Exploring blood metabolites and thyroid disorders: a bidirectional mendelian randomization study

Abstract

Background: Human blood metabolites have demonstrated close associations with thyroid disorders in observational studies. However, it's essential to determine whether these correlations imply causation. Mendelian Randomization (MR) offers a promising approach to investigate these patterns.

Aims: The primary aim of our investigation is to establish causality between blood metabolites and three thyroid disorders: TC, GD, and HT.

Methods: We employed a two-sample bidirectional MR analysis approach to assess the relationships between 452 blood metabolites and the three aforementioned thyroid disorders. Causal links were estimated using the IVW method, with sensitivity analyses conducted via MR-Egger, Weighted Median, and MR-PRESSO. We assessed potential heterogeneity and pleiotropy using MR-Egger intercept and Cochran's Q statistic. Additionally, we conducted pathway analysis to identify potential metabolic pathways.

Results: We found 46 metabolites that showed suggestive associations with thyroid disease risk, especially Aspartate (OR_IVW=7.41; 95%CI: 1.51-36.27; P_IVW=0.013) and C-glycosyltryptophan (OR_IVW=0.04; 95%CI: 0.00-0.29; P_IVW=0.001) impacted TC, Kynurenine (OR_IVW=2.69; 95%CI: 1.08-6.66; P_IVW=0.032) and 4-androsten-3beta,17beta-diol disulfate 2 (OR_IVW=0.78; 95%CI: 0.48-0.91; P_IVW=0.024) significantly impacted GD, and Alpha-ketoglutarate (OR_IVW=46.89; 95%CI: 4.65-473.28; P_IVW=0.001) and X-14189-leucylalanine (OR_IVW=0.31; 95%CI: 0.15-0.64 P_IVW=0.001) significantly impacted HT. We also detected 23 metabolites influenced by TC and GD. Multiple metabolic pathways have been found to be involved in thyroid disease.

Conclusion: Our MR findings suggest that the identified metabolites and pathways can serve as biomarkers for clinical thyroid disorder screening and prevention, while also providing new insights for future mechanistic exploration and drug target selection.

Keywords: Mendelian randomization; autoimmune thyroid disease; bidirectional; metabolites; thyroid cancer.

Figure 1

Schematic overview of the study design. (A) Mendelian randomization [MR] illustration. There are three principal assumptions in MR design: (1) IVs must be strongly correlated with exposure factors; (2) IVs was associated with outcomes only by exposure; (3) IVs cannot be associated with any confounding factors. (B) Bidirectional MR study of metabolites and thyroid disease TC, Thyroid cancer; GD, Graves' disease; HT, Hashimoto thyroiditis; IVW, Inverse variance weighted; WM, weighted median; LOO, leave-one-out.

Figure 2

Associations of genetically predicted three metabolites with risk of thyroid disorders using IVW method with different GWAS datasets. OR, odds ratio; CI, confidence. (A) 1-linoleoylglycerol (1-monolinolein) shows potential causal association in two different thyroid cancer datasets (FinnGen Consortium and IEU Open GWAS); (B) Gamma-tocopherol shows potential causal association in two different Hashimoto's thyroiditis datasets; (C) Potential causal effects of 2-hydroxyisobutyrate in two different Graves' disease datasets.

Figure 3

Potential metabolic pathways associated with thyroid disease. (A) Potential metabolic pathways involved in the pathogenesis of thyroid cancer in positive MR Analysis; (B) Hashimoto thyroiditis (The positive MR Analysis); related to Additional File 2 Table S7 (C) Thyroid cancer (The reverse MR Analysis); (D) Graves' disease (The reverse MR Analysis). related to Additional File 2 Table S8 Pathway analysis based on "Kyoto Encyclopedia of Genes and Genomes" (KEGG). The color and size of each circle is based on p-values (yellow: higher p-values and red: lower p-values) and pathway impact values (the larger the circle the higher the impact score) calculated from the topological analysis, respectively. Pathways were considered significantly enriched if p < 0.05, impact >0.1 and number of metabolite hits in the pathway >1.