Diabetologia最新文献

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.

Aims/hypothesis: The temporal suppression of insulin clearance after glucose ingestion is a key determinant of glucose tolerance for people without type 2 diabetes. Whether similar adaptations are observed after the ingestion of a mixed-macronutrient meal is unclear.

Methods: In a secondary analysis of data derived from two randomised, controlled trials, we studied the temporal responses of insulin clearance after the ingestion of a standardised breakfast meal consisting of cereal and milk in lean normoglycaemic individuals (n=12; Lean-NGT), normoglycaemic individuals with central obesity (n=11; Obese-NGT) and in people with type 2 diabetes (n=19). Pre-hepatic insulin secretion rates were determined by the deconvolution of C-peptide, and insulin clearance was calculated using a single-pool model. Insulin sensitivity was measured by an oral minimal model.

Results: There were divergent time course changes in insulin clearance between groups. In the Lean-NGT group, there was an immediate post-meal increase in insulin clearance compared with pre-meal values (p<0.05), whereas insulin clearance remained stable at baseline values in Obese-NGT or declined slightly in the type 2 diabetes group (p<0.05). The mean AUC for insulin clearance during the test was ~40% lower in the Obese-NGT (1.3 ± 0.4 l min^-1 m^-2) and type 2 diabetes (1.4 ± 0.7 l min^-1 m^-2) groups compared with Lean-NGT (1.9 ± 0.5 l min^-1 m^-2; p<0.01), with no difference between the Obese-NGT and type 2 diabetes groups. HOMA-IR and glucagon AUC emerged as predictors of insulin clearance AUC, independent of BMI, age or insulin sensitivity (adjusted R²=0.670). Individuals with increased glucagon AUC had a 40% reduction in insulin clearance AUC (~ -0.75 l min^-1 m^-2; p<0.001).

Conclusions/interpretation: The ingestion of a mixed-macronutrient meal augments differing temporal profiles in insulin clearance among individuals without type 2 diabetes, which is associated with HOMA-IR and the secretion of glucagon. Further research investigating the role of hepatic glucagon signalling in postprandial insulin kinetics is warranted.

Trial registration: ISRCTN17563146 and ISRCTN95281775.

Aims/hypothesis

Continuous glucose monitoring (CGM) improves glycaemic outcomes in the outpatient setting; however, there are limited data regarding CGM accuracy in hospital.

Methods

We conducted a prospective, observational study comparing CGM data from blinded Dexcom G6 Pro sensors with reference point of care and laboratory glucose measurements during participants’ hospitalisations. Key accuracy metrics included the proportion of CGM values within ±20% of reference glucose values >5.6 mmol/l or within ±1.1 mmol/l of reference glucose values ≤5.6 mmol/l (%20/20), the mean and median absolute relative difference between CGM and reference value (MARD and median ARD, respectively) and Clarke error grid analysis (CEGA). A retrospective calibration scheme was used to determine whether calibration improved sensor accuracy. Multivariable regression models and subgroup analyses were used to determine the impact of clinical characteristics on accuracy assessments.

Results

A total of 326 adults hospitalised on 19 medical or surgical non-intensive care hospital floors were enrolled, providing 6648 matched glucose pairs. The %20/20 was 59.5%, the MARD was 19.2% and the median ARD was 16.8%. CEGA showed that 98.2% of values were in zone A (clinically accurate) and zone B (benign). Subgroups with lower accuracy metrics included those with severe anaemia, renal dysfunction and oedema. Application of a once-daily morning calibration schedule improved accuracy (MARD 11.4%).

Conclusions/interpretation

The CGM accuracy when used in hospital may be lower than that reported in the outpatient setting, but this may be improved with appropriate patient selection and daily calibration. Further research is needed to understand the role of CGM in inpatient settings.

Graphical Abstract

Cardiovascular autonomic neuropathy (CAN) is an under-recognised yet highly prevalent microvascular complication of diabetes. CAN affects approximately 20% of people with diabetes, with recent studies highlighting the presence of CAN in prediabetes (impaired glucose tolerance and/or impaired fasting glucose), indicating early involvement of the autonomic nervous system. Understanding of the pathophysiology of CAN continues to evolve, with emerging evidence supporting a potential link between lipid metabolites, mitochondrial dysfunction and genetics. Recent advancements, such as streamlining CAN detection through wearable devices and monitoring of heart rate variability, present simplified and cost-effective approaches for early CAN detection. Further research on the optimal use of the extensive data provided by such devices is required. Despite the lack of specific pharmacological interventions targeting the underlying pathophysiology of autonomic neuropathy, several studies have suggested a favourable impact of newer glucose-lowering agents, such as sodium-glucose cotransporter 2 inhibitors and glucagon-like peptide-1 receptor agonists, where there is a wealth of clinical trial data on the prevention of cardiovascular events. This review delves into recent developments in the area of CAN, with emphasis on practical guidance to recognise and manage this underdiagnosed condition, which significantly increases the risk of cardiovascular events and mortality in diabetes.

