BMJ Open Diabetes Research & Care最新文献

Introduction: Family history (FmH) of young-onset type 2 diabetes (YOD) and 1-hour plasma glucose (PG) during the 75-g oral glucose tolerance test predicts incident diabetes, although their interactions remain unknown.

Research design and methods: In a workforce cohort established in 1998-2003, we ascertained their glycemic status in 2012-2014. We examined the interaction between FmH-YOD and 1-hour PG in predicting diabetes and used receiver operating characteristics (ROC) analysis to compare the performance of 1-hour PG in participants with or without FmH-YOD.

Results: Among 583 participants (median age (IQR)=41 (36-47) years, 43.7% men, body mass index=23.3 (21.2-26) kg/m², 40.3% (n=235) had FmH-YOD, 1-hour PG=8.1 (6.4-10.1) mmol/L), 99 (17%) had developed diabetes at a follow-up of 12.1 (11.3-13.1) years. In the FmH-YOD group, 45% in the high 1-hour PG group and 17% in the normal 1-hour PG group developed diabetes. The respective figures were 16% and 1.8% in the FmH-NONE group. Both FmH-YOD and 1-hour PG predicted diabetes with a negative interaction between FmH-YOD and 1-hour PG (OR 0.72, 95% CI 0.55 to 0.93, p=0.013). Compared with (FmH-NONE/normal 1-hour PG) group, the ORs of incident diabetes in (FmH-NONE/high 1-hour PG), (FmH-YOD/normal 1-hour PG), (FmH-YOD/high 1-hour PG) groups were 7.4 (95% CI 1.6 to 35.1, p=0.011), 18 (95% CI 3.3 to 98.1, p=0.001) and 28.2 (95% CI 5.5 to 145.9, p<0.001), respectively. In ROC analysis, the C-statistics of 1-hour PG dropped from 0.83 (95% CI 0.76 to 0.90, p<0.001) in the FmH-NONE group to 0.69 (95% CI 0.62 to 0.76, p<0.001) in the FmH-YOD group (difference=0.14 (95% CI 0.04-0.24), p=0.006) where fasting PG (FPG) was the best predictor (0.792 (95% CI 0.730-0.853), p<0.001).

Conclusions: FPG outperformed 1-hour PG in predicting incident diabetes in people with FmH-YOD, calling for precise classification and preventive strategies.

Introduction: The effects of different glucose metabolic states and diabetes-controlled status on asymptomatic carotid atherosclerosis has not been well investigated. Herein, we aimed to investigate the association of different diabetes status with asymptomatic carotid plaques and carotid intima-media thickness (CIMT).

Research design and methods: 4752 participants aged over 40 years, free of stroke or myocardial infarction, from the China National Stroke Screen Survey programme were enrolled. Carotid plaque and CIMT were assessed using duplex ultrasonography. Logistic regression analysis was used to assess the relationship between diabetes status and the presence of asymptomatic carotid plaques or abnormal CIMT.

Results: A total of 1977 (41.6%) subjects had carotid plaques and 804 (16.9%) had abnormal CIMT. In multivariate analyses, compared with normoglycemia, individuals with pre-diabetes showed significantly higher odds of asymptomatic carotid plaques (OR 1.22, 95% CI 1.03 to 1.45) and patients with diabetes also had higher odds (OR 1.66, 95% CI 1.41 to 1.92). Diabetes (OR 1.79, 95% CI 1.50 to 2.14) was associated with vulnerable plaques, while pre-diabetes was not (OR 1.17, 95% CI 0.96 to 1.43). Poorly controlled diabetes (HbA1c ≥7.5%) had higher odds of carotid plaques (OR 1.93, 95% CI 1.53 to 2.44), especially of vulnerable plaques (OR 2.03, 95% CI 1.55 to 2.67). No significant association was found between diabetes and abnormal CIMT.

Conclusions: Pre-diabetes and diabetes, especially poorly controlled diabetes, were associated with increased odds of asymptomatic carotid plaques. Implementing the most effective strategies to achieve optimal glycemic control is crucial for the prevention and management of atherosclerosis.

Introduction: Pre-diabetes comprises diverse subphenotypes linked to varying complications, type 2 diabetes, and mortality outcomes. This study aimed to explore these outcomes across different pre-diabetes subphenotypes.

Research design and methods: The dataset included adults without type 2 diabetes with baseline HbA1c and fasting plasma glucose (FPG) measurements from Siriraj Hospital, Bangkok, Thailand. The participants were classified into six subphenotypes via the k-means clustering method on the basis of age, body mass index, FPG, HbA1c, high-density lipoprotein cholesterol and alanine aminotransferase levels. The incidences of type 2 diabetes, long-term vascular complications and mortality were compared among subphenotypes over a median follow-up of 8.8 years, employing Kaplan-Meier curves and Cox regression analysis adjusted for sex, statin use and hypertension status.

