Diabetes, Obesity & Metabolism最新文献

Aim: Eloralintide (LY3841136) is a potent, long-acting selective amylin receptor agonist currently under development for the treatment of obesity with once-weekly subcutaneous dosing.

Materials and methods: This 12-week Phase 1, randomised, placebo-controlled, participant- and investigator-blinded, multiple ascending dose study evaluated the safety, tolerability, pharmacokinetics, and pharmacodynamic profiles of eloralintide upon once-weekly subcutaneous dosing without dose escalation in participants with obesity or overweight.

Results: From 30 March 2022 to 25 January 2024, at three centres in the United States, 100 participants with a mean age of 44 years, 29% female participants, and mean body mass index of 32.6 kg/m², were randomly assigned to receive either eloralintide or placebo in 5 multiple ascending dose cohorts. At Week 12, AUC_τ,ss and C_max were dose proportional with ratios of dose-normalised geometric means of 1.1 and 1.0, respectively. The most common treatment-emergent adverse events (TEAEs) with eloralintide included decreased appetite (19% of participants), headache (12%), fatigue (11%), and COVID-19 (11%). Gastrointestinal adverse events, including diarrhoea (10% of participants), nausea (8%), and vomiting (4%), were infrequent in those receiving eloralintide. Most TEAEs were mild in severity. No deaths and one serious adverse event (in the 6 mg eloralintide cohort) unrelated to eloralintide occurred. At Week 12 with eloralintide, the least squares mean percent reduction in body weight across the dose groups ranged from 2.6% to 11.3%.

Conclusion: Eloralintide once weekly was well tolerated with minimal gastrointestinal adverse events and resulted in clinically meaningful weight loss.

Glucocorticoids (GCs) are widely utilised for the treatment of inflammatory and autoimmune conditions but often precipitate significant hyperglycaemia. People with type 1 diabetes (PWT1D) may be particularly affected due to the challenges of adjusting insulin dosing, for which recommendations remain unclear. Our aim is to synthesise the evidence on the glycaemic effects of GCs in PWT1D across various formulations, doses and administration routes, and to outline management strategies. In August 2025, a systematic search of MEDLINE, Embase and CENTRAL was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Eligible studies included adult PWT1D exposed to GCs and reported glycaemic outcomes and/or management strategies. Twenty-two studies were included, comprising 368 PWT1D. GC exposure was consistently associated with marked hyperglycaemia and increased insulin requirements. Oral and intravenous GC regimens required substantial insulin dose escalation, in many cases up to 70% from baseline. During pregnancy, antenatal corticosteroids were best managed with structured subcutaneous or intravenous insulin protocols. Local injectable GCs caused delayed but prolonged excursions and required careful blood glucose (BG) monitoring. Emerging data suggest that automated-insulin delivery (AID) systems attenuate BG elevation but still require user intervention. Thus, GC therapy in PWT1D destabilises glycaemic management, though impact and timing vary by formulation, dose, administration route, patient-specific factors and clinical context. Proactive, individualised insulin adjustments aligned with GC pharmacokinetics and frequent BG monitoring or continuous glucose monitor use represent the most practical current strategy. Further research is required to develop evidence-based guidelines and to clarify the role of AID.

Aims: Numerous indices have been developed to quantify obesity and the distribution of body fat; however, none are sufficient alone, and combined usage is complicated by their potential intercorrelation. This study aims to quantify genetic and environmental influences on anthropometric measures, 12 derived obesity indices and the extent of their overlap.

Materials and methods: We used four anthropometric measurements (height, weight, waist and hip circumference) from the baseline of the multi-generational Lifelines cohort study to calculate 12 indices of obesity and body fat distribution. Variance components attributable to genetic (h²), shared (c²) and unique environmental (e²) factors along with pairwise phenotypic (r_P), genetic (r_G), shared (r_C), and unique environmental (r_E) correlations were estimated using ASReml software. Genetic and environmental contributions to the phenotypic correlations were also quantified.

Results: A total number of 152 298 adult individuals (females = 89 091, 58.4%) were included. Strong correlations were observed among most indices. (r_P, r_G, r_C, r_E > 0.8). A body shape index (ABSI) and hip index (HI) were weakly correlated with other indices, largely independent of body mass index (BMI) ( $r_P$ < 0.10), and had the highest $e^2$ , accounting for 64.2% and 75.7% of their variance. Height showed the highest heritability ( $h^2$ = 91.7%), whereas most other traits were moderately heritable ( $h^2$ = 45%-55%).

Conclusion: The high correlation between the majority of obesity indices implies their redundancy. In contrast, ABSI and HI were relatively independent of BMI and other indices and showed the greatest influence from individual-specific environmental factors, suggesting their potential utility as complementary tools in epidemiological research, clinical risk prediction, and monitoring of targeted interventions.

