Introduction and Objective: Pharmacists involved in diabetes management through a collaborative practice agreement often follow patients longitudinally or until a target is met. While this model improves outcomes, over time access for new patients diminishes. To address this issue, we developed and tested a 3-visit referral model to pharmacists between longitudinal provider visits. Methods: Endocrinology providers referred patients with diabetes who started a new medication or required titration or education. We measured change in A1c, factors that might influence change in A1c, and relationship between number of visits and outcomes. Hospitalization or emergency department visit related to diabetes and clinic wait times for diabetes appointments were reviewed. We defined A1c response categories: high (≥ 1%), medium (0.4-0.9) and low (<0.4%). Results: 374 patients were referred to a pharmacist over 20 months; 342 with A1c ≥6.5% were included for analysis. At baseline, age: 60±15 years, race: Black 26%, White 52%, Asian 4.1%; 53% were Female. Baseline A1c was 9.2%±2.4. Median number of pharmacist visits per patient was 3.0 [IQR 3.0]. Change in A1c was -1.2% ± 2.3 (p<0.01). After adjusting for baseline, A1c change was independent of age and race. Each additional visit beyond 1 was associated with an increased chance of a higher A1c response category (OR 1.12; 95% CI 1.03-1.22; p<0.01), no continuous relationship between number of visits and A1c. 16 safety events resulted in ED visit or hospitalization, none were determined related to pharmacist visit. All patients accessed appointments within 2 months. Conclusion: This unique limited-visit model for pharmacist consultation in a diabetes specialty practice for medication initiation, titration, and education is safe and effective. Disclosure G. Stern: None. E.L. Barbay: None. A. Segal: Other - Editor-in-Chief, Diabetes, Obesity, and Cardiometabolic CARE; Current; American Diabetes Association. Board Member; Current; Diabetes Education for All. M.E. McDonnell: Research Support; Ended; Dexcom, Inc. Research Support; Current; Abbott Diabetes. Advisory Panel; Ended; Vertex Pharmaceuticals Incorporated.

Introduction:Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease (CKD) and kidney failure worldwide. Reduced nicotinamide adenine dinucleotide (NAD) levels are mechanistically linked to DKD pathogenesis; and NAD augmentation by administration of its precursor, nicotinamide mononucleotide (NMN), has attenuated albuminuria and kidney injury in preclinical models. Methods:The NAD Augmentation in Diabetic Kidney Disease (NAD in DKD) trial is a phase 2a, randomized, multicenter, double-blind, placebo-controlled, parallel-group study evaluating the efficacy and safety of oral pharmaceutical-grade microcrystalline β-NMN (MIB-626) in adults with DKD. One hundred fifty-six participants aged ≥ 30 years with diabetes, urinary albumin-to-creatinine ratio (UACR) ≥ 100 mg/g, and estimated glomerular filtration rate (eGFR) > 25 ml/min per 1.73 m2 were randomized to receive MIB-626 (1000 mg twice daily) or placebo for 24 weeks, followed by 12 weeks of postintervention follow-up. The randomization was stratified for biological sex, age (30-44, 45-65, and ≥ 66 years), and enrolling site. The primary end point was change in UACR from baseline to 24 weeks. Secondary outcomes included serum creatinine, cystatin C, and eGFR, biomarkers of kidney injury and biological age, glycemic control, measures of muscle performance and physical function, and the circulating NAD metabolome. Conclusion:This phase 2a trial was designed to evaluate the safety and efficacy of NAD augmentation in improving UACR in albuminuric DKD. The results will inform whether NAD augmentation warrants further evaluation in larger trials statistically powered for long-term kidney outcomes. The study is registered at ClinicalTrials.gov (NCT05759468).

