BMJ Open Diabetes Research & Care最新文献

Introduction: The integration of continuous glucose monitoring (CGM) into clinical practice has rapidly emerged in the last decade, changing the evaluation of long-term glucose regulation in patients with diabetes. When using CGM-derived metrics to evaluate long-term glucose regulation, it is essential to determine the minimal observation period necessary for a reliable estimate. The approach of this study was to calculate mean absolute errors (MAEs) for varying window lengths, with the goal of demonstrating how the CGM observation period influences the accuracy of the estimation of 90-day glycemic control.

Research design and methods: CGM data were collected from the DIABASE cohort (ZGT hospital, The Netherlands). Trailing aggregates (TAs) were calculated for four CGM-derived metrics: time in range (TIR), time below range (TBR), glucose management indicator (GMI) and glycemic variability (GV). Arbitrary MAEs for each patient were compared between the TAs of window lengths from 1 to 89 days and a reference TA of 90 days, which is assumed to reflect long-term glycemic regulation.

Results: Using 14 days of CGM data resulted in 65% of subjects having their TIR estimation being below a MAE threshold of 5%. In order to have 90% of the subjects below a TIR MAE threshold of 5%, the observation period needs to be 29 days.

Conclusions: Although there is currently no consensus on what is an acceptable MAE, this study provides insight into how MAEs of CGM-derived metrics change according to the used observation period within a population and may thus be helpful for clinical decision-making.

Introduction: Type 1 diabetes is burdensome, requiring complex daily management and making people more prone to emotional distress. To better detect diabetes-related distress (DD) and identify at-risk patients, we aimed to provide an in-depth characterization of DD in people with type 1 diabetes.

Research design and methods: We included adults with type 1 diabetes from the Suivi en France des personnes avec un Diabète de Type 1 cohort who filled in the Problem Areas in Diabetes questionnaire (PAID ≥40 indicates high DD). Age and sex-adjusted multivariable logistic regression models analyzed individual characteristics, clinical indicators, diabetes-related complications and psychological factors. We further analyzed DD according to six data-driven subdimensions: emotional distress, fear of complications, social distress, eating distress, management distress, and diabetes burnout.

Results: In total, 1220 participants (50.6% female, age 42 years (SD 13.9), diabetes duration 24.7 years (13.6)) had a total mean PAID score of 39.6 (21.7) and 592 (48.5%) reported high DD. Leading subdimensions of DD included fear of complications (50.1 (24.4)) and diabetes burnout (45.9 (24.5)). Females, younger age, social vulnerability, smoking, and the presence of retinopathy were positively associated with high DD (p<0.05). We observed similar DD levels across HbA1c levels and treatment modalities, including automated insulin delivery and continuous glucose monitoring use. Several psychological factors, such as anxiety/depression, poor sleep quality, and treatment burden, were strongly associated with DD (p<0.001).

Conclusions: We provide a holistic clinical phenotyping approach that enables the identification of determinants and prevalence of DD, overall and according to key DD subdimensions, in a large and diverse population. Our results underscore the importance of developing DD-targeted prevention and intervention strategies focused specifically on high-risk groups and the most impactful distress subdimensions to reduce the impact of type 1 diabetes burden.

Trial registration number: NCT04657783.

Introduction: Metabolic and bariatric surgery (MBS) is an effective intervention to manage diabetes and obesity. The population-based incidence of MBS is unknown.

Objective: To estimate the incidence of MBS among US adults with obesity by diabetes status and selected sociodemographic characteristics.

Research design and methods: This cross-sectional study used data from the 2016-2020 Nationwide Inpatient Sample and Nationwide Ambulatory Surgery Sample to capture MBS procedures. The National Health Interview Survey was used to establish the denominator for incidence calculations. Participants included US non-pregnant adults aged ≥18 years with obesity. The main outcome was incident MBS without previous MBS, defined by International Classification of Diseases, Tenth Revision Procedure Codes, Diagnosis Related Group system codes, and Current Procedural Terminology codes. Adjusted incidence and annual percentage change (2016-2019) were estimated using logistic regression.

Results: Among US adults with obesity, over 900 000 MBS procedures were performed in inpatient and hospital-owned ambulatory surgical centers in the USA during 2016-2020. The age- and sex-adjusted incidence of MBS per 1000 adults was 5.9 (95% CI 5.4 to 6.4) for adults with diabetes and 2.0 (95% CI 1.9 to 2.1) for adults without diabetes. MBS incidence was significantly higher for women and adults with class III obesity regardless of diabetes status. The highest incidence of MBS occurred in the Northeast region. Sleeve gastrectomy was the most common MBS surgical approach.

