A blood-based metabolomics test to distinguish relapsing–remitting and secondary progressive multiple sclerosis: addressing practical considerations for clinical application

Yeo, Tianrong; Sealey, Megan; Zhou, Yifan; Saldaña, L.; Loveless, Samantha; Claridge, Timothy D. W.; Robertson, Neil; DeLuca, Gabriele C.; Palace, Jacqueline; Anthony, Daniel C.; Probert, Fay

doi:10.1038/s41598-020-69119-3

Cited by 10 publications

(9 citation statements)

References 48 publications

(68 reference statements)

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“…A validated, optimized blood collection protocol for serum metabolomics was employed. 16 Blood was collected in vacutainer tubes (BD 367837), left to stand for 30 min, and then centrifuged at 1300 × g for 10 min at room temperature. Serum was then immediately aliquoted and stored at −80°C until NMR experimentation.…”

Section: Methodsmentioning

confidence: 99%

“…Technical details of NMR sample preparation, experiments and spectra processing have been previously published. 13 , 16 Integral values of individual spectral ‘bins’ were computed with constant-sum-normalization and used as quantitative variables expressed in arbitrary units (AU). In all, 191 metabolite ‘bins’ were available for multivariate statistical analysis.…”

Section: Methodsmentioning

confidence: 99%

“… 12 Our previous work on metabolomics in neuroinflammatory diseases showed that metabolites can function as diagnostic and prognostic biomarkers by: (i) distinguishing multiple sclerosis from aquaporin-4-antibody disease and myelin oligodendrocyte glycoprotein-antibody disease accurately 13 ; (ii) aiding in the classification of patients with overlapping features of multiple sclerosis and neuromyelitis optica spectrum disorder 14 ; (iii) predicting clinical conversion of clinically isolated syndrome to multiple sclerosis 15 ; and (iv) distinguishing relapsing-remitting multiple sclerosis (RRMS) from secondary progressive multiple sclerosis with high accuracy. 16 , 17 With regards to the application of metabolomics in the context of multiple sclerosis relapses, two studies have reported metabolic perturbations in CSF during relapses compared to remission. 18 , 19 However, this was not the primary objective in both studies, which also had modest sample sizes (range, 50–54 multiple sclerosis patients) and the use of CSF precludes frequent sampling in the clinical setting.…”

Section: Introductionmentioning

confidence: 99%

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Objective biomarkers for clinical relapse in multiple sclerosis: a metabolomics approach

Yeo

Probert

Sealey

et al. 2021

Brain Communications

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Accurate determination of relapses in Multiple Sclerosis (MS) is important for diagnosis, classification of clinical course and therapeutic decision making, The identification of biofluid markers for MS relapses would add to our current diagnostic armamentarium and increase our understanding of the biology underlying the clinical expression of inflammation in MS. However, there is presently no biofluid marker capable of objectively determining MS relapses although some, in particular neurofilament-light chain, have shown promise. In this study, we sought to determine if metabolic perturbations are present during MS relapses, and, if so, identify candidate metabolite biomarkers and evaluate their discriminatory abilities at both group and individual levels, in comparison with neurofilament-light chain. High-resolution global and targeted 1H nuclear magnetic resonance metabolomics as well as neurofilament-light chain measurements were performed on the serum in four groups of relapsing-remitting MS (RRMS) patients, stratified by time since relapse onset: (1) in relapse (R), (2) last relapse (LR) ≥ 1 month (M) to < 6 M ago, (3) LR ≥ 6 M to < 24 M ago, and (4) LR ≥ 24 M ago. Two hundred and one RRMS patients were recruited: R (n = 38), LR 1–6 M (n = 28), LR 6–24 M (n = 34), LR ≥ 24 M (n = 101). Using supervised multivariate analysis, we found that the global metabolomics profile of R patients was significantly perturbed compared to LR ≥ 24 M patients. Identified discriminatory metabolites were then quantified using targeted metabolomics. Lysine and asparagine (higher in R), as well as, isoleucine and leucine (lower in R), were shortlisted as potential metabolite biomarkers. ANOVA of these metabolites revealed significant differences across the four patient groups, with a clear trend with time since relapse onset. Multivariable receiver operating characteristics analysis of these four metabolites in discriminating R versus LR ≥ 24 M showed an area under the curve of 0.758, while the area under the curve for serum neurofilament-light chain was 0.575. Within individual patients with paired relapse-remission samples, all four metabolites were significantly different in relapse versus remission, with the direction of change consistent with that observed at group level, while neurofilament-light chain was not discriminatory. The perturbations in the identified metabolites point towards energy deficiency and immune activation in MS relapses, and the measurement of these metabolites, either singly or in combination, are useful as biomarkers to differentiate relapse from remission at both group and individual levels.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Objective biomarkers for clinical relapse in multiple sclerosis: a metabolomics approach

