Type 1 diabetes arises from the interplay of genetic susceptibility and environmental exposures, leading to autoimmune β-cell destruction. Although disease-modifying therapies (DMTs) can delay progression to clinical (stage 3) type 1 diabetes, treatment responses remain inconsistent and transient. The marked heterogeneity of type 1 diabetes, shaped by age, sex, race/ethnicity, and genetic background, underscores the need to elucidate distinct mechanistic pathways. Among environmental contributors, obesity stands out as a compelling modifiable target. Data from The Environmental Determinants of Diabetes in the Young (TEDDY), Type 1 Diabetes TrialNet, and other longitudinal cohorts link BMI and adiposity to the onset of islet autoimmunity, progression through preclinical stages, and development of stage 3 type 1 diabetes. These associations are not uniform; heightened susceptibility to adiposity-related risk is seen among younger children, Hispanic populations, and individuals with specific HLA genotypes. Despite robust epidemiologic evidence, the biological pathways connecting elevated BMI to autoimmune β-cell destruction remain incompletely defined. Emerging data implicate a network of immunologic and metabolic disturbances, including insulin resistance, β-cell stress, chronic adipose tissue inflammation, altered adipokine signaling, and gut microbiome changes, that collectively heighten β-cell vulnerability, amplify autoreactive immune responses, and drive metabolic decompensation toward clinical disease. Elucidating these mechanisms and identifying related biomarkers are critical to advancing precision prevention. In future studies, investigators should evaluate whether modifying elevated BMI or targeting obesity-associated immunologic and metabolic pathways can alter the preclinical trajectory of type 1 diabetes. Such mechanistic understanding may help curb type 1 diabetes incidence and improve outcomes for populations most vulnerable to obesity-related risk.

Cystic fibrosis (CF)-related diabetes (CFRD) affects nearly half of adults with CF, but screening rates with the recommended oral glucose tolerance test (OGTT) are low. We evaluated the utility of a nonfasting, 50-g, 1-h oral glucose challenge test (GCT) as first-line screening for CFRD and pre-CFRD. The objectives were to define a cutoff that provides high sensitivity and reasonable specificity, and to determine how GCT compares with other opportunistic tests. Participants with CF ≥10 years old, without known diabetes, had baseline random plasma and capillary glucose (RPG and RCG), then underwent an in-clinic GCT for collection of 1-h plasma glucose (GCTpl). This was followed by hemoglobin A1C (HbA1c) and OGTT on a separate day. Receiver operating characteristic (ROC) curves were generated for identifying CFRD and pre-CFRD. Of 185 participants, 94 had normal glucose tolerance, 81 had pre-CFRD, and 10 had CFRD. For detecting pre-CFRD or CFRD, GCTpl had an area under the ROC curve (ROC-AUC) of 0.73 (95% CI 0.65-0.80), higher than ROC-AUCs for RPG of 0.56 (0.48-0.65, P = 0.003), RCG of 0.55 (0.46 = 0.63, P = 0.002), and HbA1c of 0.62 (0.53-0.67, P = 0.02). The ROC-AUC for detecting CFRD was 0.75 (0.64-0.86) for GCTpl, 0.64 (0.42-0.87) for RPG, and 0.56 (0.39-0.73) for HbA1c. A GCTpl of 147 mg/dL provides 90% sensitivity and 58% specificity for detecting CFRD and could reduce the OGTTs needed by 56%. GCT offers a practical, in-clinic method for CFRD screening. This spares low-risk individuals from OGTT, while maintaining high sensitivity for CFRD.

