Yuan-Yuei Chen1,2,3, Yi-Lin Chiu4, Tung-Wei Kao3, Tao-Chun Peng3, Hui-Fang Yang3, Wei-Liang Chen5,6. 1. Department of Pathology, Tri-Service General Hospital; and School of Medicine, National Defense Medical Center, Taipei, Taiwan, Republic of China. 2. Department of Pathology, Tri-Service General Hospital Songshan Branch; and School of Medicine, National Defense Medical Center, Taipei, Taiwan, Republic of China. 3. Division of Geriatric Medicine, Department of Family and Community Medicine, Tri-Service General Hospital; and School of Medicine, National Defense Medical Center, Number 325, Section 2, Chang-gong Rd, Nei-Hu District, 114, Taipei, Taiwan, Republic of China. 4. Department of Biochemistry, National Defense Medical Center, Taipei, Taiwan, Republic of China. 5. Division of Geriatric Medicine, Department of Family and Community Medicine, Tri-Service General Hospital; and School of Medicine, National Defense Medical Center, Number 325, Section 2, Chang-gong Rd, Nei-Hu District, 114, Taipei, Taiwan, Republic of China. weiliang0508@gmail.com. 6. Department of Biochemistry, National Defense Medical Center, Taipei, Taiwan, Republic of China. weiliang0508@gmail.com.
Abstract
BACKGROUND: Sarcopenia is a multifactorial pathophysiologic condition of skeletal muscle mass and muscle strength associated with aging. However, biomarkers for predicting the occurrence of sarcopenia are rarely discussed in recent studies. The aim of the study was to elucidate the relationship between sarcopenia and several pertinent biomarkers. METHODS: Using the Gene Expression Omnibus (GEO) profiles of the National Center for Biotechnology Information, the associations between mRNA expression of biomarkers and sarcopenia were explored, including high temperature requirement serine protease A1 (HtrA1), procollagen type III N-terminal peptide (P3NP), apelin, and heat shock proteins 70 (Hsp72). We enrolled 408 community-dwelling adults aged 65 years and older with sarcopenia and nonsarcopenia based on the algorithm proposed by the Asian Working Group for Sarcopenia (AWGS). Muscle strength is identified by hand grip strength using an analogue isometric dynamometer. Muscle mass is estimated by skeletal mass index (SMI) using a bioelectrical impedance analysis. Physical performance is measured by gait speed using 6 m walking distance. The associations between these biomarkers and sarcopenia were determined using receiver operating characteristic (ROC) curve analysis and multivariate regression models. RESULTS: From the GEO profiles, the sarcopenia gene set variation analysis score was correlated significantly with the mRNA expression of APLNR (p < 0.001) and HSPA2 (p < 0.001). In our study, apelin was significantly associated with decreased hand grip strength with β values of - 0.137 (95%CI: - 0.229, - 0.046) in men. P3NP and HtrA1 were significantly associated with increased SMI with β values of 0.081 (95%CI: 0.010, 0.153) and 0.005 (95%CI: 0.001, 0.009) in men, respectively. Apelin and HtrA1 were inversely associated with the presence of sarcopenia with an OR of 0.543 (95%CI: 0.397-0.743) and 0.003 (95%CI: 0.001-0.890) after full adjustment. The cutoff point of HtrA1 was associated with the presence of sarcopenia with an OR of 0.254 (95%CI: 0.083-0.778) in men. The cutoff point of apelin was negatively associated with the presence of sarcopenia with an OR of 0.254 (95%CI: 0.083-0.778). CONCLUSION: Our study highlights that P3NP, HtrA, and apelin are useful for diagnosis of sarcopenia in the clinical setting.
