Diabetes, Obesity & Metabolism最新文献

Objective: To assess the association between glycated haemoglobin (HbA1c) variability and risk of renal function decline in type 2 diabetes mellitus (T2DM).

Research design and methods: A comprehensive search was carried out in PubMed, Embase, Web of Science and the Cochrane Library (until 12 March 2024). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines were followed for this meta-analysis. HbA1c variability was presented as indices of the standard deviation (SD), coefficient of variation (CV), HbA1c variability score (HVS) and haemoglobin glycation index (HGI). This meta-analysis was performed using random-effect models.

Results: Eighteen studies met the objectives of this meta-analysis. The analyses showed positive associations between HbA1c variability and kidney function decline, with hazard ratio (HR) 1.26 (95% confidence interval [CI] 1.15-1.38) for high versus low SD groups, HR 1.47 (95% CI 1.30-1.65) for CV groups, HR 1.32 (95% CI 1.10-1.57) for HVS groups and HR 1.53 (95% CI 1.05-2.23) for HGI groups. In addition, each 1% increase in SD and CV was linked to kidney function decline, with HR 1.26 (95% CI 1.17-1.35), and 1.13 (95% CI 1.03-1.23), respectively. Also, each 1-SD increase in SD of HbA1c was associated with deterioration in renal function, with HR 1.17 (95% CI 1.07-1.29).

Conclusions: The four HbA1c variability indicators were all positively associated with renal function decline progression; therefore, HbA1c variability might play an important and promising role in guiding glycaemic control targets and predicting kidney function decline progression in T2DM.

Aim: To establish trust in real-world evidence (RWE) derived from CareLink Personal (CP), Medtronic's data management system for MiniMed system users, we show that this database and its analyses strictly adhere to the principles of RWE.

Methods: The methodology is applicable to all MiniMed iterations. We described every step from raw data to predefined outcomes. In addition, we showed CP's fitness-for-research by the below metrics (using last year's MiniMed 780G system data as a case study): representative population, relevant endpoints, appropriate granularity, high data completeness, high data representativity and consistency in results.

Results: The process from raw data to outcomes has been validated, and metrics/logics adhere to established definitions. Over 95% of users have a CP account; with 96% providing consent, this allows the use of >91% of the census population. There is no rationale for an over-representation of a specific phenotype among users not included. CP includes >50 endpoints, including 'International Consensus on Time in Range' based metrics. Data are recorded at 5-min intervals (maximum 288 per day), and on average there were 263 data points per person per day. Ninety-nine per cent of uploads were automated. For the last year, only 1 in 6 users had a data gap >1 day, and 1 in 50 had a gap >1 week. The time in range from in-silico studies was similar to that of real-world studies from different geographies and with ever growing populations.

Conclusion: RWE from CP adheres to the principles of RWE and can serve as robust evidence on the performance and safety of MiniMed systems.

Aim: Diabetic cognitive impairment (DCI), considered one of the most severe and commonly overlooked complications of diabetes, has shown inconsistent findings regarding the metabolic profiles in DCI patients. This systematic review and meta-analysis aimed to identify dysregulated metabolites as potential biomarkers for early DCI, providing valuable insights into the underlying pathophysiological mechanisms.

Materials and methods: A systematic search of four databases, namely PubMed, Embase, Web of Science and Cochrane, was conducted up to March 2024. Subsequently, a qualitative review of clinical studies was performed followed by a meta-analysis of metabolite markers. Finally, the sources of heterogeneity were explored through subgroup and sensitivity analyses.

Results: A total of 774 unique publications involving 4357 participants and the identification of multiple metabolites were retrieved. Of these, 13 clinical studies reported metabolite differences between the DCI and control groups. Meta-analysis was conducted for six brain metabolites and two metabolite ratios. The results revealed a significant increase in myo-inositol (MI) concentration and decreases in glutamate (Glu), Glx (glutamate and glutamine) and N-acetylaspartate/creatine (NAA/Cr) ratios in DCI, which have been identified as the most sensitive metabolic biomarkers for evaluating DCI progression. Notably, brain metabolic changes associated with cognitive impairment are more pronounced in type 2 diabetes mellitus than in type 1 diabetes mellitus, and the hippocampus emerged as the most sensitive brain region regarding metabolic changes associated with DCI.

Conclusions: Our results suggest that MI, Glu, and Glx concentrations and NAA/Cr ratios within the hippocampus may serve as metabolic biomarkers for patients with early-stage DCI.