Consensus guidelines recommend at least 14 consecutive days of continuous glucose monitoring (CGM) monitoring with 70% completeness to represent 90-day glycemic exposure. This study quantifies bias and uncertainty introduced into downstream analyses by using CGM metrics from incomplete or reduced monitoring, relative to a 90-day complete profile. Using a type 1 diabetes cohort with 1,010 complete 90-day CGM profiles, we simulated incomplete profiles by varying monitoring duration and data completeness. Consensus CGM metrics were computed on incomplete and complete profiles to quantify measurement error, which was propagated into two downstream regression models: 1) CGM metric is an outcome for a binary treatment (clinical trial setting); 2) CGM metric is an explanatory variable (covariate) for another continuous outcome. Bias was quantified using observed-to-true effect size ratios and uncertainty by the sample size increase required to maintain precision. In the clinical trial setting, treatment effects remain unbiased but lose precision; for time in range (TIR), 14 days required ≥16% more participants versus 90 days; 30 days required ≥6.5%. When the CGM metric is a covariate, associations with outcomes are attenuated (biased toward zero up to 14% at 14 days and 6% at 30 days for TIR) and less precise. Representing 90 days of glycemic exposure with 14 days can lead to bias and loss of precision in downstream analyses. We recommend study protocols require at least 30 days of CGM monitoring with 70% completeness. If 30 days is not feasible, studies should plan for increased sample sizes.

Diabetes significantly increases the risk for atherosclerotic complications, including coronary artery disease (CAD). Previous studies have suggested that adult-onset diabetes can be classified into five different clinical subtypes, including moderate obesity-related diabetes (MOD). The aim of this study was to investigate the genetic associations between the five diabetes subtypes and the risk of developing CAD and diabetes in the general population. The Malmö Diet and Cancer cohort (N = 24,025) was used to assess whether polygenic risk scores (PRSs) for the five diabetes subtypes could predict future diabetes and CAD. Genetic correlations and causal effects of the MOD subtype on CAD were investigated using data from large genome-wide association studies of the MOD subtype (N = 4,116) and CAD (N = 296,525). During follow-up, 4,105 participants (17.1%) developed diabetes (median follow-up 24.0 years) and 3,841 (16.0%) developed CAD (median follow-up 24.6 years). PRS for MOD (PRSMOD) was associated with incident diabetes and CAD. In addition, participants in the third tertile of PRSMOD had a 1.10-fold higher risk of developing CAD compared with those in the first tertile. A positive genetic correlation between MOD and CAD was observed, and Mendelian randomization analyses suggested a causal effect of MOD on CAD. The current study showed that the genetic susceptibilities for all five diabetes subtypes were associated with incident diabetes. However, only the MOD subtype was associated with incident CAD. These findings underscore the significance of a high genetic risk for MOD as an early marker for CAD.

People receiving dialysis are at high risk of cardiovascular and all-cause mortality. Semaglutide reduces major adverse cardiovascular events (MACE) in people with type 2 diabetes (T2D) and those without T2D with obesity and high cardiovascular risk. Data to establish safety and efficacy in dialysis-dependent kidney failure are scarce. We aimed to assess the safety of semaglutide in people who initiate dialysis. In this post hoc analysis of four randomized, placebo-controlled trials (Trial to Evaluate Cardiovascular and Other Long-term Outcomes With Semaglutide in Subjects With Type 2 Diabetes [SUSTAIN-6], Semaglutide Effects on Cardiovascular Outcomes in People with Overweight or Obesity [SELECT], Evaluate Renal Function With Semaglutide Once Weekly [FLOW], and Semaglutide Cardiovascular Outcomes [SOUL]), we evaluated systematically collected adverse events (AEs) from participants who initiated dialysis during study follow-up. We compared the proportion and event rates of systematically collected serious AEs (SAEs), including adjudicated MACE, and AEs leading to permanent treatment discontinuation in participants originally randomized to semaglutide or placebo who remained on treatment after dialysis initiation. Among 34,064 participants randomized across the trials, 307 initiated dialysis, of whom 165 participants randomized to semaglutide (n = 71) or placebo (n = 94) remained on treatment. After dialysis initiation, SAEs were reported in 32 of 71 (45%) and 54 of 94 (57%) participants, and the proportion of participants who permanently discontinued trial medication was 8.5% and 10.6% in the semaglutide and placebo groups, respectively. The MACE event rates were 9.7 and 16.1 events per 100 person-years, and all-cause mortality event rates were 13.8 and 18.1 events per 100 person-years, in the semaglutide and placebo groups, respectively. Although more evidence is needed, continuation of semaglutide after dialysis initiation appears safe and warrants efficacy testing regarding reduction in MACE and death.

