BMJ Open Diabetes Research & Care最新文献

Introduction: Biallelic PDX1 variants are a rare cause of isolated pancreatic agenesis and neonatal diabetes (NDM) without exocrine pancreatic insufficiency, with 17 cases reported in the literature.

Research design and methods: To determine the phenotypic variability caused by this rare genetic aetiology, we investigated 19 individuals with NDM resulting from biallelic disease-causing PDX1 variants.

Results: Of the 19 individuals, 8 (42%) were confirmed to have exocrine insufficiency requiring replacement therapy. Twelve individuals (63.2%) had extrapancreatic features, including 8 (42%) with conditions affecting the duodenum and/or hepatobiliary tract. Defects in duodenum development are consistent with previous Pdx1 ablation studies in mice which showed abnormal rostral duodenum development.

Conclusions: Our findings show that recessive PDX1 variants can cause a syndromic form of NDM, highlighting the need for clinical assessment of extrapancreatic features in individuals with NDM caused by PDX1 variants.

Introduction: The prognostic significance of red cell distribution width-SD (RDW-SD) as a promising inflammatory biomarker in individuals with non-ischemic dilated cardiomyopathy (DCM) and varying glycemic status remains unexplored.

Research design and methods: Patients hospitalized for DCM in Fuwai Hospital from 2006 to 2021 were retrospectively included. The primary outcome encompassed all-cause mortality and heart transplantations. The multivariable Cox regression was used to evaluate the association between RDW-SD and outcomes in the overall DCM population, and among patients with normoglycemia (NG), pre-diabetes mellitus (pre-DM) and DM.

Results: Among 1,102 patients with DCM, the median age was 48 years and 23.5% were women. In the overall DCM cohort, the RDW-SD was independently associated with the primary outcome (adjusted HR 1.29, 95% CI 1.15 to 1.45, p<0.001, per SD increase). When stratifying patients with glycemic status, the RDW-SD exhibited an independent association with outcome in patients with DCM with pre-DM and DM, the adjusted HRs were 1.48 (95% CI 1.21 to 1.79, p<0.001) and 1.30 (95% CI 1.06 to 1.60, p=0.011) per SD increase, respectively. However, in patients with DCM and NG, the prognostic value of RDW-SD was insignificant, with an adjusted HR of 1.20 per SD increase (95% CI: 0.97 to 1.48, p=0.101).

Conclusions: RDW-SD was independently associated with the outcome in patients with DCM with pre-DM and DM, suggesting potential individualized therapeutic targets for this subset of patients with DCM.

Introduction: There are no established methods to identify children with atypical diabetes for further study. We aimed to develop strategies to systematically ascertain cases of atypical pediatric diabetes using electronic medical records (EMR).

Research design and methods: We tested two strategies in a large pediatric hospital in the USA. Strategy 1: we designed a questionnaire to rule out typical diabetes and applied it to the EMR of 100 youth with diabetes. Strategy 2: we built three electronic queries to generate reports of three atypical pediatric diabetes phenotypes: unknown type, type 2 diabetes (T2D) diagnosed <10 years old and autoantibody-negative type 1 diabetes (AbNegT1D).

Results: Strategy 1 identified six cases (6%) of atypical diabetes (mean diagnosis age=11±2.6 years, 16.6% men, 33% non-Hispanic white (NHW) and 66.6% Hispanic). Strategy 2: unknown diabetes type: n=68 (1%) out of 6676 patients with diabetes; mean diagnosis age=12.6±3.3 years, 32.8% men, 23.8% NHW, 47.6% Hispanic, 25.4% African American (AA), 3.2% other. T2D <10 years old: n=64 (6.6%) out of 1142 patients with T2D; mean diagnosis age=8.6±1.6 years, 20.3% men, 4.7% NHW, 65.6% Hispanic, 28.1% AA, 1.6% other. AbNegT1D: n=38 (5.6%) out of 680 patients with new onset T1D; mean diagnosis age=11.3±3.8 years; 57.9% men, 50% NHW, 19.4% Hispanic, 22.3% AA, 8.3% other.

Conclusions: In sum, we identified 1%-6.6% of atypical diabetes cases in a pediatric diabetes population with high racial and ethnic diversity using systematic review of the EMR. Better identification of these cases using unbiased approaches may advance precision diabetes.

This systematic review and meta-analysis aimed to quantify differences in type 2 diabetes (T2D) complications between ethnic minority populations and European host populations, in both cross-sectional and prospective studies. Following Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines, we searched multiple databases for studies (until July 1, 2024) with T2D complications as outcome. Studies were included if they compared ethnic minority populations to the host population and were conducted in Europe. T2D complications included mortality, macrovascular and microvascular complications and mental disorders. Risk of bias was assessed with the assessment tool for observational cohort and cross-sectional studies. Risk estimates were pooled using random effects models. From a total of 2901 references, 58 studies were included, comprising 805 to 1 230 410 individuals for the meta-analyzed complications. Compared with the host population, ethnic minority populations generally had a lower risk of all-cause mortality (RR 0.70 (95% CI 0.63; 0.77); I²=87%)) and macrovascular complications (RR 0.72 (95% CI 0.58; 0.88); I²=88%). South Asians, however, showed comparable risks for most macrovascular complications and a slighthly higher risk of major adverse cardiovascular events. Increased risks for microvascular complications, nephropathy and retinopathy were observed (eg, in prospective studies RR 1.50 (95% CI 1.14; 1.96); I²=86% for nephropathy). No ethnic differences were observed for mental disorders. Ethnic minority populations with T2D in Europe are generally at reduced risk of all-cause mortality and macrovascular complications, but at higher risk of nephropathy and retinopathy. Our findings may help to further identify high-risk populations and to develop guidelines and future interventions. PROSPERO registration number:PROSPERO 2022 CRD42022366854.