Introduction and Objective: Despite benefits of modern diabetes (DM) care, population-level glycemic control has not improved, especially among groups often marginalized from traditional care due to socioeconomic and cultural factors. Methods: The DICHA (DM In Control for Hispanics through an Alliance) program is a 6-month enhanced primary care model for Latino people with diabetes (LPWD) that includes a population health manager (PHM), language concordant diabetes-trained community health worker (CHW), diabetologist and patient navigator (PN). Those in a single urban community health center with A1c >9% for > 1 year were enrolled. Interventions included addressing social determinants of health, medications, continuous glucose monitoring (CGM), diabetologist visits (DV) and weekly virtual care planning. We compared DICHA to matched LPWD contemporaneous controls on A1c change, initiation of new medication and CGM. While DICHA patients were referred to CHW, controls were referred at PCP discretion. Results: At baseline, DICHA (N=31) vs. Controls (N=49): age 61 ± 10 years vs. 58 ± 18 years; 52% vs. 43% male; BMI 30 ± 4 vs. 31 ± 6, A1c 10.9 ± 1.9% vs. 10.9% ± 1.6; 97% type 2 DM and 60% on insulin. A1c change at 6 months was greater in DICHA: -1.3 ± 1.7% vs. -0.3 ± 2.1%, p=0.04; and 12 month -1.5 ± 2.3%, vs. -0.5 ± 1.6%, p=0.05. In multivariate adjusted model, each additional hour of CHW time in DICHA was associated with a -0.46 (95% CI -0.04 to -0.88, p= 0.04) A1c change while DV time was not. In both groups (N=80), CHW time predicted CGM initiation and new medication start while age and DVs did not. Each additional hour of CHW time was associated with a >3 fold increase in the odds of CGM initiation (OR 3.64; 95% CI 1.78 to 7.46; p <0.001) and higher odds of initiating a new diabetes medication (OR 2.23; 95% CI 1.39 to 3.59; p<0.001). Conclusion: Within a non-traditional, culturally-tailored care model including diabetology and primary care, engagement with a DM-specialized CHW drove A1c-lowering and initiation of standard therapies to facilitate sustained glycemic control. Disclosure B.F. Altshuler: None. A.E. Caballero: None. K.L. Del Valle: None. M.E. McDonnell: Research Support; Ended; Dexcom, Inc. Research Support; Current; Abbott Diabetes. Advisory Panel; Ended; Vertex Pharmaceuticals Incorporated.

OBJECTIVE:To evaluate the frequency of delay in insulin access following prescription of a new insulin regimen among pregnant patients with gestational diabetes mellitus or preexisting type 2 diabetes mellitus and to examine associated factors. METHODS:We conducted a retrospective review of electronic health records of pregnant patients with gestational diabetes mellitus or preexisting type 2 diabetes mellitus seen at the Mass General Brigham between June 2018 and June 2024. Patients newly prescribed insulin during pregnancy were sampled (n = 303) for detailed chart review. Delay in insulin access (≥7 days between prescription and the day medication was dispensed) was defined using electronic prescription and fill history. Social determinants of health were assessed via the Center for Disease Control Social Vulnerability Index (SVI). Multivariable logistic regression was used to assess associations between delay and demographic/clinical factors. RESULTS:Among 303 pregnant patients newly prescribed insulin, 15.5% experienced delayed insulin access. The delayed group had higher proportions of self-identified Black race (33.3% vs 8.2%, P < .001), single marital status (38.3% vs 20.6%, P = .009), interpreter requirement (14.9% vs 5.5%, P = .019), and residence in high-SVI areas (51% vs 30.5%, P = .01). Common barriers were insurance-related (46.8%) and patient preference (27.6%). In multivariable analysis, Black race (odds ratio [OR] = 6.01, 95% CI 2.45-14.76, P < .001) and prescription year 2022-2024 vs 2018-2021 (OR = 2.35, 95% CI 1.10-5.01, P = .02), remained significant. High SVI (OR = 2.07, 95% CI 0.996-4.304, P = .05) showed borderline significance. CONCLUSION:Delays in insulin access during pregnancy are common and disproportionately affect Black women, with insurance-related barriers predominant. Strategies to reduce delays may improve maternal and fetal outcomes in pregnancies complicated by diabetes.