Results: Among the 4915 participants (mean age 60.1±10.1 years; 54.6% female), six clusters emerged: cluster 1, low risk (n=650; 13.2%); cluster 2, mild dysglycemia elderly (n=791; 16.1%); cluster 3, severe dysglycemia obese (n=1127; 22.9%); cluster 4, mild dysglycemia obese (n=963; 19.7%); cluster 5, severe dysmetabolic obese (n=337; 6.9%); and cluster 6, severe dysglycemia elderly (n=1042; 21.2%). Clusters were classified into diabetes risk subgroups: low risk (clusters 1 and 4) and high risk (clusters 3 and 5). Cluster 6 exhibited the highest risk, with significantly increased incidences of macrovascular complications (adjusted HR 2.22, 1.51-3.27) and type 2 diabetes (1.73, 1.42-2.12). In contrast, cluster 4 demonstrated the lowest risk, with significantly decreased incidences of new chronic kidney disease (0.65, 0.44-0.96), microvascular complications (0.62, 0.43-0.89) and mortality (0.25, 0.10-0.63).

Conclusions: Our pre-diabetes phenotyping approach effectively provides valuable insights into the risk of type 2 diabetes, vascular complications and mortality in individuals with pre-diabetes. Those with high-risk phenotypes should be prioritized for type 2 diabetes and cardiovascular interventions to mitigate risks.

Introduction: In a cohort of families of school-age children (8-12.99 years old) with type 1 diabetes, we examined the stability of parent and child diabetes-related distress (DRD) over 6 months and the associations between parent and child DRD and child glycated hemoglobin (HbA1c) over time.

Research design and methods: We recruited families from two large pediatric hospital systems in the USA and used validated measures of parent (Parent Problem Areas in Diabetes-Child, PPAID-C) and child (Problem Areas in Diabetes-Child, PAID-C) DRD and children's HbA1c. We collected data at baseline and 6 months. We calculated minimal clinically important differences in PPAID-C and PAID-C to examine DRD stability and used a linear regression model to examine associations between PPAID-C and PAID-C scores and child HbA1c over time.

Results: We recruited n=132 parent-child dyads (mean child age=10.23±1.5 years; 50% male, 86% non-Hispanic white). 60% of children and 55% of parents reported stable DRD levels, 20% of children and 14% of parents reported increasing DRD levels, and 20% of children and 31% of parents reported decreasing DRD levels from baseline to 6 months. In the regression model, child HbA1c and DRD scores at baseline significantly predicted child HbA1c 6 months later, β=0.013, t(157)=2.32, p=0.02.

Conclusions: Across 6 months, DRD remained stable or increased in 80% of school-aged children and 69% of parents. Only child HbA1c and DRD at baseline predicted higher child HbA1c 6 months later. Our results suggest it may be valuable to screen families of school-age children for DRD routinely and to develop treatments to help them reduce DRD.

Introduction: Metabolic abnormalities are present in 15-25% of adults with body mass index (BMI)<25 kg/m². While previous studies have shown that metabolically unhealthy individuals with lean body weight (MUL) and metabolically unhealthy individuals with obesity (MUO) exhibit increased visceral adiposity, direct comparisons between these groups have not been performed. Differences between the two groups may suggest different mechanisms of metabolic disease and may affect treatment strategies.

Research design and methods: We used the National Health and Nutrition Examination Survey data (2011-2018) that included dual energy X-ray absorptiometry. Metabolic dysfunction was defined as the presence of ≥2 components of the metabolic syndrome, excluding obesity. The differences in body fat distribution between unhealthy and healthy individuals were studied with an interaction term to evaluate whether the effect of BMI differs by the metabolic health status.

Results: We found that both MUL and MUO groups had increased android to gynoid fat ratio as compared with metabolically healthy groups with normal or lean weight (MHL) and metabolically healthy with obesity (MHO), respectively. Total fat and android fat were higher in MUL as compared with MHL individuals, in men as well as in women. Gynoid fat was higher in MUL men but not in women. However, MUO individuals had similar total fat but lower gynoid fat as compared with MHO individuals, in men as well as in women. Android fat was significantly higher in the male MUO group but not in the female MUO group.

Conclusions: The study shows increased android fat as the main abnormality in MUL individuals and decreased gynoid fat as the main abnormality in MUO individuals. The differences in android and gynoid fat patterns between MUL and MUO groups suggest different mechanisms of metabolic dysfunction in people who are lean versus those with obesity.

Introduction: The prevalence of people with type 2 diabetes (T2D) on basal insulin (BI) is rising to improve glucose control and minimize complications. However, limited evidence exists regarding the economic impact of second-generation BI analogs compared with first-generation BI in the United Kingdom.

Research design and methods: In this comparative retrospective, observational study, adults with T2D who initiated treatment with a first-generation BI (eg, glargine 100 U/mL, detemir) and switched to another first-generation or a second-generation BI (glargine 300 U/mL (Gla-300) or degludec) (index date) between 1 July 2014 and 31 March 2021 were analyzed using the Clinical Practice Research Datalink (CPRD) Aurum linked to Hospital Episode Statistics. Subjects were followed from the index date until the end of observation period, deregistration in CPRD or death. Propensity score weighting balanced baseline characteristics and healthcare resource utilization (HCRU) and costs were compared using standardized differences and zero-inflated regression models.

