Patients with diabetic foot (DF) have a high risk of cardiovascular (CV) death, yet dedicated risk-prediction tools for this population are lacking. We developed and temporally validated an interpretable machine learning (ML) model for predicting CV death in patients with DF. This single-center retrospective cohort study included 2,835 patients admitted between February 2017 and May 2025. The development cohort comprised 2,325 patients, including 748 CV deaths, and was divided into training, internal validation, and held-out test sets; an independent temporal validation cohort included 510 patients, including 220 CV deaths. Nine supervised ML algorithms were compared using the area under the receiver operating characteristic curve (AUC). Extreme gradient boosting (XGBoost) showed the best overall performance. The optimal model, incorporating demographic and diabetes-related characteristics, routine laboratory parameters, and DF-specific features, achieved AUCs of 0.829 (95% confidence interval [CI]: 0.752-0.905) in internal validation, 0.844 (95% CI: 0.806-0.881) in the held-out test set, and 0.828 (95% CI: 0.789-0.868) in temporal validation. The model demonstrated good calibration and favorable net benefit on decision curve analysis. SHapley Additive exPlanations (SHAP) identified age, serum creatinine, glycated hemoglobin, triglycerides, and body mass index as the most influential predictors of increased model-predicted risk. This interpretable XGBoost model may support early identification and individualized risk stratification of patients with DF at high risk of CV death; however, prospective multicenter validation is required before clinical implementation.

Insufficient sleep (<7h/night) affects approximately one-third of the adult population and is associated with cardiometabolic alterations. A common strategy to compensate for sleep debt accumulated during the workweek is extending sleep duration during the weekend, although its effects remain controversial. The aim of this study was to preliminarily evaluate the impact of 5 days of sleep restriction followed by 2 days of recovery sleep on sleep parameters and cardiometabolic biomarkers. A randomized crossover clinical trial was conducted in 10 healthy adults under two conditions: 1) adequate habitual sleep for 7 days (HS), and 2) 5 days of sleep restriction (-1h 30min/night) followed by 2 days of ad libitum recovery sleep (RS). Sleep duration was reduced by 102min during weekdays in the RS condition (P<.001), with no differences during the weekend. After the recovery period, sleep quality worsened in the RS condition, with increased wake after sleep onset and a higher number of awakenings. In addition, triglyceride levels and blood pressure were higher in RS compared with HS. These preliminary findings suggest that weekend recovery sleep does not fully compensate for the effects of partial sleep restriction.

As the cheapest feed ingredient, carbohydrates exert a protein-sparing effect. However, excessive carbohydrate intake can induce hepatic metabolic disorders and lead to metabolic diseases in fish. The GIP/GIPR axis plays a vital role in regulating lipid metabolism and glucose uptake. To evaluate the functions of GIP and GIPR in largemouth bass, we cloned their cDNA sequences. We analysed the tissue-specific expression of both genes and assessed the role of GIP in regulating glucose and lipid metabolism using hepatocyte studies and GIP interference assays. The largemouth bass gip gene (318 bp) encodes a protein of 105 amino acids, whereas the gipr gene (1614 bp) encodes a protein of 537 amino acids. Tissue distribution analysis revealed high expression of gip mRNA in the stomach. Additionally, gipr mRNA was highly expressed in the brain, foregut, midgut and hindgut. Both high-carbohydrate and high-fat intake in largemouth bass resulted in higher gip mRNA in the stomach and lower gipr mRNA expression in the foregut. Primary hepatocyte experiments showed that GIP treatment increased the expression of g6pase, acc1 and fas and decreased the expression of gk, pfk, hsl and lpl. Intraperitoneal injection of GIP small interfering RNA (siRNA) demonstrated that inhibiting GIP expression significantly reduced serum glucose and triglyceride levels. It also decreased the hepatic expression of g6pase, fbp1, pygl, acc1, fas and lpl, while increasing the expression of gys2 and hsl. These results suggest that GIP and GIPR regulate glucose and lipid metabolism in largemouth bass.

