Association between fibromyalgia syndrome clinical severity and body composition. A principal component analysis / Asociación entre las manifestaciones clínicas del síndrome de fibromialgia y la composición corporal. Un análisis de componentes principales

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.(AU)

Introducción: El tipo de composición corporal modula la gravedad de algunos padecimientos musculoesqueléticos; en el síndrome de fibromialgia (SFM) este tipo de asociación permanece relativamente inexplorado. Objetivo: Mediante análisis de componentes principales (PCA), analizar la asociación entre el tipo de composición corporal y medidas de desenlace clínico del SFM, como la Escala de Gravedad de Síntomas, el Índice de Dolor Generalizado y el Cuestionario de Impacto de la Fibromialgia. Métodos: Cuarenta y tres mujeres con SFM (criterios ACR 2010) fueron evaluadas clínica y antropométricamente. Los datos antropométricos se integraron en 2 indicadores mediante una metodología de PCA (PCA-Fat y PCA-Muscle). Adicionalmente, las pacientes se clasificaron en categorías alta y baja para cada indicador clínico, los cuales se utilizaron como variables dependientes en modelos de regresión logística binomial. Resultados: Encontramos correlación positiva entre PCA-Fat, el Índice de Dolor Generalizado (r=0,326, p=0,043) y el Cuestionario de Impacto de la Fibromialgia (r=0,325, p=0,044), y correlación negativa (r=−0,384, p=0,013) entre PCA-Muscle y la Escala de Gravedad de Síntomas. En el modelo de regresión logística binomial, PCA-Fat fue un predictor significativo para un Índice de Dolor Generalizado alto (OR=2,477, p=0,038), mientras que para una Escala de Gravedad de Síntomas alta, PCA-Muscle (OR=0,303, p=0,009) fue un predictor inversamente significativo. Conclusiones: Los resultados evidencian que el volumen de masa grasa puede modular negativamente la gravedad del SFM. Proponemos que la evaluación de la composición corporal debe ser un elemento básico para el abordaje clínico de los pacientes con SFM.(AU)

Association between fibromyalgia syndrome clinical severity and body composition. A principal component analysis.

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.