 inhibitors than in those treated with DPP4 inhibitors (-1.34 mL/min/1.73 m2 vs. -1.49 mL/min/1.73 m2). The advantage of SGLT2 inhibitors in mitigating eGFR decline was augmented in the individuals with higher BMI (P-value for interaction 0.0017). Furthermore, even upon adjusting the definition of outcomes to encompass a 30 or 40% reduction in eGFR, the potential advantages of SGLT2 inhibitors over DPP4 inhibitors persisted, with a trend of augmented effects with higher BMI. This interaction effect was evident in the individuals with preserved kidney function. CONCLUSION: Our nationwide epidemiological study substantiated the improved kidney outcomes in the SGLT2 inhibitor users compared with the DPP4 inhibitor users across a wide range of BMI, which was pronounced for individuals with higher BMI.