Haoran Wang

Introduction: The triglyceride-glucose–body mass index, which integrates the triglyceride-glucose index with body mass index, has emerged as a promising marker of metabolic risk for heart failure with preserved ejection fraction; however, its association with subclinical diastolic dysfunction remains underexplored. Methods: This cross-sectional study enrolled 1,467 participants from a Chinese community-based population who underwent echocardiographic evaluation and metabolic profiling. The triglyceride-glucose–body mass index was calculated as the natural logarithm of triglycerides multiplied by fasting blood glucose divided by 2, then multiplied by body mass index. Ultrasound-defined diastolic dysfunction was diagnosed according to the 2025 American Society of Echocardiography criteria. Multivariable logistic regression, restricted cubic spline analysis, and machine learning were employed to evaluate the association. Results: The prevalence of ultrasound-defined diastolic dysfunction was 33.6%. The optimal Youden-derived triglyceride-glucose–body mass index cutoff was 220.000, categorizing participants into low ( n = 543 ) and high ( n = 924 ) groups. In the fully adjusted model, each 1-standard deviation increase was not significantly associated with ultrasound-defined diastolic dysfunction in continuous analysis (odds ratio=1.35; 95% confidence interval: 0.42–4.40; P = 0.616 ); however, the binary comparison showed a significant association (odds ratio=1.60; 95% confidence interval: 1.11–2.31; P = 0.013 ). Restricted cubic spline analysis revealed a significant overall association ( P overall < 0.001 ) without significant nonlinearity ( P nonlinear = 0.194 ). Discussion: In this cohort, the triglyceride-glucose–body mass index demonstrated a significant independent association with ultrasound-defined diastolic dysfunction in the binary analysis, supporting its potential as a screening index for subclinical diastolic dysfunction, although external validation in independent populations is needed.

Objectives: This study evaluated the association between Body Roundness Index (BRI) and QUS-defined osteoporosis, its discriminative ability, empirical cutoff, and subgroup heterogeneity in middle-aged and older adults. Methods: We analyzed cross-sectional data from 1,444 community-dwelling adults aged 35–75 years. Osteoporosis was defined using tibial quantitative ultrasound-derived T-scores. Receiver operating characteristic curves assessed discrimination, restricted cubic splines examined dose-response relationships, logistic and robust regression analyses quantified associations, and subgroup analyses evaluated effect modification. Results: The Youden index identified an optimal BRI cutoff of 4.5. High BRI (≥ 4.5) was associated with higher odds of osteoporosis in the unadjusted model (OR = 1.606, 95% CI: 1.292–1.996; P < 0.001), the age- and sex-adjusted model (OR = 1.432, 95% CI: 1.141–1.797; P = 0.002), and the fully adjusted model excluding BMI (OR = 1.404, 95% CI: 1.105–1.783; P = 0.005). The association was approximately linear (P for non-linearity = 0.539; P-overall < 0.001). BRI alone showed modest discrimination (AUC = 0.562, 95% CI: 0.529–0.594), which increased after adjustment for age and sex (AUC = 0.680, 95% CI: 0.652–0.710) and after full covariate adjustment (AUC = 0.695, 95% CI: 0.674–0.731). The association was stronger among participants with metabolic syndrome (OR = 2.62, 95% CI: 1.641–4.179) than among those without it (OR = 1.39, 95% CI: 1.040–1.860; P for interaction = 0.022). Conclusions: Higher BRI was associated with QUS-defined osteoporosis, but BRI alone showed limited discrimination, and the fully adjusted model remained only moderately discriminative. BRI should therefore be considered an adjunctive anthropometric indicator within a broader assessment framework, rather than a stand-alone predictor or screening tool. Clinical trial registration: https://www.chictr.org.cn/showproj.html?proj=187762 , identifier ChiCTR2300071492.