Results: A total of 13 975 people with T2D (mean (SD) age: 62.45 (13.59) years) treated with a first-generation BI who switched to another first-generation BI (n=5654), Gla-300 (n=4737) or degludec (n=3584) were included. Mean (SD) follow-up time was 4.98 (4.27), 1.96 (1.62) and 2.05 (1.92) years for the first-generation BI, Gla-300 and degludec groups, respectively. Overall, people who switched to Gla-300 had significantly lower HCRU. Fewer people in the Gla-300 group received hypoglycemia-related healthcare compared with those in the first-generation BI group (9.1% vs 16.4%, incident rate ratio (IRR)=0.41, p<0.001) and the degludec group (9.2% vs 11.7%, IRR=0.51, p<0.001). During follow-up, diabetes-related and diabetic ketoacidosis-related total direct costs were lower for the Gla-300 group compared with the first-generation BI group by 17% and the degludec group by 60%, respectively.

Conclusions: These findings suggest that Gla-300 may offer clinical and economic benefits by reducing hypoglycemia incidents and lowering healthcare costs compared with first-generation BI.

Obesity is a chronic, relapsing, and progressive disease requiring long-term, interprofessional treatment strategies to improve health outcomes. With over 40% of US adults and nearly 20% of children affected, obesity remains a significant public health concern. Despite the American Medical Association's recognition of obesity as a chronic disease, gaps persist in education, training, and access to effective treatments. These gaps contribute to inadequate obesity management and reinforce stigma and weight bias in healthcare settings.The Standards of Care in Overweight and Obesity-2025, developed by The Obesity Association^TM, a division of the American Diabetes Association^(R), (ADA's Obesity Association), will provide evidence-based recommendations for screening, diagnosis, and management of obesity and related complications. These guidelines will emphasize a complication-centric, risk-reduction approach rather than solely focusing on weight loss. The recommendations will be intended for healthcare professionals, including but not limited to primary care physicians, endocrinologists, obesity medicine physicians, dietitians, and behavioral health specialists, as well as policymakers and insurers.The guideline development will follow a rigorous methodology, incorporating evidence from systematic literature reviews, expert consensus, and public feedback. Recommendations will be graded based on the quality and certainity of supporting evidence, with the goal of annual updates to ensure alignment with the latest research. A stringent conflict-of-interest policy will be maintained to uphold guideline integrity.By promoting personalized and equitable obesity care, these guidelines will aim to bridge existing gaps in clinical practice, enhance treatment accessibility, and improve long-term health outcomes for individuals with overweight or obesity.

Weight bias involves negative attitudes and stereotypes towards individuals based on their weight, which can be explicit or implicit. This bias contributes to weight stigma, or the mistreatment and social devaluation of individuals based on weight. Weight stigma is linked to adverse physical and mental health outcomes, leading to reduced access and quality of healthcare for individuals with obesity. The American Diabetes Association (ADA)'s Obesity Association developed guidelines on recognizing and addressing weight bias and stigma. All healthcare professionals and staff should receive training on weight bias and stigma to improve care for individuals with obesity. Training should start early and continue throughout medical education and practice. Multicomponent training that combines education with hands-on learning is recommended to reduce explicit and implicit weight bias. Clinical practices, a potential source of stigmatization for people living with obesity, should be equipped with appropriate furniture and equipment to establish an inclusive environment. Privacy and sensitivity during anthropometric measurements are essential to minimize stigmatization. Healthcare professionals should use person-centered, non-judgmental language and engage individuals in shared decision-making to consider their health and goals. Asking permission to discuss weight and respecting individual preferences is crucial. The ADA's Obesity Association encourages adopting these guidelines to reduce weight bias and stigma, emphasizing education, inclusive clinical environments, and effective communication to improve obesity care.

Weight bias involves negative attitudes and stereotypes towards individuals based on their weight, which can be explicit or implicit. This bias contributes to weight stigma, or the mistreatment and social devaluation of individuals based on weight. Weight stigma is linked to adverse physical and mental health outcomes, leading to reduced access and quality of healthcare for individuals with obesity. The American Diabetes Association (ADA)'s Obesity Association developed guidelines on recognizing and addressing weight bias and stigma. All healthcare professionals and staff should receive training on weight bias and stigma to improve care for individuals with obesity. Training should start early and continue throughout medical education and practice. Multicomponent training that combines education with hands-on learning is recommended to reduce explicit and implicit weight bias. Clinical practices, a potential source of stigmatization for people living with obesity, should be equipped with appropriate furniture and equipment to establish an inclusive environment. Privacy and sensitivity during anthropometric measurements are essential to minimize stigmatization. Healthcare professionals should use person-centered, non-judgmental language and engage individuals in shared decision-making to consider their health and goals. Asking permission to discuss weight and respecting individual preferences is crucial. The ADA's Obesity Association encourages adopting these guidelines to reduce weight bias and stigma, emphasizing education, inclusive clinical environments, and effective communication to improve obesity care.