Diabetes care最新文献

Objective: Weight gain with glucose-lowering medications may interfere with effective type 2 diabetes (T2D) management. We evaluated weight change and the effect of weight gain on outcomes over 5 years on four diabetes medications.

Research design and methods: The Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE) randomized trial compared the addition of insulin glargine, glimepiride, liraglutide, or sitagliptin to metformin in participants with T2D. We report weight change and hazard ratio (HR) per kilogram of weight change for HbA1c >7.5%; cardiovascular disease (CVD), kidney disease, and neuropathy outcomes; and diabetes treatment satisfaction.

Results: Participants (n = 4,980) were 57 ± 10 years, 44% non-White, with HbA1c 7.5% ± 0.5%, and BMI 34.3 ± 6.8 kg/m2. Mean (95% CI) weight change (kg) during the first year was -3.5 (-3.8,-3.2) with liraglutide,-1.07 (-1.4,-0.78) with sitagliptin, 0.45 (0.16, 0.74) with glargine, and 0.89 (0.60, 1.2) with glimepiride (P < 0.0001). Thereafter, weight decreased in all groups. Weight gain within the first 6 months was associated with increased risk of HbA1c >7.5%, with modest differences by treatment, and with subsequent CVD (HR 1.03 [95% CI 1.005, 1.06]). Weight gain at 1 year was associated with increased risk of HbA1c >7.5% (HR 1.05 [1.04, 1.07]) and kidney disease (HR 1.03 [1.01, 1.06]). Baseline weight, but not weight gain, was associated with new-onset neuropathy. Weight gain was associated with lower diabetes treatment satisfaction.

Conclusions: Liraglutide and sitagliptin were associated with initial weight loss and glargine and glimepiride with slight weight gain, followed by weight loss in metformin-treated T2D. Weight gain was associated with worsening glycemia and increased risk of cardiovascular and kidney outcomes largely independent of treatment.

Objective: This study aimed to evaluate the impact of islet transplantation (IT) on diabetes complications, death, and cancer incidence.

Research design and methods: This retrospective, multicenter, cohort study included patients from three IT clinical trials (intervention group) and from the French health insurance claims database Système National des Données de Santé (SNDS) (control group). Two cohorts of IT recipients were analyzed: IT recipients after kidney transplantation (IAK) and IT recipients alone (ITA). They were matched with patients living with type 1 diabetes (T1D) from the SNDS using a propensity score. The primary outcome was a composite criterion including death, dialysis, amputation, nonfatal stroke, nonfatal myocardial infarction, and transient ischemic attack. The secondary outcome was cancer. Hazard ratio (HRs) and P values were obtained using Cox proportional hazards analysis and log-rank test, respectively.

Results: The study included 61 ITA recipients matched to 610 T1D control patients and 45 IAK recipients matched to 45 T1D control patients over a median follow-up period >10 years. Compared with T1D control patients, ITA and IAK recipients had a lower composite outcome risk (HR 0.39 [95% CI 0.21-0.71; P = 0.002] and 0.52 [0.30-0.88; P = 0.014], respectively) that seemed driven by reduced mortality (0.22 [0.09-0.54]; P < 0.001) for ITA and reduced dialysis (0.19 [0.07-0.50]; P < 0.001) for IAK. Both groups showed no significant changes in cancer risk.

Conclusions: This study suggests long-term benefits of IT on diabetes-related outcomes. Furthermore, despite the use of immunosuppressive drugs following IT, we observed no significant increase in the risk of cancer. Altogether, these findings highlight a favorable risk-benefit ratio of IT in treating patients with unstable T1D.

Objective: Studies have demonstrated dose-dependent efficacy of glucagon-like peptide 1 receptor agonists for glycemic control and body weight. The aim of this trial was to characterize the dose-dependent effects of semaglutide (up to 16 mg/week) in people with type 2 diabetes and overweight or obesity.

Research design and methods: In this parallel-group, participant- and investigator-blinded, phase 2 trial, 245 individuals with type 2 diabetes and BMI ≥27 kg/m2 on metformin were randomized to weekly semaglutide (2, 8, or 16 mg s.c.) or placebo for 40 weeks. Doses were escalated every 4 weeks, followed by a maintenance period. Dose modifications were not allowed. Primary and secondary efficacy end points included change from baseline to week 40 in HbA1c and body weight, respectively.

