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Epicardial Adipose Tissue Volume and Annexin A2/Fetuin-A Crosstalk are linked to Coronary Calcification in Advanced Coronary Artery Disease: Computed Tomography and Proteomic Biomarkers from the EPICHEART StudySUPPLEMENTARY FILESJennifer Mancio1,2*, Antonio S. Barros1, Gloria Conceicao1, Guilherme Pessoa-Amorim1, Catia Santa3, Carla Bartosch4, Wilson Ferreira2, Monica Carvalho2, Nuno Ferreira2, Luis Vouga5, Isabel M. Miranda1, Rui Vitorino1, Bruno Manadas3, Ines Falcao-Pires1, Vasco Gama Ribeiro2, Adelino Leite-Moreira1,6, Nuno Bettencourt11Department of Surgery and Physiology, Cardiovascular Research Unit (UnIC), Faculty of Medicine, University of Porto, Portugal2Department of Cardiology, Centro Hospitalar de Vila Nova de Gaia, Portugal; 3CNC - Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; 4 Department of Pathology, Portuguese Oncology Institute of Porto, Porto, Portugal; 5Department of Cardiothoracic Surgery, Centro Hospitalar de Vila Nova de Gaia, Portugal; 6Department of Cardiothoracic Surgery, Centro Hospitalar de Sao Joao, Portugal.Supplemental tables: 7; Supplemental figures: 3. *Corresponding author: Jennifer Mancio MD, PhD Department of Surgery and Physiology, Faculty of Medicine of PortoAlameda Professor Hernani MonteiroPorto 4200-319, PortugalTel: +351-961529516; E-mail: up200104593@med.up.ptComplete List of Investigators’ in the EPICHEART STUDYJennifer Mancio,1,2* Gloria Conceicao,1 Marilia Pinheiro,3 Guilherme Pessoa-Amorim,1 Diana Azevedo,1 Catia Santa,4 Carla Bartosch,5 Mariana Fragao-Marques,1 Wilson Ferreira,2 Monica Carvalho,2 Rita Faria,2 Nuno Ferreira,2 David Monteiro,6 Nuno Almeida,6 Pedro Rodrigues,6 Ricardo Fontes-Carvalho,1,2 Jose Ribeiro,2 Joao Monteiro,7 Sara Costa,7 Diogo Rijo,7 Paulo Neves,7 Ricardo Ferraz,7 Rodolfo Pereira,7 Nelson Santos, 7 Filipe Carneiro,7 Fatima Neves,7 Joao Carlos Mota,7 Miguel Guerra,7 Paulo Ponce,7 Luis Vouga,7 Rui Vitorino,1 Isabel M. Miranda,1 Antonio Barros,1 Bruno Manadas,4 Ines Falcao-Pires,1 Vasco Gama Ribeiro,2 Adelino Leite-Moreira,1,8 Nuno Bettencourt11Department of Surgery and Physiology, Cardiovascular Research Unit (UnIC), Faculty of Medicine,2Department of Cardiology, Centro Hospitalar de Vila Nova de Gaia, Portugal3Faculty of Nutrition and Food Science, University of Porto4 CNC - Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal5 Department of Pathology, Portuguese Oncology Institute of Porto, Porto, Portugal6Department of Radiology, Centro Hospitalar de Vila Nova de Gaia, Portugal7Department of Cardiothoracic Surgery, Centro Hospitalar de Vila Nova de Gaia, Portugal8Department of Cardiothoracic Surgery, Centro Hospitalar de Sao Joao, Portugal*Principle investigator: Jennifer Mancio MD PhDDepartment of Surgery and Physiology, Faculty of Medicine of PortoAlameda Professor Hernani MonteiroPorto 4200-319, PortugalTel: +351-961529516; E-mail: up200104593@med.up.ptSUPPLEMENTARY METHODSDetailed definitionsSevere aortic stenosis: Aortic valve area of < 1 cm2 or 0.6 cm2/m2 by transthoracic echocardiography, Exclusion criteria: coexisting moderate to severe aortic valve regurgitation or moderate to severe mitral valve disease, bicuspid aortic valve, left ventricular dilatation (end-diastolic volume index >75 mL/m?) or left ventricular ejection fraction <55%, ADDIN EN.CITE <EndNote><Cite><Author>Roberto</Author><Year>2015</Year><RecNum>0</RecNum><IDText>Recommendations for Cardiac Chamber Quantification by Echocardiography in Adults: An Update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging</IDText><DisplayText>[1]</DisplayText><record><dates><pub-dates><date>2015-03-01</date></pub-dates><year>2015</year></dates><urls><related-urls><url> for Cardiac Chamber Quantification by Echocardiography in Adults: An Update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging</title></titles><contributors><authors><author>Roberto M. Lang</author><author>Luigi P. Badano</author><author>Victor Mor-Avi</author><author>Jonathan Afilalo</author><author>Anderson Armstrong</author><author>Laura Ernande</author><author>Frank A. Flachskampf</author><author>Elyse