Introduction and Objective: Sulfonylureas are a common choice in treatment of type 2 diabetes (T2D). Cardiovascular safety of sulfonylureas is unknown. Methods: We emulated a target trial using observational data from 12 health systems and insurance plans across the US. Eligible individuals were patients with T2D and hyperglycemia (HbA1c 7.0-11.0% or equivalent glucose levels) at moderate CV risk (no prior CV disease) on metformin monotherapy who initiated a second T2D medication between 01/01/13 and 12/31/22, with eGFR ≥ 45 ml/min/1.73m2 and with no contraindications to study medications. We compared patients initiating individual sulfonylureas (glimepiride, glipizide and glyburide) to patients initiating DPP4i (which were shown in multiple controlled trials to have CV risk similar to placebo). In each treatment arm we estimated the 5-year risk of a composite outcome (MI, ischemic CVA, HF hospitalization or CV death) after adjustment for demographics, comorbidities and laboratory values at baseline. Results: We studied 48,165 patients followed for a median of 37 (IQR 20-64) months, with a median age of 61 (IQR 52-69) years and median baseline HbA1c of 7.8% (IQR 7.3-8.5%). Of these, 13,849 patients initiated a DPP4i; 14,282 initiated glimepiride; 18,147 initiated glipizide; and 1,887 initiated glyburide. A total of 3,158 (6.6%) of patients experienced the primary outcome endpoint. The estimated 5-year risk ratios (95% CI), compared to DPP4i, were 1.04 (95% CI 0.83 to 1.24) for glyburide, 1.07 (95% CI 0.96 to 1.16) for glimepiride and 1.13 (95% CI 1.03 to 1.23) for glipizide. Sensitivity analysis excluding saxagliptin from the reference DPP4i category showed similar results. Conclusion: Patients with T2D treated with glipizide as a second agent after metformin had the highest incidence of MACE-4 events compared with patients treated with DPP4i among those initiating a sulfonylurea. Glipizide may not be the optimal agent in treatment of patients with T2D at elevated CV risk. A. Turchin: Consultant; Novo Nordisk. Research Support; Eli Lilly and Company. Consultant; Proteomics International. L. Petito: Advisory Panel; Patient-Centered Outcomes Research Institute. Research Support; Omron Healthcare Co., Ltd. Consultant; Ciconia Medical. E. Hegermiller: None. R.M. Carnahan: None. A.R. Devries: None. S. Goel: None. M.E. McDonnell: Research Support; Dexcom, Inc. M. Lansang: Research Support; Abbott, Dexcom, Inc., Neuro Solutions 100. V. Nair: None. E.L. Priest: Research Support; Boehringer-Ingelheim, AstraZeneca, Vifor Pharma, Owkin. V. Willey: Other Relationship; Carelon Research. A.F. Kaul: None. M. Hernan: None. PCORI (DB-2020C2-20308)

ObjectiveIndividuals who have metabolically healthy overweight/obesity (MHOO) do not have cardiometabolic complications despite an elevated BMI. Renin-angiotensin-aldosterone system (RAAS) activation and salt sensitivity of blood pressure (SSBP) are cardiovascular disease (CVD) risks, which are increased in individuals with higher BMI values. Little is known about the differences in RAAS activation and SSBP between MHOO and metabolically unhealthy overweight/obesity (MUOO) phenotypes. MethodsWe studied 1430 adults on controlled dietary sodium. Individuals in the MHOO group had BMI >= 25 kg/m2 without comorbidities (e.g., diabetes, dyslipidemia, hypertension, CVD), whereas individuals in the MUOO group had BMI >= 25 kg/m2 and at least one comorbidity. The control group included healthy individuals (BMI 18.5-24.9 kg/m2). ResultsBMI was similar between the MHOO (28.9 kg/m2) and MUOO groups (29.3 kg/m2; p = 0.317). On liberal sodium, the MUOO group had activated RAAS compared with the MHOO group, including higher plasma aldosterone concentration (mean [SD], 1.11 [0.48] ng/dL; p = 0.020), plasma angiotensin II levels (4.11 [2.0] pg/mL; p = 0.040), and percentage of individuals with plasma renin activity >= 1.0 ng/mL/h (+3.6%; p = 0.017). The MUOO group had higher SSBP than the MHOO group (6.0 [1.9] mm Hg; p = 0.002). Applying a zero-to-six-point metabolic health score found that a worse score was associated with higher measurements of RAAS activity and SSBP (p < 0.001). ConclusionsCompared to the MHOO group, the MUOO group was characterized by an increase in the following two CVD risk factors: higher RAAS activity and SSBP on controlled sodium diets. Therapeutic interventions targeting the effects of angiotensin II and/or aldosterone may offer cardiometabolic protection for individuals with the MUOO phenotype.

