Insulin resistance, combined with health-related lifestyles, psychological traits and adverse cardiometabolic profiles, is associated with cardiovascular diseases: findings from the BHMC study.

The triglyceride glucose (TyG) index is a reliable marker of insulin resistance; however, its combined impact with modifiable lifestyle risk factors and psychological traits on cardiovascular diseases (CVDs) remains unclear. The aim of this study was to explore the relationship between the TyG index, various behavioral factors, psychological traits, and CVDs. A total of 77 752 adults aged 18 and over from the baseline survey of the Beijing Health Management Cohort study were investigated. Associations of the TyG index, body roundness index (BRI), dietary habits, psychological traits, and sleep habits with CVDs were estimated using multivariable logistic regression models. Compared to the Q1 level, the Q4 level of the TyG index had an odds ratio (OR) and 95% confidence interval (CI) of 2.30 (1.98-2.68) for CVD risk in men and 2.12 (1.81-2.48) in women. Compared to a sleep duration of more than 7 hours, a sleep duration less than 5 hours had a 32% (8%-61%) higher risk in men and 22% (1%-48%) in women. The ORs (95% CIs) for fast eating compared to normal speed were 1.47 (1.23-1.76) in men and 1.17 (1.05-1.29) in women. Compared to individuals with a passive and depressed psychological trait, those who were positive and optimistic had a 47% (36%-56%) decreased risk in men and 43% (31%-53%) in women. In the age-stratified analysis, a higher BRI level showed a sex-differential effect on CVDs, which is potentially related to a lower risk of CVDs in elderly men. A high level of the TyG index combined with unhealthy lifestyle factors indicates a higher risk of CVDs, while maintaining a positive and optimistic psychological trait acts as a protective factor. These findings may be valuable for identifying high-risk populations for CVDs in community settings.

Lifestyle and clinical factors as predictive indicators of cardiometabolic multimorbidity in Chinese adults: Baseline findings of the Beijing Health Management Cohort (BHMC) study.

BACKGROUND: Cardiometabolic multimorbidity (CMM) is increasing globally as a result of lifestyle changes and the aging population. Even though previous studies have examined risk factors associated with CMM, there is a shortage of prediction models that can accurately identify high-risk individuals for early prevention. METHODS: In the baseline survey of the Beijing Health Management Cohort, a total of 77,752 adults aged 18 years or older were recruited from 2020 to 2021. Data on lifestyle factors, clinical profiles, and diagnoses of diabetes, coronary heart disease, and stroke were collected. Logistic regression models were used to identify risk factors for CMM. Nomograms were developed to estimate an individual's probability of CMM based on the identified risk factors. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: In men, the top three risk factors for CMM were hypertension (OR: 3.52, 95 % CI: 2.97-4.18), eating very fast (3.43, 2.27-5.16), and dyslipidemia (2.59, 2.20-3.06). In women, hypertension showed the strongest association with CMM (3.62, 2.90-4.52), followed by night sleep duration less than 5 h per day (2.41, 1.67-3.50) and dyslipidemia (1.91, 1.58-2.32). The ORs for holding passive and depressed psychological traits were 1.49 (95%CI: 1.08-2.06) in men and 1.58 (1.03-2.43) in women. Prediction models incorporating these factors demonstrated good discrimination in the test set, with AUC 0.84 (0.83-0.86) for men and 0.90 (0.89-0.91) for women. The sex-specific nomograms were established based on selected predictors. CONCLUSIONS: Modifiable lifestyle factors, metabolic health and psychological trait are associated with the risk of CMM. The developed prediction models and nomograms could facilitate early identification of individuals at high-risk of CMM.

The development and validation of a non-invasive prediction model of hyperuricemia based on modifiable risk factors: baseline findings of a health examination population cohort.

This study aims to establish a simple and non-invasive risk prediction model for hyperuricemia in Chinese adults based on modifiable risk factors. In 2020-2021, the baseline survey of the Beijing Health Management Cohort (BHMC) was conducted in Beijing city among the health examination population. Diverse life-style risk factors including dietary patterns and habits, cigarette smoking, alcohol intake, sleep duration and cell-phone use were collected. We developed hyperuricemia prediction models using three machine-learning techniques, namely logistic regression (LR), random forest (RF), and XGBoost. Performances in discrimination, calibration, and clinical applicability of the three methods were compared. Decision curve analysis (DCA) was used to assess the model's clinical usefulness. A total of 74 050 people were included in the study, of whom 55 537 (75%) were randomly selected into the training set and the other 18 513 (25%) were in the validation set. The prevalence of HUA was 38.43% in men and 13.29% in women. The XGBoost model has better performance than the LR and RF models. The area under the curve (AUC) (95% CI) in the training set for the LR, RF and XGBoost models were 0.754 (0.750-0.757), 0.844 (0.841-0.846) and 0.854 (0.851-0.856), respectively. The XGBoost model had a higher classification accuracy of 0.774 than the logistic (0.592) and RF (0.767) models. The AUC (95% CI) values in the validation set for the LR, RF and XGBoost models were 0.758 (0.749-0.765), 0.809 (0.802-0.816) and 0.820 (0.813-0.827), respectively. As demonstrated by the DCA curves, all the three models could bring net benefits within the appropriate threshold probability. XGBoost had better discrimination and accuracy. Various modifiable risk factors included in the model were helpful in facilitating the easy identification and life-style interventions of the HUA high-risk population.