Development and external validation of a risk prediction model for depression in patients with coronary heart disease.

BACKGROUND: Depression is an independent risk factor for adverse outcomes of coronary heart disease (CHD). This study aimed to develop a depression risk prediction model for CHD patients. METHODS: This study utilized data from the National Health and Nutrition Examination Survey (NHANES). In the training set, reference literature, logistic regression, LASSO regression, optimal subset algorithm, and machine learning random forest algorithm were employed to screen prediction variables, respectively. The optimal prediction model was selected based on the C-index, Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI). A nomogram for the optimal prediction model was constructed. 3 external validations were performed. RESULTS: The training set comprised 1375 participants, with a depressive symptoms prevalence of 15.2 %. The optimal prediction model was constructed using predictors obtained from optimal subsets algorithm (C-index = 0.774, sensitivity = 0.751, specificity = 0.685). The model includes age, gender, education, marriage, diabetes, tobacco use, antihypertensive drugs, high-density lipoprotein cholesterol (HDLC), and aspartate aminotransferase (AST). The model demonstrated consistent discrimination ability, accuracy, and clinical utility across the 3 external validations. LIMITATIONS: The applicable population of the model is CHD patients. And the clinical benefits of interventions based on the prediction results are still unknown. CONCLUSION: We developed a depression risk prediction model for CHD patients, which was presented in the form of a nomogram for clinical application.

Increasing the frequency of plant-based food intake in daily diets reduces the risk of cardiovascular disease among elderly Chinese: a cohort study.

Objective: There is limited research on the relationship between the frequency of plant-based food intake and the risk of cardiovascular disease (CVD) among elderly Chinese. This study aims to evaluate the association between plant-based dietary index (PDI) and CVD risks, providing evidence for elderly Chinese to reduce CVD risks by increasing the frequency of plant-based food consumption. Methods: This study analyzed data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2011-2018, employing a multivariate modified Poisson regression model, trend tests, and restricted cubic spline (RCS) analysis to assess the linear and non-linear relationship between the PDI and CVD risks. Subgroup analyses and interaction tests were conducted to evaluate the robustness and population-specificity of the results. Results: This study included a total of 1,414 elderly Chinese, and at the end of follow-up, 487 participants had developed CVD. The multivariate modified Poisson regression model revealed a negative association between PDI and CVD risks [RR = 0.983, 95%CI = (0.970, 0.997)]. Similarly, the multivariate trend test (p = 0.031) and RCS analysis (P for nonlinear = 0.600) indicated a linear relationship between PDI and CVD risks. Subgroup analyses showed that the relationship between PDI and CVD risk was not influenced by gender, BMI, smoking, alcohol use, or exercise. Conclusion: The PDI was negatively correlated with CVD risks, indicating that increasing the frequency of plant-based food intake in the diet may reduce CVD risks among elderly Chinese.

Association of sleep characteristics with cardiovascular disease risk in adults over 40 years of age: a cross-sectional survey.

Background: The relationship between sleep characteristics and cardiovascular disease (CVD) risk has yet to reach a consistent conclusion, and more research needs to be carried out. This study aimed to explore the relationship between snoring, daytime sleepiness, bedtime, sleep duration, and high-risk sleep patterns with CVD risk. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) 2015-2018 were collected and analyzed. Multivariable logistic regression was used to evaluate the relationship between snoring, daytime sleepiness, bedtime, sleep duration, high-risk sleep patterns, and CVD risk. Stratified analysis and interaction tests were carried out according to hypertension, diabetes and age. Results: The final analysis contained 6,830 participants, including 1,001 with CVD. Multivariable logistic regression suggested that the relationship between snoring [OR = 7.37,95%CI = (6.06,8.96)], daytime sleepiness [OR = 11.21,95%CI = (9.60,13.08)], sleep duration shorter than 7 h [OR = 9.50,95%CI = (7.65,11.79)] or longer than 8 h [OR = 6.61,95%CI = (5.33,8.19)], bedtime after 0:00 [OR = 13.20,95%CI = (9.78,17.80)] compared to 22:00-22:59, high-risk sleep patterns [OR = 47.73,95%CI = (36.73,62.04)] and CVD risk were statistically significant. Hypertension and diabetes interacted with high-risk sleep patterns, but age did not. Conclusions: Snoring, daytime sleepiness, excessive or short sleep duration, inappropriate bedtime, and high-risk sleep patterns composed of these factors are associated with the CVD risk. High-risk sleep patterns have a more significant impact on patients with hypertension and diabetes.

Association between different insulin resistance surrogates and all-cause mortality in patients with coronary heart disease and hypertension: NHANES longitudinal cohort study.

BACKGROUND: Studies on the relationship between insulin resistance (IR) surrogates and long-term all-cause mortality in patients with coronary heart disease (CHD) and hypertension are lacking. This study aimed to explore the relationship between different IR surrogates and all-cause mortality and identify valuable predictors of survival status in this population. METHODS: The data came from the National Health and Nutrition Examination Survey (NHANES 2001-2018) and National Death Index (NDI). Multivariate Cox regression and restricted cubic splines (RCS) were performed to evaluate the relationship between homeostatic model assessment of IR (HOMA-IR), triglyceride glucose index (TyG index), triglyceride glucose-body mass index (TyG-BMI index) and all-cause mortality. The recursive algorithm was conducted to calculate inflection points when segmenting effects were found. Then, segmented Kaplan-Meier analysis, LogRank tests, and multivariable Cox regression were carried out. Receiver operating characteristic (ROC) and calibration curves were drawn to evaluate the differentiation and accuracy of IR surrogates in predicting the all-cause mortality. Stratified analysis and interaction tests were conducted according to age, gender, diabetes, cancer, hypoglycemic and lipid-lowering drug use. RESULTS: 1126 participants were included in the study. During the median follow-up of 76 months, 455 participants died. RCS showed that HOMA-IR had a segmented effect on all-cause mortality. 3.59 was a statistically significant inflection point. When the HOMA-IR was less than 3.59, it was negatively associated with all-cause mortality [HR = 0.87,95%CI (0.78, 0.97)]. Conversely, when the HOMA-IR was greater than 3.59, it was positively associated with all-cause mortality [HR = 1.03,95%CI (1.00, 1.05)]. ROC and calibration curves indicated that HOMA-IR was a reliable predictor of survival status (area under curve = 0,812). No interactions between HOMA-IR and stratified variables were found. CONCLUSION: The relationship between HOMA-IR and all-cause mortality was U-shaped in patients with CHD and hypertension. HOMA-IR was a reliable predictor of all-cause mortality in this population.

The negative association between serum albumin levels and coronary heart disease risk in adults over 45 years old: a cross-sectional survey.

This study aimed to assess the correlation between serum albumin levels and coronary heart disease (CHD) risk in adults aged over 45 years. This cross-sectional study used the non-institutionalized US population from the National Health and Nutrition Examination Survey (NHANES 2011-2018) as the sample source. Multiple logistic regression was performed to evaluate the association between serum albumin levels and CHD risk. Smooth curve fitting was performed to explore potential nonlinear relationships. When nonlinear relationships were found, a recursive algorithm was used to calculate inflection points. Additionally, a piecewise logistic regression model was constructed. After adjusting for confounders, multiple logistic regression and smooth curve fitting indicated an inverse association between serum albumin levels and CHD risk [OR = 0.970, 95% CI = (0.948, 0.992)]. Subgroup analysis revealed that the negative correlation was statistically significant in the population of female patients, over 60 years, with hypertension, without diabetes. There was a correlation between serum albumin levels and CHD risk. Lower serum albumin levels were associated with a higher CHD risk.