Importance Sulfonylureas are commonly used to treat type 2 diabetes (T2D). Research findings on cardiovascular risk associated with sulfonylureas have been inconsistent. Objective To emulate a target trial that compares the risk of cardiovascular events after initiation of treatment with individual sulfonylureas or dipeptidyl peptidase 4 inhibitors (DPP4is). Design, Setting, and Participants This comparative effectiveness research study included individuals with T2D and moderate cardiovascular risk treated with metformin monotherapy who received care at 1 of 10 US health systems or were insured by 1 of 2 large health insurance plans between January 1, 2014, and January 1, 2023. Data were analyzed from July 2024 to March 2025. Exposure Initiation of treatment with a sulfonylurea (glimepiride, glipizide, or glyburide) or a DPP4i (reference category) as a second line therapy after metformin. Main Outcomes and Measurements The primary outcome was a 4-point composite of major adverse cardiovascular events (MACE-4): myocardial infarction, ischemic stroke, heart failure hospitalization, or cardiovascular death (from any of these conditions). The 5-year risks of each outcome were estimated. Results Among 48 165 eligible individuals (median [IQR] age, 61 [52-69] years; 22 674 female [47.1%]; median [IQR] hemoglobin A 1C , 7.8% [7.3%-8.5%]; median [IQR] low-density lipoprotein cholesterol, 89 mg/dL [70-112 mg/dL]), 18 147 started glipizide, 14 282 started glimepiride, 1887 started glyburide, and 13 849 started a DPP4i. Over the median (IQR) follow-up of 37 (20-64) months, 3158 individuals (6.6%) experienced a MACE-4. The estimated 5-year risks of MACE-4 were 8.1% (95% CI, 7.5%-8.7%) for DPP4i, 8.4% (95% CI, 6.8%-9.9%) for glyburide, 8.6% (95% CI, 7.9%-9.2%) for glimepiride, and 9.1% (95% CI, 8.7%-9.7%) for glipizide. Compared with DPP4is, the 5-year risk ratio of MACE-4 was 1.13 (95% CI, 1.03-1.23) for glipizide, 1.07 (95% CI, 0.96-1.16) for glimepiride, and 1.04 (95% CI, 0.83-1.24) for glyburide. Conclusions and Relevance In this comparative effectiveness research study of sulfonylureas vs DPP4i in patients with T2D, the risk of MACE-4 events was highest for glipizide. These findings suggest that sulfonylureas, glipizide in particular, may not be the optimal agent in treatment of individuals with T2D at moderate cardiovascular risk.

Introduction and Objective: Several clinical trials have shown that SGLT2i and GLP1-RA reduce progression of kidney disease in patients with T2D. It is not known whether these medications have renoprotective effects both in patients with and without proteinuria. Methods: We emulated a target trial using nationwide dataset from 12 US health systems and insurance plans for patients with T2D at moderate CV risk, hyperglycemia (HbA1c 7.0-11.0%) and with eGFR ≥ 45 ml/min/1.73m2 who initiated a second T2D medication after metformin between 1/1/13 and 12/31/22. We compared patients initiating a) SGLT2i or GLP1-RA or b) sulfonylureas to patients initiating DPP4i, stratifying by the presence of proteinuria (ACR ≥ 30 mg/g). We estimated 5-year risk of a composite nephropathy outcome (doubling of serum creatinine or eGFR < 15) after adjustment for demographics, comorbidities and laboratory values. Results: We studied 76,263 patients (14,067 with and 62,196 without baseline proteinuria) followed for a median of 32 (IQR 18-57) months, with a median age of 60 (IQR 51 - 68) years and median baseline HbA1c of 7.8% (IQR 7.3% - 8.5%) Of these, 26,306 patients initiated either an SGLT2i or a GLP1-RA, 36,059 initiated a sulfonylurea and 13,898 initiated a DPP4i. A total of 1,512 (2.0%) of patients experienced the composite nephropathy endpoint. The estimated 5-year risk ratios (95% CI), compared to DPP4i, were 0.57 (95% CI 0.40-0.79) for the combined SGLT2i-GLP1-RA arm among patients with proteinuria and 1.07 (95% CI 0.86-1.32) for patients without proteinuria. Sensitivity analyses among patients with a baseline eGFR < 60 showed similar results. There was no evidence of effect modification by proteinuria for sulfonylureas vs. DPP4i. Conclusion: Patients with T2D treated with an SGLT2i or GLP1-RA as a second agent after metformin had a lower risk of incident / worsening nephropathy compared with patients treated with DPP4i only if they had baseline proteinuria. A. Turchin: Consultant; Novo Nordisk. Research Support; Eli Lilly and Company. Consultant; Proteomics International. L. Petito: Advisory Panel; Patient-Centered Outcomes Research Institute. Research Support; Omron Healthcare Co., Ltd. Consultant; Ciconia Medical. E. Hegermiller: None. R.M. Carnahan: None. A.R. Devries: None. S. Goel: None. M. Lansang: Research Support; Abbott, Dexcom, Inc., Neuro Solutions 100. M.E. McDonnell: Research Support; Dexcom, Inc. V. Nair: None. E.L. Priest: Research Support; Boehringer-Ingelheim, AstraZeneca, Vifor Pharma, Owkin. V. Willey: Other Relationship; Carelon Research. A.F. Kaul: None. M. Hernan: None. PCORI DB-2020C2-20308