Conclusions: Incident MBS procedures were nearly threefold higher among adults with obesity and diabetes than those with obesity but without diabetes. Continued monitoring of the trends of MBS and other treatment modalities can inform our understanding of treatment accessibility to guide prevention efforts aimed at reducing obesity and diabetes.

Introduction: Diabetes self-management education (DSME) is endorsed by the American Diabetes Association (ADA) as an essential component of diabetes management. However, the utilization of DSME remains limited in the USA. This study aimed to investigate current DSME participation among the older population and to identify comprehensive factors of DSME engagement through employing various machine learning (ML) models based on a US nationally representative survey linked to claims data.

Research design and methods: Data from the Medicare Current Beneficiary Survey were employed, and this study included data on US Medicare beneficiaries with diabetes from 2017 to 2019. Comprehensive variables following the National Institute on Aging Health Disparities Research Framework were employed to ensure a comprehensive evaluation of factors associated with DSME using five common ML approaches.

Results: In our study, 37.94% of participants received DSME after the application of inclusion and exclusion criteria. A total of 95 variables were used and all ML models achieved accuracy scores exceeding 70%. Random forest had better predictive performance, with an accuracy of 85%. Seventy-four of 95 variables were identified as key variables. Racial/ethnic disparities in predictors for DSME were identified in this study.

Conclusions: This study identified comprehensive and critical factors associated with DSME engagement from biological, behavioral, sociocultural, and environmental domains using different ML models, as well as related racial/ethnic disparities. Aligning these findings with the DSME National Standards from the ADA would enhance the guidelines' effectiveness, promoting tailored and equal diabetes management approaches that cater to diverse races/ethnicities.

Introduction: The study aimed to assess the effect of COVID-19 on hepatic lipid (HL) content, fibrosis risk, and adiposity in persons with type 2 diabetes.

Research design and methods: Participants with type 2 diabetes with a history of mild COVID-19 (n=15, age 58±12 years, body mass index 30.9±5.2 kg/m²) were examined before (baseline) and 1 year (12±2 months) after (follow-up) recovery from COVID-19. Investigations for changes in metabolic risk comprised clinical examination, fasting blood sampling and MR-based measurements. Potential changes were corrected with the time course of the respective parameters in a group of participants who did not contract COVID-19 over the same time course (n=14, 61±6 years, 30.0±4.6 kg/m²).

Results: COVID-19 resulted in a relative increase in HL content of 56% (95% CI 18%, 106%; p=0.04) measured as proton density fat fraction (HL-PDFF), corrected for the time course in the absence of COVID-19. While no changes in hepatic stiffness and volume, intramyocellular lipids, whole-body, subcutaneous and visceral adipose tissue volumes as well as homeostatic model assessment of insulin resistance and beta-cell function were observed.

Conclusions: History of COVID-19 in persons with type 2 diabetes is associated with higher HL-PDFF after 1 year following recovery from infection.

Trial registration number: NCT01055093.

Depression is associated with diabetes, but the underlying causes remain unclear. To better understand depression in diabetes, this study investigated associations between 135 inflammatory and neurological protein biomarkers and depressive symptoms in individuals with diabetes.This cross-sectional study included 430 adults with a known diabetes duration <1 year from the German Diabetes Study (GDS), in whom biomarkers were measured in serum and depressive symptoms were evaluated at baseline and annually over 5 years using the Center for Epidemiological Studies Depression Scale (CES-D). Based on the information on depressive symptoms from the baseline and follow-up visits (n=305, ≥3 time points), we subdivided the sample into individuals with persistent or recurrent and transient or never depressive symptoms. We assessed the associations of each biomarker with baseline CES-D score (continuous) and persistent/recurrent depressive symptoms using multiple linear and logistic regression models, respectively.After adjustment for covariates, we identified a three-protein signature associated with baseline CES-D score and persistent/recurrent depressive symptoms. CUB domain-containing protein 1 (CDCP1) and NAD-dependent protein deacetylase sirtuin-2 (SIRT2) were positively associated with baseline (β 1.24 (95% CI 0.19 to 2.29); β 0.89 (95% CI 0.06 to 1.72)), respectively) and persistent/recurrent depressive symptoms (OR 1.58 (95% CI 1.08 to 2.31); OR 1.32 (95% CI 1.03 to 1.71), respectively), whereas leptin receptor (LEPR) was inversely associated with baseline (β -0.99 (95% CI -1.87 to -0.11)) and persistent/recurrent depressive symptoms (OR 0.70 (95% CI 0.49 to 0.99)). However, results were not significant after adjustment for multiple testing.In conclusion, the three-protein signature identified may provide insights into mechanisms underlying depressive symptoms in diabetes and might open new therapeutic avenues.The trial registration number of the study is NCT01055093.

