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Clinical Trial
. 2020 Dec 14;10(1):21912.
doi: 10.1038/s41598-020-78951-6.

Serum metabolites as biomarkers in systemic sclerosis-associated interstitial lung disease

C Meier  1 , K Freiburghaus  2 , C Bovet  2 , J Schniering  1 , Y Allanore  3 , O Distler  1 , C Nakas  2   4 , B Maurer  5
Affiliations
Clinical Trial

Serum metabolites as biomarkers in systemic sclerosis-associated interstitial lung disease

C Meier et al. Sci Rep. .

Abstract

Systemic sclerosis (SSc) is a severe multi-organ disease with interstitial lung disease (ILD) being the major cause of death. While targeted therapies are emerging, biomarkers for sub-stratifying patients based on individual profiles are lacking. Herein, we investigated how levels of serum metabolites correlated with different stages of SSc and SSc-ILD. Serum samples of patients with SSc without ILD, stable and progressive SSc-ILD as well as of healthy controls (HC) were analysed using liquid targeted tandem mass spectrometry. The best discriminating profile consisted of 4 amino acids (AA) and 3 purine metabolites. L-tyrosine, L-tryptophan, and 1-methyl-adenosine distinguished HC from SSc patients. L-leucine, L-isoleucine, xanthosine, and adenosine monophosphate differentiated between progressing and stable SSc-ILD. In SSc-ILD, both, L-leucine and xanthosine negatively correlated with changes in FVC% predicted. Additionally, xanthosine was negatively correlated with changes in DLco% predicted and positively with the prognostic GAP index. Validation of L-leucine and L-isoleucine by an enzymatic assay confirmed both the sub-stratification of SSc-ILD patients and correlation with lung function and prognosis score. Serum metabolites may have potential as biomarkers for discriminating SSc patients based on the presence and severity of ILD. Confirmation in larger cohorts will be needed to appreciate their value for routine clinical care.

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Conflict of interest statement

CM, KF, CB, JS, CN: none to declare. OD had speaker fees from Actelion, Bayer, Boehringer Ingelheim, Medscape, Novartis, Roche, Menarini, Mepha, MSD, iQone, Pfizer, consultancy fees from Abbvie, Actelion, Acceleron Pharma, Amgen, AnaMar, Bayer, Baecon Discovery, Blade Therapeutics, Boehringer, CSL Behring, ChemomAb, Curzion Pharmaceuticals, Ergonex, Galapagos NV, GSK, Glenmark Pharmaceuticals, Inventiva, Italfarmaco, iQvia, medac, Medscape, Mitsubishi Tanabe Pharma, MSD, Roche, Sanofi, UCB, Amgen, Lilly, Target BioScience, Pfizer, project scoring fees from Abbvie, interview fees from Catenion, travel support from Pfizer, research grants from Actelion, Bayer, Boehringer Ingelheim, Competitive Drug Development International Ltd, Mitsubishi Tanabe, patent issued: mir-29 for the treatment of systemic sclerosis (US8247389, EP2331143). BM had grant/research support from AbbVie, Protagen, Novartis, and congress support from Pfizer, Roche, Actelion, and MSD, speakers honorary from Novartis. In addition, B. Maurer has a patent mir-29 for the treatment of systemic sclerosis registered (US8247389, EP2331143). The real or perceived potential conflicts listed above are accurately stated. YA had consultancy relationships and/or has received research funding from Actelion, Bayer, Boehringer Ingelheim, ChemomAb, Genentech/Roche, Inventiva, Pfizer, Sanofi, Servier, in the area of potential treatments of scleroderma and its complications. AH received consulting fees and/or research funding from Actelion, ARXX, Bayer, Boehringer Ingelheim, Medscape, MSD and Roche.

Figures

Figure 1
Figure 1
Clinical characteristics of SSc patients from the Zurich cohort at time point of sample collection. mRSS modified Rodnan skin score, dcSSc diffuse cutaneous SSc, lcSSc limited cutaneous SSc, ACA anti-Centromere antibodies, AT-1 anti-topoisomerase I antibodies, FVC forced vital capacity, DLco carbon monoxide diffusion capacity. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, #not significant. N = 12 per group.
Figure 2
Figure 2
Hierarchical clustering of metabolomics data using Euclidean distance and direct linkage.
Figure 3
Figure 3
Group-wise PLS-DA analysis of metabolomics data. ESI+/− positive/negative electrospray ionization. (a) Healthy (n = 12) versus all SSc (n = 36); (b) stable versus progressive SSc-ILD (n = 12 per group); (c) all SSc-ILD (n = 24) versus non-ILD SSc (n = 12).
Figure 4
Figure 4
Significant metabolites in univariate analysis of Z-score-normalized peak areas and PLS-DA analysis. * = p ≤ 0.05, ** = p ≤ 0.01, *** = p ≤ 0.001, # = not significant in univariate analysis and no post-hoc test performed. N = 12 per group.
Figure 5
Figure 5
ROC analyses of significant metabolites for healthy (n = 12) versus all SSc (n = 36) (a) and stable (n = 12) versus progressive (n = 12) SSc-ILD (b). AUC area under the curve. Brackets show confidence intervals.
Figure 6
Figure 6
Pearson correlation analysis of l-leucine (a) and xanthosine (b) in SSc-ILD patients (n = 24). *p ≤ 0.05, **p ≤ 0.01, n.s. = p > 0.05.
Figure 7
Figure 7
Results of t-test (a), ROC analysis (b), and Pearson’s correlation analysis (c,d) of enzymatic analysis of BCAA levels in SSc-ILD patients. **p ≤ 0.01. N = 11 (progressive) and 12 (stable).
See this image and copyright information in PMC

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