Plasma Metabolomics Reveals Systemic Metabolic Alterations of Subclinical and Clinical Hypothyroidism

Abstract

CONTEXT: Clinical hypothyroidism (CH) and subclinical hypothyroidism (SCH) have been linked to various metabolic comorbidities but the underlying metabolic alterations remain unclear. Metabolomics may provide metabolic insights into the pathophysiology of hypothyroidism. OBJECTIVE: We explored 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 Multivariate Linear Regression analysis. Unbiased Variable selection in R algorithm and 3 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. Significant associations for 65 metabolites were found with levels of thyrotropin, free thyroxine, thyroid peroxidase antibody, or thyroglobulin antibody. We successfully selected and validated 17 metabolic biomarkers to differentiate 3 groups. CONCLUSION: SCH and CH have significantly altered metabolic patterns associated with hypothyroidism, and metabolomics coupled with machine learning algorithms can be used to develop diagnostic models based on selected metabolites.