Related Experiment Video
Updated: Aug 23, 2025
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Association between fibromyalgia syndrome clinical severity and body composition. A principal component analysis
José Álvarez-Nemegyei1, Elda Leonor Pacheco-Pantoja2, Lililana Judith Olán-Centeno3
1Star Medica Hospital, Rheumatology Staff, Mexico.
Introduction:
The type of body composition modulates the severity of some musculoskeletal conditions, in fibromyalgia syndrome (FMS), this type of association remains relatively unexplored.
Objective:
To analyze the association between the type of body composition and FMS using Principal Component Analysis (PCA). The FMS clinical outcome measures were: Symptom Severity Scale (SSS), Widespread Pain Index (WPI; and Fibromyalgia Impact Questionnaire (FIQ).
Methods:
Forty-three women with FMS (ACR 2010 criteria) were clinically and anthropometrically evaluated. The anthropometric data were integrated into two indicators using a PCA methodology (PCA-Fat and PCA-muscle). Additionally, the patients were classified into high and low categories for each clinical indicator, which were used as dependent variables in binomial logistic regression (BLR) models.
Results:
We found a positive correlation between PCA-Fat with WPI (r=0.326, P=.043) and FIQ (r=0.325, P=.044), and negative correlation (r=-0.384, P=.013) between PCA-muscle and SSS. In the BLR analysis, PCA-Fat was a significant predictor for high WPI (OR=2.477, P=.038); while for high SSS, PCA-muscle (OR=0.303, P=.009) was an inversely significant predictor.
Conclusions:
The results suggest that the volume of fat mass can negatively modulate the severity of FMS. We propose that the evaluation of body composition should be a basic element for the clinical approach of patients with FMS.