Non-alcoholic fatty liver disease (NAFLD), a metabolic-associated fatty liver disease, has become the most common chronic liver disease worldwide. Recently, the discovery of cuproptosis, a newly identified mode of cell death, further highlighted the importance of copper in maintaining metabolic homeostasis. An increasing number of studies have confirmed that liver copper metabolism is closely related to the pathogenesis of NAFLD. However, the relationship between NAFLD and copper metabolism, especially cuproptosis, remains unclear. In this review, we aim to summarize the current understanding of copper metabolism and its dysregulation, particularly the role of copper metabolism dysregulation in the pathogenesis of NAFLD. More importantly, this review emphasizes potential gene-targeted therapeutic strategies, challenges and the future of cuproptosis-related genes in the treatment of NAFLD. This review aims to provide innovative therapeutic strategies for NAFLD.

Aim: To study the association between femoral neck (FN) bone mineral density (BMD) T-score and fracture risk in individuals with and without type 2 diabetes (T2D).

Materials and methods: We performed a single-centre retrospective cohort study using the Danish National Health Service. BMD of the FN was measured by dual-energy X-ray absorptiometry. Cox proportional hazards regression models were used to study the association between FN BMD T-score and fractures in individuals with and without T2D separately, adjusted for age, comorbidities and comedication. The results from this analysis were used to estimate the 10-year absolute fracture risk.

Results: In total, there were 35,129 women (2362 with T2D) and 7069 men (758 with T2D). The FN BMD T-score was significantly associated with risk of any, hip and major osteoporotic fracture in men and women with [adjusted hazard risk ratios (aHR) women, hip: 1.57; 95% confidence interval (CI) 1.24-2.00, incidence rate (IR) 8.7; aHR men, hip: 1.55; 95% CI 1.01-2.36, IR 4.6] and without T2D (aHR women, hip: 1.75; 95% CI 1.64-1.87, IR 7.0; aHR men, hip: 1.97, 95% CI 1.73-2.25, IR 6.3), and its ability to predict fracture risk was similar. Fracture IRs were not significantly different for individuals with or without T2D, nor was the estimated cumulative 10-year fracture risk.

Conclusions: The FN BMD T-score was significantly associated with hip, non-spine and major osteoporotic fracture risk in men and women with and without T2D. Fracture risk for a given T-score and age was equal in individuals with and without T2D, as was the ability of the FN BMD T-score to predict fracture risk.

Aim: Non-invasive diagnostics for metabolic dysfunction-associated fatty liver disease (MAFLD) remain challenging. We aimed to identify novel key genes as non-invasive biomarkers for MAFLD, elucidate causal relationships between biomarkers and MAFLD and determine the role of immune cells as potential mediators.

Materials and methods: Utilizing published transcriptome data of patients with biopsy-proven MAFLD, we applied linear models for microarray data, least absolute shrinkage and selector operation (LASSO) regressions and receiver operating characteristic (ROC) curve analyses to identify and validate biomarkers for MAFLD. Using the expression quantitative trait loci database and a cohort of 778 614 Europeans, we used Mendelian randomization to analyse the causal relationships between key biomarkers and MAFLD. Additionally, mediation analysis was performed to examine the involvement of 731 immunophenotypes in these relationships.

Results: We identified 31 differentially expressed genes, and LASSO regression showed three hub genes, IGFBP2, PEG10, and P4HA1, with area under the receiver operating characteristic (AUROC) curve of 0.807, 0.772 and 0.791, respectively, for identifying MAFLD. The model of these three genes had an AUROC of 0.959 and 0.800 in the development and validation data sets, respectively. This model was also validated using serum-based enzyme-linked immunosorbent assay data from MAFLD patients and control subjects (AUROC: 0.819, 95% confidence interval: 0.736-0.902). PEG10 was associated with an increased MAFLD risk (odds ratio = 1.106, p = 0.032) via inverse variance-weighted analysis, and about 30% of this risk was mediated by the percentage of CD11c + CD62L- monocytes.

Conclusions: The MAFLD panels have good diagnostic accuracy, and the causal link between PEG10 and MAFLD was mediated by the percentage of CD11c + CD62L- monocytes.

Aim: To examine changes in the use of glucose-lowering medicine (GLM) 12 months before and 12 months after long-term care facility (LTCF) entry among people with diabetes.

Materials and methods: A national retrospective cohort study was conducted using linked health and aged care data from the Registry of Senior Australians National Historical Cohort. Residents of LTCFs with diabetes aged 65 years or older from 2015 to 2019 were included. Prevalence of GLM use and the number of defined daily doses (DDDs) dispensed per 1000 resident-days were estimated quarterly (91-day) using Poisson regression models, or negative binomial regression when overdispersion was present.

