Sarcopenia significantly influences the treatment outcomes and prognosis of colorectal cancer patients, and dietary patterns show a notable correlation with sarcopenia. Elucidating the characteristics of Chinese healthy dietary patterns and their association with sarcopenia is crucial for developing targeted nutritional interventions based on traditional Chinese dietary practices.
To explore the correlation between Chinese healthy dietary pattern and sarcopenia in colorectal cancer patients, utilizing the Chinese Healthy Eating Index (CHEI).
From July 2024 to May 2025, a total of 326 colorectal cancer patients from Tangshan People's Hospital, North China University of Science and Technology Affiliated Hospital, and Kailuan General Hospital were enrolled as study subjects. The CHEI and various diagnostic indicators of sarcopenia were collected. Binary Logistic regression analysis was employed to examine the associations between the total CHEI score and its dietary components with low muscle mass, impaired physical function and sarcopenia in colorectal cancer patients.
Among the 326 patients, 195 (59.82%) were male and 131 (40.18%) were female, with an average age of (64.0±8.8) years. Among them, there were 49 cases (15.03%) with reduced muscle mass, and the CHEI score was 50 (40, 55). Muscle strength was reduced in 220 cases (67.48%), and the CHEI score was 55 (45, 60). Physical function declined in 217 cases (66.56%), with CHEI scores of 55 (45, 60). There were 46 patients (14.11%) with sarcopenia, with a CHEI score of 478 (40, 55). Binary Logistic regression analysis results indicated that CHEI scores were negatively associated with the risk of low muscle mass (OR=0.934, 95%CI=0.891-0.979, P=0.004), impaired physical function (OR=0.968, 95%CI=0.942-0.995, P=0.020), and sarcopenia (OR=0.931, 95%CI=0.889-0.975, P=0.003). Analysis of the correlation between various dietary components and low muscle mass, impaired physical function, and sarcopenia revealed that scores for fruits (OR=0.880, 95%CI=0.786-0.985, P=0.026), legumes (OR=0.480, 95%CI=0.290-0.794, P=0.004), poultry intake (OR=0.799, 95%CI=0.644-0.991, P=0.041), and sodium (OR=0.897, 95%CI=0.805-1.000, P=0.049) were negatively correlated with the risk of low muscle mass. Scores for whole grains (OR=0.839, 95%CI=0.714-0.986, P=0.033) and seafood (OR=0.848, 95%CI=0.730-0.985, P=0.031) were negatively correlated with the risk of impaired physical function, while red meat (OR=1.256, 95%CI=1.048-1.506, P=0.014) was positively correlated with the risk of impaired physical function. Grains (OR=1.608, 95%CI=1.115-2.317, P=0.011) showed a significant positive correlation with the risk of sarcopenia. Scores for fruits (OR=0.886, 95%CI=0.788-0.996, P=0.043), legumes (OR=0.409, 95%CI=0.238-0.703, P=0.001), and poultry (OR=0.731, 95%CI=0.584-0.916, P=0.007) were negatively correlated with the risk of sarcopenia.
The CHEI is negatively associated with the risk of low muscle mass, impaired physical function, and sarcopenia in colorectal cancer patients. Adequate intake of fruits, legumes, poultry, whole grains, and seafood, moderate red meat consumption, and controlled intake of sodium and grains may help lower the risk of sarcopenia in colorectal cancer patients.
With global population aging, cognitive decline has become a major public-health challenge. Moderate-intensity continuous training (MICT) has been shown to improve cognition but is time-consuming, whereas high-intensity interval training (HIIT) is considered a time-efficient alternative with comparable promise. Prior meta-analyses have largely relied on traditional two-level models and have not adequately addressed the dependence among multiple cognition outcomes within studies; a three-level meta-analytic approach is therefore warranted to obtain more robust effect estimates and inferences.
To investigate the effects of HIIT and MICT on cognitive function in healthy individuals, as well as the moderating factors, in order to establish precise prescription guidelines for these two training modalities in enhancing cognitive function.
