Triglyceride-glucose (TyG) related indices, which serve as simple markers for insulin resistance, have been closely linked to metabolic dysfunction-associated steatotic liver disease (MASLD), cardiovascular disease (CVD), and mortality. However, the prognostic utility of TyG-related indices in predicting the risk of CVD and mortality among patients with MASLD remains unclear. Data of 97,331 MASLD patients, with a median age of 58.0 years and free of CVD at baseline, were obtained from the UK Biobank. The TyG index, along with its combination with adiposity parameters (i.e. body mass index [BMI], waist circumference [WC], and waist-to-height ratio [WHtR]), were calculated. Cox proportional hazards models and restricted cubic spline (RCS) were performed to evaluate the associations between TyG-related indices and the risk of overall CVD, coronary heart disease (CHD), stroke, all-cause mortality, and cardiovascular mortality. Additionally, Harrell’s C-index, net reclassification index (NRI), and integrated discrimination improvement index (IDI) were used to assess the predictive performance of these indices. Over a median follow-up of 13.56 years, we identified 13,256 cases of overall CVD, 10,980 CHD, 2,926 stroke, 8,809 all-cause mortality, and 1,796 cardiovascular mortality. Compared with the lowest quartile of TyG-related indices, participants with MASLD in the high quartile of TyG-related indices had a significantly increased risk of incident overall CVD, CHD, stroke, and mortality. Specifically, the hazard ratios of occurring overall CVD in the fourth versus the first quartiles were 1.19 (95% confidence interval: 1.13–1.25) for TyG, 1.35 (1.28–1.42) for TyG-BMI, 1.33 (1.26–1.40) for TyG-WC, and 1.39 (1.32–1.46) for TyG-WHtR. RCS analyses indicated a nonlinear association of TyG with CVD outcomes (all P values for nonlinearity < 0.05), whereas there exhibited linear trends in TyG-BMI, TyG-WC, and TyG-WHtR with CVD outcomes (all P values for nonlinearity > 0.05, except for TyG-WC with stroke). Furthermore, TyG-WC and TyG-WHtR demonstrated the significantly higher C-index, NRI, and IDI in predicting risk of CVD and mortality in MASLD patients. TyG-related indices, especially TyG-WC and TyG-WHtR, had significant predictive values for the risk of incident CVD and mortality in individuals with MASLD. TyG-related indices may serve as effective surrogate predictors of CVD and mortality in MASLD patients.

Despite established associations between insulin resistance (IR)-related indices and cardiometabolic diseases (CMDs), most studies are limited to single CMD outcomes. The study aimed to examine the influence of IR-related indices on the incidence, predictive value, and progression trajectory of cardiometabolic multimorbidity (CMM), as well as potential biological mechanisms. This prospective study included 374,274 individuals from the UK Biobank who were free of CMDs at baseline. CMM was defined as the presence of two or more CMDs, including type 2 diabetes (T2D), coronary heart disease (CHD), and stroke. Five indices were developed to assess IR levels: triglyceride-glucose (TyG) index, TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), TyG-waist-height ratio (TyG-WHtR), and triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio. Cox proportional hazards and multi-state models were utilized to examine the associations between IR-related indices and CMM incidence and transition, respectively, with results expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). The predictive utility of these indices was assessed using the net reclassification index (NRI) and integrated discrimination improvement index (IDI). Mediation analyses were conducted to quantify the potential mediating roles of biomarkers. During a mean follow-up period of 13.7 years, 5048 (1.3%) individuals developed CMM. Elevated baseline IR-related indices were associated with higher risks of incident CMM. The HRs (95% CIs) for each 1-standard deviation increase were as follows: 1.30 (1.26–1.34) for the TyG index, 1.42 (1.39–1.46) for the TyG-BMI, 1.54 (1.49–1.59) for the TyG-WC, 1.52 (1.48–1.57) for the TyG-WHtR, and 1.19 (1.17–1.21) for the TG/HDL-C ratio. Besides, TyG-WHtR and TyG-WC exhibited significantly higher NRI and IDI, indicating superior predictive performance for CMM risk. These indices played critical yet distinct roles in the progression of CMM. For transitions from being free of CMDs to single CMDs, these indices had the strongest impact on T2D (all P < 0.001). Participants initially diagnosed with CHD were more likely to progress to CMM when exposed to higher IR-related indices (all P < 0.001). The effect sizes for TyG-WC and TyG-WHtR were greater than those of other indices across all transitions. Mediation analyses revealed that biomarkers associated with liver function, renal function, and inflammation collectively mediated approximately one-third of the associations of the TyG-WHtR and TyG-WC indices with incident CMM. Our findings highlight the critical role of IR-related indices, particularly TyG-WHtR and TyG-WC, in the incidence, progression, and prevention of CMM. The mediation effects of biomarkers indicate the potential for targeted interventions to reduce CMM risk in high-IR individuals.