Introduction and Objective: Continuous Glucose Monitoring (CGM) is established to improve glycemic control and quality of life (QoL), reduce diabetes-related hospitalization and utilization of the emergency department in people with diabetes (PWD). While guidelines have recommended CGM, there are multiple known barriers including cost, insurance coverage and access to specialized education and clinical care that supports use. We designed and implemented an interdisciplinary Diabetes Technology Transition of Care Program in an urban academic medical center to address barriers and leverage hospital resources to target patients most likely to benefit from CGM. Methods: Hospitalized patients with plan for insulin use at discharge were offered CGM through a pharmacy-led Meds to Beds Program that included bedside education, sensor placement, device set up and transition of care planning by a pharmacist or inpatient diabetes team member. Participants were offered a one-time follow up visit with a diabetes clinician after discharge, and were contacted post discharge to identify and address barriers to CGM use and remote data sharing. We assessed individual change in glycemic control by comparing baseline A1c with the CGM-derived glucose management indicator (GMI) and measured patient satisfaction using CGM Satisfaction Survey (CGM-SAT). Results: Baseline characteristics (N=43): 65% female, mean age 46.3 (±15.9), A1c 9.9% (±2.4). The mean GMI was lower than baseline mean A1c at 2 weeks (N=26, -3.1% (±2.8), p<0.001); 3 months (N=12, -2.7% (±2.3), p<0.01); and 6 months (N=5, -3.3% (±1.4), p<0.01). CGM-SAT at baseline and 6 months confirm patient reported benefit with greatest impact on domains of trust and openness. Conclusion: Employing a hospital-based interdisciplinary service to initiate CGM in insulin-treated patients for use upon discharge was feasible and associated with improvement in glycemic control and patient satisfaction over 6 months. N.E. Palermo: Other Relationship; Dexcom, Inc. E.G. Thurber: Other Relationship; SimulConsult. G.M. Rickards: None. J. Harrod: None. B. Altshuler: None. A. Shahani: None. M.E. McDonnell: Research Support; Dexcom, Inc. Brigham and Women's Hospital Department of Medicine Health Equity Innovation Pilot Grant

AimsIn patients with breast cancer (BCa) and diabetes (DM), diabetes distress (DD) and treatment satisfaction (DTS) can influence BCa management and outcomes. We assessed the impact of implementing a personalized diabetes care model in patients with BCa. MethodsPatients in active treatment or surveillance for BCa with an HbA1c > 53 mmol/mol (7%) or random blood glucose >11.1 mmol/L were included. Participants were offered continuous glucose monitoring (CGM), virtual care and a dedicated diabetes provider for 6 months. Primary outcomes included DD measured by the Diabetes Distress Survey (DDS) and DTS measured by the Diabetes Treatment Satisfaction Questionnaire (DTSQ). Questionnaires were conducted at 0, 3 and 6 months. ResultsThirty-one women were enrolled (median age 61, IQR 49.0-69.0). Compared to baseline, the mean DDS score was lower at both 3 months (2.2 vs. 1.8 [n = 27], p = 0.004, SD = 0.70) and 6 months (2.3 vs. 1.8 [n = 23], p = 0.002, SD = 0.70). The mean DTSQ score was higher at 3 months (baseline: 20.5 vs. 3 months: 28.7 [n = 28], p < 0.001, SD = 9.2) and 6 months (baseline: 20.4 vs. 6 months: 30.0 [n = 26], p < 0.001, SD = 9.7). ConclusionsPersonalized diabetes care models that emphasize remote management and optimize access for those with BCa may lower DD and improve DTS.

Importance:The effect of testosterone replacement therapy (TRT) in men with hypogonadism on the risk of progression from prediabetes to diabetes or of inducing glycemic remission in those with diabetes is unknown. Objective:To evaluate the efficacy of TRT in preventing progression from prediabetes to diabetes in men with hypogonadism who had prediabetes and in inducing glycemic remission in those with diabetes. Design, Setting, and Participants:This nested substudy, an intention-to-treat analysis, within a placebo-controlled randomized clinical trial (Testosterone Replacement Therapy for Assessment of Long-Term Vascular Events and Efficacy Response in Hypogonadal Men [TRAVERSE]) was conducted at 316 trial sites in the US. Participants included men aged 45 to 80 years with hypogonadism and prediabetes or diabetes who were enrolled in TRAVERSE between May 23, 2018, and February 1, 2022. Intervention:Participants were randomized 1:1 to receive 1.62% testosterone gel or placebo gel until study completion. Main Outcomes and Measures:The primary end point was the risk of progression from prediabetes to diabetes, analyzed using repeated-measures log-binomial regression. The secondary end point was the risk of glycemic remission (hemoglobin A1c level <6.5% [to convert to proportion of total hemoglobin, multiply by 0.01] or 2 fasting glucose measurements <126 mg/dL [to convert to mmol/L, multiply by 0.0555] without diabetes medication) in men who had diabetes. Results:Of 5204 randomized participants, 1175 with prediabetes (mean [SD] age, 63.8 [8.1] years) and 3880 with diabetes (mean [SD] age, 63.2 [7.8] years) were included in this study. Mean (SD) hemoglobin A1c level in men with prediabetes was 5.8% (0.4%). Risk of progression to diabetes did not differ significantly between testosterone and placebo groups: 4 of 598 (0.7%) vs 8 of 562 (1.4%) at 6 months, 45 of 575 (7.8%) vs 57 of 533 (10.7%) at 12 months, 50 of 494 (10.1%) vs 67 of 460 (14.6%) at 24 months, 46 of 359 (12.8%) vs 52 of 330 (15.8%) at 36 months, and 22 of 164 (13.4%) vs 19 of 121 (15.7%) at 48 months (omnibus test P = .49). The proportions of participants with diabetes who experienced glycemic remission and the changes in glucose and hemoglobin A1c levels were similar in testosterone- and placebo-treated men with prediabetes or diabetes. Conclusions and Relevance:In men with hypogonadism and prediabetes, the incidence of progression from prediabetes to diabetes did not differ significantly between testosterone- and placebo-treated men. Testosterone replacement therapy did not improve glycemic control in men with hypogonadism and prediabetes or diabetes. These findings suggest that TRT alone should not be used as a therapeutic intervention to prevent or treat diabetes in men with hypogonadism. Trial Registration:ClinicalTrials.gov Identifier: NCT03518034.