Results: Among the 50 993 residents studied (median age 84 years), the prevalence of GLM use was 58.4% (95% confidence interval [CI] 58.0%-58.8%) in the 9-12 months pre-LTCF entry and 56.3% (95% CI 55.9%-56.8%) in the 9-12 months post-entry. The number of DDDs/1000 resident-days increased from 1015.2 (95% CI 1002.3-1028.1) to 1253.8 (95% CI 1168.4-1339.3) during the same period. GLM use in the 3 months pre-entry was 56.8% (95% CI 56.4%-57.2%) compared with 61.7% (95% CI 61.3%-62.1%) in the 3 months post-entry, with the increased use driven mainly by insulin. No marked changes in the number of GLMs dispensed or GLM type were observed at 9-12 months post-entry compared with 3 months pre-entry. Among 22 792 individuals dispensed a GLM in the 3 months prior to LTCF entry, 50.2% continued the same GLM at 9-12 months post-entry.

Conclusions: GLM use peaked in the first 3 months following LTCF entry, driven mainly by insulin, hence, residents may benefit from close monitoring of diabetes treatment during this period.

Aim: To describe the use of composite endpoints (CEs) in cardiovascular outcome trials (CVOTs) of type 2 diabetes and to evaluate the significance of the individual outcomes included within these CEs from the perspectives of both patients and clinicians. Secondary objectives were to estimate the gradient of treatment effects and events across outcomes.

Materials and methods: Eligible studies were randomized controlled trials assessing CV outcomes for patients with diabetes from 2008 and onwards. Trials were identified by searching the reports from the CVOT Summit of the Diabetes & CV Disease EASD (European Association for the Study of Diabetes) Study Group. The individual outcomes comprising the CE were compared for differences in importance for patients and clinicians, proportion of events, and effect size.

Results: We included 22 trials randomizing a mean of 8098 patients to an active intervention or a comparator group for an average of 33 months (standard deviation 16). All primary outcomes were CEs, and from a patient perspective there was no gradient of importance across outcomes in 22 of 22 (100%) CEs, while the gradient was small in 22 of 22 (100%) from a clinician perspective. The gradient of effect was moderate to large in 9 of 18 (50%) reporting studies, while assessment of events was available in 15 of 22 studies (68%), finding that three of 15 (20%) had a gradient of effect of more than 5% points between included outcomes. In 10 of 22 (45%) trial reports, the results were not clearly presented as based on a CE.

Conclusions: To avoid misinterpretation, clinicians and regulatory authorities should be careful when interpreting the results of trials, of which the main outcomes are CEs.

Aim: To conduct post hoc analyses of the VERTIS CV (NCT01986881) trial to explore the effects of ertugliflozin on serum uric acid (UA) and gout-related outcomes.

Materials and methods: Participants with type 2 diabetes and atherosclerotic cardiovascular disease were randomised (1:1:1) to placebo, ertugliflozin 5 mg or ertugliflozin 15 mg. Mean UA over time (260 weeks) was evaluated for pooled ertugliflozin versus placebo overall, and by baseline quintile of UA (≤4.3 mg/dL [≤255.8 µmol/L], >4.3-5.1 mg/dL [>255.8-303.4 µmol/L], >5.1-5.8 mg/dL [>303.4-345.0 µmol/L], >5.8-6.9 mg/dL [>345.0-410.4 µmol/L] and >6.9 mg/dL [>410.4 µmol/L]), glycated haemoglobin level, albuminuria status, estimated glomerular filtration rate and KDIGO (Kidney Disease: Improving Global Outcomes in Chronic Kidney Disease) risk category. The effect of ertugliflozin on a composite of gout onset or initiation of anti-gout medication was assessed.

Results: The mean UA levels at baseline were 5.67 and 5.62 mg/dL in the placebo and ertugliflozin groups, respectively. Ertugliflozin reduced UA over Weeks 6-260 compared with placebo, with least squares mean (LSM) changes (95% confidence interval [CI]) from baseline at Week 260 of 0.07 mg/dL (-0.02, 0.15) and -0.19 mg/dL (-0.25, -0.13) in the placebo and pooled ertugliflozin groups, respectively. At Week 260, placebo-adjusted LSM change (95% CI) from baseline in UA was -0.26 mg/dL (-0.36, -0.16) with ertugliflozin. Ertugliflozin was associated with reductions in UA across baseline UA quintiles compared with placebo. The incidence of the composite of gout-related outcomes was 84/2539 (3.3%) for placebo and 133/5091 (2.6%) for ertugliflozin (hazard ratio for the composite 0.76 [95% CI 0.580, 1.002]).

Conclusions: Ertugliflozin was generally associated with lowering UA overall and across subgroups compared with placebo, and numerically reduced rates of gout-related outcome events.