Background: Lipid accumulation product (LAP), derived from waist circumference and triglycerides, may reflect central lipid overaccumulation, but its relevance for a screening-defined cardiovascular disease (CVD) high-risk label is uncertain. We examined whether LAP could help flag community adults who may need more formal risk assessment rather than serve as a stand-alone prognostic tool. Methods: This cross-sectional analysis initially considered 6,860 adults aged 35–75 years from the ChinaHEART screening program in Luohe. After excluding participants with missing LAP or screening-defined CVD high-risk outcome data, 6,803 participants were included in the base analytic sample. LAP was analyzed continuously and dichotomized using a rounded cutoff of 42 derived from the Youden index in the LAP-only receiver operating characteristic (ROC) analysis. Logistic regression, restricted cubic splines, subgroup analyses, and ROC analyses were conducted under four stepwise models. Exploratory least absolute shrinkage and selection operator (LASSO), XGBoost, and TreeSHAP analyses were added as supplementary pattern-recognition analyses. Results: ROC analysis identified an optimal LAP cutoff of 42.12 in the LAP-only model; the rounded value of 42 was used for categorical analyses. LAP alone showed modest discrimination for the concurrent screening-defined CVD high-risk label (AUC 0.583, 95% CI 0.566–0.600), which increased to 0.628 (95% CI 0.613–0.643) after adding age and sex and to 0.632 (95% CI 0.619–0.649) in Model 3. Using the rounded cutoff of 42, 42.3% of participants were classified as high LAP. The prevalence of screening-defined CVD high-risk status was higher in the high-LAP group than in the low-LAP group (28 vs. 17%, P < 0.001). Conclusions: Higher LAP was associated with a greater likelihood of meeting a contemporaneous screening-defined CVD high-risk criterion. These findings support LAP as a pragmatic screening trigger for further formal assessment in community settings, not as evidence of causal risk stratification or prediction of hard cardiovascular events.

Background: While diastolic dysfunction (DD) is a critical heart failure precursor linked to metabolic stress, the specific association between the promising triglyceride-glucose waist circumference (TyG-WC) index and DD remains insufficiently characterized under contemporary diagnostic standards. This study aimed to evaluate the association between the TyG-WC index and the risk of DD in a community-based population of middle-aged and older adults. Methods: In this cross-sectional study of 1,413 participants, DD was adjudicated according to the age-stratified echocardiographic criteria. Multivariable logistic regression and restricted cubic splines (RCS) were employed to assess independent associations and dose-response relationships. Furthermore, machine learning techniques, including LASSO regression for feature selection and Random Forest models interpreted by Shapley Additive exPlanations (SHAP), were utilized to quantify the predictive contribution of TyG-WC. Results: After full adjustment for demographics, blood pressure, and clinical comorbidities, TyG-WC was independently associated with an increased risk of DD (OR = 1.002, 95% CI: 1.001-1.004, P < 0.001). Individuals in the high TyG-WC group ( ≥ 776.5) faced a significantly higher risk of DD compared to the low group (OR = 1.656, 95% CI: 1.266-2.166, P < 0.001). RCS analysis confirmed a significant linear dose-response relationship ( P _overall <0.001), indicating that the probability of DD escalates with increasing TyG-WC. The full predictive model achieved an AUC of 0.687, demonstrating incremental value beyond traditional risk factors. Subgroup analyses demonstrated the robustness of these findings, with significant interactions observed for hypertension and diabetes status. Machine learning identified TyG-WC as an independent contributor for DD, with SHAP values consistently linking higher index levels to increased risk. Conclusions: Elevated TyG-WC is independently associated with an increased risk of DD. As a cost-effective and easily accessible marker of cardiometabolic stress, TyG-WC may serve as a practical screening tool for the early identification and risk stratification of subclinical heart failure.

Background: The cardiometabolic index (CMI), a composite marker reflecting central adiposity and dyslipidemia, may offer a pragmatic tool for community cardiovascular risk screening. Methods: We analyzed cross-sectional screening data from the ChinaHEART Luohe cohort. WHO chart-defined CVD high risk was a 10-year predicted risk ≥20%. CMI was evaluated per 1-unit increase and, for descriptive and stratified analyses, dichotomized using a rounded pragmatic analytic threshold of 0.7 derived from a crude ROC/Youden optimal threshold of 0.723. Discrimination (AUC with bootstrap 95% CIs), raincloud plots, restricted cubic splines (RCS), stepwise logistic regression, subgroup/interaction analyses, and an explainable ML pipeline (LASSO-random forest-SHAP) were applied. Results: Among 6,701 participants, 1,439 (21%) were classified as CVD high risk; prevalence was higher in the high-CMI group (≥0.7) than the low-CMI group (29% vs. 18%). CMI alone showed modest discrimination for WHO-defined CVD high-risk status (AUC: 0.571, 95% CI: 0.555–0.586), whereas multivariable models incorporating CMI showed higher discrimination (Model 3 AUC: 0.642, 95% CI: 0.626–0.659). The raincloud plot showed higher CMI in the high-risk group ( P < 0.001), and RCS suggested nonlinearity ( P for overall < 0.001; P for nonlinearity = 0.020). In Model 3, CMI was associated with higher odds of CVD high risk (OR: 1.31, 95% CI: 1.16–1.48 per 1-unit; OR: 1.50, 95% CI: 1.32–1.71 for ≥0.7 vs. <0.7). In ML, random forest achieved AUC 0.814; SHAP ranked CMI 3rd of 7 LASSO-selected features. Conclusions: Higher CMI was associated with WHO-defined CVD high-risk status with a nonlinear pattern and consistent importance across conventional and explainable ML analyses, supporting its potential utility as an adjunct screening marker.

