ABSTRACTBackground and AimDiabetic neuropathy (DN) is a recognised risk factor for fragility fractures. However, the mechanisms linking DN, bone health, and falling risk remain unclear. We aimed to assess bone health and risk of falls, with their contributing factors, in young elderly patients with type 2 diabetes (T2D) and mild‐to‐moderate DN.MethodsWe enrolled 144 subjects with T2D, excluding those with severe DN (neuropathy disability score ‐NDS‐ ≥ 9) or fracture history. Clinical and biochemical data were collected, including surrogate markers of insulin resistance, such as the triglycerides/HDL (TG/HDL) ratio and triglycerides/glucose (TyG) index. Bone mineral density (BMD) and trabecular bone score (TBS) were evaluated using DXA scans. Falls were self‐recorded prospectively over 4 years using diaries.ResultsSubjects with DN (27%) had higher BMI (p = 0.036), fasting blood glucose (p = 0.04), serum triglycerides (p = 0.016), TG/HDL ratio (p = 0.012) and TyG index (p = 0.003) compared with those without DN. After adjustment for gender, age, BMI, HbA1c, TyG index and TG/HDL ratio, subjects with DN showed significantly lower BMD at the femoral neck (0.702 [0.638–0.850] g/cm2 vs. 0.789 [0.717–0.860] g/cm2, p = 0.015) and total femur (0.890 [0.820–1.055] g/cm2 vs. 0.983 [0.889–1.076] g/cm2, p = 0.027). No differences were observed in spine BMD or TBS. However, TBS was negatively correlated with the TG/HDL ratio (r = −0.215, p = 0.013) and visceral adipose tissue (r = −0.310, p < 0.001). After 4 years of follow‐up, subjects with painful neuropathy at baseline had a higher rate of falls (p = 0.011).ConclusionDN is associated with decreased BMD and increased risk of falls. Among factors associated with DN, insulin resistance was also associated with decreased bone quality.

Gestational diabetes mellitus (GDM) is a common metabolic complication during pregnancy that poses significant risks to both maternal and foetal health. Although its pathogenesis is multifactorial, emerging evidence highlights a potential role of iron metabolism and its dysregulation in the development of GDM. Iron is essential for foetal growth and maternal physiological adaptation during pregnancy. However, both iron deficiency and excess iron are associated with adverse pregnancy outcomes. In particular, excess iron accumulation has been associated with elevated oxidative damage and impaired glucose regulation, potentially contributing to the onset of GDM. Ferroptosis, a regulated cell death caused by iron-dependent lipid peroxidation, has recently emerged as a potential mechanistic link between iron overload and cellular dysfunction in GDM. This review highlights the dynamic regulation of iron metabolism during normal pregnancy and its disruption in GDM. In the context of GDM, ferroptosis is implicated in promoting oxidative stress and lipid peroxidation that disrupts metabolic regulation. Existing research suggests that maternal iron status could serve as a biomarker for early GDM risk assessment and a potential therapeutic target. However, the molecular pathways linking iron metabolism, ferroptosis, and metabolic abnormalities remain uncertain. Further investigations are needed to understand these mechanisms and assess the potential of ferroptosis inhibitors in GDM. Bridging these knowledge gaps could lead to improved strategies for the prediction, prevention, and management of GDM and its associated complications.

Metabolic Dysfunction-associated Steatotic Liver Disease (MASLD) is a prevalent liver disease worldwide, with its prevalence rising alongside the increase in metabolic syndrome (MetS), obesity and ageing. Machine learning (ML), as a powerful analysis tool to handle and analyse massive data/information, has been employed to enhance and refine the diagnosis, risk assessment, non-invasive screening, and treatment options against MASLD. This review thoroughly explores the application of ML in identifying MASLD-related genes and lipidomic biomarkers, non-invasive screening technologies such as ultrasound and imaging, and predicting the risk of disease progression to metabolic dysfunction-associated steatohepatitis (MASH) or more advanced stages, such as cirrhosis. Additionally, ML models have shown potential and definitive performance in accurately predicting and effectively managing the risk of comorbidities in relation to MASLD. By integrating clinical data, biochemical markers, imaging techniques, and an individual's biochemical metrics, ML offers a personalised medical approach that improves therapeutic strategies and holds promise for significant contributions to public health in the future.

No abstract is available for this article.

Glycaemic variability (GV) has emerged as an important prognostic indicator in critical illness, yet its predictive value among patients with sepsis remains unclear. This systematic review and meta-analysis aimed to evaluate the association between GV metrics and mortality outcomes in adult patients with sepsis. Cohort studies enrolling septic patients and reporting in-hospital, 28-day, or 30-day mortality in relation to GV were identified through PubMed, Embase, Cochrane Library, Scopus, CNKI, and Wanfang databases. Pooled odds ratios (ORs) were calculated using a random-effects model. Sensitivity analyses were performed to assess the robustness of the findings. Ten studies comprising 18,337 patients were included. For categorical analysis, high-GV patients had nearly twice the mortality risk (OR=1.99, 95% CI: 1.66-2.40, p<0.0001; I2=45%). For continuous analysis, all 4GV metrics showed significant associations with mortality: CoV (OR=1.050, I2=76.6%), SD (OR=1.0037, I2=83.5%), GLI (OR=1.0171, I2=0.0%), and MAGE (OR=1.0062, I2=0.0%). High GV was associated with prolonged ICU stay (0.95days, p=0.0018). Sensitivity analyses confirmed the result robustness. Elevated GV is independently linked to an increased risk of death among patients with sepsis. GLI and MAGE are the most reliable GV metrics for prognostic assessment, whereas CoV and SD are less consistent. Standardised GV measurement and prospective studies are warranted to evaluate whether interventions targeting GV can improve outcomes in this population.

