Table_1_Investigating the shared genetic architecture between hypothyroidism and rheumatoid arthritis.docx

BackgroundThere is still controversy regarding the relationship between hypothyroidism and rheumatoid arthritis (RA), and there has been a dearth of studies on this association. The purpose of our study was to explore the shared genetic architecture between hypothyroidism and RA.MethodsUsing public genome-wide association studies summary statistics of hypothyroidism and RA, we explored shared genetics between hypothyroidism and RA using linkage disequilibrium score regression, ρ-HESS, Pleiotropic analysis under a composite null hypothesis (PLACO), colocalization analysis, Multi-Trait Analysis of GWAS (MTAG), and transcriptome-wide association study (TWAS), and investigated causal associations using Mendelian randomization (MR).ResultsWe found a positive genetic association between hypothyroidism and RA, particularly in local genomic regions. Mendelian randomization analysis suggested a potential causal association of hypothyroidism with RA. Incorporating gene expression data, we observed that the genetic associations between hypothyroidism and RA were enriched in various tissues, including the spleen, lung, small intestine, adipose visceral, and blood. A comprehensive approach integrating PLACO, Bayesian colocalization analysis, MTAG, and TWAS, we successfully identified TYK2, IL2RA, and IRF5 as shared risk genes for both hypothyroidism and RA.ConclusionsOur investigation unveiled a shared genetic architecture between these two diseases, providing novel insights into the underlying biological mechanisms and establishing a foundation for more effective interventions.</p

Spaghetti plot for TKV over entire observation time in individual patients (n = 421).

<p>Spaghetti plot for TKV over entire observation time in individual patients (n = 421).</p

Baseline demographics and clinical characteristics of Chinese ADPKD cohort.

<p>Baseline demographics and clinical characteristics for female (n = 251) and male (n = 290) patients with ADPKD are reported separately, and for both sexes combined (n = 541). Data show mean ± standard deviation or relative frequency (%), respectively.</p

Baseline kidney and cyst volumes and yearly volume growth rates stratified by age.

<p>Baseline kidney volumes (TKV and TCV) and yearly volume growth rates for age categories ≤18 years, 19–30 years, 31–40 years, 41–50 years, 51–60 years, and >60 years are reported. Data show mean ± standard deviation.</p

Linear regression model with 97% windsorized yearly eGFR change as dependent variable.

<p>Adjusted R² was 0.1943, F-statistic was 10.58 on 7 and 271 DF (<i>P</i><0.001).</p

Thrombocytes were correlated to age, TKV and eGFR in ADPKD patients.

<p>A) Scatter plot between age and thrombocytes (n = 399). Pearson correlation coefficient is –0.286 (<i>P</i><0.001). B) Scatter plot between thrombocytes and log₁₀ TKV (n = 394). Pearson correlation coefficient is –0.134 (<i>P</i> = 0.008). C) Scatter plot between thrombocytes and eGFR (n = 391). Pearson correlation coefficient is 0.222 (<i>P</i><0.001).</p

Renal function in different age categories of ADPKD patients.

<p>A) Creatinine (μmol/l) for age categories ≤18 years (n = 20), 19–30 years (n = 74), 31–40 years (n = 171), 41–50 years (n = 128), 51–60 years (n = 90), and >60 years (n = 19). B) eGFR (ml/min/1.73 m²) for age categories ≤18 years (n = 20), 19–30 years (n = 74), 31–40 years (n = 171), 41–50 years (n = 128), 51–60 years (n = 90), and >60 years (n = 19). Boxes show the median and the 25^th and 75^th percentile. Whiskers extend to the farthest points that are not outliers (i.e., that are within 3/2 times the interquartile range) and dots indicate outliers. C) Spaghetti plots for course of creatinine and D) eGFR over entire observation time in individual patients.</p

Correlations between yearly TKV growth and eGFR change in ADPKD patients.

<p>A) Scatter plot between yearly eGFR change and yearly TKV volume growth (cm³) (n = 372). Spearman’s rho is –0.119 (<i>P</i> = 0.022). Only patients >18 years and ≤60 years are included. B) Scatter plot between yearly eGFR change and yearly TKV volume growth (%) (n = 372). Spearman’s rho is –0.055 (<i>P</i> = 0.288). Only patients >18 years and ≤60 years are included.</p

TKV and TCV in different age categories, and the correlations between kidney volumes (KV) and cyst volumes (CV).

<p>A) TKV and B) TCV for age categories ≤18 years (n = 23), 19–30 years (n = 81), 31–40 years (n = 180), 41–50 years (n = 134), 51–60 years (n = 95), and >60 years (n = 19). Boxes show the median and the 25^th and 75^th percentile. Whiskers extend to the farthest points that are not outliers (i.e., that are within 3/2 times the interquartile range) and dots indicate outliers. C) Scatter plot between RKV and LKV (n = 532). The regression line is defined by LKV = 0.337+0.889*RKV. Pearson correlation coefficient is 0.902 (<i>P</i><0.001). D) Scatter plot between RCV and LCV (n = 530). Pearson correlation coefficient is 0.857 (<i>P</i><0.001). E) Scatter plot between baseline TKV and baseline TCV (n = 532). Pearson correlation coefficient is 0.951 (<i>P</i><0.001).</p