Colorectal cancer is among the leading causes of cancer worldwide. Processed meat was known to be positively associated with a higher risk of gastrointestinal cancer. This study focused on the long-time trends of colorectal cancer mortality attributable to high processed meat intake in China from 1990 to 2019 and the projection for the next decade based on data obtained from the Global Burden of Disease 2019 study. We used an age-period-cohort model to fit the long-time trend. The joinpoint model was conducted to estimate the average and annual change of the attributable mortality. The Bayesian age-period-cohort model was used to project the crude attributable mortality from 2020 to 2030. An upward trend in colorectal cancer mortality attributable to high processed meat intake was observed for both sexes in China from 1990 to 2019, with an overall net drift of 4.009% for males and 2.491% for females per year. Projection analysis suggested that the burden of colorectal cancer incidence and mortality would still be high. Our findings suggested that colorectal cancer death attributable to high processed meat intake is still high in China, and elderly males were at higher risk. Gradually decreasing the intake of processed meat could be an effective way to reduce colorectal cancer mortality.

BackgroundStudies have suggested that there is a significant association between social engagement and depression symptoms. However, this association may differ in people with different features such as different sociodemographic characteristics and health conditions.MethodsResearch data were obtained from the CHARLS database. The causal inference was performed with the propensity score. We used the linear mixed-effects model tree algorithm under the causal inference frame for subgroup identification analysis.ResultsWe included 13,521 participants, and the median follow-up time is 4 years. Under the casual inference frame, the association between social engagement and depression symptoms is confirmed for all included individuals (OR = 0.957, P = 0.016; 95%CI: 0.923–0.992). Using the linear mixed-effects model tree, we found two subgroups, including middle-aged and elderly residents who live in rural areas with <6 h of sleep and those living in urban areas, could benefit more from social engagement. After using the propensity score method, all the two subgroups selected are statistically significant (P = 0.007; P = 0.013) and have a larger effect size (OR = 0.897, 95%CI: 0.830–0.971; OR = 0.916, 95%CI: 0.854–0.981) than the whole participants. As for sex difference, this associations are statistically significant in male (OR: 0.935, P = 0.011, 95%CI: 0.888–0.985) but not in female (OR: 0.979, P = 0.399, 95%CI: 0.931–1.029).ConclusionsOur findings indicate that social engagement may reduce the risks of depressive symptoms among all individuals. The identified subgroups of middle-aged and elderly residents who live in rural areas with <6 h of sleep and those who live in urban areas may benefit more from the social engagement than the whole participants.

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Since most patients with heart failure are re-admitted to the hospital, accurately identifying the risk of re-admission of patients with heart failure is important for clinical decision making and management. This study plans to develop an interpretable predictive model based on a Chinese population for predicting six-month re-admission rates in heart failure patients. Research data were obtained from the PhysioNet portal. To ensure robustness, we used three approaches for variable selection. Six different machine learning models were estimated based on selected variables. The ROC curve, prediction accuracy, sensitivity, and specificity were used to evaluate the performance of the established models. In addition, we visualized the optimized model with a nomogram. In all, 2002 patients with heart failure were included in this study. Of these, 773 patients experienced re-admission and a six-month re-admission incidence of 38.61%. Based on evaluation metrics, the logistic regression model performed best in the validation cohort, with an AUC of 0.634 (95%CI: 0.599–0.646) and an accuracy of 0.652. A nomogram was also generated. The established prediction model has good discrimination ability in predicting. Our findings are helpful and could provide useful information for the allocation of healthcare resources and for improving the quality of survival of heart failure patients.

Objectives Depression is one of the most serious mental disorder worldwide. Published studies indicated that nutrients such as folic acid and magnesium may provide a protective effect against it. The purpose of this study was to analyze whether dietary patterns defined by nutrients are associated with the risk of depression. Methods Research data content of 23 464 adults was obtained from the NHANES database. Dietary data were assessed with a valid food frequency questionnaire. Dietary patterns were derived by reduced rank regression with EPA + DHA, folate, Mg and Zn as response variables. The Patient Health Questionnaire was used to assess depressive symptoms (cutoff = 10). We applied logistic regression analyses to test the association between dietary patterns and depressive symptoms. Finally, all samples were divided into three groups: low, medium and high adherence to dietary patterns according to the trinomial score of dietary patterns, and the differences of depression risk among the three groups were compared. Results In total, 3 020 cases with depression were observed. We identified a dietary pattern that was strongly associated with EPA + DHA, folate, Mg and Zn (response variables) intake, which was also characterized by the consumption of vegetables, grains, meat, nuts, beans, peas, and lentils, milk, cheese, oils and solid fats. After adjustment for confounders, a statistically significant association was observed (OR = 0.42, 95%CI: 0.36,0.50; P < 0.001). In addition, compared with the low-adherence group, increasing adherence to this dietary pattern significantly reduced the risk of depression (medium-adherence: OR = 0.62, 95%CI: 0.55,0.71; high-adherence: OR = 0.43, 95%CI: 0.36,0.51; P < 0.001). Conclusions Adults living in the United States have been linked to a lower risk of depression with a high-nutrient eating pattern. Funding Sources National Natural Science Foundation of China and National Key R&D Program of China.