The purpose of this study was to investigate how aerobic exercise affects oxidative stress (OS) in patients with chronic kidney disease (CKD). Retrieval dates range from the date the database was established to 19 July 2023, without languages being restricted. A meta-analysis and sensitivity analysis were conducted using RevMan 5.3 and Stata 16.0. The meta-analysis showed that, compared to usual activity or no exercise, aerobic exercise significantly reduced the oxidative markers malondialdehyde (MDA) (mean differences (MD) - 0.96 (95% CI -1.33, - 0.59); p < 0.00001), advanced oxidation protein product (AOPP) (MD - 3.49 (95% CI - 5.05, - 1.93); p < 0.00001), F2-isoprostanes (F2-iso) (MD - 11.02 (95% CI - 17.79, - 4.25); p = 0.001). Aerobic exercise also increased the antioxidant marker superoxide dismutase (SOD) in CKD patients (standardized mean differences (SMD) 1.30 (95% CI 0.56, 2.04); p = 0.0005). Subgroup analysis showed a significant increase in glutathione peroxidase (GPX) in patients aged ≥60 years (SMD 2.11 (95% CI 1.69, 2.54); p < 0.00001). The change in total antioxidant capacity (TAC) after aerobic exercise was insignificant in patients with CKD. The trial sequential analysis supported aerobic exercise's effectiveness in improving MDA, SOD, AOPP, and F2-iso in patients with CKD. The results of this review suggest that aerobic exercise improves OS indicators (MDA, SOD, AOPP, and F2-iso) in CKD patients compared to conventional treatment or no exercise and that the effects on GPX and TAC indicators need further confirmation. For better validation of benefits and exploration of the best aerobic exercise regimen to improve OS status with CKD, further studies with high methodological quality and large sample sizes are needed.

Background: Omega-3 has been extensively studied for its cardiovascular disease (CVD) benefits. However, the results of this evidence are inconsistent. Therefore, in this study, dietary omega-3 intake was investigated further in relation to coronary heart disease (CHD) risk among U.S. adults. Methods: We used data from the National Health and Nutrition Examination Survey (NHANES) database for people ages 20 years and older between 1999 and 2018 to conduct a cross-sectional survey. The Medical Condition Questionnaire (MCQ) was used to determine CHD status. We measured dietary omega-3 intake using two 24-hour dietary recall interviews. Multivariate logistic regression and subgroup analysis were used to explore the correlation between dietary omega-3 intake and CHD. The dose-response relationship between the two was analyzed with a restricted cubic spline (RCS). Results: 31,184 study subjects were included, of whom 1,604 (5.14%) were patients with CHD. By quintile (Q) of dietary omega-3 intake, after adjusting for all confounding factors, compared with Q1, when total dietary omega-3, alpha-linolenic acid (ALA), docosapentaenoic acid (DPA), eicosatetraenoic acid (ETA), eicosapentaenoic acid (EPA), and docosahexenoic acid (DHA) intake reached Q5, the odds ratio (95% confidence interval, CI) of CHD were 0.76 (0.60, 0.96), 0.73 (0.57, 0.94), 0.70 (0.54, 0.92), 0.66 (0.50, 0.85), 0.84 (0.69, 1.02), and 0.83 (0.64, 1.07), respectively, while EPA and DHA were not significantly associated with the disease (Trend p > 0.05). Intake of omega-3 and CHD were linearly related (P for nonlinear = 0.603). No significant interactions were found within subgroups except for the age group (P for interaction = 0.001). Sensitivity analysis and multivariate logistic regression results are generally in agreement. Conclusions: Total dietary omega-3, ALA, DPA, and ETA intake were negatively associated with CHD risk. In contrast, EPA and DHA had no significant correlation with CHD.

Objective. This nationwide, multicenter prospective observational study with a patient registry was designed to evaluate the efficacy of Fufang E’jiao Jiang (FEJ) in Chinese patients with Qi and blood deficiency syndrome (QBDS). Methods. QBDS patients were consecutively recruited from 81 investigational sites in China from July, 2019, to December, 2020. Patients who met the eligibility criteria were enrolled in a prospective registry database. Baseline characteristics and changes in scores on the traditional Chinese medicine (TCM) symptom evaluation scale for Qi and blood deficiency, the clinical global impression (CGI) scale, the fatigue scale‐14 (FS‐14), and the Pittsburgh sleep quality index (PSQI) were analyzed to determine the clinical efficacy of FEJ. Results. A total of 3,203 patients were recruited. The average remission rate (i.e., the sum of the cure rate and improvement rate) of the 20 symptoms of QBDS was 92.49% after 4 weeks of FEJ treatment, which was higher than at baseline; the rate increased to 94.69% at 8 weeks. The CGI scale revealed that the number of total remissions at 4 and 8 weeks was 3,120 (97.41%) and 415 (100%), respectively. The total FS‐14 scores decreased by 1.67 ± 4.11 (p < 0.001) at 4 weeks and 1.72 ± 3.09 (p < 0.001) at 8 weeks of treatment. The PSQI scores were 6.6 ± 4.7 and 6.52 ± 3.07 at 4 and 8 weeks, respectively, which were significantly lower than the baseline scores (p < 0.001; p = 0.0033). Both the subhealth fatigue (SF) and iron deficiency anemia (IDA) groups showed significantly improved clinical symptoms of QBDS (p < 0.01). Between‐group comparisons revealed significantly greater improvements in FS‐14 and PSQI scores in the SF group than in the IDA group (p < 0.05). A multivariate logistic regression analysis showed that disease course, FS‐14 score at baseline, and four‐week FEJ doses were independent risk factors for the degree of symptom relief in QBDS patients (p < 0.05). Conclusion. In real‐world settings, FEJ has a promising effect in treating QBDS and can significantly improve the severity of its symptoms.