The Mexican population experiences a notably high prevalence of type 2 diabetes (T2D) and high T2D-associated disease risks. We used targeted plasma nuclear magnetic resonance metabolomics data within a Mendelian randomization framework to characterize the metabolomic profile of genetically predicted liability to T2D in this population. Between 1998 and 2004, 50,000 men and 100,000 women aged ≥35 years were recruited from Mexico City. Mendelian randomization analyses used a genetic risk score (GRS) comprising 1,055 established T2D-associated risk variants and eight pathway-specific T2D GRSs constructed from nonoverlapping subsets of these variants to estimate associations with 143 metabolic biomarkers (including lipids, lipoproteins, fatty acids, amino acids, ketone bodies, and other low-molecular-weight biomarkers). Among 125,587 included participants, the T2D GRS explained 6.0% of T2D liability and was not associated with major potential confounders of the relationships of T2D with the circulating metabolome. Genetically predicted liability to T2D was strongly positively associated with concentrations of VLDL particles and lipids within these, with triglycerides, branch-chain amino acids, and glycoprotein acetyls, and more modestly positively associated with intermediate-density lipoprotein and LDL, particularly small LDL, particles. Inverse associations were found with relative concentrations of several fatty acids. Pathway-specific T2D GRSs all associated with higher T2D risk but showed differential relationships with circulating metabolic biomarkers. T2D is associated with widespread changes in the circulating metabolome among adults in Mexico, reflecting diverse biological mechanisms and highlighting the importance of effective T2D management, including control of T2D-associated dyslipidemia, in this population.

To compare the glycemic response and pharmacokinetics (PK) of Lyumjev versus Humalog during manual basal rate reductions (BRRs) before exercise with conventional continuous subcutaneous insulin infusion (CSII) in active adults with type 1 diabetes. This study was a double-blind, four-period, crossover, randomized controlled trial. Exercise (60 min walking at 45-55% VO2max) was performed 4 h after a standardized meal, with either a 50% (-60 min) or 100% (-15 min) BRR before exercise using Lyumjev or Humalog. The primary endpoint was the change in glucose during exercise, and insulin lispro PK was the secondary endpoint. Twenty-five participants (mean ± SD age 36.7 ± 10.3 years; mean ± SD HbA1c 50.4 ± 8.5 mmol/mol [6.76 ± 0.8%]; 48% female) completed the study. Lyumjev significantly attenuated the reduction in glucose level compared with Humalog during both the 50% BRR (-26.8 ± 37 vs. -39.0 ± 39 mg/dL; P < 0.05) and the 100% BRR (-46.9 ± 32 vs. -60.5 ± 39 mg/dL; P < 0.01). Circulating insulin lispro concentrations rose in the first 15 min of exercise, but maximum concentration was lower with Lyumjev versus Humalog within each BRR strategy. Numerically less hypoglycemia was observed during exercise with Lyumjev (6%) compared with Humalog (16%). Following a postexercise meal, faster insulin absorption of Lyumjev resulted in an earlier postprandial glucose lowering compared with Humalog. Using Lyumjev in conventional CSII pump therapy provides more effective BRRs prior to exercise compared with Humalog, with fewer hypoglycemic events in active adults with type 1 diabetes.

