## 2 Materials and Methods
### Targeted Metabolomics  
Metabolite concentrations in serum samples were quantified using the AbsoluteIDQ\({}^{\circledr}\)p180 kit from Biocrates (Biocrates Life Sciences AG, Innsbruck, Austria). In brief, 10 \(\mu\)L of serum samples from study participants, quality control samples (provided by Biocrates and pool of all serum samples), calibration curve samples, one double blank sample (Milli Q water), and one blank sample (PBS) were transferred to Biocrates filter plates using the Fluent 780 automatic pipetting system. Samples were further processed according to the manufacturer's protocol, which included an initial filtering step followed by a derivatization employing phenyl isothiacetate and sample extraction with 5 mM ammonium acetate solution in MeOH. Sample aliquots were then transferred into two distinct 96-well plates and filled with LC-MS and FIA-specific dilution solutions. Plates were then analyzed with a LC-MS system consisting of an Agilent UHPLC 1290 coupled with a Sciex 6500 QTRAP. For each sample, 42 metabolites (21 amino acids and 21 biogenic amines) were fully quantified by UHPLC-MS/MS using the calibration curve samples. Semi quantitative FIA-MS/MS was employed to assess the remaining metabolites concentration (40 acylcarnitines, 90 glycerophospholipids, 15 sphingolipids, and 1 sum of hexoses). For UHPLC-MS/MS data, integration of peaks was visually checked using Multiquant software v. 3.0.2 from SCIEX, and results were stored together with FIA-MS/MS data using the MetIDQ software v. 6.0.0 from Biocrates. Finally, metabolite raw concentrations were exported for subsequent data quality assessment and normalization, which was performed in R (version 4.0.3). Between-plate differences were adjusted for each metabolite considering the concentrations measured for the QC samples per plate. For quality assessment, the coefficient \begin{table}
\begin{tabular}{c c c} \hline \hline
**Primer Name** & **Sequence 5\({}^{\prime}\)-3\({}^{\prime}\)** & **Used for Sequencing** \\ \hline rs528903211-F & GCCCAGACCGTGAAAACATT & \\ \hline rs528903211-R & GGCATTCTGCTCTGCCCTAG & Yes \\ \hline rs573729400-F & ACAATCTGATTGTCCCAGCG & \\ \hline rs573729400-R & CATGGGATGGTGCAAATTCTATGC & Yes \\ \hline rs745582874-F & CCACTATGGCCTTGG & Yes \\ \hline rs745582874-R & GACATGCTCTAGACCTG & \\ \hline rs768476991-F & TCTGTCACACGGCTTAGATG & Yes \\ \hline rs768476991-R & GOTAATCCAACACTGTCCAAAA & \\ \hline F: forward; R: reverse. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PCR and sequencing primers. of variation in QC samples, the number of missing values, and the signal distribution was considered. In total, 13 of the 188 metabolites were excluded because of poor quality or high number of missing values. For data imputation, missing values due to a signal below the detection limit of the instrument were replaced by random numbers from a uniform distribution ranging from the smallest measured value for that analyte to half of this smallest value. Metabolomics concentrations were available for 5688 study participants, including 64 AF cases. To identify metabolites with different concentrations between AF cases and controls, linear regression models were fitted separately to each analyte using the log2 transformed concentration as a response variable and a binary variable for AF, sex, age, body mass index (BMI), self-reported fasting status, and a binary variable for AF medication as covariates. In addition, linear regression models were fitted to metabolite concentrations standardized to mean of 0 and standard deviation of 1. Coefficients from this analysis, where differences in one unit are equal to a standard deviation of 1, are reported as \"effect size\". Raw \(p\)-values were adjusted for multiple hypotheses testing with Bonferroni correction. Metabolites were considered significant if they had an adjusted \(p\)-value smaller than 0.05 and if, in addition, their difference in concentration was at least twice as large as the coefficient of variation (CV) of that metabolite.