Diabetologia最新文献

Aims/hypothesis: The aim of this study was to investigate how diabetes mellitus affects longer term outcomes in individuals presenting to hospital with non-ST segment elevation myocardial infarction (NSTEMI).

Methods: We analysed data from 456,376 adults hospitalised between January 2005 and March 2019 with NSTEMI from the UK Myocardial Ischaemia National Audit Project (MINAP) registry, linked with Office for National Statistics death reporting. We compared outcomes and quality of care by diabetes status.

Results: Individuals with diabetes were older (median age 74 vs 73 years), were more often of Asian ethnicity (13% vs 4%) and underwent revascularisation (percutaneous coronary intervention or coronary artery bypass graft surgery) (38% vs 40%) less frequently than those without diabetes. The mortality risk for those with diabetes compared with those without was significantly higher at 30 days (HR 1.19, 95% CI 1.15, 1.23), 1 year (HR 1.28, 95% CI 1.26, 1.31), 5 years (HR 1.36, 95% CI 1.34, 1.38) and 10 years (HR 1.39, 95% CI 1.36, 1.42). In individuals with diabetes, higher quality inpatient care, assessed by opportunity-based quality indicator (OBQI) score category ('poor', 'fair', 'good' or 'excellent'), was associated with lower mortality rates compared with poor care (good: HR 0.74, 95% CI 0.73, 0.76; excellent: HR 0.69, 95% CI 0.68, 0.71). In addition, compared with poor care, excellent care in the diabetes group was associated with the lowest mortality rates in the diet-treated and insulin-treated subgroups (diet-treated: HR 0.64, 95% CI 0.61, 0.68; insulin-treated: HR 0.69, CI 0.66, 0.72).

Conclusion/interpretation: Individuals with diabetes experience disparities during inpatient care following NSTEMI. They have a higher risk of long-term mortality than those without diabetes, and higher quality inpatient care may lead to better long-term survival.

Aims/hypothesis: The aim of this study was to investigate insulin secretion, insulin sensitivity, disposition index and insulin clearance by glucose tolerance status in individuals with cystic fibrosis (CF) and exocrine pancreatic insufficiency.

Methods: In a cross-sectional study, we conducted an extended (ten samples) OGTT in individuals with pancreatic-insufficient CF (PI-CF). Participants were divided into normal glucose tolerance (NGT), early glucose intolerance (EGI), impaired glucose tolerance (IGT) and CF-related diabetes (CFRD) groups. We used three different oral minimal models to assess insulin secretion, insulin sensitivity and insulin clearance during the OGTT. We evaluated insulin secretion using total secretion (Φ total), first-phase secretion (Φ dynamic) and second-phase secretion (Φ static) from the model, and we estimated the disposition index by multiplying Φ total and insulin sensitivity.

Results: Among 61 participants (NGT 21%, EGI 33%, IGT 16%, CFRD 30%), insulin secretion indices (Φ total, dynamic and static) were significantly lower in the CFRD group compared with the other groups. Insulin sensitivity declined with worsening in glucose tolerance (p value for trend <0.001) and the disposition index declined between NGT and EGI and between IGT and CFRD. Those with CFRD had elevated insulin clearance compared with NGT (p=0.019) and low insulin secretion (Φ total) was also associated with high insulin clearance (p<0.001).

Conclusions/interpretation: In individuals with PI-CF, disposition index declined with incremental impairment in glucose tolerance due to a reduction in both insulin secretion and insulin sensitivity. Moreover in CF, reduced insulin secretion was associated with higher insulin clearance.

Aims/hypothesis: The aim of this work was to investigate the association between macronutrient intakes and continuous glucose monitoring (CGM) metrics in individuals with type 1 diabetes.

Methods: In 470 individuals with type 1 diabetes of the GUTDM1 cohort (65% female, median age 40 [IQR 28-53] years, median diabetes duration 15 [IQR 6-29] years), we used logistic regression to establish associations between macronutrient intakes and the CGM metrics time in range (TIR, time spent between 3.9-10.0 mmol/l blood glucose, optimally set at ≥70%) and time below range (TBR, <3.9 mmol/l blood glucose, optimally set at <4%). ORs were expressed per 1 SD intake of nutrient and were adjusted for other macronutrient intakes, age, sex, socioeconomic status, BMI, duration of type 1 diabetes, pump use, insulin dose and alcohol intake.

Results: The median (IQR) TIR was 67 (51-80)% and TBR was 2 (1-4)%; the mean ± SD energy intake was 6879±2001 kJ, fat intake 75±31 g, carbohydrate intake 162±63 g, fibre intake 20±9 g and protein intake 70±24 g. A higher fibre intake and a lower carbohydrate intake were associated with higher odds of having a TIR≥70% (OR [95% CI] 1.64 [1.22, 2.24] and 0.67 [0.51, 0.87], respectively), whereas solely a higher carbohydrate intake was associated with TBR<4% (OR 1.34 [95% CI 1.02, 1.78]).

Conclusions/interpretation: A higher fibre intake is independently associated with a higher TIR. A higher carbohydrate intake is associated with less time spent in hypoglycaemia, a lower TIR and a higher time above range. These findings warrant confirmatory (interventional) investigations and may impact current nutritional guidelines for type 1 diabetes.