The use of continuous glucose monitoring (CGM) with remote patient monitoring (RPM) continues to grow. We evaluated the cost-effectiveness of CGM with RPM compared with self-monitoring of blood glucose (SMBG) and CGM alone. We simulated type 1 diabetes progression with a Markov model in 5-year-old patients over a 20-year, 50-year, and lifetime horizon. We tracked diabetic ketoacidosis (DKA), severe hypoglycemia (SH), and seven chronic complications: retinopathy, neuropathy, nephropathy, cardiovascular disease, end-stage renal disease, lower-extremity amputation, and blindness. We compared three interventions: SMBG, CGM, and CGM with RPM. Efficacy estimates were derived from meta-analyses of pediatric CGM studies and the results of the Teamwork, Targets, Technology, and Tight Glycemia Study (4T Study 1). We evaluated quality-adjusted life years (QALYs) and health care costs (2022 U.S. dollars) discounted at 3% annually. We performed extensive sensitivity analyses. Compared with SMBG, CGM increased QALYs by 0.09 and costs by $8,900 over 20 years; CGM with RPM increased QALYs by 0.37, and costs by $10,300. CGM with RPM yielded more QALYs at a lower incremental cost-effectiveness ratio compared with CGM ($27,400/QALY vs. $103,700/QALY, respectively). Results were robust across sensitivity analyses and time horizons. CGM with RPM remained cost-effective when achieving at least 30% of 4T's clinical efficacy. CGM with RPM delivers superior health outcomes compared with SMBG and CGM and is likely cost-effective for patients with newly diagnosed type 1 diabetes. Despite higher intervention costs, CGM with RPM can reduce complications costs and generate net health care savings.

Social determinants of health (SDoHs) account for more than half of the variance in racial and ethnic disparities in health. However, few studies have examined how SDoHs may cluster in ways that affect health. We aimed to identify patterns of social adversity and their differential associations with both diabetes status at baseline and change in diabetes status across ∼12 years among Hispanic/Latino adults. Participants were from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL; N = 16,415; aged 18-74 years). Diabetes status (defined as normoglycemia, prediabetes, or diabetes per American Diabetes Association criteria) was measured by clinical assessment and self-reported medications at baseline (2008-2011) and two follow-up visits (2014-2017 and 2020-2024). SDoHs were assessed at baseline and as part of the HCHS/SOL Sociocultural Ancillary Study (2010-2012). Latent class analyses of nine SDoHs (income, education, employment status, home ownership, language and social acculturation, chronic stressors, family cohesion, and social support) revealed four distinct patterns of social adversity: 1) low adversity, 2) social/educational strengths, 3) acculturated and underresourced, and 4) high adversity. Compared with the low-adversity group, the high-adversity group had the highest odds of worse diabetes status at baseline and had greater odds of worsening diabetes status over time. SDoHs cluster in distinct ways that affect diabetes outcomes; social adversities must be addressed to mitigate diabetes burden among Hispanic/Latino adults.

Exposure to per- and polyfluoroalkyl substances (PFAS) may increase the risk of gestational diabetes mellitus (GDM), with adverse consequences for pregnant women and their offspring. However, epidemiologic studies have shown inconsistent results. We addressed this question in a large, pooled sample of U.S. women. Participants (n = 5,229) from 16 cohorts had singleton pregnancies. PFAS were quantified in a single plasma or serum sample during pregnancy (1999-2021); six PFAS detected in ≥60% of participants were analyzed. The primary outcome was GDM diagnosis based on self-report or medical record documentation. The secondary outcome, among 1,213 participants, was fasting glucose. We estimated associations between each PFAS and GDM using generalized estimating equations models with Poisson distribution and robust variance, and estimated associations between each PFAS and fasting glucose using generalized estimating equations models for linear regression. Effect modification by prepregnancy BMI or race and ethnicity was evaluated via interaction terms and stratification. We quantified the combined effect of the PFAS mixture using quantile-based g-computation. Associations between individual PFAS and GDM were null or weakly inverse; the association with the six-PFAS mixture was negative (prevalence ratio [95% CI] per quartile increase: 0.75 [0.58, 0.96]). Certain PFAS were more strongly negatively associated with GDM among participants with BMI <25 kg/m2. Associations between PFAS and fasting glucose were largely null, although both positive and negative associations were observed in specific race and ethnicity strata. In a large, pooled sample of U.S. pregnant women, greater concentrations of PFAS were not associated with higher prevalence of GDM.