TY - JOUR

T1 - Clinical, Immunological, and Vesicular Markers in Sarcopenia and Presarcopenia

AU - Shuliko, Liudmila M

AU - Svarovsky, Dmitry A

AU - Spirina, Liudmila V

AU - Ogieuhi, Ikponmwosa Jude

AU - Akbasheva, Olga E

AU - Matveeva, Mariia V

AU - Samoilova, Iuliia G

AU - Shokalo, Valeria A

AU - Timoshenko, Sofia S

AU - Merkulova, Sofia M

AU - Ragimov, Amin I

AU - Shukyurova, Mar'yam P

AU - Tarasenko, Natalia V

N1 - © 2025 The Author(s). Published by IMR Press.

PY - 2025/8/27

Y1 - 2025/8/27

N2 - BACKGROUND: Sarcopenia is a complex, multifactorial condition characterized by progressive loss of muscle mass, strength, and function. Despite growing awareness, the early diagnosis and pathophysiological characterization of this condition remain challenging due to the lack of integrative biomarkers.OBJECTIVE: This study aimed to conduct a comprehensive multilevel profiling of clinical parameters, immune cell phenotypes, extracellular vesicle (EV) signatures, and biochemical markers to elucidate biological gradients associated with different stages of sarcopenia.MATERIALS AND METHODS: A prospective cohort study enrolled adults aged 45-85 years classified as control, presarcopenic, or sarcopenic based on European Working Group on Sarcopenia in Older People 2 (EWGSOP2) criteria. Clinical evaluation included anthropometry, muscle strength, sarcopenia screening (SARC-F) questionnaire/Short Physical Performance Battery (SPPB) questionnaires, and quality-of-life assessment. Flow cytometry was used to characterize blood monocyte/macrophage subsets (cluster of differentiation 14 (CD14), CD68, CD163, CD206). EVs were isolated from plasma and profiled for surface tetraspanins and matrix metalloproteinases (MMP2, MMP9, tissue inhibitor of metalloproteinase-1 (TIMP-1)) using bead-based flow cytometry. Biochemical assays measured metabolic, inflammatory, and extracellular matrix (ECM)-related markers. Data were analyzed via Kruskal-Wallis testing, discriminant analysis, and principal component analysis (PCA).RESULTS: Sarcopenia, a muscle-wasting condition linked to aging, is characterized by chronic inflammation, proteolytic imbalance, and metabolic disturbances. Clinical deterioration is evident through reduced appendicular lean mass (ALM), appendicular skeletal muscle index (ASMI), SPPB scores, and sarcopenia quality of life (SarQoL) domains. Principal component analysis (PCA) identified four functional marker clusters: ECM degradation (MMP-positive EVs), inflammatory and homeostasis-stabilizing macrophages, and metabolic disruption (glucose, asprosin, triglycerides). Discriminant analysis emphasized vesicular and immune markers with significant classification potential, even when univariate differences were non-significant. Metabolic destabilization and inflammatory activation are detectable in presarcopenia stages. Chronic inflammation, characterized by CD14-CD163+206+ cells releasing pro-inflammatory cytokines, accelerates muscle degradation. Proteolytic dysfunction, with an imbalance between proteases and inhibitors, further contributes to muscle loss. Metabolic disorders impair energy production and nutrient utilization, exacerbating muscle wasting. A comprehensive assessment, including anthropometric, functional, physical activity, and QoL measures, is crucial for identifying high-risk individuals and understanding sarcopenia's mechanisms. Vesicular biomarkers, regulating tissue remodeling and inflammation, provide valuable insights. Standardized assessment methods are essential for enhancing diagnostic accuracy and intervention effectiveness. Future research should focus on developing and refining biomarkers to improve specificity and sensitivity, enabling targeted therapies and better QoL.CONCLUSIONS: Integrating clinical, immunological, and biochemical markers with EVs helps stratify sarcopenia effectively. Our data shows that EVs and macrophage profiles reflect systemic changes and metabolic stress. However, age- and gender-related variability in our cohort warrants caution in generalizing the findings. Artificial intelligence (AI) enhances patient clustering by combining these data types, enabling precise, personalized sarcopenia management, predicting disease progression, and identifying high-risk patients. AI also standardizes and optimizes analytical protocols, improving diagnostic and monitoring reliability and reproducibility.

