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Thyroid function, nutritional status, inflammation, and sarcopenia in hemodialysis patients: an integrated
Karina Schiavoni Scandelai1, Hércules Rezende Freitas2, Maria das Graças Coelho de Souza3,4
1Postgraduate Program in Clinical and Experimental Physiopathology (Fisclinex), Faculty of Medical Sciences - Rio de Janeiro State University , Rio de Janeiro, RJ, Brazil.
Objective:
The objective of this study was to investigate the interrelationships among thyroid function, nutritional status, inflammation, and sarcopenia in patients undergoing hemodialysis (HD) and to identify variables associated with sarcopenia using a penalized regression approach.
Design:
This is a cross-sectional study.
Methods:
A total of 101 HD patients and 33 euthyroid controls were evaluated. Thyroid hormones, inflammatory biomarkers, leptin, and albumin were measured. Nutritional status was assessed using the Geriatric Nutritional Risk Index (GNRI), and body composition by bioelectrical impedance analysis. Sarcopenia was defined by reduced handgrip strength and confirmed by skeletal muscle mass or skeletal muscle index. Penalized regression (LASSO) with internal validation was applied to identify variables associated with sarcopenia.
Results:
Compared with controls, HD patients were older and had higher TSH, C-reactive protein, IL-6, TNF-α, and leptin levels and lower FT3, FT4, albumin, and GNRI values (all P < 0.05), with no difference in BMI. Probable and confirmed sarcopenia were more prevalent in HD patients (48.5 and 26.7%) than in controls (18.1 and 6.0%, P = 0.004). In the penalized regression model, FT3, TSH, GNRI, and dialysis status were selected as variables associated with sarcopenia (bootstrap-corrected AUC = 0.72; recalibrated AUC = 0.79).
Conclusion:
HD patients exhibited concurrent thyroid, inflammatory, and nutritional alterations associated with sarcopenia. FT3, TSH, GNRI, and dialysis status formed an integrated biological signature associated with sarcopenia. These findings support an integrative modelling approach and require validation in larger independent cohorts.
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