Xiangyu Wang

Background/Objectives: We compared next-morning fecal metabolic and microbial responses to five acute fueling strategies. Methods: Eight male recreational rowers with complete specimens from 12 crossover completers received an erythritol comparator, two carbohydrate doses, fixed carbohydrate–protein, and personalized carbohydrate–protein (P-CPS). Eighty specimens yielded 40 paired changes. Participant-blocked, period-adjusted models used restricted permutations and Benjamini–Hochberg false discovery rate (FDR) correction. Results: No primary global test passed across-modality FDR correction: SCFAs, p = 0.0321, q = 0.0963; metabolome, q = 0.1334; microbiome, q = 0.2813. No metabolite or originally annotated genus passed its omnibus FDR family. Post hoc SILVA 138.2 reclassification retained a nonsignificant global result but identified three low-count genus-level omnibus signals. Secondary change-score contrasts indicated higher isovaleric and isobutyric post/pre fold changes under P-CPS, fixed carbohydrate–protein, and high carbohydrate than under the comparator. However, no concentration or composite-endpoint contrast remained FDR-positive in post hoc models adjusting for same-trial pre-trial values. Caproic acid evidence also weakened with expanded correction and analytical-range checks. Two metabolome–SCFA principal-component correlations survived FDR correction and persisted after exclusion of overlapping annotations. Conclusions: This pilot identified model-sensitive secondary chemical signals without FDR-supported global intervention effects. Baseline imbalance, unmeasured beverage osmolality and fecal water, and quantification limitations preclude attributing these signals to increased fermentation or nutritional benefit.

Objective: Dietary fiber and physical activity are independent determinants of blood pressure. However, their interaction regarding hypertension remains poorly understood. This study examined the association between dietary fiber intake and hypertension. It also aimed to determine if this relationship is modified by physical activity in a representative sample of U.S. adults. Methods: This cross-sectional study used data from 26,556 U.S. adults (National Health and Nutrition Examination Survey 2007–2018). Dietary fiber intake was assessed via two 24-h recalls and categorized into quartiles. Hypertension was defined by measured blood pressure, self-reported diagnosis, or antihypertensive medication use. Physical activity was quantified and divided into three levels. Missing covariate data were addressed using multiple imputations. Survey-weighted multivariable logistic regression was used to examine the association. A multiplicative interaction term was introduced to assess effect modification by physical activity. An XGBoost was also developed and interpreted using SHapley Additive exPlanation to explore nonlinear associations. Results: In the fully adjusted model, lower dietary fiber intake was associated with increased odds of hypertension (OR for Q3 vs. Q4: 1.25; 95% CI [1.08, 1.44]). This relationship showed a potential interaction with physical activity ( p for interaction = .067). Stratified analysis showed no association in the low or moderate physical activity groups. Among individuals with high physical activity, lower fiber intake was associated with significantly higher odds of hypertension (OR for Q1 vs. Q4: 1.50; 95% CI [1.10, 2.04]). The machine learning model confirmed that higher fiber intake was protectively associated with hypertension, an effect most prominent in physically active individuals. Conclusion: The protective association of dietary fiber with hypertension was observed primarily in highly active adults, but not in less active individuals. These findings highlight the importance of the diet-activity interplay for hypertension prevention.

Objective: Lean mass preservation does not guarantee sustained muscle strength in aging populations. Aggregate physical activity metrics obscure temporal movement patterns and fail to explain this mass-function dissociation. Unsupervised machine learning is required to identify multidimensional activity phenotypes and clarify their specific neuromuscular impacts. This study examined the associations of accelerometer-derived activity phenotypes with lean mass versus function, comparing phenotypic models against aggregate volume metrics. Methods: This cross-sectional study analyzed National Health and Nutrition Examination Survey 2011–2014 data from United States adults aged analyzed. K-Means clustering derived activity phenotypes from wrist-accelerometry features representing rhythm and fragmentation. Survey-weighted linear regression assessed independent associations with appendicular lean mass ( n = 1756) and grip strength ( n = 3890), adjusting for covariates. The Akaike information criterion compared the explanatory power of phenotype-based versus traditional volume-based models. Results: Two phenotypes emerged: high-volume/consolidated (HVC: higher MIMS volume, longer sedentary bouts) and low-volume/fragmented (LVF: lower MIMS volume, shorter sedentary bouts). Compared to LVF, HVC exhibited significantly higher appendicular lean mass index ( β = 0.11; 95% CI, 0.03 to 0.18), equivalent to offsetting five years of age-related decline. It demonstrated no significant advantage in grip strength ( β = −0.07; 95% CI, −0.70 to 0.56). Phenotype-based models demonstrated statistical equivalence to traditional volume-based models (ΔAIC < 1.0) for both outcomes. Conclusions: High activity volume accumulated via structured patterns is associated with preserved appendicular lean mass. However, this structural advantage does not translate to improved grip strength. This dissociation implies that total activity volume is insufficient to ensure generalized neuromuscular performance in the presence of prolonged sedentary time. This decoupling implies that consolidated sedentary behavior compromises neuromuscular performance despite adequate total movement. Consequently, sarcopenia guidelines must integrate sedentary fragmentation targets rather than relying solely on aggregate volume.

