Frailty is characterized by biomarker patterns reflecting inflammation or muscle catabolism in multi‐morbid patients. Issue 1 (14th November 2022)

• Record Type: Journal Article
• Authors: Kochlik, Bastian; Franz, Kristina; Henning, Thorsten; Weber, Daniela; Wernitz, Andreas; Herpich, Catrin; Jannasch, Franziska; Aykaç, Volkan; Müller‐Werdan, Ursula; Schulze, Matthias B.; Grune, Tilman; Norman, Kristina
• Is Part Of: Journal of cachexia, sarcopenia and muscle. Volume 14:Issue 1(2023)
• Page Start: 157
• Page End: 166
• Publication Date: 2022-11-14
• Subjects: Biomarker -- Frail -- Inflammation -- Methylhistidine -- Micronutrients -- Muscle; Cachexia -- Periodicals; Muscles -- Aging -- Periodicals; Muscles -- Periodicals; Cachexia; Sarcopenia; Muscles; Cachexia; Muscles; Muscles -- Aging; Periodicals; Periodicals; 616
• DOI: 10.1002/jcsm.13118
• Languages: English
• ISSNs: 2190-5991

Abstract

Abstract: Background: Frailty development is partly dependent on multiple factors like low levels of nutrients and high levels of oxidative stress (OS) and inflammation potentially leading to a muscle‐catabolic state. Measures of specific biomarker patterns including nutrients, OS and inflammatory biomarkers as well as muscle related biomarkers like 3‐methylhistidine (3MH) may improve evaluation of mechanisms and the complex networks leading to frailty. Methods: In 220 multi‐morbid patients (≥ 60 years), classified as non‐frail ( n = 104) and frail ( n = 116) according to Fried's frailty criteria, we measured serum concentrations of fat‐soluble micronutrients, amino acids (AA), OS, interleukins (IL) 6 and 10, 3MH (biomarker for muscle protein turnover) and serum spectra of fatty acids (FA). We evaluated biomarker patterns by principal component analysis (PCA) and their cross‐sectional associations with frailty by multivariate logistic regression analysis. Results: Two biomarker patterns [principal components (PC)] were identified by PCA. PC1 was characterized by high positive factor loadings (FL) of carotenoids, anti‐inflammatory FA and vitamin D3 together with high negative FL of pro‐inflammatory FA, IL6 and IL6/IL10, reflecting an inflammation‐related pattern. PC2 was characterized by high positive FL of AA together with high negative FL of 3MH‐based biomarkers, reflecting a muscle‐related pattern. Frail patients had significantly lower factor scores than non‐frail patients for both PC1 [median: −0.27 (interquartile range: 1.15) vs. 0.27 (1.23); P = 0.001] and PC2 [median: −0.15 (interquartile range: 1.13) vs. 0.21 (1.38); P = 0.002]. Patients with higher PC1 or PC2 factor scores were less likely to be frail [odds ratio (OR): 0.62, 95% CI: 0.46–0.83, P = 0.001 for PC1; OR: 0.64, 95% CI: 0.48–0.86, P = 0.003 for PC2] compared with patients with lower PC1 or PC2 factor scores. This indicates that increasing levels of anti‐inflammatory biomarkers and increasing levels of muscle‐anabolic biomarkers are associated with a reduced likelihood (38% and 36%, respectively) for frailty. Significant associations remained after adjusting the regression models for potential confounders. Conclusions: We conclude that two specific patterns reflecting either inflammation‐related or muscle‐related biomarkers are both significantly associated with frailty among multi‐morbid patients and that these specific biomarker patterns are more informative than single biomarker analyses considering frailty identification.