Metformin, a widely used first-line treatment for type 2 diabetes (T2D), is known to reduce blood glucose levels and suppress appetite. Here we report a significant elevation of the appetite-suppressing metabolite N -lactoyl phenylalanine (Lac-Phe) in the blood of individuals treated with metformin across seven observational and interventional studies. Furthermore, Lac-Phe levels were found to rise in response to acute metformin administration and post-prandially in patients with T2D or in metabolically healthy volunteers.

BACKGROUND:Androgen deprivation therapy (ADT) in prostate cancer (PCa) has been associated with development of insulin resistance. However, the predominant site of insulin resistance remains unclear. METHODS:The ADT & Metabolism Study was a single-center, 24-week, prospective observational study that enrolled ADT-naive men without diabetes who were starting ADT for at least 24 weeks (ADT group, n = 42). The control group comprised men without diabetes with prior history of PCa who were in remission after prostatectomy (non-ADT group, n = 23). Prevalent diabetes mellitus was excluded in both groups using all three laboratory criteria defined in the American Diabetes Association guidelines. All participants were eugonadal at enrollment. The primary outcome was to elucidate the predominant site of insulin resistance (liver or skeletal muscle). Secondary outcomes included assessments of body composition, and hepatic and intramyocellular fat. Outcomes were assessed at baseline, 12, and 24 weeks. RESULTS:At 24 weeks, there was no change in hepatic (1.2; 95% confidence interval [CI], -2.10 to 4.43; p = .47) or skeletal muscle (-3.2; 95% CI, -7.07 to 0.66; p = .10) insulin resistance in the ADT group. No increase in hepatic or intramyocellular fat deposition or worsening of glucose was seen. These changes were mirrored by those observed in the non-ADT group. Men undergoing ADT gained 3.7 kg of fat mass. CONCLUSIONS:In men with PCa and no diabetes, 24 weeks of ADT did not change insulin resistance despite adverse body composition changes. These findings should be reassuring for treating physicians and for patients who are being considered for short-term ADT.