AB - BACKGROUND: Sarcopenia is a complex, multifactorial condition characterized by progressive loss of muscle mass, strength, and function. Despite growing awareness, the early diagnosis and pathophysiological characterization of this condition remain challenging due to the lack of integrative biomarkers.OBJECTIVE: This study aimed to conduct a comprehensive multilevel profiling of clinical parameters, immune cell phenotypes, extracellular vesicle (EV) signatures, and biochemical markers to elucidate biological gradients associated with different stages of sarcopenia.MATERIALS AND METHODS: A prospective cohort study enrolled adults aged 45-85 years classified as control, presarcopenic, or sarcopenic based on European Working Group on Sarcopenia in Older People 2 (EWGSOP2) criteria. Clinical evaluation included anthropometry, muscle strength, sarcopenia screening (SARC-F) questionnaire/Short Physical Performance Battery (SPPB) questionnaires, and quality-of-life assessment. Flow cytometry was used to characterize blood monocyte/macrophage subsets (cluster of differentiation 14 (CD14), CD68, CD163, CD206). EVs were isolated from plasma and profiled for surface tetraspanins and matrix metalloproteinases (MMP2, MMP9, tissue inhibitor of metalloproteinase-1 (TIMP-1)) using bead-based flow cytometry. Biochemical assays measured metabolic, inflammatory, and extracellular matrix (ECM)-related markers. Data were analyzed via Kruskal-Wallis testing, discriminant analysis, and principal component analysis (PCA).RESULTS: Sarcopenia, a muscle-wasting condition linked to aging, is characterized by chronic inflammation, proteolytic imbalance, and metabolic disturbances. Clinical deterioration is evident through reduced appendicular lean mass (ALM), appendicular skeletal muscle index (ASMI), SPPB scores, and sarcopenia quality of life (SarQoL) domains. Principal component analysis (PCA) identified four functional marker clusters: ECM degradation (MMP-positive EVs), inflammatory and homeostasis-stabilizing macrophages, and metabolic disruption (glucose, asprosin, triglycerides). Discriminant analysis emphasized vesicular and immune markers with significant classification potential, even when univariate differences were non-significant. Metabolic destabilization and inflammatory activation are detectable in presarcopenia stages. Chronic inflammation, characterized by CD14-CD163+206+ cells releasing pro-inflammatory cytokines, accelerates muscle degradation. Proteolytic dysfunction, with an imbalance between proteases and inhibitors, further contributes to muscle loss. Metabolic disorders impair energy production and nutrient utilization, exacerbating muscle wasting. A comprehensive assessment, including anthropometric, functional, physical activity, and QoL measures, is crucial for identifying high-risk individuals and understanding sarcopenia's mechanisms. Vesicular biomarkers, regulating tissue remodeling and inflammation, provide valuable insights. Standardized assessment methods are essential for enhancing diagnostic accuracy and intervention effectiveness. Future research should focus on developing and refining biomarkers to improve specificity and sensitivity, enabling targeted therapies and better QoL.CONCLUSIONS: Integrating clinical, immunological, and biochemical markers with EVs helps stratify sarcopenia effectively. Our data shows that EVs and macrophage profiles reflect systemic changes and metabolic stress. However, age- and gender-related variability in our cohort warrants caution in generalizing the findings. Artificial intelligence (AI) enhances patient clustering by combining these data types, enabling precise, personalized sarcopenia management, predicting disease progression, and identifying high-risk patients. AI also standardizes and optimizes analytical protocols, improving diagnostic and monitoring reliability and reproducibility.

KW - Humans

KW - Sarcopenia/immunology

KW - Aged

KW - Biomarkers/blood

KW - Middle Aged

KW - Female

KW - Male

KW - Extracellular Vesicles/metabolism

KW - Aged, 80 and over

KW - Prospective Studies

KW - Quality of Life

KW - Muscle Strength

KW - Muscle, Skeletal/metabolism

UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105015611700&origin=inward

UR - https://pubmed.ncbi.nlm.nih.gov/40917056/

U2 - 10.31083/FBL42063

DO - 10.31083/FBL42063

M3 - Article

C2 - 40917056

VL - 30

JO - Frontiers in Bioscience - Landmark

JF - Frontiers in Bioscience - Landmark

SN - 2768-6701

IS - 8

M1 - 42063

ER -