Integration of magnetic resonance imaging and protein and metabolite CSF measurements to enable early diagnosis of secondary progressive multiple sclerosis

Abstract

Molecular networks in neurological diseases are complex. Despite this fact, contemporary biomarkers are in most cases interpreted in isolation, leading to a significant loss of information and power. We present an analytical approach to scrutinize and combine information from biomarkers originating from multiple sources with the aim of discovering a condensed set of biomarkers that in combination could distinguish the progressive degenerative phenotype of multiple sclerosis (SPMS) from the relapsing-remitting phenotype (RRMS).Methods: Clinical and magnetic resonance imaging (MRI) data were integrated with data from protein and metabolite measurements of cerebrospinal fluid, and a method was developed to sift through all the variables to establish a small set of highly informative measurements. This prospective study included 16 SPMS patients, 30 RRMS patients and 10 controls. Protein concentrations were quantitated with multiplexed fluorescent bead-based immunoassays and ELISA. The metabolome was recorded using liquid chromatography-mass spectrometry. Clinical follow-up data of the SPMS patients were used to assess disease progression and development of disability.Results: Eleven variables were in combination able to distinguish SPMS from RRMS patients with high confidence superior to any single measurement. The identified variables consisted of three MRI variables: the size of the spinal cord and the third ventricle and the total number of T1 hypointense lesions; six proteins: galectin-9, monocyte chemoattractant protein-1 (MCP-1), transforming growth factor alpha (TGF-α), tumor necrosis factor alpha (TNF-α), soluble CD40L (sCD40L) and platelet-derived growth factor AA (PDGF-AA); and two metabolites: 20β-dihydrocortisol (20β-DHF) and indolepyruvate.The proteins myelin basic protein (MBP) and macrophage-derived chemokine (MDC), as well as the metabolites 20β-DHF and 5,6-dihydroxyprostaglandin F1a (5,6-DH-PGF1), were identified as potential biomarkers of disability progression.Conclusion: Our study demonstrates, in a limited but well-defined and data-rich cohort, the importance and value of combining multiple biomarkers to aid diagnostics and track disease progression.

Improved Differential Diagnosis of Alzheimer’s Disease by Integrating ELISA and Mass Spectrometry-Based Cerebrospinal Fluid Biomarkers

“…We have here demonstrated that adding a limited number of CSF protein measurements in a model-based manner can improve the differential diagnosis. In line with this, we have recently demonstrated that integration of CSF protein, metabolite, and MRI measurements can improve differential diagnosis in multiple sclerosis [44]. Integrative diagnostics holds great potential in future diagnostic assessments, yet the challenge is to identify a limited number of markers that holds complementary information and that preferably can be acquired in a clinical setting.…”

Biochemical Differences in Cerebrospinal Fluid between Secondary Progressive and Relapsing–Remitting Multiple Sclerosis

“…Nuclear magnetic resonance spectroscopy-based methods have previously been used to study the CSF metabolome, comparing MS with controls and other neurological diseases [12,13,14,15,16,17]. Using high-resolution mass spectrometry (HRMS), we recently demonstrated that CSF metabolites integrated with protein and magnetic resonance imaging (MRI) information can improve early detection of SPMS [18].…”

Targeted metabolomics of CSF in healthy individuals and patients with secondary progressive multiple sclerosis using high-resolution mass spectrometry

“…Due to its proximity to the central nervous system (CNS), CSF is better for study diseases affecting the brain. Today, more than 450 metabolites have been identified and quantified in human CSF (Wishart et al 2008) and several of these have been found altered in neurological diseases, such as Alzheimer's disease (Wilkins and Trushina 2018), Parkinson's disease (Willkommen et al 2018), Huntington's disease (Herman et al 2019b) and multiple sclerosis (MS) (Herman et al 2018(Herman et al , 2019aReinke et al 2014).…”

“…We have previously used non-targeted metabolomics methods of CSF to increase understanding of SPMS (Herman et al 2019a) and provided a methodology to integrate multiple layers of information to improve early detection of transitioning patients (Herman et al 2018).…”