Objective: Though updated American Diabetes Association (ADA) guidelines recommend initiating GLP1 receptor agonists (GLP1) or SGLT2 inhibitors (SGLT2i) for persons with type 2 diabetes mellitus (PWT2DM) with high CV risk, metformin was previously recommended as 1st-line treatment. For PWT2DM at moderate CV risk, it is unknown which 2nd-line drug optimizes CV outcomes. Our multicenter observational study describes US-wide trends in 2nd-line T2DM medication use (DPP4 inhibitors [DPP4i], GLP1s, SGLT2i and sulfonylureas [SU]) overall and in key sociodemographic subgroups between 2014-2023. Methods: This is a secondary analysis of the oBservational Evaluation of Second line Therapy Medications in Diabetes (BESTMED) study, which used a new-user design to evaluate associations between 2nd-line medications and CV events in PWT2DM at moderate CV risk. The database comprised EHRs and insurance claims for 75,224 PWT2DM from 10 health systems and 2 insurance plans on metformin who initiated a 2nd medication from 1/2013 to 1/2023. Data were obtained from orders or prescription fills. Prescription class was regressed on calendar time via multinomial logistic regression to model trends in prescribing patterns, adjusted for sociodemographic variables. Subgroup analyses were performed by adjustment factors; CIs and p-values for differences were calculated via nonparametric bootstrap (B=200). Due to large sample size, statistical significance was interpreted at p<0.05 and annual rate of change >0.5%. Results: We included 75,224 T2DM adults (median A1c 7.8%), of whom 18%,17%, 18%, and 47% initiated DPP4i, GLP1, SGLT2i, SU respectively. From 2014 - 2023, SGLT2i and GLP1 increased: 5.7 to 28.8%, and 3.5 to 30.3%, and DPP4i and SUs decreased: 25.3 to 11.6%, and 65.6 to 29.3%, respectively. Annual rate of change for SGLT2i was higher in men(3.0% vs 2.1%/year; p<0.001) and in > 65 y (3.0% vs 2.4%; p<0.001), and for GLP1s was higher in those <65 y (3.6% vs 2.1%; p<0.001), women (3.5% vs 2.5%; p<0.001), Hispanic ethnicity (3.2% vs 2.7%; p=0.005), and with class II/III obesity (4.1% versus 2.6% per year; p<0.001). There was no evidence that these trends differed by race, insurance, or SDI. Conclusion: In this nationwide study of prescribing patterns PWT2DM at moderate CV risk, prevalences of 2nd-line T2DM therapies are approaching current ADA guidelines for initial therapy for those at high CV risk. Differences between sociodemographic groups appear aligned with clinical expectations.

ImportanceThe effect of testosterone replacement therapy (TRT) in men with hypogonadism on the risk of progression from prediabetes to diabetes or of inducing glycemic remission in those with diabetes is unknown.ObjectiveTo evaluate the efficacy of TRT in preventing progression from prediabetes to diabetes in men with hypogonadism who had prediabetes and in inducing glycemic remission in those with diabetes.Design, Setting, and ParticipantsThis nested substudy, an intention-to-treat analysis, within a placebo-controlled randomized clinical trial (Testosterone Replacement Therapy for Assessment of Long-Term Vascular Events and Efficacy Response in Hypogonadal Men [TRAVERSE]) was conducted at 316 trial sites in the US. Participants included men aged 45 to 80 years with hypogonadism and prediabetes or diabetes who were enrolled in TRAVERSE between May 23, 2018, and February 1, 2022.InterventionParticipants were randomized 1:1 to receive 1.62% testosterone gel or placebo gel until study completion.Main Outcomes and MeasuresThe primary end point was the risk of progression from prediabetes to diabetes, analyzed using repeated-measures log-binomial regression. The secondary end point was the risk of glycemic remission (hemoglobin A1c level <6.5% [to convert to proportion of total hemoglobin, multiply by 0.01] or 2 fasting glucose measurements <126 mg/dL [to convert to mmol/L, multiply by 0.0555] without diabetes medication) in men who had diabetes.ResultsOf 5204 randomized participants, 1175 with prediabetes (mean [SD] age, 63.8 [8.1] years) and 3880 with diabetes (mean [SD] age, 63.2 [7.8] years) were included in this study. Mean (SD) hemoglobin A1c level in men with prediabetes was 5.8% (0.4%). Risk of progression to diabetes did not differ significantly between testosterone and placebo groups: 4 of 598 (0.7%) vs 8 of 562 (1.4%) at 6 months, 45 of 575 (7.8%) vs 57 of 533 (10.7%) at 12 months, 50 of 494 (10.1%) vs 67 of 460 (14.6%) at 24 months, 46 of 359 (12.8%) vs 52 of 330 (15.8%) at 36 months, and 22 of 164 (13.4%) vs 19 of 121 (15.7%) at 48 months (omnibus test P = .49). The proportions of participants with diabetes who experienced glycemic remission and the changes in glucose and hemoglobin A1c levels were similar in testosterone- and placebo-treated men with prediabetes or diabetes.Conclusions and RelevanceIn men with hypogonadism and prediabetes, the incidence of progression from prediabetes to diabetes did not differ significantly between testosterone- and placebo-treated men. Testosterone replacement therapy did not improve glycemic control in men with hypogonadism and prediabetes or diabetes. These findings suggest that TRT alone should not be used as a therapeutic intervention to prevent or treat diabetes in men with hypogonadism.Trial RegistrationClinicalTrials.gov Identifier: NCT03518034