Aims: Impaired insulin sensitivity is an under-recognised risk in Type 1 diabetes but is challenging to measure with 'gold-standard' euglycaemic clamps. Adding stable-isotope glucose distinguishes hepatic and muscle insulin action (assessed by endogenous glucose production [EGP] and glucose infusion rate [GIR], respectively). We therefore searched for a blood-biomarker alternative.

Methods: Two-step clamps were conducted in 40 adults with Type 1 diabetes, participating in the INTIMET trial (INsulin resistance in Type 1 diabetes managed with METformin, ACTRN12619001440112). Participants were characterised with 33 baseline biomarkers.

Results: Exhaustive search analyses derived a formula predicting an 'unfavourable GIR' (dichotomous variable: below median of 60.4 μmol/kg fat-free mass [FFM]/min) using: total daily insulin dose (TDI), fasting triglycerides (TGs), insulin-like growth factor 1 and aspartate aminotransferase levels (area under the receiver operating characteristic curve (AUROC) 0.97, p < 0.0001, R² [Nagelkerke] = 0.83, 92.5% accuracy). An 'unfavourable EGP level' (above median of 6.2 μmol/kg FFM/min) during low-dose clamp was predicted by TDI, TGs, alkaline phosphatase and uric acid levels (AUROC 0.86, p = 0.001, R² (Nagelkerke) = 0.50, 80% accuracy). A free online tool (https://bit.ly/EGP-GIR-calculator) converts these variables into dichotomised EGP and GIR estimates.

Conclusions: We demonstrate that clinical and research biomarkers can be used to estimate tissue specific insulin sensitivity in adults with Type 1 diabetes.

Aim: To estimate the prevalence of new-onset diabetes in adults (≥ 18 years) following SARS-CoV-2 infection.

Materials and methods: This meta-analysis includes studies written in English that measured the number of adults (≥ 18 years) diagnosed with diabetes following SARS-CoV-2 infection. Studies underwent dual independent review; quality was assessed by using the New Castle Ottawa Scale. A random-effects meta-analysis was conducted to obtain the pooled estimate of new-onset diabetes. To understand the relationship between patient characteristics (age, sex) and study variable (duration of follow-up), a random effects meta-regression was used.

Results: A total of 33 articles were retained for analysis. The overall estimated prevalence of new-onset diabetes (combined T1DM and T2DM or undefined) was 8.33% (95% CI 7.47, 9.18%, z = 19.04, p < 0.001; Q = 6791.24, I², 99.68%). The overall estimated prevalence of new-onset T2DM in COVID-19 was 8.92% (95% CI 7.88%, 9.96%, z = 16.77, p < 0.001; Q = 27659.74; p < 0.001, I² = 99.96%). The overall estimated prevalence of new-onset T1DM was 0.86% (95% CI 0.0072%, 0.0099%, z = 12.59, p < 0.001; Q = 9456.28; p < 0.001, I² = 99.94%). At the study level, there was no significant relationship identified with age, sex, or follow-up duration.

Conclusions: This systematic review and meta-analysis revealed a notable increase in T2DM or combined (T1DM, T2DM, or undefined) conditions. As such, it may be important to understand the underlying factors contributing to increased prevalence.

Aims: Conflicting data have explored the association between lipoprotein(a) [Lp(a)] and atherosclerotic cardiovascular disease (ASCVD) among individuals with different glucose metabolism statuses. We aimed to prospectively evaluate this association and to assess whether it is modified by C-reactive protein (CRP).

Materials and methods: This population-based cohort study was derived from the UK Biobank database. Lp(a) and CRP were measured between 2006 and 2010. Cox proportional hazards models and restricted cubic spline curves were employed to assess the relationship between Lp(a) levels and time to ASCVD events.

Results: A total of 307 269 participants without prevalent ASCVD were included, comprising 253 746 individuals with normal glucose regulation (NGR), 38 020 with prediabetes, and 15 503 with diabetes. The mean age was 57 years (Q1-Q3: 50-63), and 55.3% were female. Over a median follow-up of 13.2 years, 29 521 ASCVD events occurred. Higher Lp(a) levels were associated with an increased risk of ASCVD across all glucose metabolism statuses. In fully adjusted models, the hazard ratio (95% confidence interval) for ASCVD comparing participants in the top 10% of Lp(a) with those in the bottom 33% was 1.28 (1.22-1.34) among those with NGR, 1.23 (1.12-1.35) among those with prediabetes, and 1.16 (1.02-1.31) among those with diabetes. No significant interactions were observed after stratification by CRP (<2/≥2 mg/L) across glucose metabolism groups (P for interaction >0.05).

Conclusions: Elevated Lp(a) levels were associated with a higher risk of ASCVD across different glucose metabolism statuses, particularly among individuals with NGR and prediabetes, independent of baseline CRP levels.

