上海科技大学知识管理系统

Context: Clinical hypothyroidism (CH) and subclinical hypothyroidism (SCH) have been
linked to various metabolic co-morbidities but the underlying metabolic alterations remain
unclear. Metabolomics may provide metabolic insights into the pathophysiology of
hypothyroidism.
Objective: To explore metabolic alterations in SCH and CH and identify potential metabolite
biomarkers for the discrimination of SCH and CH from euthyroid individuals.
Methods: Plasma samples from a cohort of 126 human subjects including 45 patients with CH,
41 patients with SCH, and 40 euthyroid controls were analyzed by high-resolution mass
spectrometry–based metabolomics. Data were processed by multivariate principal components
analysis and orthogonal partial least squares discriminant analysis. Correlation analysis was
performed by a Multivarate Linear Regression analysis. Unbiased Variable selection in R
(MUVR) algorithm and three machine learning models were utilized to develop prediction
models based on potential metabolite biomarkers.
Results: The plasma metabolomic patterns in SCH and CH groups were significantly different
from those of control groups, while metabolite alterations between SCH and CH groups were
dramatically similar. Pathway enrichment analysis found that SCH and CH had a significant
impact on primary bile acid biosynthesis, steroid hormone biosynthesis, lysine degradation,
tryptophan metabolism, and purine metabolism. About 65 metabolites were found significantly
associated with levels of thyrotropin, free thyroxine, thyroid peroxidase antibody, or
thyroglobulin antibody. Seventeen metabolic biomarkers were successfully selected and
validated to differentiate three groups.

Conclusion: SCH and CH have significantly altered 63 metabolic patterns associated with
hypothyroidism, and metabolomics coupled with machine learning algorithms can be used to
develop diagnostic models based on selected metabolites.