Yeo

Probert

Sealey

et al. 2021

Brain Communications

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“…CSF spectra were acquired using a 1D-nuclear overhauser effect spectroscopy (NOESY) presaturation scheme (2 s presaturation, 64 data collections, 1.5 s acquisition time, fixed receiver gain) for attenuation of the water resonance. Details of NMR spectra processing have been previously published [ 10 ]. Following this, integral values of individual spectral ‘bins’ were computed with constant-sum-normalization and used as quantitative variables expressed in arbitrary units (AU).…”

Section: Methodsmentioning

confidence: 99%

Metabolomics detects clinically silent neuroinflammatory lesions earlier than neurofilament-light chain in a focal multiple sclerosis animal model

Yeo

Bayuangga

Augusto-Oliveira

et al. 2022

J Neuroinflammation

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Background Despite widespread searches, there are currently no validated biofluid markers for the detection of subclinical neuroinflammation in multiple sclerosis (MS). The dynamic nature of human metabolism in response to changes in homeostasis, as measured by metabolomics, may allow early identification of clinically silent neuroinflammation. Using the delayed-type hypersensitivity (DTH) MS rat model, we investigated the serum and cerebrospinal fluid (CSF) metabolomics profiles and neurofilament-light chain (NfL) levels, as a putative marker of neuroaxonal damage, arising from focal, clinically silent neuroinflammatory brain lesions and their discriminatory abilities to distinguish DTH animals from controls. Methods 1H nuclear magnetic resonance (NMR) spectroscopy metabolomics and NfL measurements were performed on serum and CSF at days 12, 28 and 60 after DTH lesion initiation. Supervised multivariate analyses were used to determine metabolomics differences between DTH animals and controls. Immunohistochemistry was used to assess the extent of neuroinflammation and tissue damage. Results Serum and CSF metabolomics perturbations were detectable in DTH animals (vs. controls) at all time points, with the greatest change occurring at the earliest time point (day 12) when the neuroinflammatory response was most intense (mean predictive accuracy [SD]—serum: 80.6 [10.7]%, p < 0.0001; CSF: 69.3 [13.5]%, p < 0.0001). The top discriminatory metabolites at day 12 (serum: allantoin, cytidine; CSF: glutamine, glucose) were all reduced in DTH animals compared to controls, and correlated with histological markers of neuroinflammation, particularly astrogliosis (Pearson coefficient, r—allantoin: r = − 0.562, p = 0.004; glutamine: r = − 0.528, p = 0.008). Serum and CSF NfL levels did not distinguish DTH animals from controls at day 12, rather, significant differences were observed at day 28 (mean [SEM]—serum: 38.5 [4.8] vs. 17.4 [2.6] pg/mL, p = 0.002; CSF: 1312.0 [379.1] vs. 475.8 [74.7] pg/mL, p = 0.027). Neither serum nor CSF NfL levels correlated with markers of neuroinflammation; serum NfL did, however, correlate strongly with axonal loss (r = 0.641, p = 0.001), but CSF NfL did not (p = 0.137). Conclusions While NfL levels were elevated later in the pathogenesis of the DTH lesion, serum and CSF metabolomics were able to detect early, clinically silent neuroinflammation and are likely to present sensitive biomarkers for the assessment of subclinical disease activity in patients.

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“…Using multivariate approaches like non-linear kernel function and PLS-DA, MS patients can be correctly classified according to their disease stage, for example CIS or confirmed MS [25,30,31,86].…”

Section: Evidence From the Use Of Multivariate Approachesmentioning

confidence: 99%

Contribution of Metabolomics to Multiple Sclerosis Diagnosis, Prognosis and Treatment

Rispoli

Valentinuzzi

Luca

et al. 2021

IJMS

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Metabolomics-based technologies map in vivo biochemical changes that may be used as early indicators of pathological abnormalities prior to the development of clinical symptoms in neurological conditions. Metabolomics may also reveal biochemical pathways implicated in tissue dysfunction and damage and thus assist in the development of novel targeted therapeutics for neuroinflammation and neurodegeneration. Metabolomics holds promise as a non-invasive, high-throughput and cost-effective tool for early diagnosis, follow-up and monitoring of treatment response in multiple sclerosis (MS), in combination with clinical and imaging measures. In this review, we offer evidence in support of the potential of metabolomics as a biomarker and drug discovery tool in MS. We also use pathway analysis of metabolites that are described as potential biomarkers in the literature of MS biofluids to identify the most promising molecules and upstream regulators, and show novel, still unexplored metabolic pathways, whose investigation may open novel avenues of research.

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A blood-based metabolomics test to distinguish relapsing–remitting and secondary progressive multiple sclerosis: addressing practical considerations for clinical application

Cited by 10 publications

References 48 publications

Objective biomarkers for clinical relapse in multiple sclerosis: a metabolomics approach

Objective biomarkers for clinical relapse in multiple sclerosis: a metabolomics approach

Metabolomics detects clinically silent neuroinflammatory lesions earlier than neurofilament-light chain in a focal multiple sclerosis animal model

Contribution of Metabolomics to Multiple Sclerosis Diagnosis, Prognosis and Treatment

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