Elevated triglyceride-glucose (TyG) index has been linked to cardiovascular risk and metabolic disorders, but its relationship with all-cause mortality in postmenopausal women remains unclear. We aimed to investigate the association between baseline TyG index and all-cause mortality among postmenopausal women, and to determine whether a nonlinear (threshold) relationship exists. We conducted a prospective cohort study using data from the U.S. National Health and Nutrition Examination Survey 2001-2014. Participants were 3925 postmenopausal women (weighted mean age 63.1 years). Fasting subsample survey weights were applied, and the complex multistage sampling design (stratification and clustering) was fully accounted for in all analyses. TyG index was calculated from fasting triglyceride and glucose measurements. The main outcome was all-cause mortality. We used restricted cubic splines and design-based two-piecewise Cox proportional hazards regression to model nonlinear associations and identify a potential threshold. Over a median follow-up of approximately 9.9 years, 1088 deaths occurred (27.7% of participants). The association was nonlinear: the two-piecewise Cox analysis identified a significant threshold at TyG = 9.06 (95% confidence interval [CI] 8.92-9.26). Below the threshold, TyG index was not significantly associated with mortality (hazard ratio 0.88, 95% CI 0.69-1.11). Above this threshold, each 1-unit increase in TyG was associated with a 44% higher risk of all-cause mortality (hazard ratio 1.44, 95% CI 1.05-1.96, P = .020) after full adjustment for covariates. In this cohort of U.S. postmenopausal women, an elevated TyG index above 9.06 was associated with increased all-cause mortality. The relationship was nonlinear, with a risk threshold. This threshold should be considered exploratory and requires external validation before clinical use.

Chronic obstructive pulmonary disease (COPD) is a leading cause of global mortality and has been linked to systemic inflammation and insulin resistance. The C-reactive protein-triglyceride-glucose index (CTI), a composite biomarker integrating inflammatory and metabolic components, may reflect these intertwined pathways. However, its association with COPD in the general population remains unclear. In this cross-sectional study, we analyzed data from 3442 adults aged ≥20 years participating in the National Health and Nutrition Examination Survey 2015 to 2018. Weighted multivariable logistic regression models were used to assess the association between CTI and self-reported COPD. Dose-response relationships were evaluated using restricted cubic splines. Subgroup and interaction analyses were performed across demographic and socioeconomic factors. Receiver operating characteristic (ROC) curves were constructed to compare the discriminatory ability of CTI with individual biomarkers. Mean CTI levels were higher among participants with COPD compared to those without COPD (9.29 vs 8.95; P < .001). After adjustment for potential confounders, participants in the highest CTI quartile had a modestly increased likelihood of COPD compared with those in the lowest quartile (odds ratio = 1.04, 95% confidence interval: 1.02-1.07). A linear dose-response association was observed (P for nonlinearity = .319). A statistically significant interaction by sex was identified, with a stronger association observed in males. In ROC curve analysis, CTI demonstrated slightly higher discriminatory ability (area under the ROC curve = 0.630) than individual biomarkers, although overall predictive performance remained limited. In this nationally representative cross-sectional analysis, higher CTI levels were independently associated with COPD prevalence, with a modest effect size. While CTI showed slightly improved discriminatory performance compared with single biomarkers, its overall predictive capacity was limited. Prospective studies are warranted to clarify its potential role in COPD risk assessment.

Post-transplant diabetes mellitus (PTDM) is the most frequent metabolic complication after liver transplantation. The role of triglyceride-rich lipoprotein cholesterol (TRL-c) in PTDM risk remains poorly defined. We characterized TRL-c in liver transplant recipients (LTRs) from the TransplantLines cohort (NCT03272841) compared to the general population (PREVEND cohort; NCT03073018) and evaluated its association with incident PTDM. We compared 367 adult LTRs and 4,837 PREVEND participants with TRL-c measurements derived from total cholesterol, high-density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c), all measured by nuclear magnetic resonance spectroscopy. Incident PTDM was assessed using Cox models and adjusted for clinically relevant covariates. Compared with PREVEND participants, LTRs had higher triglycerides, TRL-c, and HDL-c but lower total and LDL-c, irrespective of baseline diabetes status. Among LTRs, TRL-c was highest in those with pre-existing diabetes and lowest in those who remained non-diabetic. During a median follow-up of 7.1 years (IQR 6.2-7.8), 31 (12.6%) LTRs developed PTDM. Incidence was highest in the top TRL-c tertile (p = 0.016). Each 1-SD increase in TRL-c was associated with a 64% higher PTDM risk, independent of other risk factors. TRL-c may help risk stratification after transplant, given it likely captures a dyslipidemic phenotype not represented by conventional lipid measurements.

