BMJ Open Diabetes Research & Care最新文献

Aim: Genetic variants associated with gestational diabetes mellitus (GDM, n=14 SNPs) were recently classified into two groups: type 2 diabetes predominant effects (Class-T, three SNPs) and GDM-predominant effects (Class-G, eight SNPs; three SNPs unclassified). We aimed to compare the effects of GDM-associated variants on glucose levels (fasting glucose and 2-hour post-OGTT) measured during versus post pregnancy.

Research design and methods: We calculated genetic scores (GS) by class (T_GS and G_GS) and overall (All_GS) in 10 225 pregnant women and 4763 women post pregnancy (mean 10.5 years post pregnancy) from eight datasets representing four ancestrally-diverse cohorts: Exeter Family Study of Childhood Health, Genetics of Glucose Regulation in Gestation and Growth, Hyperglycaemia and Adverse Pregnancy Outcome, and Finnish Gestational Diabetes. We used linear regression models adjusted for ancestry principal components to investigate associations between standardized GS and glucose levels during or post pregnancy. Analyses were performed separately in each dataset and then combined using inverse-variance weighted random-effects meta-analyses.

Results: All_GS was associated with fasting glucose both during and post pregnancy (β (95% CI), in mmol/L per 1 SD higher GS=0.06 (0.04 to 0.08) during vs 0.06 (0.04 to 0.07) post pregnancy). All_GS was also associated with 2-hour post-OGTT glucose levels during pregnancy but not after (0.10 (0.04 to 0.15) during vs 0.01 (-0.04 to 0.07) post pregnancy). Both G_GS and T_GS showed consistent associations with fasting glucose during and post pregnancy (0.06 (0.04 to 0.08) during and 0.05 (0.03 to 0.07) post pregnancy for G_GS; 0.02 (0.01 to 0.02) during and 0.02 (-0.001; 0.05) post pregnancy for T_GS). G_GS showed weak evidence of association with 2-hour glucose levels during pregnancy (0.06 (-0.002 to 0.11)) and no association with 2-hour glucose levels post pregnancy (-0.03 (-0.08 to 0.03)). However, T_GS was associated with 2-hour glucose during pregnancy and post pregnancy (0.10 (0.04 to 0.16) and 0.06 (0.01 to 0.12)).

Conclusion: Consistent associations with fasting glucose levels during and after pregnancy may suggest that biological pathways underlying GDM genetic susceptibility to fasting hyperglycemia are not pregnancy specific. However, the results for All_GS and 2-hour glucose provide evidence that some genetic associations with postprandial glucose may be stronger in pregnancy and should be followed up in larger samples.

Introduction: Achieving optimal glycemic control remains challenging for many patients with diabetes. Text message-based interventions offer a scalable approach to enhance management. This systematic review and meta-analysis evaluated the impact of texting interventions on glycemic control in adults with diabetes.

Research design and methods: We searched EMBASE, PubMed, and Cochrane CENTRAL for randomized controlled trials comparing texting interventions to standard care in high-income countries. The primary outcome was the between-group difference in hemoglobin A1c (HbA1c) change from baseline. Risk of bias and overall quality of evidence were assessed using the Cochrane and Grading of Recommendations Assessment, Development, and Evaluation tools respectively. Results were pooled using an inverse variance random-effects model. Heterogeneity was evaluated using the I² statistic.

Results: Over 3 months of follow-up (14 trials, n=1,460 intervention, n=1,487 control), texting interventions were associated with a 0.29-unit greater reduction in percent HbA1c over control (95% CI 0.14 to 0.45, p=0.0001, I²=57%). At 6 months (20 trials, n=2,332 intervention, n=2,371 control), texting was associated with 0.19-unit greater HbA1c reduction (95% CI 0.07 to 0.30, p=0.001 I²=45%). At 12 months (seven trials, n=2,038), there was a non-significant benefit associated with texting. Among studies with a mean baseline HbA1c ≥8.6%, texting was associated with 0.48- and 0.36-unit greater HbA1c reductions at 3 (p=0.004) and 6 (p=0.004) months, respectively. Subgroups were not significantly different.

Conclusion: Text messaging interventions are associated with modest improvements in glycemic control over 3-6 months, particularly in patients with poorer baseline HbA1c. These effects may be meaningful at scale and support texting as a potential adjunct to routine diabetes care. Benefits appear to diminish by 12 months, underscoring the need for high-quality trials focused on long-term impact and intervention optimization.

Prospero registration number: CRD42023416462.

Background: Diabetic foot ulcer (DFU) is a severe complication of diabetes mellitus, often characterized by a chronic disease course and a high recurrence rate, posing significant challenges to patient management. Accurately predicting DFU recurrence is essential for enhancing patient care and outcomes through timely treatment and intervention. This study aimed to develop a machine learning (ML) model to predict the 3-year recurrence risk in patients with DFU.