Following PRISMA guidelines, we searched PubMed, Web of Science, Cochrane Library, Scopus, and Embase from inception to August 2025 for randomized controlled trials examining the effects of HIIT and MICT on cognitive function in healthy populations. Meta-analysis was conducted in R 4.4.3 using random-effects models for primary pooling. Subgroup analyses, meta-regression, and sensitivity analyses were performed to probe sources of heterogeneity and identify determinants of intervention efficacy.
A total of 19 randomized controlled trials involving 2 277 participants were included. Compared with the control group, HIIT significantly improved executive function (Hedges'g=0.36, 95%CI=0.10-0.61, P=0.01) and memory function (Hedges'g=0.61, 95%CI=0.12-1.09, P<0.01) in healthy individuals, but had no significant effect on information processing (Hedges'g=0.69, 95%CI=-0.65 to 2.03, P=0.25) or attention (Hedges'g=0.36, 95%CI=-0.20 to 0.92, P=0.15). Regression analysis revealed that older participants showed greater improvement in memory function (β=0.02, P<0.01), and the greatest memory enhancement was observed in participants with a BMI of 27.66 kg/m2 (β=2.05, P<0.01). The duration of rest intervals was negatively associated with improvements in executive function (β=-0.01, P=0.04), indicating better outcomes with shorter rest periods; among these, a 150-second rest interval demonstrated the most significant effect (β=2.39, P<0.01). Single training duration was positively correlated with improvements in executive function (β=0.10, P=0.03); specifically, a 6-minute training session showed the most pronounced benefit (β=2.31, P<0.01).
HIIT significantly improves executive and memory functions in healthy individuals, offering health benefits similar to those of MICT and serving as a time-efficient alternative for cognitive enhancement. Greater cognitive improvements were observed in older adults and those with moderate-to-high BMI through HIIT. Based on previous research findings, this study suggests that an optimal training prescription may include approximately 150 seconds of intermittent exercise per session, each session lasting about 6 minutes, and consistent intervention over 6 to 12 weeks.
Sub-health status, as a subclinical and reversible stage between health and chronic disease, has received widespread attention. Previous studies have indicated that various lifestyle factors significantly influence the dynamic changes of sub-health.
This study aims to explore the association between diverse lifestyle factors and sub-health status using machine learning methods. In addition, it seeks to develop a predictive model for sub-health to enable early identification, diagnosis, and intervention in sub-health populations.
This study was a cross-sectional survey conducted from September 2017 to January 2024. A multistage cluster sampling method was employed to randomly select survey sites and units in stages from seven representative cities in Guangdong Province, with a total of 20 375 participants ultimately included. Data were collected on-site during routine health examinations by uniformly trained investigators using standardized questionnaires, including the Sub-health Assessment Scale and the Health-promoting Lifestyle ProfileⅡ (HPLP-Ⅱ). For statistical analysis, SPSS 25.0 and Python 3.9 were used for data processing and analysis. Feature selection was first performed using recursive feature elimination, and sub-health prediction models were then developed based on Logistic regression, support vector machine, k-nearest neighbors, and XGBoost. Subsequently, five-fold cross-validation and data balancing techniques were employed to evaluate and optimize model performance, aiming to identify associated factors and predict sub-health status.
A total of 20 375 participants were included. Among them, 2 586 (12.7%) participants were classified as healthy, and 17 789 (87.3%) were classified as having sub-health status. In the sub-health group, there were 8 581 (48.2%) males and 9 208 (51.8%) females. Univariate analysis showed that there were statistically significant differences between the healthy and sub-health groups in sex, age, educational level, marital status, BMI, alcohol consumption, and the scores of all 52 HPLP-Ⅱ items (P<0.05), whereas no statistically significant difference was observed in smoking status (P>0.05). Feature selection results indicated that BMI and sleep quality were the key factors most closely associated with sub-health status. The model constructed based on the XGBoost algorithm demonstrated good performance, with an accuracy of 0.860 and an F1 score of 0.672. SHAP analysis further confirmed that these variables had high contributions to the model and played an important role in predicting sub-health status.
This study indicates that BMI and sleep quality are key factors associated with suboptimal health status, providing a scientific basis for the prevention and management of chronic diseases. Against the backdrop of rapid advances in predictive, preventive, and personalized medicine, early prediction and precise intervention for suboptimal health may facilitate disease state reversal and ultimately improve population health outcomes.
