Nuclear Magnetic Resonance‐Based Metabolomic Profiling in Fibromyalgia Patients

Abstract

ABSTRACTFibromyalgia (FM) is a multifactorial syndrome with poorly understood pathophysiology, characterized by chronic widespread pain and fatigue affecting the central and peripheral nervous systems, as well as the endocrine and muscular systems. These characteristics create significant challenges in diagnosis and treatment. This study compared the metabolic profiles of FM patients and healthy controls using one‐dimensional (1D) 1H nuclear magnetic resonance (NMR) spectroscopy and multivariate statistical analysis to identify potential biomarkers associated with FM symptoms. Urine and serum samples from 50 FM patients and 50 healthy individuals underwent untargeted metabolic profiling. Quantitative 1H‐NMR spectroscopy was used to determine metabolic changes. Chemometric models and receiver operating characteristic (ROC) curves were generated using the MetaboAnalyst platform. Fold change (FC) analysis and t‐tests were conducted to determine statistically significant differences between groups. Our findings showed significant differences at the metabolic level between FM patients and healthy controls. Increased urea, glutamate, valine, taurine, proline, glycine, and homoserine metabolites, and decreased benzoate, leucine, π‐methylhistidine, galactitol, τ‐methylhistidine, glutamine, 3‐hydroxykynurenine, and fructose levels were observed in FM serum; while increased malate, dimethylamine, trimethylamine N‐oxide (TMAO), creatine phosphate, N‐phenylacetylglycine, N‐acetylglutamate (NAG), hippurate, and urea levels were observed in FM urine, and decreased guanidoacetate, creatine, malonate, serine, glucuronate, creatinine, and uracil levels were observed. Serum glutamate was positively correlated with waist/hip ratio (WHR) and negatively correlated with Fibromyalgia Impact Questionnaire (FIQ) and Visual Analog Scale (VAS); negative correlations existed between taurine and FIQ and VAS; a positive correlation existed between urea and WHR; there was a positive correlation between anserine and body mass index (BMI). Also, in urine, there were positive correlations between TMAO, N‐phenylacetylglycine, glutamate, and xylose and WHR; negative correlations existed between glutamine and FIQ; and a positive correlation existed between glucuronate and FIQ. Our findings provide important information about potential biomarkers, associated different metabolites, and metabolic pathways of FM patients; we think that they will provide new insight into the pathogenesis of the disease and help expand our knowledge.