This review outlines some of the extraordinary recent advances in diabetes technology, which are transforming the management of type 1 diabetes before, during and after pregnancy. It highlights recent improvements associated with use of continuous glucose monitoring (CGM) but acknowledges that neither CGM nor insulin pump therapy are adequate for achieving the pregnancy glucose targets. Furthermore, even hybrid closed-loop (HCL) systems that are clinically effective outside of pregnancy may not confer additional benefits throughout pregnancy. To date, there is only one HCL system, the CamAPS FX, with a strong evidence base for use during pregnancy, suggesting that the pregnancy benefits are HCL system specific. This is in stark contrast to HCL system use outside of pregnancy, where benefits are HCL category specific. The CamAPS FX HCL system has a rapidly adaptive algorithm and lower glucose targets with benefits across all maternal glucose categories, meaning that it is applicable for all women with type 1 diabetes, before and during pregnancy. For women of reproductive years living with type 2 diabetes, the relative merits of using non-insulin pharmacotherapies vs diabetes technology (dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter 2 inhibitors) are unknown. Despite the urgent unmet need and potential benefits, studies of pharmacotherapy and technology use are extremely limited in pregnant women with type 2 diabetes.

Aims/hypothesis: Metabolic risk factors and plasma biomarkers for diabetes have previously been shown to change prior to a clinical diabetes diagnosis. However, these markers only cover a small subset of molecular biomarkers linked to the disease. In this study, we aimed to profile a more comprehensive set of molecular biomarkers and explore their temporal association with incident diabetes.

Methods: We performed a targeted analysis of 54 proteins and 171 metabolites and lipoprotein particles measured in three sequential samples spanning up to 11 years of follow-up in 324 individuals with incident diabetes and 359 individuals without diabetes in the Danish Blood Donor Study (DBDS) matched for sex and birth year distribution. We used linear mixed-effects models to identify temporal changes before a diabetes diagnosis, either for any incident diabetes diagnosis or for type 1 and type 2 diabetes mellitus diagnoses specifically. We further performed linear and non-linear feature selection, adding 28 polygenic risk scores to the biomarker pool. We tested the time-to-event prediction gain of the biomarkers with the highest variable importance, compared with selected clinical covariates and plasma glucose.

Results: We identified two proteins and 16 metabolites and lipoprotein particles whose levels changed temporally before diabetes diagnosis and for which the estimated marginal means were significant after FDR adjustment. Sixteen of these have not previously been described. Additionally, 75 biomarkers were consistently higher or lower in the years before a diabetes diagnosis. We identified a single temporal biomarker for type 1 diabetes, IL-17A/F, a cytokine that is associated with multiple other autoimmune diseases. Inclusion of 12 biomarkers improved the 10-year prediction of a diabetes diagnosis (i.e. the area under the receiver operating curve increased from 0.79 to 0.84), compared with clinical information and plasma glucose alone.

Conclusions/interpretation: Systemic molecular changes manifest in plasma several years before a diabetes diagnosis. A particular subset of biomarkers shows distinct, time-dependent patterns, offering potential as predictive markers for diabetes onset. Notably, these biomarkers show shared and distinct patterns between type 1 diabetes and type 2 diabetes. After independent replication, our findings may be used to develop new clinical prediction models.

Children with type 1 diabetes and their caregivers face numerous challenges navigating the unpredictability of this complex disease. Although the burden of managing diabetes remains significant, new technology has eased some of the load and allowed children with type 1 diabetes to achieve tighter glycaemic management without fear of excess hypoglycaemia. Continuous glucose monitor use alone improves outcomes and is considered standard of care for paediatric type 1 diabetes management. Similarly, automated insulin delivery (AID) systems have proven to be safe and effective for children as young as 2 years of age. AID use improves not only blood glucose levels but also quality of life for children with type 1 diabetes and their caregivers and should be strongly considered for all youth with type 1 diabetes if available and affordable. Here, we review key data on the use of diabetes technology in the paediatric population and discuss management issues unique to children and adolescents.

Over the past two decades there has been a substantial rise in the adoption of diabetes therapeutic technology among children, adolescents and younger adults with type 1 diabetes, and its use is now also advocated for older individuals. Older people with diabetes are more prone to experience hypoglycaemia because of numerous predisposing factors and are at higher risk of hypoglycaemic events requiring third-party assistance as well as other adverse sequelae. Hypoglycaemia may also have long-term consequences, including cognitive impairment, frailty and disability. Diabetes in older people is often characterised by marked glucose variability related to age-associated changes such as variable appetite and levels of physical activity, comorbidities and polypharmacotherapy. Preventing hypoglycaemia and mitigating glucose excursions may have considerable positive impacts on physical and cognitive function and general well-being and may even prevent or improve frailty. Technology for older people includes continuous glucose monitoring systems, insulin pumps, automated insulin delivery systems and smart insulin pens. Clinical trials and real-world studies have shown that older people with diabetes benefit from technology in terms of glucose management, reductions in hypoglycaemic events, emergency department attendance and hospital admissions, and improvement in quality of life. However, ageing may bring physical impairments and other challenges that hinder the use of technology. Healthcare professionals should identify older adults with diabetes who may benefit from therapeutic technology and then adopt an individualised approach to education and follow-up for individuals and their caregivers. Future research should explore the impact of diabetes technology on outcomes relevant to older people with diabetes.