Genetically predicted metabolites mediate the causal associations between autoimmune thyroiditis and immune cells

Introduction We aimed to comprehensively investigate the causal relationship between 731 immune cell traits and autoimmune thyroiditis (AIT) and to identify and quantify the role of 1400 metabolic traits as potential mediators in between. Methods Using summary-level data from genome-wide association studies (GWAS) we performed a two-sample bidirectional Mendelian randomization (MR) analysis of genetically predicted AIT and 731 immune cell traits. Furthermore, we used a two-step MR analysis to quantify the proportion of the total effects (that the immune cells exerted on the risk of AIT) mediated by potential metabolites. Results We identified 24 immune cell traits (with odds ratio (OR) ranging from 1.3166 6 to 0.6323) and 10 metabolic traits (with OR ranging from 1.7954 to 0.6158) to be causally associated with AIT, respectively. Five immune cell traits (including CD38 on IgD+ CD24-, CD28 on CD28+ CD45RA+ CD8br, HLA DR+ CD4+ AC, TD CD4+ %CD4+, and CD8 on EM CD8br) were found to be associated with the risk of AIT, which were partially mediated by metabolites (including glycolithocholate sulfate, 5alpha-androstan-3alpha,17beta-diol disulfate, arachidonoylcholine, X-15486, and kynurenine). The proportion of genetically predicted AIT mediated by the identified metabolites could range from 5.58% to 17.7%. Discussion Our study identified causal associations between AIT and immune cells which were partially mediated by metabolites, thus providing guidance for future clinical and basic research.


INTRODUCTION 1 Background
Explain the scientific background and rationale for the reported study.What is the exposure?Is a potential causal relationship between exposure and outcome plausible?Justify why MR is a helpful method to address the study question 1 The contents of Introduction include general information of AIT, potential causal relationship between AIT and immune cells, potential causal relationship between immune cells and metabolites, potential causal relationship between AIT and metabolites, and that MR is a helpful method to address the study question.

Objectives
State specific objectives clearly, including pre-specified causal hypotheses (if any).State that MR is a method that, under specific assumptions, intends to estimate causal effects 1 "In the present study, we aimed to determine the specific immune cell signature that was causally associated with AIT and to assess the extent to which a specific metabolic trait could mediate the effect of the immune cell on AIT."

METHODS 2 Study design and data sources
Present key elements of the study design early in the article.Consider including a table listing sources of data for all phases of the study.For each data source contributing to the analysis, describe the following: 2.1-2.3 a) Setting: Describe the study design and the underlying population, if possible.
Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection, when available.

2.1
"Figure 1 shows a schematic summary of the analysis….i. Provide justification of the similarity of the genetic variant-exposure associations between the exposure and outcome samples ii.Provide information on the number of individuals who overlap between the exposure and outcome studies 3.1-3.4 Characteristics of significant SNPs with genomewide associations for exposure and outcome were all included in Supplementary Tables S2, S9, and S16, S20, and S24.

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Main results 3 a) Report the associations between genetic variant and exposure, and between genetic variant and outcome, preferably on an interpretable scale 3.1 3.2 3.3

3.4
Associations between immune cells and AIT.

Associations between immune cells and HT (MR validation).
Associations between metabolites and AIT.
Associations between immune cells and metabolites.
b) Report MR estimates of the relationship between exposure and outcome, and the measures of uncertainty from the MR analysis, on an interpretable scale, such as odds ratio or relative risk per SD difference 3.1 3.2 3.3

3.4
Information including OR, 95% CI, and p values for immune cells associated with AIT; the data were all included in Figure 2-3, Supplementary Figure S1.
Information including OR, 95% CI, and p values for HT associated with immune cells; the data were all included in Supplementary Figure S2.
b) Report results from other sensitivity analyses or additional analyses 3.5 Leave-one-out analyses were all included in Supplementary Figures S14-S16.
c) Report any assessment of direction of causal relationship (e.g., bidirectional MR) 3.1-3.2Results of reverse causal associations of AIT or HT with immune cells were all included in Supplementary Tables S5-6, S12-13, and Figure 3. d) When relevant, report and compare with estimates from non-MR analyses Not applicable.
e) Consider additional plots to visualize results (e.g., leave-one-out analyses) 3.5 The results were all included in Supplementary Figures S14-S16.

Key results
Summarize key results with reference to study objectives 4 "We identified 27 immune cells and 10 metabolites to be causally associated with AIT (at least in the IVM method), among which 5 immune cells…The results suggest that metabolites were able to, at least partially, mediate the causal relationship between immune cells and AIT." 15 Limitations Discuss limitations of the study, taking into account the validity of the IV assumptions, other sources of potential bias, and imprecision.Discuss both direction and magnitude of any potential bias and any efforts to address them Provide the data used to perform all analyses or report where and how the data can be accessed, and reference these sources in the article.Provide the statistical code needed to reproduce the results in the article, or report whether the code is publicly accessible and if so, where Interest financial relationships that could be construed as a potential conflict of interest."This checklist is copyrighted by the Equator Network under the Creative Commons Attribution 3.0 Unported (CC BY 3.0) license.1. Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, et al.Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) Statement.JAMA.2021;under review.2. Skrivankova VW, Richmond RC, Woolf BAR, Davies NM, Swanson SA, VanderWeele TJ, et al.Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): Explanation and Elaboration.BMJ.2021;375:n2233.