Plasma metabolomic profiling as a tool to identify predictive biomarkers of methotrexate efficacy in rheumatoid arthritis

Medcalf, Matthew R.; Bantis, Leonidas E.; Shi, Peng; Bhadbhade, Pooja; Gundry, Rebekah L.; Mikuls, Ted R.; England, Bryant R.; O'Dell, James Robert; Funk, Ryan S.

doi:10.1016/j.semarthrit.2022.152056

Cited by 8 publications

(6 citation statements)

References 27 publications

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“…However, these findings support previous findings that the gut microbiome may play a significant role in autoimmune arthritis disease activity and MTX response [48,49]. Of these compounds, N-methylisoleucine appears to be the most promising biomarker of MTX response as it has been demonstrated to be a discriminatory plasma metabolite of MTX response in patients with RA [46] and in CIA studies using both RBCs and plasma analyses, as described here. N-methylisoleucine also represented the greatest absolute difference of any of the proposed biomarker with mean RBC levels of N-methylisoleucine three-fold lower in MTX treated disease mice compared to disease mice, a finding that was consistent in both plasma and RBC analyses.…”

Section: Discussionsupporting

confidence: 90%

“…These results reinforce the hypothesis that MTX induced folate depletion results in a reduction in methyltransferase activity and thus the methylation of various biomolecules, including amino acids [3,[41][42][43][44][45]. However, it remains unclear whether these methylated amino acid biomarkers are a direct result of endogenous metabolism, or a product of exogenous metabolites generated from the gut microbiome [3,46,47]. However, these changes in methylation were not observed in our healthy control mice treated with MTX, which may suggest that the antagonism of folates by MTX may not be responsible for these changes.…”

Section: Discussionsupporting

confidence: 79%

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Metabolomic Profiling of Red Blood Cells to Identify Molecular Markers of Methotrexate Response in the Collagen Induced Arthritis Mouse Model

Salamoun

Polireddy

Cho

et al. 2022

Future Pharmacology

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Although methotrexate (MTX) is the first line disease-modifying therapy used in the treatment of autoimmune arthritis, it is limited by its unpredictable and variable response profile and lack of therapeutic biomarkers to predict or monitor therapeutic response. The purpose of this work is to evaluate the utility of red blood cell (RBC) metabolite profiles to screen for molecular biomarkers associated with MTX response. Methods: Utilizing the collagen-induced arthritis mouse model, DBA/1J mice were treated with subcutaneous MTX (20 mg/kg/week) and RBC samples were collected and analyzed by semi-targeted global metabolomic profiling and analyzed by univariate analysis. Results: MTX treatment normalized the following RBC metabolite levels that were found to be altered by disease induction: N-methylisoleucine, nudifloramide, phenylacetylglycine, 1-methyl-L-histidine, PC 42:1, PE 36:4e, PC 42:3, PE 36:4e (16:0e/20:4), and SM d34:0. Changes in the RBC metabolome weakly but significantly correlated with changes in the plasma metabolome following MTX treatment (ρ = 0.24, p = 1.1 × 10−13). The RBC metabolome resulted in the detection of nine significant discriminatory biomarkers, whereas the plasma metabolome resulted in two. Overall, the RBC metabolome yielded more highly sensitive and specific biomarkers of MTX response compared to the plasma metabolome. N-methylisoleucine was found to be highly discriminatory in both plasma and RBCs. Conclusions: Our results suggest that RBCs represent a promising biological matrix for metabolomics and future studies should consider the RBC metabolome in their biomarker discovery strategy.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionsupporting

confidence: 79%

Metabolomic Profiling of Red Blood Cells to Identify Molecular Markers of Methotrexate Response in the Collagen Induced Arthritis Mouse Model

Salamoun

Polireddy

Cho

et al. 2022

Future Pharmacology

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“…We also performed Spearman's correlation coefficients analysis to assess the associations between metabolites and muscle mass. [21][22][23] We used the selected metabolites to build a prediction model and determine the accuracy of the model by using receiver operating characteristic (ROC) curves. We combined ROC curve and logistic regression analyses to establish a diagnostic accuracy between muscle mass and the metabolites.…”

Section: Discussionmentioning

confidence: 99%

“…We cross checked the list of metabolites through KEGG (http://www.genome.jp/kegg), PUBCHEM (pubchem.ncbi.nlm.nih.gov), and HMDB (http://www.hmdb.ca) websites, where we checked whether there was a direct relationship with muscle metabolism. We also performed Spearman's correlation coefficients analysis to assess the associations between metabolites and muscle mass 21–23 …”

Section: Methodsmentioning

confidence: 99%

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

Oliveira

Santo

Silva

et al. 2023

J cachexia sarcopenia muscle

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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.

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“… 623 Another study used metabolomic profiling and small metabolites (N-methylisoleucine, nornicotine, 2,3-dihydroxybutanoic acid) were able to discriminate rheumatoid arthritis patients with early response to methotrexate therapy. 624 These clinical applications of small metabolites provide excellent examples to illustrate new channels for targeted therapies and enabling precision treatment.…”

Section: Enabling Precision Treatmentmentioning

confidence: 99%

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Qiu

Cai

et al. 2023

Sig Transduct Target Ther

228

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Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject’s phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

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Plasma metabolomic profiling as a tool to identify predictive biomarkers of methotrexate efficacy in rheumatoid arthritis

Cited by 8 publications

References 27 publications

Metabolomic Profiling of Red Blood Cells to Identify Molecular Markers of Methotrexate Response in the Collagen Induced Arthritis Mouse Model

Metabolomic Profiling of Red Blood Cells to Identify Molecular Markers of Methotrexate Response in the Collagen Induced Arthritis Mouse Model

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

Small molecule metabolites: discovery of biomarkers and therapeutic targets

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