Daily Endocrinology Research Analysis

Clinical practice guidelines recommend defined weight loss goals for the prevention of type 2 diabetes (T2D) in those individuals with increased risk, such as prediabetes. However, achieving prediabetes remission, that is, reaching normal glucose regulation according to American Diabetes Association criteria, is more efficient in preventing T2D than solely reaching weight loss goals. Here we present a post hoc analysis of the large, multicenter, randomized, controlled Prediabetes Lifestyle Intervention Study (PLIS), demonstrating that prediabetes remission is achievable without weight loss or even weight gain, and that it also protects against incident T2D. The underlying mechanisms include improved insulin sensitivity, β-cell function and increments in β-cell-GLP-1 sensitivity. Weight gain was similar in those achieving prediabetes remission (responders) compared with nonresponders; however, adipose tissue was differentially redistributed in responders and nonresponders when compared against each other-while nonresponders increased visceral adipose tissue mass, responders increased adipose tissue in subcutaneous depots. The findings were reproduced in the US Diabetes Prevention Program. These data uncover essential pathways for prediabetes remission without weight loss and emphasize the need to include glycemic targets in current clinical practice guidelines to improve T2D prevention.

Achieving optimal glycemic control remains challenging for many individuals with type 1 diabetes using multiple daily injections. We report results from a 12-week, open-label, randomized controlled trial evaluating a decision support system (DSS) consisting of a mobile application and a titration algorithm that provides weekly basal and prandial insulin recommendations. Eighty-four adults with type 1 diabetes and suboptimal glycemic control (HbA1c ≥ 7.5%) are randomized 1:1 to receive the DSS or a non-adaptive bolus calculator (control), alongside Freestyle Libre glucose sensors. The primary endpoint is change in HbA1c from baseline; secondary endpoints include additional glycemic and insulin-related metrics. The DSS reduces mean HbA1c from 8.6% (SD 1.1) to 8.1% (0.8) (p = 0.0002), while the control reduces HbA1c from 8.6% (1.0) to 8.5% (1.0) (p = 0.22); yielding a treatment effect of -0.40% (95% CI: -0.75 to -0.051; p = 0.025). There are no reported severe hypoglycemia or diabetic ketoacidosis events. Our DSS improves HbA1c in this population without compromising safety. ClinicalTrials.gov: NCT04123054 .

Teplizumab, a humanized anti-CD3 mAb, represents a breakthrough in autoimmune type 1 diabetes (T1D) treatment, by delaying clinical onset in stage 2 and slowing progression in early stage 3 of the disease. However, therapeutic responses are heterogeneous. To better understand this variability, we applied single-cell transcriptomics to paired peripheral blood and pancreas samples from anti-mouse CD3-treated nonobese diabetic (NOD) mice and identified distinct gene signatures associated with the therapy outcome, with consistent patterns across compartments. Success-associated signatures were enriched in NK or CD8+ T cells and other immune cell types, whereas resistance signatures were predominantly expressed by neutrophils. The immune cell communities underlying these response signatures were confirmed in human whole blood sequencing data from the Autoimmunity-blocking Antibody for Tolerance (AbATE) study at 6 months, which assessed teplizumab therapy in individuals with stage 3 T1D. Furthermore, baseline expression profiling in the human TrialNet Anti-CD3 Prevention (TN10) (stage 2) and AbATE (stage 3) cohorts identified immune signatures predictive of therapy response, T cell-enriched signatures in responders, and neutrophil-enriched signatures in nonresponders, highlighting the roles of both adaptive and innate immunity in determining teplizumab treatment outcomes. Using an elastic net logistic regression model, we developed a 26-gene blood-based signature predicting the response to teplizumab (AUC = 0.97). These findings demonstrate the predictive potential of immune gene signatures and the value of transcriptomics profiling in guiding individualized treatment strategies with teplizumab in individuals with T1D.