Methods: A total of 494 patients with DFU were included and assigned to a training set and a test set at a 4:1 ratio. Four feature selection methods-least absolute shrinkage and selection operator, minimum redundancy maximum relevance, Fisher score and recursive feature elimination-were applied to the training set, and intersecting features were selected to construct the final feature set. Seven ML algorithms, including logistic regression, support vector machine, random forest, gradient boosting decision tree, AdaBoost, extreme gradient boosting (XGBoost) and light gradient boosting machine, were employed to develop predictive models. The models' parameters were optimized using fivefold cross-validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). The best-performing model was calibrated using Platt scaling, with calibration performance assessed by the Brier score. ML model interpretability was enhanced using SHapley Additive exPlanations (SHAP) analysis.

Results: The XGBoost model demonstrated superior predictive performance, achieving an AUROC of 0.924 (95% CI 0.867 to 0.967). Following calibration with Platt scaling, the model exhibited a Brier score of 0.096, indicating good calibration. SHAP analysis identified key risk factors that aligned with existing literature and clinical expertise, further validating the model's interpretability and clinical relevance.

Conclusion: The XGBoost model demonstrated strong predictive accuracy and clinical relevance in assessing DFU recurrence risk. However, further multicenter validation with a larger sample size is needed to improve its generalizability and clinical applicability.

Introduction: Prednisolone is widely prescribed to hospitalized patients for a range of conditions. Up to 40% of hospitalized patients treated with prednisolone will experience hyperglycemia. Current guidelines recommend management of acute hyperglycemia in hospitalized patients with subcutaneous basal-bolus insulin (BBI), but the optimum treatment strategy has not been defined. We aimed to assess the performance of an individualized subcutaneous BBI regimen for management of prednisolone-associated hyperglycemia in hospitalized patients.

Research design and methods: This cross-sectional study included 23 adult inpatients prescribed subcutaneous BBI based on total daily insulin requirements estimated from a 24-hour intravenous insulin infusion and 24 historical controls who were prescribed a standard, institutional weight-based subcutaneous BBI to treat prednisolone-associated hyperglycemia. The primary endpoint was the mean 24-hour point-of-care (POC) glucose concentration on day 1. Exploratory end points included proportion of glucose measurements within target glucose range, SD of glucose, and stress hyperglycemia ratio (SHR).

Results: There was no significant difference in mean POC glucose on day 1 between participants prescribed an individualized insulin regimen and patients receiving a standard body weight-based BBI regimen (10.7±3.4 vs 11.9±3.2 mmol/L, p=0.07). Proportion of glucose measurements within the target glucose range was higher (52.0±4.8 vs 37.0±4.5%, p=0.0007) and SD for glucose lower (3.1±1.5 vs 4.0±1.6, p=0.04) on day 1 of individualized BBI insulin. Over 2 days, there was an increase in glucose SD in both groups, but no significant difference in mean glucose and SHR between groups.

Conclusions: Individualizing a subcutaneous BBI regimen for management of prednisolone-associated hyperglycemia was associated with a modest reduction in mean POC glucose, an increased proportion of blood glucose measurements within the target range, and reduced short-term glycemic variability.

Trial registration number: ACTRN12618001211257.

Introduction: Diabetes technologies may improve glycemic control and psychological well-being among adolescents and young adults (AYA) with type 1 diabetes. This cross-sectional study examines perceptions of automated insulin dosing (AID) systems and their association with glycemic and psychological outcomes compared with multiple daily insulin injections (MDI) and continuous subcutaneous insulin infusion (CSII).

Research design and methods: Participants were recruited from the largest diabetes camp for AYA in Germany. A total of 151 participants (70% female, mean age 20.7±2.9 years, 33% AID users) completed a questionnaire that included self-reported glycated hemoglobin A1c (HbA1c), global health status, emotional well-being (WHO-5), Generalized anxiety (GAD-7) and diabetes distress (PAID-5). AID users also rated their experiences with the system.

Results: AID users reported significantly lower HbA1c levels (7.3±1.0%) than CSII users (7.5±1.1%) and MDI users (8.4±2.0%, p=0.003). Approximately 75% of AID users viewed their current system as an improvement over previous therapy, reporting greater ease (84%), comfort (82%) and safety (80%). They reported higher treatment satisfaction than CSII users (p=0.044) and lower diabetes burden than MDI users (p=0.044) after controlling for age and gender. Treatment groups did not differ in well-being or anxiety. Better global health status was associated with the absence of other chronic health conditions (p=0.024), greater well-being (WHO-5; p<0.001), lower HbA1c (p=0.038) and fewer anxiety symptoms (GAD-7, p=0.007). Despite these positive indicators, a substantial proportion of participants reported symptoms of depression (18%), anxiety (30%), and diabetes distress (39%).

