Metachromatic leukodystrophy (MLD) is a rare, autosomal recessive lysosomal storage disease.Deficient activity of arylsulfatase A causes sulfatides to accumulate in cells of different tissues, including those in the central and peripheral nervous systems, leading to progressive demyelination and neurodegeneration.Although there is some association between specific arylsulfatase A alleles and disease severity, genotype-phenotype correlations are not fully understood.We aimed to identify biomarker candidates of early tissue damage in MLD using a modeling approach based on systems biol.A review of the literature was performed in an initial disease characterization step, allowing identification of pathophysiol. processes involved in MLD and proteins relating to these processes.Three math. models were generated to simulate different stages of MLD at the mol. level: an early pro-inflammatory stage model (including only processes considered to be active in the early stages of disease), a pre-demyelination stage model (including addnl. processes that are active after some disease progression), and a demyelination stage model (in which all pathophysiol. processes are active).The models evaluated 3457 proteins of interest, individually and by pairs through data mining techniques, applying five filters to prioritize biomarkers that could differentiate between the models.Sixteen potential biomarkers were identified, including effectors relating to mitochondrial dysfunction, remyelination, and neurodegeneration.The findings were corroborated in a gene expression data set from T lymphocytes of patients with MLD; all candidates formed combinations that were able to distinguish patients with MLD from controls, and all but one candidate distinguished late-infantile MLD from juvenile MLD as part of a combinatorial biomarker pair.In particular, pro-neuregulin-1 appeared as differential on all comparisons (patients with MLD vs controls and within clin. subtypes); casein kinase II subunit alpha was detected as a potential individual marker within clin. subtypes.These findings provide a panel of biomarker candidates suitable for exptl. validation and highlight the utility of math. models to identify biomarker candidates of early tissue damage in MLD with a high degree of accuracy and sensitivity.