Plasma Short-Chain Fatty Acids and Cytokine Profiles in Chronic Kidney Disease: A Potential Pathophysiological Link.

Sarcopenia was present in nearly half of patients with CKD and CHF.

• 39 of 80 patients (49%) met criteria for sarcopenia
• Sarcopenia was diagnosed according to EWGSOP2 criteria
• The study was cross-sectional in design
• All 80 patients had both chronic kidney disease and chronic heart failure

Patients with sarcopenia exhibited significantly lower body fat percentage, reduced appendicular skeletal muscle mass (ASM), and slower gait speed compared to those without sarcopenia.

• Body fat percentage was significantly lower in the sarcopenic group
• ASM (appendicular skeletal muscle mass) was reduced in sarcopenic patients
• Gait speed was slower in sarcopenic patients
• Body composition was assessed by bioimpedance analysis

Hexanoic acid (C6) showed an independent positive association with sarcopenia in multivariable analysis.

• OR = 2.24, 95% CI: 1.08–5.37 for hexanoic acid (C6)
• This association was identified as independent in multivariable logistic regression
• LASSO feature selection was used to identify key determinants prior to final modeling
• C6 was associated with stronger pro-inflammatory clustering in SCFA-cytokine correlation networks

IL-8 showed an inverse association with sarcopenia, indicating lower IL-8 levels were more frequently observed in individuals with sarcopenia.

• OR = 0.38, 95% CI: 0.13–0.94 for IL-8
• Lower IL-8 levels were associated with the presence of sarcopenia
• This association was identified in multivariable logistic regression
• IL-8 was identified as an independent predictor alongside C6 and body-composition metrics

Correlation heatmaps revealed distinct SCFA-cytokine coupling patterns depending on sarcopenia status.

• Stronger pro-inflammatory clustering was observed in C6-associated networks
• SCFA-cytokine coupling patterns differed between sarcopenic and non-sarcopenic patients
• Analysis methods included correlation heatmaps and principal component analysis (PCA)

The final multivariable model integrating SCFAs, cytokines, and body-composition metrics achieved excellent discrimination and good calibration.

• AUC = 0.911 for the final multivariable model
• The model was described as having 'excellent discrimination' and 'good calibration'
• Model building used multivariable logistic regression, LASSO feature selection, and Random Forest modeling
• The model integrated three domains: SCFAs, cytokines, and body-composition metrics