Metabolomics studies have been reported for virtually all of the main rheumatologic diseases, although notably none yet for SSc. Our own group has demonstrated the use of urinary metabolic fingerprint analysis to predict responses to anti-TNF, and we have suggested that metabolites resulting from TNF-driven cachexia are among the useful predictive biomarkers.
Serum metabolic profiling can differentiate four types of human arthritis, and we have shown the predictive value of the serum metabolite profile in early synovitis patients, with differences between those with self-limiting disease and those who went on to develop persistent RA.
The value of combining omics approaches has been demonstrated in a study using proteomics and metabolomics to show alterations in both vitamin D3 metabolites and proteins in patients with AS. The combination of genetic and metabolomic data has shown the potential to identify genotype-influenced metabotypes in a number of chronic diseases.