Foster</author><author>Steven A. Goldstein</author><author>Tatiana Kuznetsova</author><author>Patrizio Lancellotti</author><author>Denisa Muraru</author><author>Michael H. Picard</author><author>Ernst R. Rietzschel</author><author>Lawrence Rudski</author><author>Kirk T. Spencer</author><author>Wendy Tsang</author><author>Jens-Uwe Voigt</author></authors></contributors><language>en</language><added-date format="utc">1482773659</added-date><ref-type name="Journal Article">17</ref-type><rec-number>124</rec-number><publisher>Oxford University Press</publisher><last-updated-date format="utc">1486143351</last-updated-date><electronic-resource-num>10.1093/ehjci/jev014</electronic-resource-num></record></Cite></EndNote>[1] stage 3 to 5 chronic renal failure <30 mL/min/1.73m2 (defined as glomerular filtration rate (GFR) estimated by Cockcroft-Gault formula adjusted for body surface area), moderate to severe chronic obstructive pulmonary disease (defined as forced expiratory volume in one second <50% according to the 2011 Global Initiative for Chronic Obstructive Pulmonary Disease guidelines),PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MZWl2c2V0aDwvQXV0aG9yPjxZZWFyPjIwMTM8L1llYXI+

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ADDIN EN.CITE.DATA [5, 6]Computed tomography protocol All patients underwent non-contrast CT (Somatom Sensation Cardiac 64, Siemens, Forchheim, Germany) one to three months before cardiac surgery. We acquired a thoracic scan using a prospective ECG-triggered scanning protocol (tube voltage 120 kV, tube current 190 mA, gantry rotation 330 ms, collimation 24×1.2 mm, pitch 0.2; image reconstruction 3 mm). Additionally, an abdominal single slice acquisition was performed between L4 and L5-S1 (radiographic factors: 120 kV and 216 mAs; 5 mm thickness). Coronary calcium score (CCS): CCS was quantified by the Agatston method on non-contrast cardiac computed tomography (CT) scans using the commercially available software (SyngoCalciumScoring, Siemens Healthcare, Erlangen, Germany).Adipose tissue depots quantification: Fat depots including epicardial adipose tissue (EAT) and visceral abdominal adipose tissue (VAT) were quantified using baseline CT scan in an offline workstation (SyngoVolume, Siemens Healthcare, Erlangen, Germany), according to the predefined image display setting [window with, -150 to -50 HU] to identify voxels that correspond to adipose tissue. To measure EAT volume, the pericardium was manually traced for every 10 mm from the right pulmonary artery to the diaphragm to determine the region of interest; this task took about 10 minutes per patient. Total abdominal fat area was obtained as the sum of adipose tissue presented in the examined abdominal slice (L4-L5/S1); and VAT area as adipose tissue located inside the region of interest defined by delineating the abdominal wall muscular layer. Intra – and interrater reliabilities for each fat measurement were evaluated in 55 random patients as reported previously. ADDIN EN.CITE <EndNote><Cite><Author>Mancio</Author><Year>2017</Year><RecNum>4227</RecNum><DisplayText>[7]</DisplayText><record><rec-number>4227</rec-number><foreign-keys><key app="EN" db-id="920aswsp0t5wfsetsptvp59wvr02apwtw22x" timestamp="1561241819">4227</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Mancio, J.</author><author>Barros, A.</author><author>Leite-Moreira, A.</author><author>Falcao-Pires, I.</author><author>Bettencourt, N.</author><author>Ferreira, W.</author><author>Carvalho, M.</author><author>Ferreira, N.</author><author>Ribeiro, V. G.</author><author>Pinheiro, M.</author><author>Vouga, L.