Diabetes is the leading cause and a common comorbidity of advanced chronic kidney disease. Glycaemic management in this population is challenging and characterised by frequent excursions of hypoglycaemia and hyperglycaemia. Current glucose monitoring tools, such as HbA_1c, fructosamine and glycated albumin, have biases in this population and provide information only on mean glucose exposure. Revolutionary developments in glucose sensing and insulin delivery technology have occurred in the last decade. Newer factory-calibrated continuous glucose monitors provide real-time glucose data, with predictive alarms, allowing improved assessment of glucose excursions and preventive measures, particularly during and between dialysis sessions. Furthermore, integration of continuous glucose monitors and their predictive alerts with automated insulin delivery systems enables insulin administration to be decreased or stopped proactively, leading to improved glycaemic management and diminishing glycaemic fluctuations. While awaiting regulatory approval, emerging studies, expert real-world experience and clinical guidelines support the use of diabetes technology devices in people with diabetes and advanced chronic kidney disease.

Aims/hypothesis: Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal the heterogeneity of the at-risk population by identifying clinically meaningful clusters are lacking. We aimed to identify and characterise clusters of islet autoantibody-positive individuals who share similar characteristics and type 1 diabetes risk.

Methods: We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention study data (n=1123). The outcome of the analysis was the time to development of type 1 diabetes, and variables in the model included demographic characteristics, genetics, metabolic factors and islet autoantibodies. An independent dataset (the Diabetes Prevention Trial of Type 1 Diabetes Study) (n=706) was used for validation.

Results: The analysis revealed six clusters with varying type 1 diabetes risks, categorised into three groups based on the hierarchy of clusters. Group A comprised one cluster with high glucose levels (median for glucose mean AUC 9.48 mmol/l; IQR 9.16-10.02) and high risk (2-year diabetes-free survival probability 0.42; 95% CI 0.34, 0.51). Group B comprised one cluster with high IA-2A titres (median 287 DK units/ml; IQR 250-319) and elevated autoantibody titres (2-year diabetes-free survival probability 0.73; 95% CI 0.67, 0.80). Group C comprised four lower-risk clusters with lower autoantibody titres and glucose levels (with 2-year diabetes-free survival probability ranging from 0.84-0.99 in the four clusters). Within group C, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels and age. A decision rule for assigning individuals to clusters was developed. Use of the validation dataset confirmed that the clusters can identify individuals with similar characteristics.

Conclusions/interpretation: Demographic, metabolic, immunological and genetic markers may be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.

Aims/hypothesis: Diabetic kidney disease (DKD) is a severe diabetic complication that affects one third of individuals with type 1 diabetes. Although several genes and common variants have been shown to be associated with DKD, much of the predicted inheritance remains unexplained. Here, we performed next-generation sequencing to assess whether low-frequency variants, extending to a minor allele frequency (MAF) ≤10% (single or aggregated) contribute to the missing heritability in DKD.

Methods: We performed whole-exome sequencing (WES) of 498 individuals and whole-genome sequencing (WGS) of 599 individuals with type 1 diabetes. After quality control, next-generation sequencing data were available for a total of 1064 individuals, of whom 541 had developed either severe albuminuria or end-stage kidney disease, and 523 had retained normal albumin excretion despite a long duration of type 1 diabetes. Single-variant and gene-aggregate tests for protein-altering variants (PAV) and protein-truncating variants (PTV) were performed separately for WES and WGS data and combined in a meta-analysis. We also performed genome-wide aggregate analyses on genomic windows (sliding window), promoters and enhancers using the WGS dataset.

Results: In the single-variant meta-analysis, no variant reached genome-wide significance, but a suggestively associated common THAP7 rs369250 variant (p=1.50 × 10^-5, MAF=49%) was replicated in the FinnGen general population genome-wide association study (GWAS) data for chronic kidney disease and DKD phenotypes. The gene-aggregate meta-analysis provided suggestive evidence (p<4.0 × 10^-4) at four genes for DKD, of which NAT16 (MAF_PAV≤10%) and LTA (also known as TNFβ, MAF_PAV≤5%) are replicated in the FinnGen general population GWAS data. The LTA rs2229092 C allele was associated with significantly lower TNFR1, TNFR2 and TNFR3 serum levels in a subset of FinnDiane participants. Of the intergenic regions suggestively associated with DKD, the enhancer on chromosome 18q12.3 (p=3.94 × 10^-5, MAF_variants≤5%) showed interaction with the METTL4 gene; the lead variant was replicated, and predicted to alter binding of the MafB transcription factor.

Conclusions/interpretation: Our sequencing-based meta-analysis revealed multiple genes, variants and regulatory regions that were suggestively associated with DKD. However, as no variant or gene reached genome-wide significance, further studies are needed to validate the findings.