Introduction: The risk of cardiovascular disease is increased in individuals with type 1 diabetes, despite good glycemic control. This study aims to evaluate early signs of atherosclerosis and predisposing factors in individuals with childhood-onset type 1 diabetes compared with healthy controls.

Research design and methods: The Atherosclerosis and Childhood Diabetes study is a prospective population-based cohort study with follow-up every fifth year. The cohort consists of 329 subjects with type 1 diabetes and 173 controls. Carotid intima-media thickness (cIMT) was measured at baseline and 5 and 10 years of follow-up. Data from the Norwegian Childhood Diabetes Registry were used in assessment of traditional risk factors.

Results: Mean cIMT in young women with type 1 diabetes increased significantly over a 10-year period compared with healthy controls (∆0.019 mm (0.001-0.035), p=0.035). At the 10-year follow-up the group with type 1 diabetes had a mean age of 24.2±2.9 years (13.7±2.8 years at baseline), diabetes duration of 15.6±3.4 years (5.4±3.3 years at baseline) and HbA1c of 8.2±3.6% (66±16 mmol/mol) (8.4±3.4% (68±13 mmol/mol) at baseline). Women with type 1 diabetes had significantly higher mean weight, body mass index, waist circumference, diastolic blood pressure (DBP), serum low-density lipoprotein (LDL)-cholesterol and apolipoprotein B, while men with type 1 diabetes had significantly higher mean DBP and urinary albumin-creatinine ratio compared with the control group. Mean cIMT change over time was not associated with long-term HbA1c or LDL-cholesterol burden in childhood and adolescence.

Conclusion: Young women with childhood-onset type 1 diabetes of relatively short diabetes duration had a higher mean cIMT over a 10-year period compared with their healthy female controls, with values similar to males.

Introduction: Elevated glycosylated hemoglobin (HbA1c) in individuals with type 2 diabetes is associated with increased risk of hospitalization and death after acute COVID-19, however the effect of HbA1c on Long COVID is unclear.

Objective: Evaluate the association of glycemic control with the development of Long COVID in patients with type 2 diabetes (T2D).

Research design and methods: We conducted a retrospective cohort study using electronic health record data from the National COVID Cohort Collaborative. Our cohort included individuals with T2D from eight sites with longitudinal natural language processing (NLP) data. The primary outcome was death or new-onset recurrent Long COVID symptoms within 30-180 days after COVID-19. Symptoms were identified as keywords from clinical notes using NLP in respiratory, brain fog, fatigue, loss of smell/taste, cough, cardiovascular and musculoskeletal symptom categories. Logistic regression was used to evaluate the risk of Long COVID by HbA1c range, adjusting for demographics, body mass index, comorbidities, and diabetes medication. A COVID-negative group was used as a control.

Results: Among 7430 COVID-positive patients, 1491 (20.1%) developed symptomatic Long COVID, and 380 (5.1%) died. The primary outcome of death or Long COVID was increased in patients with HbA1c 8% to <10% (OR 1.20, 95% CI 1.02 to 1.41) and ≥10% (OR 1.40, 95% CI 1.14 to 1.72) compared with those with HbA1c 6.5% to <8%. This association was not seen in the COVID-negative group. Higher HbA1c levels were associated with increased risk of Long COVID symptoms, especially respiratory and brain fog. There was no association between HbA1c levels and risk of death within 30-180 days following COVID-19. NLP identified more patients with Long COVID symptoms compared with diagnosis codes.

Conclusion: Poor glycemic control (HbA1c≥8%) in people with T2D was associated with higher risk of Long COVID symptoms 30-180 days following COVID-19. Notably, this risk increased as HbA1c levels rose. However, this association was not observed in patients with T2D without a history of COVID-19. An NLP-based definition of Long COVID identified more patients than diagnosis codes and should be considered in future studies.