</author></authors></contributors><auth-address>(Mancio, Barros, Leite-Moreira, Falcao-Pires, Bettencourt) Department of Cardiothoracic Surgery and Physiology, Faculty of Medicine, University of Porto, Portugal&#xD;(Mancio, Ferreira, Carvalho, Ferreira, Ribeiro) Department of Cardiology, Centro Hospitalar de Vila Nova de Gaia, Portugal&#xD;(Pinheiro) Faculty of Food and Nutrition, University of Porto, Portugal&#xD;(Vouga) Department of Cardiothoracic Surgery, Centro Hospitalar de Vila Nova de Gaia, Portugal&#xD;(Leite-Moreira) Department of Cardiothoracic Surgery, Centro Hospitalar de Sao Joao, Portugal</auth-address><titles><title>Gender differences in the association of epicardial adipose tissue and coronary artery calcification: EPICHEART study: EAT and coronary calcification by gender</title><secondary-title>International Journal of Cardiology</secondary-title></titles><periodical><full-title>Int J Cardiol</full-title><abbr-1>International journal of cardiology</abbr-1></periodical><pages>419-425</pages><volume>249</volume><dates><year>2017</year><pub-dates><date>Dec 2017</date></pub-dates></dates><publisher>Elsevier Ireland Ltd</publisher><urls></urls><remote-database-provider>Embase</remote-database-provider></record></Cite></EndNote>[7]Invasive coronary angiography protocol and data analysis Invasive coronary angiography was performed using standard angiographic techniques at least one week before or after CT, with the purpose of excluding coronary artery disease (CAD) before aortic valve replacement. An experienced invasive cardiologist (15 years in invasive coronary angiography execution and interpretation) characterized, visually, the most severe stenosis within each coronary segment. Using the standard definitions of flow-limiting stenoses, we categorized each patient according to: Severity of CAD, classified by the degree of stenosis in obstructive CAD (defined as a coronary artery stenosis greater than 50% in any epicardial coronary artery), and severe CAD (defined as any stenosis 50% or greater in the left main coronary artery and/or 70% or greater in any other coronary artery);Distribution of CAD, classified by CAD severity in a single, double, or triple-vessel disease; we defined vessel distribution by the left anterior descending artery and its tributaries, the left circumflex artery and its tributaries, and the right coronary artery and its tributaries.Fat proteomics analysisFat preparation for mass spectrometry: Selected fat samples were subsequently homogenized in 125mM Tris-HCl, 2% sodium dodecyl sulfate (SDS), 50mM dithiothreitol (DTT), 8M urea, 2M thiourea, 2% 3-[(3-cholamidopropyl) dimethylammonio] propane sulfonate (CHAPS), 2% ampholytes (pH 3-10), 1% NP-40, 50mM DTT and 10μL PMSF by Potter-Elvehjem with Teflon pestle. The lysate was centrifuged at 14,000g for 30 min at 4?C and the supernatant was carefully recovered and protein concentration was estimated using RC DC Protein assay (Bio-Rad, Hercules, CA, USA). Protein extract was alkylated with 50 mM iodoacetamide and the concentrate was diluted with 0.1 mL of 50 mM ammonium bicarbonate and digested overnight (37 ?C) with the addition of 5μg of trypsin. The resulting tryptic peptides were collected by centrifugation. Eluted peptides were acidified with TFA to a final concentration of 0.1% and cleaned up with PepCleanTM C-18 Spin Columns (Pierce) according to manufacturer’s instructions. Peptides were resuspended in 15μl of 5% ACN and 0.1% TFA. To monitor sample loss during sample preparation, samples were spiked with 2μg of recombinant green fluorescent protein and Maltose-binding periplasmic protein (malE-GFP) before digestion.IDA and SWATH acquisition: Samples were analyzed on a triple TOF? 5600 System (ABSciex?, Framingham, MA) operating in two phases: information-dependent acquisition (IDA) of each pooled sample; followed by SWATH (Sequential Windowed data independent Acquisition of the Total High-resolution Mass Spectra) acquisition of each individual sample. Peptides were resolved by LC (nanoLC Ultra 2D, Eksigent?, Dublin, CA) on a ChromXP? C18R reverse phase column (300 ?m ID x 15 cm length, 3 ?m particles, 120 ? pore size, Eksigent?) at 5?l/min, and eluted into the mass spectrometerin a 45 minutes gradient and a 65 minutes total run ( 5% mobile phase B (ACN in 0.1% FA, where mobile phase A is H2O in 0.1% FA) for the first 2 minutes; followed by a linear gradient from 10% to 30% mobile phase B until minute 45 and one minute to reach 35%; the last minutes of the run were used for column wash, reaching 98% mobile phase B, and reequilibration of the column at 5% mobile phase B), using an electrospray ionization source (DupoSpray? Source, ABSciex). Pooled mixtures were analyzed in IDA mode, where the mass spectrometer was set for IDA scanning full spectra (350-1250 m/z) for 250 ms, followed by up to 100 MS/MS scans (100–1500 m/z from a dynamic accumulation time – minimum 30 ms for precursor above the intensity threshold of 1000 counts per second (cps) – in order to maintain a cycle time of 3.3 s). Candidate ions with a charge state between +2 and +5 and counts above a minimum threshold of 10 cps were isolated for fragmentation and one MS/MS spectra was collected before adding those ions to the exclusion list for 25 seconds (mass spectrometer operated by Analyst? TF 1.6, ABSciex). Rolling collision energy was used with a collision energy spread of 5. To improve sample coverage, an additional IDA experiment was performed for each pool, using an exclusion list of the previously identified peptides. The SWATH setup was essentially as described by Anjo et al. ADDIN EN.CITE <EndNote><Cite><Author>Anjo</Author><Year>2015</Year><RecNum>19</RecNum><DisplayText>[8]</DisplayText><record><rec-number>19</rec-number><foreign-keys><key app="EN" db-id="z0rw2sdd7dpa0fe0wt7pt90rz55zxv2d2zre">19</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Anjo, S. I.</author><author>Santa, C.</author><author>Manadas, B.</author></authors></contributors><auth-address>CNC - Center for Neuroscience and Cell Biology, University of Coimbra, Portugal; Faculty of Sciences and Technology, University of Coimbra, Portugal.</auth-address><titles><title>Short GeLC-SWATH: a fast and reliable quantitative approach for proteomic screenings</title><secondary-title>Proteomics</secondary-title><alt-title>Proteomics</alt-title></titles><periodical><full-title>Proteomics</full-title><abbr-1>Proteomics</abbr-1></periodical><alt-periodical><full-title>Proteomics</full-title><abbr-1>Proteomics</abbr-1></alt-periodical><pages>757-62</pages><volume>15</volume><number>4</number><keywords><keyword>Animals</keyword><keyword>Cerebral Cortex/chemistry</keyword><keyword>Chromatography, Liquid/*methods</keyword><keyword>Membrane Proteins/analysis/chemistry/metabolism</keyword><keyword>Peptide Fragments/*analysis/chemistry/metabolism</keyword><keyword>Proteome/*analysis/chemistry/metabolism</keyword><keyword>Proteomics/*methods</keyword><keyword>Rats</keyword><keyword>Reproducibility of Results</keyword></keywords><dates><year>2015</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>1615-9861 (Electronic)&#xD;1615-9853 (Linking)</isbn><accession-num>25418953</accession-num><urls><related-urls><url>;[8]. The mass spectrometer was operated in a looped product ion mode. The SWATH-MS setup was designed specifically for the samples analyzed (Table 1), in order to adapt SWATH windows to the complexity of this batch of samples. A set of 60 windows of variable width (containing 1 m/z for the window overlap) was constructed covering the precursor mass range of 350-1250 m/z. A 100 ms survey scan (350-1500 m/z) was acquired at the beginning of each cycle for instrument calibration, and SWATH MS/MS spectra were collected from 100–1500 m/z for 50 ms from the precursors ranging from 350 to 1250 m/z resulting in a cycle time of 3.1 s. Collision energy for each window was determined according to the calculation for a charge +2 ion centered upon the window with variable collision energy spread (CES) according with the window.Protein identification: Peptide identification and library generation were performed with Protein Pilot software (v4.5, ABSciex?), using the following parameters: (1) search against a database composed by Homo Sapiens from SwissProt (downloaded in April 2016), malE-GFP, using (2) trypsin digestion and (3) iodoacetamide as alkylating agent. An independent False Discovery Rate (FDR) analysis, using the target-decoy approach provided by Protein Pilot?, was used to assess the quality of identifications. Positive identifications were considered when identified proteins and peptides reached a 5% local FDR PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5UYW5nPC9BdXRob3I+PFllYXI+MjAwODwvWWVhcj48UmVj