ABSTRACTAimsThis multi‐cohort study evaluated whether the cardiometabolic index (CMI)—a composite of waist‐to‐height ratio and triglyceride‐to‐HDL cholesterol ratio—serves as an early predictor of cardio‐kidney risk and whether its predictive value varies across glycaemic states.MethodsWe analysed 327,902 adults in the UK Biobank to examine the association of CMI with baseline cardio‐kidney comorbidities, incident cardio‐kidney events (CKE)—defined as the composite occurrence of cardiovascular and chronic kidney outcomes, and mortality. Baseline comorbidities was assessed using logistic regression, and Cox models with stratified analyses and restricted cubic splines (RCS) evaluated prospective associations. Findings were externally validated in CHARLS and NHANES. Machine‐learning survival models further assessed predictive performance.ResultsHigher CMI was associated with baseline cardio‐kidney comorbidities (OR 2.25, 95% CI 2.10–2.42). Among 303,113 participants free of cardiovascular and/or kidney disease at baseline, CMI predicted incident CKE (HR 2.18, 95% CI 2.01–2.38; median follow‐up 14.3 years), all‐cause death (HR 1.10, 95% CI 1.06–1.14; 15.8 years), and cardio‐kidney death (HR 1.55, 95% CI 1.37–1.76; 15.8 years). The strength of associations was greatest in normoglycemia and progressively attenuated in prediabetes and diabetes. RCS analyses revealed nonlinear dose–response relationships, with steep increases in CKE and cardio‐kidney mortality below CMI thresholds (∼0.70 and ∼0.95) and more gradual rises thereafter. Results were directionally consistent in external cohorts, particularly for cardio‐kidney comorbidities and incident CKE. ML models demonstrated strong discrimination and consistently ranked CMI among the top predictors of incident CKE.ConclusionsCMI is a simple, robust predictor of cardio‐kidney risk especially in earlier metabolic states, with particularly strong prognostic value in normoglycaemic individuals where excess risk appears at lower CMI levels.

To explore the impact of BMI-metabolic phenotypes and their changes on chronic multimorbidity. Data were drawn from the China Health and Retirement Longitudinal Study (CHARLS), with participants aged ≥ 45. Analysing the metabolic heterogeneity of obesity through four BMI-metabolic phenotypes: metabolically healthy normal weight (MHNW), metabolically unhealthy overweight/obesity (MUOO), metabolically healthy overweight/obesity (MHOO), and metabolically unhealthy normal weight (MUNW). Transition of BMI-metabolic phenotype was assessed between 2011 and 2015. Chronic multimorbidity refers to the coexistence of ≥ 2 chronic diseases among 14 specified diseases. The association between changes in BMI-metabolic phenotypes and chronic multimorbidity was applied using Cox regression. Among 2528 individuals, the median age was 56.00years, and 1244 (49.21%) had chronic multimorbidity. After adjusting for all variables at baseline, participants in the MUOO phenotype exhibited a 1.66-fold increased risk of chronic multimorbidity compared with the MHNW phenotype (95% CI: 1.42-1.94, p<0.001), followed by the MUNW phenotype with a 1.25-fold increased risk (95% CI: 1.06-1.47, p=0.008). However, in the MHOO phenotype, no statistically significant association was found (p>0.05), which may reflect its heterogeneity and instability as a transient rather than benign metabolic state. In addition, obesity or unhealthy metabolism can also increase the risk of chronic multimorbidity. Overall, for individuals aged ≥ 45, especially those with the MUOO phenotype, managing body weight and improving metabolic health are crucial for preventing chronic multimorbidity.

ABSTRACTBackgroundType 2 diabetes (T2D) and chronic kidney disease (CKD) increase the risk of ischaemic and haemorrhagic strokes. However, the effect of sodium‐glucose cotransporter 2 inhibitors (SGLT2i) on reducing the risk of ischaemic and haemorrhagic strokes in patients with T2D and CKD remains unclear. Thus, this study was conducted to explore the role of SGLT2i in the prevention of ischaemic and haemorrhagic strokes.MethodsIn this retrospective cohort study, Cox regression analysis was employed to examine the hazard ratio (HR) between users and nonusers of SGLT2i on incident ischaemic and haemorrhagic strokes following 1:1 propensity score matching. The Kaplan–Meier method was used to determine the risk of study outcome over time between users and nonusers of SGLT2i. Finally, a sensitivity analysis of the HR was performed between users and nonusers of SGLT2i on incident ischaemic and haemorrhagic strokes after 1:2 sex and age matching.ResultsAfter 1:1 propensity score matching of patients by age, sex, T2D duration, and comorbidities, 107,819 users of SGLT2i and 107,819 nonusers were enrolled for analysis. SGLT2i therapy was associated with significantly reduced incidence of ischaemic and haemorrhagic strokes (HR 0.86, [95% CI, 0.81–0.90]; HR 0.80, [95% CI, 0.74–0.87]). Furthermore, the HR was even more significant in the sensitivity test for incident ischaemic and haemorrhagic strokes.ConclusionSGLT2i reduced the risk of incident ischaemic and haemorrhagic strokes among patients with T2D and CKD. The protective profile of the SGLT2i against incident ischaemic and haemorrhagic strokes makes it a clinical option for those with T2D with CKD.