The widespread use of pesticides performs a vital role in safeguarding crop yields and quality, providing the opportunity for multiple pesticides to co-exist, which poses a significant potential risk to human health. To assess the toxic effects caused by exposures to individual pesticides (chlorpyrifos, carbofuran and acetamiprid), binary combinations and ternary combinations, individual and combined exposure models were developed using HepG2 cells and the types of combined effects of pesticide mixtures were assessed using concentration addition (CA), independent action (IA) and combination index (CI) models, respectively, and the expression of biomarkers related to oxidative stress, apoptosis and cell necrosis was further examined. Our results showed that both individual pesticides and mixtures exerted toxic effects on HepG2 cells. The CI model indicated that the toxic effects of pesticide mixtures exhibited synergistic effects. The results of the lactate dehydrogenase (LDH) release and apoptosis assay revealed that the pesticide mixture increased the release of LDH and apoptosis levels. Moreover, our results also showed that individual pesticides and mixtures disrupted redox homeostasis and that pesticide mixtures produced more intense oxidative stress effects. In conclusion, we have illustrated the enhanced combined toxicity of pesticide mixtures by in-vitro experiments, which provides a theoretical basis and scientific basis for further toxicological studies.

Due to the high prevalence of multimorbidity and realistic health service demands for fall prevention, there is growing interest in the association between multimorbidity and falls. Our study aimed to identify multimorbidity patterns among Chinese older adults and explore the association between multimorbidity patterns and falls. Data from 4,579 Chinese community-dwelling older adults was included in this analysis. Information regarding falls and 10 chronic conditions was collected. An exploratory factor analysis was performed to determine multimorbidity patterns. Regression models were fitted to explore the associations of individual chronic disease or multimorbidity patterns with falls. Among 4,579 participants, 368 (8.0%) were defined as fallers, including 92 (2.0%) frequent fallers, and multimorbidity affected 2,503 (54.7%) participants. Older adults with multimorbidity were more likely to be fallers [odds ratio (OR) = 1.3, P = 0.02] and frequent fallers (OR = 1.7, P = 0.04). Three multimorbidity patterns were identified (i.e., cardiovascular-metabolic diseases, psycho-cognitive diseases and organic diseases), and the associations between psycho-cognitive diseases/organic diseases and prevalent falls or frequent falls were found to be significant. The psycho-cognitive disease pattern and organic disease pattern are significantly associated with falls. Therefore, more attention should be paid to patients with psycho-cognitive diseases and timely, targeted diagnostic and treatment services should be provided in fall prevention.

