TY  - JOUR
N1  - This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/). See http://ivyspring.com/terms for full terms and conditions.
IS  - 16
SP  - 4477
VL  - 8
JF  - Theranostics
A1  - Herman, S
A1  - Khoonsari, PE
A1  - Tolf, A
A1  - Steinmetz, J
A1  - Zetterberg, H
A1  - Ĺkerfeldt, T
A1  - Jakobsson, P-J
A1  - Larsson, A
A1  - Spjuth, O
A1  - Burman, J
A1  - Kultima, K
SN  - 1838-7640
UR  - http://dx.doi.org/10.7150/thno.26249
TI  - Integration of magnetic resonance imaging and protein and metabolite CSF measurements to enable early diagnosis of secondary progressive multiple sclerosis
EP  - 4490
AV  - public
Y1  - 2018///
KW  - biomarker
KW  -  data integration
KW  -  disease progression
KW  -  metabolomics
KW  -  multiple sclerosis
ID  - discovery10056834
N2  - 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-?), tumor necrosis factor alpha (TNF-?), soluble CD40L (sCD40L) and platelet-derived growth factor AA (PDGF-AA); and two metabolites: 20?-dihydrocortisol (20?-DHF) and indolepyruvate. The proteins myelin basic protein (MBP) and macrophage-derived chemokine (MDC), as well as the metabolites 20?-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.
ER  -