BACKGROUND: Sarcopenia is a multifactorial pathophysiologic condition of skeletal muscle mass and muscle strength associated with aging. However, biomarkers for predicting the occurrence of sarcopenia are rarely discussed in recent studies. The aim of the study was to elucidate the relationship between sarcopenia and several pertinent biomarkers. METHODS: Using the Gene Expression Omnibus (GEO) profiles of the National Center for Biotechnology Information, the associations between mRNA expression of biomarkers and sarcopenia were explored, including high temperature requirement serine protease A1 (HtrA1), procollagen type III N-terminal peptide (P3NP), apelin, and heat shock proteins 70 (Hsp72). We enrolled 408 community-dwelling adults aged 65 years and older with sarcopenia and nonsarcopenia based on the algorithm proposed by the Asian Working Group for Sarcopenia (AWGS). Muscle strength is identified by hand grip strength using an analogue isometric dynamometer. Muscle mass is estimated by skeletal mass index (SMI) using a bioelectrical impedance analysis. Physical performance is measured by gait speed using 6 m walking distance. The associations between these biomarkers and sarcopenia were determined using receiver operating characteristic (ROC) curve analysis and multivariate regression models. RESULTS: From the GEO profiles, the sarcopenia gene set variation analysis score was correlated significantly with the mRNA expression of APLNR (p < 0.001) and HSPA2 (p < 0.001). In our study, apelin was significantly associated with decreased hand grip strength with β values of - 0.137 (95%CI: - 0.229, - 0.046) in men. P3NP and HtrA1 were significantly associated with increased SMI with β values of 0.081 (95%CI: 0.010, 0.153) and 0.005 (95%CI: 0.001, 0.009) in men, respectively. Apelin and HtrA1 were inversely associated with the presence of sarcopenia with an OR of 0.543 (95%CI: 0.397-0.743) and 0.003 (95%CI: 0.001-0.890) after full adjustment. The cutoff point of HtrA1 was associated with the presence of sarcopenia with an OR of 0.254 (95%CI: 0.083-0.778) in men. The cutoff point of apelin was negatively associated with the presence of sarcopenia with an OR of 0.254 (95%CI: 0.083-0.778). CONCLUSION: Our study highlights that P3NP, HtrA, and apelin are useful for diagnosis of sarcopenia in the clinical setting.
Authors: Claire Vinel; Laura Lukjanenko; Aurelie Batut; Simon Deleruyelle; Jean-Philippe Pradère; Sophie Le Gonidec; Alizée Dortignac; Nancy Geoffre; Ophelie Pereira; Sonia Karaz; Umji Lee; Mylène Camus; Karima Chaoui; Etienne Mouisel; Anne Bigot; Vincent Mouly; Mathieu Vigneau; Allan F Pagano; Angèle Chopard; Fabien Pillard; Sophie Guyonnet; Matteo Cesari; Odile Burlet-Schiltz; Marco Pahor; Jerome N Feige; Bruno Vellas; Philippe Valet; Cedric Dray Journal: Nat Med Date: 2018-07-30 Impact factor: 53.440
Authors: Jérémie Boucher; Bernard Masri; Danièle Daviaud; Stéphane Gesta; Charlotte Guigné; Anne Mazzucotelli; Isabelle Castan-Laurell; Ivan Tack; Bernard Knibiehler; Christian Carpéné; Yves Audigier; Jean-Sébastien Saulnier-Blache; Philippe Valet Journal: Endocrinology Date: 2005-01-27 Impact factor: 4.736
Authors: Shalender Bhasin; E Jiaxiu He; Miwa Kawakubo; E Todd Schroeder; Kevin Yarasheski; Gregory J Opiteck; Alise Reicin; Fabian Chen; Raymond Lam; Jeffrey A Tsou; Carmen Castaneda-Sceppa; Ellen F Binder; Stanley P Azen; Fred R Sattler Journal: J Clin Endocrinol Metab Date: 2009-10-16 Impact factor: 5.958
Authors: Alfonso J Cruz-Jentoft; Jean Pierre Baeyens; Jürgen M Bauer; Yves Boirie; Tommy Cederholm; Francesco Landi; Finbarr C Martin; Jean-Pierre Michel; Yves Rolland; Stéphane M Schneider; Eva Topinková; Maurits Vandewoude; Mauro Zamboni Journal: Age Ageing Date: 2010-04-13 Impact factor: 10.668
Authors: A Besse-Patin; E Montastier; C Vinel; I Castan-Laurell; K Louche; C Dray; D Daviaud; L Mir; M-A Marques; C Thalamas; P Valet; D Langin; C Moro; N Viguerie Journal: Int J Obes (Lond) Date: 2013-08-27 Impact factor: 5.095