Background and Objective: Machine learning models offer a practical approach for estimating body fat percentage from simple anthropometric data. However, the scarcity of biomedical data frequently leads to model overfitting, compromising predictive accuracy. Generative data augmentation presents a promising strategy to address this limitation. This study develops and evaluates a generative data augmentation framework to enhance body fat prediction from limited anthropometric data. Methods: A public dataset comprising 249 male subjects was partitioned into development (80%) and test (20%) sets. The fidelity of Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), random noise injection, and mixup was compared to select the optimal method. Subsequently, XGBoost, Support Vector Regression, and Multi-layer Perceptron models were trained and validated, comparing performance with and without the selected augmentation. Final model generalization was assessed on the independent test set using the coefficient of determination (R²), Mean Absolute Error, and Root Mean Squared Error. Results: Among the evaluated augmentation techniques, the WGAN-GP generated synthetic data with the highest fidelity. On the original data, the baseline XGBoost model achieved a R² of 0.67; this performance increased to 0.77 on the test set when using WGAN-GP augmentation. Feature importance analysis of the final model identified abdominal circumference as the most significant predictor of body fat percentage. Conclusion: The WGAN-GP is a highly effective method for generating realistic synthetic anthropometric data. Integrating these synthetic samples into the training pipeline substantially improves the generalization and predictive accuracy of machine learning models. This methodology offers a robust solution for developing more accurate and accessible predictive health models in data-scarce environments.

Background: Taurine (TAU) and creatine (Cr) are common ergogenic aids used by athletes to enhance performance; however, the effect of their combined supplementation, and on recovery in high temperature and humidity environments, has not been studied. Hypothesis: Combined TUA and Cr will have greater effect on physiological indicators and repetitive sprint performance recovery after exhaustive exercise under hot and humid conditions than single supplementation or placebo. Study Design: Single-blind crossover randomized controlled study. Level of Evidence: Level 2. Methods: Participants (12 sports students) were assigned randomly to 1 of 4 supplementation intervention groups: placebo (P), taurine (T), creatine (C), or taurine + creatine (T+C). Exercise protocol included exhaustion tests and repeated sprinting exercises were conducted in a laboratory environment at 35 °C/65% relative humidity. Heartrate, blood lactate (BLa), tympanic temperature, thermal sensation, and rating of perceived exertion were monitored throughout. Heartrate variability, time to exhaustion (TTE), reaction time, and countermovement jump (CMJ) height were tracked before and after exhaustion exercise and before sprint exercise. Results: TTE was significantly higher in the T+C group than in the P group ( P = 0.04). BLa and tympanic temperature increased rapidly in all 4 conditions, then decreased gradually, and T group peak values were higher than those of P group ( P = 0.04; P < 0.01). CMJ decreased in the C and T+C groups ( P = 0.04; P = 0.04) after exhaustive exercise, unlike other groups ( P > 0.05). Indicators of repeated sprint exercise, peak power, mean power, and power decrement showed a decreasing trend within groups but no difference between groups ( P > 0.05). Conclusion: In this small student group, under hot and humid conditions, T+C supplementation significantly enhanced TTE. Clinical Relevance: TAU, Cr, and their combined supplementation do not significantly improve repeated sprint performance after exhaustive exercise under hot and humid conditions.

Carbohydrate-protein supplement (CPS) intake is a well-established strategy for enhancing athletic performance, promoting glycogen replenishment, maintaining a positive nitrogen balance, and minimizing muscle damage in endurance athletes. Current CPS intake recommendations often rely solely on weight, lacking personalization. This study aimed to develop a machine learning-based personalized CPS intake recommendation system for endurance sports enthusiasts. We recruited 171 participants and collected 45 indicators from 12 diverse aspects, including lifestyle, psychological state, sleep quality, demographics, anthropometrics and body composition, physical activity levels, exercise capacity, blood markers and central nervous system parameters, cardiovascular metrics, meal timings, and beverage composition. Additionally, we assessed each subject’s performance in the Jensen Kurt’s 60-minute rowing ergometer distance race. Utilizing back propagation (BP) neural networks with 5-fold cross-validation, we identified the relationship between the 45 indicators and the 1-hour rowing distance, and observed a well-fitted model. We further employed an enumeration method to tailor the CPS intake protocol for each individual. Our results demonstrate the feasibility and potential of using machine learning to deliver personalized CPS intake recommendations. Future work will focus on expanding the dataset’s dimensions to iterate, update, and enhance the model’s robustness.

Generally, adequate motor coordination (MC) ability is one among the critical factors for the overall development of children. In this paper, we have thoroughly analyzed the effects of equine-assistant activity (EAA) training on MC in children. For this purpose, MC test, specifically for children, was used to the Körperkoordinationstest für Kinder (KTK), and a total of 100 children, particularly those in 8 to 10 age, were equally separated into equine-assistant activity group (EAAG) and control group (CG), respectively. The EAAG group has attended a 14-week EAA training program, while the CG joined in physical education activity once per week. The experimental results have indicated that four indices of KTK test (i.e., backward walk [WB], height jump [HH], jumping sideways [JS] and moving sideways [MS], and motor quotient [MQ] score) showed significant differences ( ) after a 14-week EAA training. Furthermore, the indices of physical fitness test, standing long jump (SLJ), and sit and reach (SAR) showed significant differences ( ), but the handgrip (HG) increased slightly without significant difference ( ) after a 14-week EAA training. In conclusion, there were improvements in MC, lower limb strength, and flexibility by EAAG for those who participated in a 14-week EAA training, and this study has demonstrated the effectiveness of the KTK assessment of MC in children 8 to 10 years.