Conclusions: AID systems were associated with improved glycemic control and high satisfaction among AYA. However, psychological distress remained prevalent across all treatment modalities, underscoring a discrepancy between metabolic benefits and persistent mental health challenges. These findings highlight the need to integrate psychological support alongside technological advances in the care of AYA living with diabetes.

Background: Diabetes technology can improve glycemic variability and diabetes outcomes, but there are disparities in patient use.

Aims: Identify racial, ethnic, and socioeconomic disparities in technology utilization and determine provider-, patient-, and parent-identified barriers.

Methods: Technology (continuous glucose monitors (CGM) and pump) usage and demographic data on patients at a large urban pediatric hospital were obtained from a clinical database. Providers (16 attending physicians, five fellow physicians, five nurse practitioners, 13 diabetes educators) completed a survey on diabetes technology prescribing habits. English and Spanish-speaking patients ages 8 to 17 years with diabetes (n=109) and caregivers of pediatric patients with diabetes (n=117) completed surveys that assessed attitudes and perceived benefits/burdens of diabetes technology.

Results: From August 2020 to 2021, independent of insurance payor status, non-Hispanic Black (NHB) and Hispanic youth with type 1 diabetes were less likely to use pump therapy (OR 0.4 and 0.35, respectively) or CGM (OR 0.74 and 0.54) compared with non-Hispanic White (NHW) youth. For pump eligibility, diabetes educators placed higher importance on subjective factors such as parental education, health literacy, and psychosocial stability (p value <0.05) compared with physicians. Hispanic and NHB patients and parents learned about diabetes technology later after diabetes diagnosis compared with NHW patients/caregivers. Physicians and diabetes educators, but not patients themselves, identified patient-perceived barriers to CGM use (ie, embarrassment and/or discomfort in wearing devices) as reasons for not utilizing diabetes technology.

Conclusions: There are marked disparities in diabetes technology use among youth with diabetes. Findings from the provider surveys showed reliance on subjective variables as opposed to objective criteria. Youth with diabetes and caregivers of underrepresented race/ethnicities learned about diabetes technology later and were less likely to use technology. Though clinic providers perceived multiple barriers to technology utilization, responses from families showed low perceived burden and highly positive attitudes.

Introduction: Sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) improve cardiovascular outcomes in type 2 diabetes (T2D), and SGLT2i reduces events in heart failure (HF). However, the benefit of their combination in patients with both conditions remains unclear. This study assessed the risk of all-cause death and hospitalization with combination therapy versus SGLT2i monotherapy.

Research design and methods: This multicenter, retrospective, observational study used the TriNetX database between January 1, 2018, and December 31, 2021. We identified 928,981 patients aged ≥18 years with HF and T2D. Of these, 168,422 received an SGLT2i. The exposure group comprised patients who initiated a GLP-1 RA within 6 months of SGLT2i initiation, while the control group included those who did not receive a GLP-1 RA after SGLT2i initiation. The index date was defined as 6 months after SGLT2i. 25,989 patients received SGLT2i and GLP-1 RA and 54,619 received SGLT2i monotherapy. Following propensity score matching, each group comprised 23,240 patients.

Results: Over 1 year, the risk of all-cause death in patients who received SGLT2i and GLP-1 RA relative to those who received SGLT2i monotherapy was significantly lower (2.8% vs 6.3%, p<0.001; HR 0.43; 95% CI 0.39 to 0.48). Similarly, the risk of hospitalization in patients who received SGLT2i and GLP-1 RA was also lower (32.9% vs 36.4%, p<0.001; HR, 0.87; 95% CI 0.84 to 0.90).

Conclusions: The risk of all-cause death and hospitalization in patients who received combination therapy with SGLT2i and GLP-1 RA relative to those who received SGLT2i monotherapy was significantly lower in patients with HF and T2D.

Introduction: This analysis investigated whether use of real-time continuous glucose monitoring (CGM) compared with blood glucose monitoring (BGM) results in rapidly improved glycemic management in adults with type 2 diabetes (T2D) treated with basal insulin.

Research design and methods: Using data from the MOBILE study where adults (n=175) with T2D treated with basal insulin without prandial insulin were randomized (2:1) to either CGM (n=116) or BGM (n=59), the treatment effect on glycemic management was determined over 3 months. The main outcome was a between-group difference in hemoglobin A_1c (HbA_1c) at 3 months adjusted for baseline value. Other outcomes included changes in CGM-derived glucose metrics and hypoglycemic events.

