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Cholelithiasis is a common digestive disease that drives a myriad of adverse complications. The correlation between sarcopenia and various digestive disorders has been extensively researched, whereas its association with cholelithiasis remains unreported. We aimed to investigate the association through prospective and Mendelian randomization (MR) analyses and establish a quantitative score reflecting the impact of sarcopenia‐related markers on cholelithiasis. The prospective study involved 448 627 participants from the UK Biobank. Cox proportional hazard models were employed to investigate the correlation between sarcopenia‐related markers and cholelithiasis. To quantitatively assess cholelithiasis risk, the SARCHO score was derived from a multivariable Cox model. Bidirectional two‐sample MR analysis was conducted to validate the causal association. A total of 16 738 individuals developed cholelithiasis during a median follow‐up of 12 years. Hazard ratios (HRs) of cholelithiasis decreased stepwise over skeletal muscle index tertiles (highest tertile: reference; middle tertile: 1.23, p <.001; lowest tertile: 1.33, p <.001). The tertiles of grip strength showed a similar pattern. Individuals with slow walking pace had a higher risk of cholelithiasis compared to those with normal walking pace (HR 1.23; p <.001). Our SARCHO score better quantifies the risk of cholelithiasis. MR analysis showed a causal relationship between muscle mass and cholelithiasis (OR 0.81; p <.001). No causal effect of cholelithiasis on lean mass was observed. Prospective and MR analyses have consistently demonstrated an increased risk of cholelithiasis in individuals with decreased muscle mass. Additionally, SARCHO score further quantified the cholelithiasis occurrence risk. These findings provide compelling evidence for muscle strengthening in preventing cholelithiasis. [ABSTRACT FROM AUTHOR]

Objective: Metabolic dysfunction‐associated steatotic liver disease (MASLD) affects many populations, and screening out the high‐risk populations at an early stage is a challenge. As a sarcopenia index, the relationship between creatinine to cystatin C ratio (CCR) and MASLD remains unclear. This cross‐sectional, prospective study aimed to explore the relationship between CCR and MASLD. Design Firstly, explored the correlation between CCR and MASLD in cross‐sectional analyses. Then excluded the population with baseeline diagnosis of MASLD and analyzed the association with baseline CCR levels and the onset of MASLD in the population with available follow‐up data. Univariate and multivariate logistic regression analyses were used to calculate odds ratios (ORs) to evaluate the association between CCR levels and MASLD. Patients and Measurements: This study included 368,634 participants from the UK Biobank for cross‐sectional and prospective analyses. The demographic characteristics and laboratory measurements of all participants were obtained from the UK Biobank. MASLD was diagnosed according to the multi‐society consensus nomenclature. Hepatic steatosis was defined as FLI ≥60. Results: We grouped the study participants according to CCR tertiles. In cross‐sectional analyses, participants in CCR tertile 1 had the highest MASLD risk (OR: 1.070, 95% CI: 1.053−1.088, p <.001). And the similar association was observed in the prospective analyses (CCR tertile 1 OR: 1.340, 95% CI: 1.077−1.660, p =.009; CCR tertile 2 OR: 1.217, 95% CI: 1.021−1.450, p =.029, respectively). After stratification by gender, the significant association between CCR and the onset of MASLD was only observed in males (CCR tertile 1 OR: 1.639, 95% CI: 1.160−2.317, p =.005; CCR tertile 2 OR: 1.322, 95% CI: 1.073−1.628, p =.005, respectively). Conclusion: Our results indicated that lower CCR was significantly associated with higher risk of MASLD, based on which predictive models can be developed to screen populations at high risk of developing MASLD. [ABSTRACT FROM AUTHOR]

Fe-Cu-kaolinites were prepared by co-precipitation and hydrothermal methods, and characterized by X-ray diffraction (XRD) and scanning electron microscope (SEM), where 2 wt.% natural kaolinite was dispersed, and the ratios of (Al + Fe + Cu)/clay = 10 mmol/g and Al/(Fe + Cu) = 5/1 were maintained. The effect of different drying methods (vacuum drying, ethanol exchange drying, freeze-drying, microwave drying, normal oven drying) and different Fe/Cu molar ratio (0/2, 0.4/1.6, 0.8/1.2, 1/1, 1.2/0.8, 1.6/0.4, 2/0) was also assessed. Catalytic wet peroxide oxidation (CWPO) reaction of 4-chlorophenol (4-CP) was used to probe the reactivity and activity of the materials prepared. The results showed that Fe and Cu could be successfully intercalated into the interlayer of kaolinite by hydrothermal method, where specific surface area and pore volume increased by 19 times and 7 times, respectively; the intensity of basal space (001) reflection peak was reduced by 80%, and tip width was doubly increased. The catalyst possessed higher reactivity, with 85.5% of 4-CP conversion being observed, whereas only 15.2% of 4-CP was removed over raw kaolinite. High-power microwave drying (720 W) was the best drying method, because it resulted in greater microstructure and thus higher reactivity (85.3% of 4-CP conversion), with lower active metal (Fe or Cu) leaching (3.96 mg L−1). Fe/Cu molar ratio of 0.8–1.0/1.2–1.0 was considered as the optimum ratio in pillaring solution, for maintaining higher catalytic activity (85–90% of 4-CP conversion) and lower metal (Fe or Cu) leaching (7–9.3 mg L−1). [ABSTRACT FROM AUTHOR]

Hyperuricemia (HUA) has emerged as the second most prevalent metabolic disorder characterized by prolonged and asymptomatic period, triggering gout and metabolism-related outcomes. Early detection and prognosis prediction for HUA and gout are crucial for pre-emptive interventions. Integrating genetic and clinical data from 421287 UK Biobank and 8900 Nanfang Hospital participants, a stacked multimodal machine learning model is developed and validated to synthesize its probabilities as an in-silico quantitative marker for hyperuricemia (ISHUA). The model demonstrates satisfactory performance in detecting HUA, exhibiting area under the curves (AUCs) of 0.859, 0.836, and 0.779 within the train, internal, and external test sets, respectively. ISHUA is significantly associated with gout and metabolism-related outcomes, effectively classifying individuals into low- and high-risk groups for gout in the train (AUC, 0.815) and internal test (AUC, 0.814) sets. The high-risk group shows increased susceptibility to metabolism-related outcomes, and participants with intermediate or favorable lifestyle profiles have hazard ratios of 0.75 and 0.53 for gout compared with those with unfavorable lifestyles. Similar trends are observed for other metabolism-related outcomes. The multimodal machine learning-based ISHUA marker enables personalized risk stratification for gout and metabolism-related outcomes, and it is unveiled that lifestyle changes can ameliorate these outcomes within high-risk group, providing guidance for preventive interventions., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)