Gut Microbial, Inflammatory and Metabolic Signatures in Older People with Physical Frailty and Sarcopenia: Results from the BIOSPHERE Study

• Published in: Nutrients Vol. 12; no. 1; p. 65
• Main Authors: Picca, Anna, Ponziani, Francesca Romana, Calvani, Riccardo, Marini, Federico, Biancolillo, Alessandra, Coelho-Júnior, Hélio José, Gervasoni, Jacopo, Primiano, Aniello, Putignani, Lorenza, Del Chierico, Federica, Reddel, Sofia, Gasbarrini, Antonio, Landi, Francesco, Bernabei, Roberto, Marzetti, Emanuele
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.12.2019; MDPI
• Subjects: analytical methods; aspartic acid; Biomarkers; Biosphere; blood serum; classification; covariance; Deoxyribonucleic acid; DNA; elderly; Frailty; Inflammation; intestinal microorganisms; Metabolism; Microbiota; nutrients; Nutrition; nutritional status; Older people; Oscillospira; Oxidative stress; pathophysiology; prediction; regression analysis; Ruminococcus; Sarcopenia; threonine; United States > US; Italy; systemic inflammation; profiling; biomarkers; amino acids; muscle; multi-marker; physical performance; metabolism; aging; gut microbiota
• ISSN: 2072-6643; 2072-6643
• DOI: 10.3390/nu12010065

Physical frailty and sarcopenia (PF&S) share multisystem derangements, including variations in circulating amino acids and chronic low-grade inflammation. Gut microbiota balances inflammatory responses in several conditions and according to nutritional status. Therefore, an altered gut-muscle crosstalk has been hypothesized in PF&S. We analyzed the gut microbial taxa, systemic inflammation, and metabolic characteristics of older adults with and without PF&S. An innovative multi-marker analytical approach was applied to explore the classification performance of potential biomarkers for PF&S. Thirty-five community dwellers aged 70+, 18 with PF&S, and 17 nonPF&S controls were enrolled. Sequential and Orthogonalized Covariance Selection (SO-CovSel), a multi-platform regression method developed to handle highly correlated variables, was applied. The SO-CovSel model with the best prediction ability using the smallest number of variables was built using seven mediators. The model correctly classified 91.7% participants with PF&S and 87.5% nonPF&S controls. Compared with the latter group, PF&S participants showed higher serum concentrations of aspartic acid, lower circulating levels of concentrations of threonine and macrophage inflammatory protein 1α, increased abundance of Oscillospira and Ruminococcus microbial taxa, and decreased abundance of Barnesiellaceae and Christensenellaceae. Future investigations are warranted to determine whether these biomediators are involved in PF&S pathophysiology and may, therefore, provide new targets for interventions.