Plasma Metabolomics Reveals Systemic Metabolic Alterations of Subclinical and Clinical Hypothyroidism

Feifei Shao; Rui Li; Qian Guo; Rui Qin; Wenxiu Su; Huiyong Yin; Limin Tian


J Clin Endocrinol Metab. 2023;108(1):13-25. 

In This Article

Abstract and Introduction


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.


As the common endocrine system disease, clinical hypothyroidism (CH) affects 4% to 10% of the population, and the prevalence of subclinical hypothyroidism (SCH) is as high as 10%.[1] CH is diagnosed when thyroid-stimulating hormone (TSH) concentrations are increased above the reference range and free thyroxine (FT4) concentrations fall below the reference range. SCH, which is commonly regarded as the sign of early thyroid failure, is diagnosed when TSH levels are detected above the upper limit of the reference range with FT4 within the normal range. The annual risk of SCH progression to CH is from 1% to 5%, depending on the presence of other risk factors.[2]

The definition of hypothyroidism is primarily determined by biochemical parameters because clinical manifestations vary widely and often lack specific symptoms. However, in recent years, it has been a matter of debate whether the existing reference ranges for TSH and thyroid hormones (THs) should be applied to the diagnosis of thyroid dysfunction, as symptoms or the risk of adverse disease are not considered.[2] A series of studies have confirmed an increased risk of stroke, sudden cardiac death, hyperlipidemia, and Alzheimer disease with variations in thyroid function even within their reference ranges.[1–6] Thus, a reliable prediction of hypothyroidism consist of metabolic changes and biochemical testing is helpful to improve our understanding of the normal physiology and the pathophysiology and is of clinical importance in determining clinical treatment.

Metabolomics is a rapidly developing field of life science that uses advanced analytical techniques, such as mass spectrometry (MS) or nuclear magnetic resonance (NMR), and sophisticated statistical methods to comprehensively characterize the metabolome, a collection of all the small molecular metabolites and metabolic pathways for a given biological system.[7] Metabolomics has increasingly been used to uncover novel metabolite biomarkers and metabolic pathways under various metabolic disease conditions.[8] Furthermore, metabolomics is yielding important new insights into how metabolites influence organ function, immune modulate, and gut physiology.[9] The capacity to detect a large number of widely varying metabolites makes metabolomics particularly attractive for the study and diagnosis of metabolic disorders.[10–13] As a result, a number of important concepts indicating the role of "metabotoxins" in disease has been proposed, such as "diabetogens", which refer to compounds that lead to diabetes and insulin resistance at chronically high concentrations.[7]

Genome-wide association studies have explained only a small proportion of thyroid function variability in patients with hypothyroidism.[14,15] In other words, it indicates that many other factors can also regulate TSH and TH production, including demographic factors (age and sex),[2,16,17] intrinsic factors (microbiota,[18] stress),[19] and environmental factors.[20] "Metabotoxins", including endogenous compounds and xenobiotic molecules in serum or organs probably play a role in regulating TSH and TH levels. On the other hand, TH regulates metabolic processes essential for normal growth and development and plays an important role in regulating metabolism in the adults.[21] TSH increases hepatic gluconeogenesis,[22] controls lipid homeostasis,[23] and alters monoaminergic function.[24] Thus, a systemic profiling of metabolic alterations associated with SCH and CH by metabolomics may provide novel insights into the underlying mechanisms of hypothyroidism-associated complications.

In this study, we applied an ultrahigh performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS)-based plasma metabolomic approach to characterize the global metabolic perturbations of SCH and CH. The aim of the study is to provide new insights into the pathogenesis of hypothyroidism at the metabolic level and to develop prediction models based on potential metabolic biomarkers for the discrimination of euthyroid controls, individuals with SCH, and patients with CH. Such an approach may have a tremendous impact not only on understanding the molecular basis of SCH and CH, but also on improving current clinical practice in SCH and CH.