Early identification of high-risk individuals for cardiovascular disease (CVD) and effective intervention and management can significantly reduce the incidence of CVD. The ratio of total cholesterol to high-density lipoprotein cholesterol (TC/HDL-C) reflects both the protective effect of high HDL-C levels and the detrimental impact of high low-density lipoprotein cholesterol levels and residual cholesterol. However, whether the TC/HDL-C ratio can effectively identify the high CVD risk population has not been fully verified. We conducted a cross-sectional analysis using community screening data from the ChinaHEART program in Luohe, China (2021–2022). Among 6860 adults aged 35 to 75 years, 6586 with valid lipid measurements were included in the final analyses. We evaluated the association between the TC/HDL-C ratio and World Health Organization-chart–defined high 10-year CVD risk (≥20%). According to restricted cubic spline analysis, the odds of high CVD risk were increased in individuals with a higher level of TC/HDL-C. Receiver operating characteristic analysis indicated that the TC/HDL-C ratio had good discriminatory power for high CVD risk, with an area under the curve of 0.847 in the fully adjusted model. Logistic regression analysis showed that for each 1-unit increase in the TC/HDL-C ratio, the odds ratio for high CVD risk was 1.97 (95% confidence interval: 1.83–2.13, P < .001). Compared with the lowest quartile of TC/HDL-C, the odds ratio for high CVD risk in the highest quartile was 2.95 (95% confidence interval: 2.42–3.60, P < .001). This study demonstrated that the TC/HDL-C ratio was independently linked to high CVD risk. These findings highlighted the importance of considering the TC/HDL-C ratio as a valuable tool in assessing CVD risk and guiding preventive strategies.

Introduction: TyHGB is a composite metabolic index integrating triglyceride-to-high-density lipoprotein cholesterol ratio, fasting plasma glucose, and body mass index, but its association with World Health Organization (WHO)-defined cardiovascular disease (CVD) high-risk status in community settings remains unclear. Methods: We analyzed 6,755 adults from a community-based ChinaHEART Luohe screening cohort (2021–2022). CVD high-risk was defined as an estimated 10-year WHO CVD risk ≥20%. TyHGB was examined per 1-unit increase and as high versus low using a receiver operating characteristic (ROC)-derived cutoff (Youden index; approximately 7.6). Logistic regression and restricted cubic splines were used to assess associations and potential nonlinearity. Discrimination was evaluated using ROC curves with DeLong’s tests, and incremental value beyond conventional risk factors was assessed using integrated discrimination improvement (IDI) and net reclassification improvement (NRI). Results: The prevalence of CVD high-risk was 22%. TyHGB was independently associated with CVD high-risk (fully adjusted OR 1.131 per 1-unit increase), and high TyHGB (≥7.6) was associated with higher odds (OR 1.642). In unadjusted analyses, TyHGB showed higher discrimination than TyG (AUC 0.624 vs 0.585). Adding TyHGB to the base model modestly improved reclassification (IDI 0.003; NRI 0.216). TyHGB was also retained in exploratory machine-learning analyses. Discussion: TyHGB was positively associated with WHO-defined CVD high-risk and provided modest incremental discrimination and reclassification for cross-sectional high-risk classification beyond conventional risk factors, supported by sensitivity analyses. These findings relate to estimated risk status rather than adjudicated incident CVD events.