Vitamin D plays a critical role in many physiological functions, including calcium metabolism and musculoskeletal health. This commentary aims to explore the intricate relationships among skin complexion, race, and 25-hydroxyvitamin D (25[OH]D) levels, focusing on challenges the Endocrine Society encountered during clinical practice guideline development. Given that increased melanin content reduces 25(OH)D production in the skin in response to UV light, the guideline development panel addressed the potential role for 25(OH)D screening in individuals with dark skin complexion. The panel discovered that no randomized clinical trials have directly assessed vitamin D related patient-important outcomes based on participants' skin pigmentation, although race and ethnicity often served as presumed proxies for skin pigmentation in the literature. In their deliberations, guideline panel members and selected Endocrine Society leaders underscored the critical need to distinguish between skin pigmentation as a biological variable and race and ethnicity as socially determined constructs. This differentiation is vital to maximize scientific rigor and, thus, the validity of resulting recommendations. Lessons learned from the guideline development process emphasize the necessity of clarity when incorporating race and ethnicity into clinical guidelines. Such clarity is an essential step toward improving health outcomes and ensuring equitable healthcare practices.

Introduction: In patients with T2D at high cardiovascular (CV) risk, several classes of medications have been found to reduce incidence of CV events. However, long-term CV outcomes of T2D medications in lower risk patients are unknown. Methods: We conducted a causal inference study by emulating a target trial using observational data from 12 health systems and insurance plans across the US. Eligible individuals were patients with T2D and hyperglycemia (HbA1c 7.0-11.0% or equivalent glucose levels) at moderate CV risk (no CV disease) on metformin monotherapy who initiated a second T2D medication between 01/01/13 and 09/01/21, with eGFR ≥ 45 ml/min/1.73m2 and with no contraindications to study medications. We compared patients initiating one of the following medication classes: DPP4s, GLP1s, SGLT2s and sulfonylureas (SUs). In each treatment arm we estimated the 5-year risk of a composite outcome (MI, ischemic CVA, HF hospitalization or CV death) after adjustment for demographics, comorbidities and laboratory values at baseline. Results: We studied 55,441 patients followed for a median of 32 (IQR 18-54) months, with a median age of 60 (IQR 52-68) years and median baseline HbA1c of 7.8% (IQR 7.3-8.6%). Of these, 11,195 patients initiated a DPP4, 6,920 a GLP1, 7,911 an SGLT2 and 29,415 an SU; 40,673 (73.4)% of patients stopped the original study drug class before the end of follow-up after a median of 11 (IQR 3-22) months. A total of 2,566 (4.6%) of patients experienced the primary outcome endpoint. The estimated 5-year risk ratios (95% CI), compared to DPP4s, were 0.93 (0.73 - 1.13) for GLP1s, 0.96 (0.83 - 1.14) for SGLT2s and 1.08 (0.98 - 1.19) for SUs. Conclusions: Among patients with T2D at moderate CV risk on metformin monotherapy, incidence of CV events was similar among four classes of most commonly used non-insulin T2D medications. Further research is needed to evaluate the risk of non-CV events to help patients and clinicians to make informed decisions on T2D therapy. Disclosure A. Turchin: Research Support; Eli Lilly and Company, Novo Nordisk. Consultant; Novo Nordisk, Proteomics International. Research Support; AstraZeneca. L. Petito: Research Support; Omron Healthcare Co., Ltd. E. Hegermiller: None. R.M. Carnahan: None. H. Kitzman: Advisory Panel; Novo Nordisk. M. Lansang: Research Support; Abbott, Dexcom, Inc. Consultant; Glooko, Inc. Research Support; Xeris Pharmaceuticals, Inc. M.E. McDonnell: None. V. Nair: None. E.L. Priest: Research Support; Boehringer-Ingelheim, AstraZeneca, Owkin. V. Willey: Other Relationship; Carelon Research. S. Goel: None. A.F. Kaul: None. M. Hernan: Consultant; ProPublica, Cytel. Other Relationship; ADIA Lab, Flatiron, Foundation Medicine. Funding PCORI (DB-2020C2-20308)

Text messaging can promote healthy behaviors, like adherence to medication, yet its effectiveness remains modest, in part because message content is rarely personalized. Reinforcement learning has been used in consumer technology to personalize content but with limited application in healthcare. We tested a reinforcement learning program that identifies individual responsiveness (“adherence”) to text message content and personalizes messaging accordingly. We randomized 60 individuals with diabetes and glycated hemoglobin A1c [HbA1c] ≥ 7.5% to reinforcement learning intervention or control (no messages). Both arms received electronic pill bottles to measure adherence. The intervention improved absolute adjusted adherence by 13.6% (95%CI: 1.7%–27.1%) versus control and was more effective in patients with HbA1c 7.5- < 9.0% (36.6%, 95%CI: 25.1%–48.2%, interaction p < 0.001). We also explored whether individual patient characteristics were associated with differential response to tested behavioral factors and unique clusters of responsiveness. Reinforcement learning may be a promising approach to improve adherence and personalize communication at scale.