Introduction: Low carbohydrate ketogenic diets have received renewed interest for the treatment of obesity and type 2 diabetes. These diets promote weight loss, improve glycemic control, and reduce insulin resistance. However, whether the improvements in glycemic control and insulin sensitivity are secondary to the weight loss or result from a direct effect of hyperketonemia is controversial.

Research design and methods: 29 overweight obese subjects were randomized to one of three dietary interventions for 10 days: (1) Weight-maintaining standard diet; (2) Weight-maintaining ketogenic diet; (3) Weight-maintaining ketogenic diet plus supplementation with the ketone ester of beta-hydroxybutyrate (β-OH-B), 8 g every 8 hours. At baseline, all subjects had oral glucose tolerance test, 2-step euglycemic insulin clamp (20 mU/m².min and 60 mU/m².min) with titrated glucose and indirect calorimetry.

Results: Body weight, fat content, and per cent body fat (DEXA) remained constant over the 10-day dietary intervention period in all three groups. Plasma β-OH-B concentration increased twofold, while carbohydrate oxidation decreased, and lipid oxidation increased demonstrating the expected shifts in substrate metabolism with institution of the ketogenic diet. Glucose tolerance either decreased slightly or remained unchanged in the two ketogenic diet groups. Whole body (muscle), liver, and adipose tissue sensitivity to insulin remained unchanged in all 3 groups, as did the plasma lipid profile and blood pressure.

Conclusion: In the absence of weight loss, a low carbohydrate ketogenic diet has no beneficial effect on glucose tolerance, insulin sensitivity, or other metabolic parameters.

Introduction: To examine the long-term health and economic impact of a lifestyle diabetes prevention program in people with high risk of developing type 2 diabetes in Germany.

Research design and methods: We assessed the lifetime cost-effectiveness of a 2-year pragmatic lifestyle program for preventing type 2 diabetes targeting German adults aged 35-54 and 55-74 years old with hemoglobin A1c (HbA1c) from 6.0% to 6.4%. We used the Centers for Disease Control and Prevention RTI Diabetes Cost-Effectiveness Model to run a simulation on the program effectiveness. We estimated incremental health benefits in quality-adjusted life years (QALYs) and costs using an established simulation model adapted to the German context, from a healthcare system and societal perspective. The cost-effectiveness of the program was measured by incremental cost-effectiveness ratios (ICERs) in cost per QALY. We projected the number of type 2 diabetes cases prevented by participation rate if the program was implemented nationwide.

Results: The lifestyle program would result to more QALYs and higher costs. The lifetime ICERs were 14 690€ (35-54 years old) and 14 372€ (55-74 years old) from a healthcare system perspective and cost saving (ICER=-3805€) and cost-effective (ICER=4579€), respectively, from a societal perspective. A total of 10 527 diabetes cases would be prevented over lifetime if the program was offered to all eligible people nationwide and 25% of those would participate in the program.

Conclusions: Implementing the lifestyle intervention for people with HbA1c from 6.0% to 6.4% could be a cost-effective at standard willingness to pay level strategy for type 2 diabetes prevention. The intervention in the younger cohort could be cost saving from a societal perspective. The successful implementation of a lifestyle-based diabetes prevention program could be an important component of a successful National Diabetes Strategy in Germany.

Introduction: Ethnic differences associated with oral glucose tolerance test (OGTT) phenotypes is less studied in Southeast Asian ethnicities, especially in women with hyperglycemia in pregnancy (HIP).

Research design and methods: We retrospectively examined 3027 women at KK Women's and Children's Hospital in 2019. Of these, 508 (16.8%) women were diagnosed with HIP using the IADPSG (International Association of Diabetes and Pregnancy Study Groups) criteria at 24-28 weeks. OGTT phenotypes were classified into four mutually exclusive groups based on abnormal plasma glucose at (1) 0 hour only; (2) 1 hour only; (3) 2 hour only; (4) ≥2 timepoints (reference). Multinomial logistic regression was used to examine the association between ethnicity and OGTT phenotypes, adjusting for maternal age, parity, and first-trimester body mass index.

Results: Overall HIP prevalence was 16.8%, highest among Indians (20.5%), then Chinese (18.3%) and Malays (14.2%). Indians (relative risk ratio (RRR) 3.05) and Chinese (RRR 2.33) were at higher risk of displaying a fasting-only phenotype compared with Malays. Chinese were at increased risk of displaying a 2-hour postprandial phenotype with an RRR of 2.88 as compared with Malays.

