Additional file 3 of The European BestAgeing Study on microRNA candidates reveals distinct signatures with diagnostic and prognostic potential in cardiovascular disease

Additional file 3: Supplementary tables. Table S1 Patient recruitment by center and disease. This table lists the number of patients recruited at each participating centre who underwent miRNA profiling and the specific diseases for which they were recruited. Recruiting centers were Amsterdam UMC- Department of Cardiology, Universitätsklinikum Frankfurt - Department of Cardiology, Frankfurt am Main, Universitätsklinikum Heidelberg - Department of Cardiology, Institut National de la Santé et de la Recherche Médicale (INSERM), National Scientific Center, Institute of Cardiology Named After Academician M.D. Strazhesko of the National Academy of Medical Sciences of Ukraine - Department of Cardiology, Kyiv, Servicio Madrileño de Salud - Department of Cardiology, Madrid, Spain, Azienda Ospedaliera San Filippo Neri - Department of Cardiology, Rome, Italy, Azienda Ospedaliera di Padova (Università degli Studi di Padova) - Department of Cardiology, Padua, Italy, Ethniko kai Kapodistriako Panepistimio Athinon (National and Kapodistrian University of Athens) - Department of Cardiology, Athens, Greece, Uppsala Universitetssjukhus (Uppsala Universitet) - Department of Cardiology, Uppsala, Sweden. Table S2 Summary of abstract extraction and miRNA Identification. The table provides a detailed breakdown of the total number of PubMed abstracts loaded, the count of abstracts successfully extracted using the miRetrieve package, and the subsequent identification of unique miRNAs. Table S3 Intersections of miRNAs in Distinct Cardiovascular Phenotypes. This table details the shared top 50 literature miRNAs per disease as indicated by the weighted biomarker score retrieved via miRetrieve across different cardiovascular conditions. This summary provides insights into the common miRNAs observed in various cardiovascular diseases. Table S4 Selected top 50 miRNAs identified from the literature search and grouped by disease of interest. The weighted biomarker score was calculated using the miRetrieve package, taking into account the number of associated PMIDs and biomarker keywords. Univariate AUCs were calculated for each miRNA and miRNAs ranked accordingly. AUROC; area under the receiver operating characteristic curve, PMID; PubMed unique identifier. Table S5 Result metrics for the comparison of ACS versus control. This table shows the statistical results from matched analysis and logistic regression, adjusted for age and sex. The 'ttest_rawp' column shows the raw p-values from the matched analysis t-test, 'ttest_adjp' shows the Bonferroni-Holm adjusted p-values, 'glm_rawp' shows the raw p-values from the logistic regression model, 'AUC' shows the area under the curve values reflecting the diagnostic accuracy, and 'log2FoldChange' shows the magnitude of differential expression on a logarithmic scale. Table S6 Result metrics for the comparison of CAD versus control. Table S7 Result metrics for the comparison of DCM versus control. Table S8 Result metrics for the comparison of ICM versus control. Table S9 Intersections of differentially expressed miRNAs in distinct cardiovascular phenotypes. This table details the shared top differentially expressed miRNAs per disease as indicated by an adjusted p-value of 0.05 and an abs(log2 Fold Change) > 0.2 across different cardiovascular conditions. This summary provides insights into the common miRNAs observed in various cardiovascular diseases. This data-driven table mirrors Supplementary Table S3 (literature-based overlaps), enabling comparison between reported and observed patterns. “Number of miRNAs” indicates the total count for each intersection. Table S10 ACS risk groups stratified by aPRIORI ACS model probabilities. This table provides the patient demographics and laboratory values stratified by tertiles of probability for ACS derived from the aPRIORI model, analysing only patients from the University Hospital of Heidelberg. ACS, acute coronary syndrome. Table S11 DCM risk groups stratified by aPRIORI DCM model probabilities. This table provides the patient demographics and laboratory values stratified by tertiles of probability for DCM derived from the aPRIORI model, analysing only patients from the University Hospital of Heidelberg. DCM, dilated cardiomyopathy. Table S12 CAD risk groups stratified by aPRIORI DCM model probabilities (tertiles). This table provides the patient demographics and laboratory values stratified by tertiles of probability for CAD derived from the aPRIORI model, analysing only patients from the University Hospital of Heidelberg. CAD, coronary artery disease. Table S13 CAD risk groups stratified by aPRIORI CAD model probabilities (median). This table provides the patient demographics and laboratory values stratified by median probability values for CAD derived from the aPRIORI model, analysing only patients from the University Hospital of Heidelberg. CAD, coronary artery disease. Table S14 Diagnostic performance of miRNAs and combined models with NT-proBNP in all DCM patients. Performance metrics for individual miRNAs, the DCM aPRIORI miRNA signature, and combined NT-proBNP + miRNA models are shown. Incremental Δ represents the absolute AUC gain of the combined model compared with NT-proBNP alone. P-values were obtained from likelihood-ratio (χ²) tests comparing nested logistic regression models.