Aims/hypothesis: Regulators worldwide are reviewing safety data on glucagon-like peptide-1 receptor agonists (GLP-1RA), following reports by the Icelandic Medicines Agency in July 2023 of suicidal ideation and self-injury (SIS) in individuals taking liraglutide and semaglutide. We aimed to assess the risk of SIS in new users of GLP-1RA when compared with sodium-glucose cotransporter 2 inhibitors (SGLT-2i) users, prescribed to treat type 2 diabetes in individuals with obesity.

Methods: This is a cohort study combining several population-wide databases and covering a Spanish population of five million inhabitants, including all adults with obesity who initiated treatment with either GLP-1RA or SGLT-2i for type 2 diabetes from 2015 to 2021. To estimate the comparative effect of GLP-1RA on the risk of SIS, we employed a new user, active comparator design and we carried out multivariable Cox regression modelling with inverse probability of treatment weighting (IPTW) based on propensity scores. We performed several stratified and sensitivity analyses.

Results: We included 3040 patients initiating treatment with GLP-1RA and 11,627 with SGLT-2i. When compared with patients treated with SGLT-2i, those in the GLP-1RA group were younger (55 vs 60 years old, p<0.001), had more anxiety (49.4% vs 41.5%, p<0.001), sleep disorders (43.2% vs 34.1%, p<0.001) and depression (24.4% vs 19.0%, p<0.001), and were more obese (35.1% of individuals with BMI ≥40 vs 15.1%, p<0.001). After propensity score weighting, standardised mean differences between groups were <0.1 for all covariates, showing adequate balance between groups at baseline after adjustment. In the main per-protocol analyses we found no evidence that GLP-1RA increased the incidence of SIS (HR 1.04; 95% CI 0.35, 3.14). Intention-to-treat analyses resulted in an HR of 1.36 (95% CI 0.51, 3.61). In analyses excluding individuals with no BMI information and using imputation for BMI missing values, respective HRs were 0.89 (95% CI 0.26, 3.14) and 1.29 (95% CI 0.42, 3.92). Stratified analyses showed no differences between subgroups.

Conclusions/interpretation: Our findings do not support an increased risk of SIS when taking GLP-1RA in individuals with type 2 diabetes and obesity; however, the rarity of SIS events and the wide uncertainty of effect size (although null, effect may be compatible with a risk as high as threefold) calls for a cautious interpretation of our results. Further studies, including final evaluations from regulatory bodies, are called for to discard a causal link between GLP-1RA and suicidality.

Internationally, governments and scientists are bound by legal and treaty rights when working with Indigenous nations. These rights include the right of Indigenous people to control the conduct of science with Indigenous nations. Unfortunately, in some cases, individual scientists and scientific teams working with biological and genetic data collected from Indigenous people have not respected these international rights. Here, we argue that the scientific community should understand and acknowledge the historical harms experienced by Indigenous people under the veil of scientific progress (truth) and implement existing standards for ethical conduct of research and sovereign control of data collected within Indigenous communities (reconciliation). Specifically, we outline the rationale for why scientists, scientific journals and research integrity and institutional review boards/ethics committees should adopt, and be held accountable for upholding, current international standards of Indigenous data sovereignty and ethical use of Indigenous biological samples.

Aims/hypothesis: It is unclear whether kidney transplant candidates with diabetes have equitable transplantation opportunities or have divergent survival probabilities stratified by kidney replacement therapy. The aim of this study was to investigate these two issues using national transplant registry data in the UK.

Methods: A cohort study was undertaken of prospectively collected registry data of all wait-listed people with kidney failure receiving dialysis in the UK. All people listed for their first kidney-alone transplant between 2000 and 2019 were included. Stratification was done for cause of kidney failure. Primary outcome was all-cause mortality. Time-to-death from listing was analysed using adjusted non-proportional hazard Cox regression models, with transplantation handled as a time-dependent covariate.

Results: A total of 47,917 wait-listed people with kidney failure formed the total study cohort, of whom 6594 (13.8%) had diabetes classified as cause of kidney failure. People with kidney failure with diabetes comprised 27.6% of the cohort (n=3681/13,359) that did not proceed to transplantation vs only 8.4% (n=2913/34,558) of the cohort that received a transplant (p<0.001). Kidney transplant candidates with diabetes were more likely to be older, of male sex and of ethnic minority background compared with those without diabetes. In an adjusted analysis, compared with remaining on dialysis, any kidney transplant provided survival benefit for wait-listed kidney transplant candidates regardless of diabetes as cause of kidney failure (RR 0.26 [95% CI 0.25, 0.27], p<0.001).

Conclusions/interpretation: Kidney transplant candidates with diabetes have a lower chance of transplantation despite better survival after kidney transplantation vs remaining on dialysis. The reasons for this require further investigation to ensure equal transplantation opportunities.