Conclusions: Unique OGTT phenotypes exist across ethnic groups among women who developed HIP in a multi-ethnic Asian population.

Introduction: This study aimed to assess recent trends in the US use of glucagon-like peptide-1 receptor agonist (GLP-1 RA) and sodium-glucose cotransporter 2 inhibitor (SGLT2i) in people with type 2 diabetes (T2D) and atherosclerotic cardiovascular disease (ASCVD), including incident use following newly diagnosed ASCVD.

Research design and methods: This real-world, retrospective observational study used de-identified data from the TriNetX Dataworks-USA network. A longitudinal analysis of cross-sectional data (interval: January 01, 2018 to December 31, 2022) assessed the yearly prevalent use of GLP-1 RA and SGLT2i. A nested cohort study (January 01, 2017 to January 31, 2023) assessed the proportions of patients with T2D newly prescribed GLP-1 RAs and SGLT2is after incident ASCVD diagnosis.

Results: Prevalent use of GLP-1 RA and/or SGLT2i increased from 9.2% of patients in 2018 to 27.1% in 2022, with eligible annual patient numbers ranging from 279,474 to 348,997. GLP-1 RA-alone use rose from 5.2% to 9.9% and SGLT2i-alone use rose from 2.8% to 12.2% over this interval. Incident use of GLP-1 RA and/or SGLT2i within the year following ASCVD diagnosis increased from 5.9% to 17.0% (2018-2022). For GLP-1 RA alone, this increase was from 3.6% to 7.8%, while for SGLT2i alone, it was from 1.8% to 7.0%.

Conclusions: Use of GLP-1 RAs/SGLT2is in patients with T2D and ASCVD has increased in recent years in the USA, but remains suboptimal given the prevalence of ASCVD and its high morbidity and mortality.

Introduction: We describe the identification and management of general population screen-detected type 1 diabetes (T1D) and share learnings for best practice.

Research design and methods: Children diagnosed with T1D through a general population screening initiative, the EarLy Surveillance for Autoimmune diabetes (ELSA) study, were reviewed and described.Parents provided written, informed consent for inclusion in the case series.

Results: 14 children with insulin requiring (stage 3) T1D are described. These cases offer unique insights into the features of screen-detected T1D. T1D is identified sooner through screening programs, characterized by absent/short symptom duration, median presenting glycated hemoglobin 6.6% (49 mmol/mol) and insulin requirements<0.5 units/kg/day. ELSA identified four children at stage 3 and another 4 progressed within 4 months of ELSA completion, including two single seropositive children. Six children developed stage 3 T1D prior to ELSA completion, including two children (14%, n=2/14) with diabetic ketoacidosis prior to confirmed antibody status.

Conclusions: There are three main learnings from this case series. First, T1D identified through screening is at an earlier stage of its natural history and requires personalized insulin regimens with lower total daily insulin doses. Second, single autoantibody seropositivity can rapidly progress to stage 3. Finally, insulin requirement can manifest at any stage of the T1D screening pathway, and therefore early education around symptom recognition is essential for families participating in screening programs.

Introduction: Body mass index (BMI) is inadequately recorded in US administrative claims databases. We aimed to validate the sensitivity and positive predictive value (PPV) of BMI-related diagnosis codes using an electronic medical records (EMR) claims-linked database. Additionally, we applied machine learning (ML) to identify features in US claims databases to predict obesity status.

Research design and methods: This observational, retrospective analysis included 692 119 people ≥18 years of age, with ≥1 BMI reading in MarketScan Explorys Claims-EMR data (January 2013-December 2019). Claims-based obesity status was compared with EMR-based BMI (gold standard) to assess BMI-related diagnosis code sensitivity and PPV. Logistic regression (LR), penalized LR with L1 penalty (Least Absolute Shrinkage and Selection Operator), extreme gradient boosting (XGBoost) and random forest, with features drawn from insurance claims, were trained to predict obesity status (BMI≥30 kg/m²) from EMR as the gold standard. Model performance was compared using several metrics, including the area under the receiver operating characteristic curve. The best-performing model was applied to assess feature importance. Obesity risk scores were computed from the best model generated from the claims database and compared against the BMI recorded in the EMR.

Results: The PPV of diagnosis codes from claims alone remained high over the study period (85.4-89.2%); sensitivity was low (16.8-44.8%). XGBoost performed the best at predicting obesity with the highest area under the curve (AUC; 79.4%) and the lowest Brier score. The number of obesity diagnoses and obesity diagnoses from inpatient settings were the most important predictors of obesity. XGBoost showed an AUC of 74.1% when trained without an obesity diagnosis.

Conclusions: Obesity prevalence is under-reported in claims databases. ML models, with or without explicit obesity, show promise in improving obesity prediction accuracy compared with obesity codes alone. Improved obesity status prediction may assist practitioners and payors to estimate the burden of obesity and investigate the potential unmet needs of current treatments.