Results: Estimated treatment difference between 16 and 2 mg was -0.3 percentage points (%-points) (95% CI -0.7 to 0.2; P = 0.245) for HbA1c change and -3.4 kg (-6.0 to -0.8; P = 0.011) for weight change for the treatment policy estimand and -0.5%-points (-1.0 to -0.1; P = 0.015) and -4.5 kg (-7.6 to -1.4; P = 0.004), respectively, for the hypothetical estimand. Dose-response modeling confirmed these findings. Treatment-emergent adverse events (AEs) and treatment discontinuations due to AEs, primarily gastrointestinal, were more frequent in the semaglutide 8 and 16 mg groups than in the 2 mg group. No severe hypoglycemic episodes were reported.

Conclusions: Higher semaglutide doses for type 2 diabetes and overweight or obesity provide modest additional glucose-lowering effect, with additional weight loss, at the expense of more AEs and treatment discontinuations. A study for evaluating high-dose semaglutide in obesity is currently underway.

Objective: Screening for advanced fibrosis (AF) resulting from metabolic dysfunction-associated steatotic liver disease (MASLD) is recommended in diabetology. This study aimed to compare the performance of noninvasive tests (NITs) with that of two-step algorithms for detecting patients at high risk of AF requiring referral to hepatologists.

Research design and methods: We conducted a planned interim analysis of a prospective multicenter study including participants with type 2 diabetes and/or obesity and MASLD with comprehensive liver assessment comprising blood-based NITs, vibration-controlled transient elastography (VCTE), and two-dimensional shear-wave elastography (2D-SWE). AF risk stratification was determined by a composite criterion of liver biopsy, magnetic resonance elastography, or VCTE ≥12 kPa depending on availability.

Results: Of 654 patients (87% with type 2 diabetes, 56% male, 74% with obesity), 17.6% had an intermediate/high risk of AF, and 9.3% had a high risk of AF. The area under the empirical receiver operating characteristic curves of NITs for detection of high risk of AF were as follows: fibrosis-4 index (FIB-4) score, 0.78 (95% CI 0.72-0.84); FibroMeter, 0.74 (0.66-0.83); FibroTest, 0.78 (0.72-0.85); Enhanced Liver Fibrosis (ELF) test, 0.82 (0.76-0.87); and SWE, 0.84 (0.78-0.89). Algorithms with FIB-4 score/VCTE showed good diagnostic performance for referral of patients at intermediate/high risk of AF to specialized care in hepatology. An alternative FIB-4 score/ELF test strategy showed a high negative predictive value (NPV; 88-89%) and a lower positive predictive value (PPV; 39-46%) at a threshold of 9.8. The FIB-4 score/2D-SWE strategy had an NPV of 91% and a PPV of 58-62%. The age-adapted FIB-4 score threshold resulted in lower NPVs and PPVs in all algorithms.

Conclusions: The FIB-4 score/VCTE algorithm showed excellent diagnostic performance, demonstrating its applicability for routine screening in diabetology. The ELF test using an adapted low threshold at 9.8 may be used as an alternative to VCTE.

Objective: The Diabetes in Children, Adolescents, and Young Adults (DiCAYA) network seeks to create a nationwide electronic health record (EHR)-based diabetes surveillance system. This study aimed to develop a DiCAYA-wide EHR-based computable phenotype (CP) to identify prevalent cases of diabetes.

Research design and methods: We conducted network-wide chart reviews of 2,134 youth (aged <18 years) and 2,466 young adults (aged 18 to <45 years) among people with possible diabetes. Within this population, we compared the performance of three alternative CPs, using diabetes diagnoses determined by chart review as the gold standard. CPs were evaluated based on their accuracy in identifying diabetes and its subtype.

Results: The final DiCAYA CP requires at least one diabetes diagnosis code from clinical encounters. Subsequently, diabetes type classification was based on the ratio of type 1 diabetes (T1D) or type 2 diabetes (T2D) diagnosis codes in the EHR. For both youth and young adults, the sensitivity, specificity, and positive and negative predictive values (PPV and NPV, respectively) in finding diabetes cases were >90%, except for the specificity and NPV in young adults, which were slightly lower at 83.8% and 80.6%, respectively. The final DiCAYA CP achieved >90% sensitivity, specificity, PPV, and NPV in classifying T1D, and demonstrated lower but robust performance in identifying T2D, consistently maintaining >80% across metrics.

Conclusions: The DiCAYA CP effectively identifies overall diabetes and T1D in youth and young adults, though T2D misclassification in youth highlights areas for refinement. The simplicity of the DiCAYA CP enables broad deployment across diverse EHR systems for diabetes surveillance.