The increasing prevalence of cardiovascular diseases (CVD) and type 2 diabetes, particularly among younger populations, highlights the urgent need for early, cost-effective risk assessment tools. The Triglyceride-Glucose (TyG) index, derived from routine triglyceride and glucose levels, has emerged as a reliable predictor of insulin resistance and is gaining recognition for its role in forecasting cardiovascular risk, including Major Adverse Cardiac and Cerebrovascular Events (MACCE). Unlike more complex and costly insulin resistance tests, the TyG index offers a simpler approach to early detection, particularly in individuals without obvious clinical signs of metabolic disease. This narrative review synthesizes evidence on the association between the TyG index and various cardiovascular outcomes, focusing on early-onset conditions such as atherosclerotic cardiovascular disease (ASCVD), myocardial infarction (MI), coronary artery disease (CAD), stroke, heart failure, and all-cause mortality. Notably, the TyG index has shown predictive value across diverse patient populations, including those free from diabetes or dyslipidemia.

The menopause transition is associated with a notable increase in the risk of cardiovascular disease (CVD), making this period a critical window for early prevention and intervention. This heightened CVD risk is driven in part by adverse changes in lipid metabolism. As estrogen levels decline, women experience significant increases in low-density lipoprotein cholesterol (LDL-C) and triglycerides, along with alterations in high-density lipoprotein cholesterol (HDL-C) composition and function that reduce its cardioprotective properties. These lipid changes occur independent of chronological aging, with the most pronounced increases occurring within one year of the final menstrual period. Dyslipidemia is an important modifiable risk factor for CVD during this life stage. Evidence-based lifestyle interventions, such as Mediterranean and Dietary Approaches to Stop Hypertension (DASH) dietary patterns along with regular aerobic or resistance training, can help mitigate these adverse lipid changes. Additional behavioral modifications, including smoking cessation and limiting alcohol intake, further reduce cardiovascular risk. When lifestyle interventions are insufficient, statins remain first-line therapy for dyslipidemia management. While menopausal hormone therapy favorably affects lipid profiles, current guidelines do not recommend its use for dyslipidemia management or CVD prevention due to inconsistent benefits and potential risks. This review evaluates current evidence on dyslipidemia across the menopause transition, distinguishing menopause-related lipid changes from those of chronological aging, and examines the impact of modifiable lifestyle factors and therapeutic interventions on lipid trajectories during this reproductive stage.

Persistent organic pollutants such as organochlorine pesticides (OCPs) have been implicated in obesity and metabolic dysfunction. As they bioaccumulate in adipose tissue, OCPs may act as obesogens, but in vivo evidence for aldrin, endosulfan and lindane is limited. This study investigated whether subacute exposure to aldrin, endosulfan and lindane elicits obesogenic effects in male rats by examining weight gain, serum biochemistry and metabolic and inflammatory gene expression in white adipose tissue (WAT) and brown adipose tissue (BAT). Thirty-two adult male Sprague-Dawley rats were randomly assigned to receive corn oil (control) or oral aldrin, endosulfan or lindane (1 mg/kg every other day for 28 days). Body weight was recorded weekly. Serum liver enzymes and lipid profile were determined. In WAT and BAT, we quantified mRNA expression of thermogenic and adipogenic markers (Pparg, Ucp1, Ucp3, Fndc5), and the pro- and anti-inflammatory cytokines (Il-1b, Tnf-α, Il-10, Il-4). Lindane caused significant weight gain from the first week (p < 0.0001), whereas aldrin and endosulfan increased body weight only at week four (p < 0.05). Endosulfan-treated rats had reduced total and high-density lipoprotein cholesterol (p < 0.05) without changes in low-density lipoprotein cholesterol, very-low-density lipoprotein cholesterol, or triglycerides. In WAT, Pparg, Ucp1, and Fndc5 transcripts were unchanged, while Ucp3 tended to decrease in the endosulfan group (p = 0.056). In BAT, endosulfan increased Ucp1 and decreased Fndc5 levels (p < 0.05), with no changes in Pparg or Ucp3. Endosulfan suppressed Il-1b, Tnf-α, and Il-10 (p < 0.05) transcripts in both WAT and BAT, and all pesticides reduced Il-4 expression in BAT (p < 0.05). Overall, short-term exposure to aldrin, endosulfan or lindane interfered with metabolic and inflammatory pathways in a compound-specific manner, with lindane causing early weight gain and endosulfan eliciting complex effects on lipid metabolism and adipose gene expression. We suggest that even short-term exposure to OCPs may affect metabolic regulation in male rats, with each chemical exerting distinct effects.