Background: The increasing prevalence of nonalcoholic fatty liver disease (NAFLD) in China presents a significant public health concern. Traditional ultrasound, commonly used for fatty liver screening, often lacks the ability to accurately quantify steatosis, leading to insufficient follow-up for patients with moderate-to-severe steatosis. Transient elastography (TE) provides a more quantitative diagnosis of steatosis and fibrosis, closely aligning with biopsy results. Moreover, machine learning (ML) technology holds promise for developing more precise diagnostic models for NAFLD using a variety of laboratory indicators. Objective: This study aims to develop a novel ML-based diagnostic model leveraging TE results for staging hepatic steatosis. The objective was to streamline the model’s input features, creating a cost-effective and user-friendly tool to distinguish patients with NAFLD requiring follow-up. This innovative approach merges TE and ML to enhance diagnostic accuracy and efficiency in NAFLD assessment. Methods: The study involved a comprehensive analysis of health examination records from Suzhou Municipal Hospital, spanning from March to May 2023. Patient data and questionnaire responses were meticulously inputted into Microsoft Excel 2019, followed by thorough data cleaning and model development using Python 3.7, with libraries scikit-learn and numpy to ensure data accuracy. A cohort comprising 978 residents with complete medical records and TE results was included for analysis. Various classification models, including logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost), were constructed and evaluated based on the area under the receiver operating characteristic curve (AUROC). Results: Among the 916 patients included in the study, 273 were diagnosed with moderate-to-severe NAFLD. The concordance rate between traditional ultrasound and TE for detecting moderate-to-severe NAFLD was 84.6% (231/273). The AUROC values for the RF, LightGBM, XGBoost, SVM, KNN, and LR models were 0.91, 0.86, 0.83, 0.88, 0.77, and 0.81, respectively. These models achieved accuracy rates of 84%, 81%, 78%, 81%, 76%, and 77%, respectively. Notably, the RF model exhibited the best performance. A simplified RF model was developed with an AUROC of 0.88, featuring 62% sensitivity and 90% specificity. This simplified model used 6 key features: waist circumference, BMI, fasting plasma glucose, uric acid, total bilirubin, and high-sensitivity C-reactive protein. This approach offers a cost-effective and user-friendly tool while streamlining feature acquisition for training purposes. Conclusions: The study introduces a groundbreaking, cost-effective ML algorithm that leverages health examination data for identifying moderate-to-severe NAFLD. This model has the potential to significantly impact public health by enabling targeted investigations and interventions for NAFLD. By integrating TE and ML technologies, the study showcases innovative approaches to advancing NAFLD diagnostics.

Insulin resistance (IR)-related indices are validated prognostic markers in metabolic disorders, but have not been applied to preclinical or clinical obesity. This study aimed to investigate the relationship between IR-related indices and cardiovascular disease (CVD) incidence, considering genetic factors and biomarkers. This prospective study analyzed 112,866 UK Biobank participants with preclinical or clinical obesity. IR-related indices were evaluated: triglyceride-glucose (TyG) index, TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), and TyG-waist-to-height ratio (TyG-WHtR). Genetic risk was estimated using the polygenic risk score. Outcomes, including total CVD, coronary artery disease (CAD), and stroke, were ascertained through medical records linkage. Cox proportional hazard models were used to evaluate the associations and modification effects of genetic risk. Incremental predictive value was assessed by net reclassification index (NRI) and integrated discrimination improvement index (IDI). Mediation analyses explored the role of inflammatory, hepatic, and renal biomarkers. Over a median follow-up period of 13.45 years, 21,601 total CVD, 11,942 CAD, and 3347 stroke cases were documented. Compared with the lowest quartile of IR-related indices, participants in the highest quartile presented increased CVD risk. For total CVD, hazard ratios (HRs) (95% confidence intervals, CIs) for the fourth versus the first quartiles were 1.33 (1.28–1.39) for TyG-BMI, 1.41 (1.34–1.48) for TyG-WC, and 1.25 (1.20–1.31) for TyG-WHtR. All IR-related indices demonstrated significant associations with CAD. Borderline significant associations were observed for stroke. Distinct dose-response association patterns with total CVD were observed: TyG-BMI and TyG-WHtR exhibited nonlinear relationships, while TyG-WC demonstrated a linear association. The CVD risk was highest in individuals with high genetic risk and high IR indices, with an additive interaction between TyG-WC and genetic risk being observed. Significantly higher NRI and IDI were observed for TyG-WC, TyG-BMI, and TyG-WHtR in predicting CVD, with TyG-WC achieving the highest performance. Mediation analyses indicated that inflammation, liver, and renal biomarkers might partially mediate the relationship. Elevated IR-related indices, particularly TyG-WC, were associated with increased total CVD and CAD risks in preclinical or clinical obesity. Additive effects of TyG-WC and genetic risk on CVD were revealed, with mediating biomarkers suggesting potential targeted interventions for CVD risk reduction.