Metformin remains the most widely prescribed drug for diabetes management, yet recent studies have explored additional benefits, whose mechanisms are not completely understood. Recent research has highlighted the central role of the intestine in mediating metformin's therapeutic effects, involving interactions with the gut microbiota, intestinal epithelial cells, and the immune system. Among its various properties, metformin also exhibits immunomodulatory activity, drawing growing attention to its impact on neutrophil function. In particular, the excessive formation of neutrophil extracellular traps (NETs) and the process of NETosis have been linked to diabetes and its complications. Emerging evidence suggests that NETosis is influenced by alterations in gut microbiota composition and may itself contribute to metabolic dysregulation. This review explores intestinal NETosis as a novel and promising target of metformin, emphasizing its potential therapeutic relevance and the need for further investigation. A deeper understanding of these molecular pathways is essential to explore new therapeutic applications, guide the development of more personalized therapies that minimize adverse effects, and inspire next-generation drugs that improve metformin's efficacy.

Conventional lipid-lowering agents, such as statins, ezetimibe, fibrates, bile acid sequestrants, nicotinic acid, bempedoic acid, and omega-3, play a pivotal role in managing dyslipidemia. Despite their benefits, these agents are associated with increased levels of plasma proprotein convertase subtilisin kexin 9 (PCSK9), a serine protease linked to elevated cardiovascular risk. The literature lacks comprehensive data on how these commonly used drugs collectively impact circulating PCSK9 levels alongside their lipid-modifying effects. This review addresses this gap by systematically analysing the effects of these agents on PCSK9 and lipid profiles. Following the preferred reporting items for systematic review and meta-analysis protocols guidelines, this study conducts a systematic search across multiple databases including MEDLINE, Cochrane Central, EMBASE, Web of Science, SCOPUS, and ScienceDirect. We include published, peer-reviewed randomised controlled trials (RCTs) that involve adult patients undergoing monotherapy or combination therapy with the mentioned lipid-lowering drugs for at least 2 weeks, with recorded PCSK9 levels at baseline and post-treatment. Our analysis encompasses data extraction and bias assessment independently performed by two researchers, utilising standardised mean difference for continuous data and risk ratios for dichotomous data. We also conduct subgroup and sensitivity analyses to assess treatment intensity, drug types, comorbidities, and geographical variations in study outcomes. Publication bias is evaluated through funnel plot and Egger's test. The meta-analysis includes 14 RCTs with 21 treatment arms, involving 1313 participants. Results indicate a significant increase in plasma PCSK9 levels following treatment with conventional lipid-lowering drugs (weighted mean difference: 23.25 ng/mL; 95% confidence interval: 17.00, 29.50; p < 0.01; I² = 56%). Notably, subgroup analyses reveal significant differences based on treatment intensity, type of lipid-lowering agent, underlying diseases, and trial location. This systematic review confirms that conventional lipid-lowering drugs significantly elevate plasma PCSK9 levels. These findings highlight the necessity for clinicians to monitor PCSK9 concentrations in patients undergoing lipid-lowering treatment to optimise therapeutic strategies and mitigate associated cardiovascular risks. Our study contributes to a nuanced understanding of the biochemical effects of lipid-lowering therapies, potentially guiding future clinical practices and research in cardiovascular risk management.

Objective: This study aimed to develop and validate a health-economic model incorporating glycaemic variability to support the economic evaluation of novel interventions.

Methods: We developed a Monte Carlo-based Markov model, primarily using data from the United Kingdom Prospective Diabetes Study. An innovative glycaemic variability module was constructed, with its key parameter being the time in range (TIR). TIR was defined as the percentage of time a patient's glucose level remained within the target range of 3.9-10.0 mmol/L. The module was parameterized based on quantitative clinical evidence. The base model underwent face, internal, and external validation, with performance assessed using the R², root mean square percentage error (RMSPE) and symmetric mean absolute percentage error (SMAPE). To evaluate the structural uncertainty introduced by the new module, we conducted an exploratory scenario analysis in which the incremental improvement in TIR was modelled from 0% to 100%.

Results: The model showed good overall reliability. Specifically, face validity was confirmed, while internal and external validation both demonstrated R² values exceeding 0.95. For the internal and external validation, the RMSPE was 0.328 and 0.347, and the corresponding SMAPE values were 0.175 and 0.279, respectively. The exploratory scenario analysis supported the structural validity of the module. Over the first decade, the cumulative incidence of background retinopathy, microalbuminuria, peripheral vascular disease, and mortality decreased as TIR improved. However, the incidence gaps narrowed in the long run, which was consistent with clinical observations.

Conclusion: This study developed and validated a health-economic model incorporating glycaemic variability, which provides a potentially robust tool to inform economic evaluations of novel interventions.