Urinary metabolomic biomarker candidates for skeletal muscle wasting in patients with rheumatoid arthritis

Abstract

Background

Rheumatoid arthritis (RA) is an autoimmune disease that affects the joints, leading to chronic synovial inflammation and local tissue destruction. Extra-articular manifestations may also occur, such as changes in body composition. Skeletal muscle wasting is often observed in patients with RA, but methods for assessing loss of muscle mass are expensive and not widely available. Metabolomic analysis has shown great potential for identifying changes in the metabolite profile of patients with autoimmune diseases. In this setting, urine metabolomic profiling in patients with RA may be a useful tool to identify skeletal muscle wasting.

Methods

Patients aged 40–70 years with RA have been recruited according to the 2010 ACR/EULAR classification criteria. Further, the Disease Activity Score in 28 joints using the C-reactive protein level (DAS28-CRP) determined the disease activity. The muscle mass was measured by Dual X-ray absorptiometry (DXA) to generate the appendicular lean mass index (ALMI) by summing the lean mass measurements for both arms and legs and dividing them by height squared (kg/height²). Finally, urine metabolomic analysis by ¹H nuclear magnetic resonance (¹H-NMR) spectroscopy was performed and the metabolomics data set analysed using the BAYESIL and MetaboAnalyst software packages. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the ¹H-NMR data, followed by Spearman's correlation analysis. The combined receiver operating characteristic curve (ROC) was calculated, as well as the logistic regression analyses to establish a diagnostic model. The significance level at P < 0.05 was set for all analyses.

Results

The total set of subjects investigated included 90 patients with RA. Most patients were women (86.7%), with a mean age of 56.5 ± 7.3 years old and a median DAS28-CRP of 3.0 (IQR 1.0–3.0). Fifteen metabolites were identified in the urine samples with high variable importance in projection (VIP scores) by MetaboAnalyst. Of these, dimethylglycine (r = 0.205; P = 0.053), oxoisovalerate (r = −0.203; P = 0.055), and isobutyric acid (r = −0.249; P = 0.018) were significantly correlated with ALMI. Based on the low muscle mass (ALMI ≤6.0 kg/m² for women and ≤8.1 kg/m² for men) a diagnostic model have been established with dimethylglycine (area under the curve [AUC] = 0.65), oxoisovalerate (AUC = 0.49), and isobutyric acid (AUC = 0.83) with significant sensitivity and specificity.

Conclusions

Isobutyric acid, oxoisovalerate, and dimethylglycine from urine samples were associated with low skeletal muscle mass in patients with RA. These findings suggest that this group of metabolites may be further tested as biomarkers for identification of skeletal muscle wasting.