Results: After 3 months, there was a greater reduction from baseline in mean HbA_1c in the CGM group compared with the BGM group, from 9.1±1.0% (76±11 mmol/mol) to 8.0±1.2% (64±13 mmol/mol) in the CGM group and from 9.0±0.9% (75±10 mmol/mol) to 8.5±1.5% (69±16 mmol/mol) in the BGM group (adjusted difference, -0.6% (95% CI -0.9% to -0.3%); -6.6 mmol/mol (95% CI -10.2 to -2.9), p<0.001). Mean time spent in range 70-180 mg/dL (3.9-10.0 mmol/L) increased significantly more in the CGM group than the BGM group (adjusted difference, +9.3% (95% CI 2.1% to 16.4%), p<0.001). There also was a greater reduction in mean time >250 mg/dL (>13.9 mmol/L) with CGM (adjusted difference, -5.8% (95% CI -10.4% to -1.2%), p<0.001) without an increase in time <70 mg/dL (<3.9 mmol/L). Mean weekly hypoglycemic event rate was lower with CGM than BGM (adjusted difference, -0.2 events per week (95% CI -0.4 to -0.1), p<0.001). Further, in the CGM group, significant improvements in CGM metrics were observed during the first 7 days of CGM use.

Conclusions: In adults with basal insulin-treated T2D, use of CGM compared with BGM resulted in rapidly improved glycemic management, with a substantial reduction in HbA_1c over 3 months.

Trial registration number: NCT03566693.

Introduction: The prevalence of type 2 diabetes (T2D) has surged, yet body mass index (BMI) fails to explain the 30%-40% of cases that occur in individuals with a healthy weight. Emerging evidence suggests that regional fat distribution differentially impacts glucose metabolism, independent of total adiposity. This study investigated the independent association between regional body composition and T2D risk using BMI-matched National Health and Nutrition Examination Survey (NHANES) data to identify sex-specific effects and the mediating role of insulin resistance.

Research design and methods: Our study employed data from the 2011-2018 cycles of NHANES. Participants were classified into a high-risk T2D group if they met one or more of the following criteria: fasting blood glucose≥6.1 mmol/L, 2-hour blood glucose≥7.8 mmol/L following an oral glucose tolerance test or self-reported physician's diagnosis of diabetes or pre-diabetes. Body composition data were assessed via dual-energy X-ray absorptiometry, which provides a precise assessment of regional fat and muscle mass distribution.

Results: Participants at high T2D risk exhibited significantly reduced lower limb fat mass compared with healthy controls (p<0.001), with higher amounts of lower limb fat serving as a protective factor against both diabetes and insulin resistance. Notably, this protective effect of lower-limb fat (OR 0.86 (0.76-0.97), p=0.01) along with the detrimental impact of visceral fat (OR 7.35 (1.57-34.4), p=0.01) was particularly pronounced in male subjects. Additionally, 36.18% of the protective effect of lower limb fat on diabetes is mediated by improved insulin sensitivity.

Conclusions: This study delineates a protective role for lower-body fat in diabetes pathogenesis, mediated substantially through ameliorating insulin resistance. The sex-specific associations underscore the protective effect of lower-body fat and the detrimental impact of visceral adiposity in men after controlling for BMI.

Introduction: Hepatic fibrosis caused by metabolic dysfunction-associated steatotic liver disease (MASLD) predicts adverse atherosclerotic cardiovascular disease (ASCVD) outcomes in the general patient population. However, it is unclear whether this association extends to type 2 diabetes mellitus (T2DM) patients, who have distinct metabolic profiles and high comorbidity of both MASLD and ASCVD. To address this gap, we investigated the association between hepatic fibrosis caused by MASLD and ASCVD risk in T2DM patients as well as potentially moderators of this association.

Research design and methods: This multisite cross-sectional study included 1238 hospitalized T2DM patients with MASLD (mean age=57.81±10.23, 37% female). Hepatic fibrosis was assessed via the Steatosis-Associated Fibrosis Estimator (SAFE) score, and 10-year ASCVD risk was assessed via the ASCVD Risk Calculator.

Results: Advanced fibrosis was present in 25.6% of patients. Multivariable regression revealed a significant association between the SAFE score and 10-year ASCVD risk (p<0.001), after adjusting for covariates. Each unit increase in SAFE score was associated with 0.07-unit increase in 10-year ASCVD risk score. Increase in SAFE score was associated with greater increase in 10-year ASCVD risk score among patients with hypertension, insulin resistance and elevated low-density lipoprotein (LDL) cholesterol (ps<0.05). Overweight/obesity, triglycerides, high-density lipoprotein cholesterol, uric acid, thyroid-stimulating hormone, hemoglobin A1c and high-sensitivity C reactive protein showed no moderating effects.

Conclusions: In T2DM patients, hepatic fibrosis caused by MASLD is associated with elevated ASCVD risks, particularly among those with hypertension, insulin resistance and elevated LDL cholesterol. It is crucial to incorporate hepatic fibrosis assessment into ASCVD risk stratification in T2DM patients with comorbid MASLD to inform early prevention of ASCVD.