Objective: Social risk factors are key determinants of migraine occurrence and progression. This study assessed the association between the social risk profile (SRP) and the prevalence of self-reported severe headache or migraine and all-cause mortality in US adults, and developed machine learning prediction models to explore feature contributions to internal risk stratification. Methods: Using data from the National Health and Nutrition Examination Survey (NHANES) 1999–2004, weighted multivariate logistic regression evaluated the SRP–migraine association, and a weighted Cox proportional hazards model assessed the influence of SRP on all-cause mortality among migraine patients. The Boruta and Lasso algorithms selected predictive features for nine machine learning classifiers to predict migraine risk and four survival models to assess mortality risk. SMOTE was applied within cross-validation folds to address class imbalance. SHAP values were utilized to identify the most critical features. Results: Among 11,861 participants, 2,355 self-reported severe headache or migraine; 2,351 were included in the mortality analysis after excluding 4 individuals with missing survival status. Over a median follow-up of 206 months (IQR: 187–224), 471 deaths occurred. Higher SRP scores were associated with lower migraine prevalence (OR = 0.44, 95% CI: 0.34–0.57) and lower all-cause mortality (HR = 0.31, 95% CI: 0.19–0.50). XGBoost achieved the best performance for migraine prediction (AUC = 0.732, 95% CI: 0.712–0.753), while Random Survival Forest performed best for mortality prediction (AUC = 0.882). SHAP analysis identified age, SRP, and cotinine as key predictors. Decision curve and calibration analyses demonstrated acceptable internal performance, supported by ten-fold cross-validation. Conclusion: SRP is an independent predictor of migraine risk and long-term survival. The machine learning analysis provides exploratory insights into feature importance for risk stratification within the development sample, while the regression-based association estimates support the epidemiological significance of social determinants in migraine.

Background: Cardiovascular disease (CVD) is the leading cause of mortality worldwide. The C-reactive protein–triglyceride–glucose (CTI) index captures metabolic inflammation and insulin resistance, while obesity indices reflect adiposity-related cardiometabolic risk. However, the predictive value of combining CTI with obesity indices for CVD remains unclear. Objectives: To examine the prognostic utility of CTI combined with established and novel obesity indicators for CVD, and to benchmark their predictive efficacy. Design: Nationally representative prospective cohort study. Methods: Using data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative prospective cohort, 7847 adults aged 45 years and above without baseline CVD were enrolled. CTI was calculated from C-reactive protein, fasting blood glucose, and triglycerides (TG); each obesity index was combined with CTI to form composite parameters (CTI-body mass index (CTI-BMI), CTI-waist circumference (CTI-WC), CTI-waist-to-height ratio (CTI-WHtR), CTI-body roundness index (CTI-BRI), CTI-weight-adjusted waist index (CTI-WWI), CTI-Chinese visceral adiposity index (CTI-CVAI), CTI-a body shape index (CTI-ABSI)). We employed Cox proportional hazard models, restricted cubic spline analyses, Kaplan–Meier survival plots, ROC curve evaluations, and weighted quantile sum (WQS) regression to examine the links and prognostic utility of each metric with incident CVD across baseline and cumulative exposure tiers. Results: Over a 9-year follow-up period, 1938 new-onset CVD cases were documented. Following multivariable adjustment, each CTI-obesity composite metric showed a significant positive link to CVD risk ( p < 0.05). Moreover, all CTI‑obesity composite parameters showed stronger associations with CVD than CTI alone. CTI‑CVAI demonstrated the relatively best predictive accuracy among the evaluated indices, though absolute discriminatory ability remained modest (baseline area under the ROC curve (AUC): 0.596; cumulative AUC: 0.600). Trajectory clustering found that, versus the well-managed group, the poorly managed subgroup had 21%–71% higher CVD risk (max 71% for CTI-WC). WQS regression indicated that CRP and obesity indices (especially CVAI) were the major contributors in the mixed exposure; cumulative exposure WQS analysis showed that cumulative TG and cumulative obesity parameters contributed the most. Conclusion: CTI combined with obesity indices, particularly CTI-CVAI, was associated with modestly improved predictive ability for CVD risk compared with CTI alone. This parameter may offer incremental information for CVD risk stratification by integrating metabolic inflammation and visceral fat markers.