Background: Efficient identification of individuals at high cardiovascular disease (CVD) risk is essential for prevention in middle-aged and older adults. The body roundness index (BRI), derived from waist circumference and height, may capture body-shape-related risk beyond conventional measures. We examined the association of BRI with World Health Organization (WHO)-defined CVD high-risk status in a community-based screening population. Methods: This cross-sectional study used baseline data from the Luohe branch of the ChinaHEART cohort, a community-based health screening program in Luohe, Henan, China (March 2021 to February 2022), including adults aged 35–75 years. WHO-defined CVD high-risk status was determined using WHO CVD risk charts, with an estimated 10-year risk ≥20% classified as high risk. BRI was analyzed as a continuous variable (per 1-unit increase), quartiles, and a binary variable using a receiver operating characteristic (ROC)-derived threshold. Multivariable logistic regression, restricted cubic splines, ROC analysis with bootstrap confidence intervals, and subgroup/interaction analyses were performed. An explainable machine-learning workflow (LASSO, random forest, and SHAP) was also applied. Results: Among 6,858 participants, 1,489 (22%) were classified as WHO-defined CVD high risk. Higher BRI remained associated with high-risk status in fully adjusted models. ROC analysis showed only modest standalone discrimination, while subgroup analyses suggested heterogeneity by sex and cardiometabolic strata. In machine-learning analyses, BRI was retained among selected predictors and contributed meaningfully within the multivariable model. Conclusion: In this community screening population, BRI was positively associated with WHO-defined CVD high-risk status and may serve as a low-cost adjunct marker to prioritize individuals for comprehensive risk evaluation in primary-care screening settings.

Background: Community screening programs increasingly use World Health Organization (WHO) cardiovascular disease (CVD) risk charts to identify individuals at high predicted 10-year risk. The atherogenic index of plasma (AIP), derived from triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C), may capture atherogenic dyslipidemia and support pragmatic risk stratification. Methods: We conducted a cross-sectional analysis of baseline data from the China Health Evaluation And risk Reduction through nationwide Teamwork (ChinaHEART) community screening program in Luohe, China. Among 6,860 screened participants, 6,702 with complete data for AIP computation, WHO risk classification, and prespecified covariates were included. The outcome was the WHO CVD risk chart-defined predicted 10-year CVD high-risk category (high risk: ≥20%), rather than adjudicated or incident CVD events. AIP was calculated as log10(TG [mmol/L]/HDL-C [mmol/L]) and modeled as both a continuous and categorical exposure; spline models tested nonlinearity, and ROC analyses evaluated discrimination and derived a Youden-index cutoff. In addition, we performed an explainable machine-learning pipeline for CVD high-risk prediction using LASSO logistic regression for feature selection (AIP forced-in), followed by a random forest classifier and SHAP-based interpretation. Results: Of 6,860 screened participants, 6,702 were included in the analytic sample (median age 58 years; 38% men). The WHO CVD risk chart-defined predicted 10-year CVD high-risk category was present in 1,440 (21%) participants and was more frequent in the high-AIP group than in the low-AIP group. Higher AIP was associated with higher odds of CVD high-risk status. Restricted cubic splines supported a non-linear association. Discrimination was modest for AIP alone (AUC 0.557) and improved in adjusted models (AUC 0.650). In the machine-learning pipeline (LASSO + random forest), the random forest model achieved an AUC of 0.792, and SHAP analyses ranked LDL-C and history of hypertension as the strongest contributors, with AIP remaining among the top predictive features. Conclusion: In this community-based ChinaHEART population, higher AIP was non-linearly associated with the WHO CVD risk chart-defined predicted 10-year CVD high-risk category. Although AIP alone had limited discrimination, it may serve as a simple adjunct marker to triage individuals for intensified risk assessment in primary-care screening settings.

Osteoporosis is associated with cardiovascular disease, but the relationship between bone mineral density (BMD) and cardiac structure and function remains incompletely understood. This cross-sectional study analyzed baseline data from 1233 participants (median age 60 years; 59% female) in the Osteoarthritis and Cardiovascular Health Cohort. BMD was categorized as normal (T-score ≥ − 1.0, n = 364), osteopenia (− 2.5 < T-score < − 1.0, n = 404), or osteoporosis (T-score ≤ − 2.5, n = 465). Cardiac parameters were measured via echocardiography. Multivariable linear regression adjusted for age, sex, and cardiovascular risk factors. Osteoporosis was associated with higher LAVI (median: 29 vs. 26 mL/m², P < 0.001) and RWT (0.38 vs. 0.37, P = 0.047) compared to normal BMD. After adjustment, T-score was inversely associated with LAVI (β = − 0.358, P = 0.043) and RWT (β = − 0.003, P = 0.013). Subgroup analyses showed stronger effects in women (LAVI β = − 0.737, P = 0.001) and participants < 50 years (LAVI β = − 0.909, P = 0.022). No significant associations were observed for left ventricular mass index, ejection fraction, or diastolic function metrics. Lower BMD, measured by tibial ultrasound, is independently associated with left atrial enlargement and increased ventricular wall thickness, particularly in women and younger adults. These findings suggest shared pathophysiological mechanisms between bone loss and cardiac remodeling, warranting more in-depth research.