ArticleMedicine2025
Intertwined and unequal causality of type 1 diabetes and autoimmune thyroid disease: Insights from Mendelian randomization.
Article in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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6 authors.
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Abstract
The relationship between type 1 diabetes (T1D) and autoimmune thyroid disease (AITD) remains controversial. This study aims to analyze the potential causal relationship between T1D and AITD using bidirectional Mendelian randomization (MR). Single nucleotide polymorphisms associated with T1D, autoimmune thyroiditis (AIT), and Graves' disease (GD) were sourced from FinnGen. Inverse variance weighted, MR-Egger, and weighted median methods were employed to analyze these associations. The MR-Egger intercept was utilized to assess horizontal pleiotropy. Cochran Q and leave-one-out analyses were conducted to evaluate heterogeneity and sensitivity, respectively. The MR analysis indicated that T1D was potentially associated with an increased risk of AIT (odds ratio [OR] = 1.128, 95% confidence interval [CI] = 1.041-1.222, P = .003) but not with GD (OR = 1.001, 95% CI = 0.948-1.057, P = .970). Additionally, both AIT (OR = 1.032, 95% CI = 1.003-1.063, P = .032) and GD (OR = 1.049, 95% CI = 1.006-1.094, P = .025) were potentially associated with an increased risk of T1D. The MR-Egger intercept indicated no evidence of horizontal pleiotropy (P > .05) for these findings. Cochran Q and leave-one-out sensitivity analyses demonstrated that these results were robust and showed no heterogeneity (P > .05). This MR analysis suggests a potential causal relationship between AIT and GD with the risk of T1D, as well as a potential causal relationship between T1D and the risk of AIT. However, these findings need to be validated through large-scale clinical trials.
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