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ADDIN EN.CITE.DATA [11]. Peak group confidence threshold was determined based on a FDR analysis using the target-decoy approach and 1% extraction FDR threshold was used for all the analyses. Peptides that met the 1% FDR threshold in at least half the replicates were retained. Peak areas of the target fragment ions of those peptides were extracted across experiments, using an extracted-ion chromatogram (XIC) window of 4 min and 100 ppm XIC width. Protein levels were estimated by summing all peptide transitions for a given protein and were normalized to the total intensity of the sample. PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Db2xsaW5zPC9BdXRob3I+PFllYXI+MjAxMzwvWWVhcj48

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ADDIN EN.CITE.DATA [12] RNA Extraction and Quantitative Real Time-Polymerase Chain Reaction Messenger RNA (mRNA) of FABP4 and ANXA2 was quantified in EAT. After total RNA extraction (Tripure, Roche) and reverse transcription, equal amounts of cDNA from every sample underwent real-time PCR (StepOne Plus, Applied Biosystems) experiments for each gene, using SYBR green as marker (Qiagen). Relative gene expression was normalized to 18S Ribosomal RNA (18S) expression. Specific PCR primer pairs for the studied genes were: r18S – fw 5’- CGTCTGCCCTATCAACTTTCG-3’ and rev 5’-CTTGGATGTGGTAGCCGTTT-3’; rFABP4 – fw 5’-TCAGTGTGAATGGGGATGTGAT-3’ and rev 5’-GGTTATGGTGCTCTTGACTTTC-3’ and rANXA2 – fW 5’-CCTGTATTTTGCTGATCGGCTG-3’ and rev 5’-TTCACTGCGGGAGACCAT-3’.Enzyme-Linked Immunosorbent Assay (ELISA) Peripheral venous blood was obtained after 8 hours of fasting, on the morning of cardiac surgery, and span in EDTA-containing tubes at 5000 r.p.m. for 15 min at 4 ?C and plasma separated and frozen at -80 ?C until analysis. Pericardial fluid samples were collected during cardiac surgery, immediately after opening the pericardial sac, and frozen at -80?C until analysis. Quantitative measurement of Fetuin A concentrations in plasma and pericardial fluid were quantified using a human Fetuin A ELISA Kit (Abcam) according to manufacturer instructions. Each sample was analysed in duplicate. Absorbance was recorded at 450nm using and ELISA plate reader (Perkin-Elmers) and a standard curve was plotted and used to calculate fetuin A concentration in samples.Power and sample size calculationAs we have previously described, patients with CAD or CAC (CCS>0) are expected to have higher EAT volume than those without CAD or CCS of zero (estimated mean difference of 17.3 mL or 19.1 mL, respectively); assuming a beta-value of 0.20 and an alpha-value of 0.05, we calculated that a minimum of 294 patients are needed to detect EAT volume differences between CAD and non-CAD patients and at least 260 patients are needed to detect EAT volume differences between patients with and without CAC.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5NYW5jaW88L0F1dGhvcj48WWVhcj4yMDE3PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA [13] SUPPLEMENTARY FIGURESSupplementary Figure S1: Venn diagram of proteins identified among EAT, MAT and SAT in non-CAD and CAD patients. We quantified a total of 85 proteins for both non-CAD and CAD patients; among them, 59 and 58 proteins were common to the three fat depots in non-CAD and CAD groups, respectively.Supplementary Figure S2: PLS-DA loading weights scatter plot showing each protein contribution for the differentiation between visceral (EAT+MAT) vs. subcutaneous (SAT) fat depots in controls (A) and between EAT vs. MAT+SAT in patients with stenosis (B).Supplementary Figure S3: Multivariate modeling of CAD vs non-CAD. Bootstrapped (1000 models) OPLS-DA loading weights (LV1), sorted in in descending order (A) showing proteins with VIP > 1 (B). Proteins are colored as a function of group contribution. Errors bars depict uncertainty. CAD: coronary artery disease.Supplementary Figure S4: Pearson’s correlation network Set of 31 proteins derived from EAT proteome and their intertwined relationships. ANX2=annexin-A2; EAT=epicardial adipose tissue; FETUA=fetuin-A; VTDB=vitamin D binding protein. SUPPLEMENTARY TABLESSupplementary Table S1: Windows used in SWATH acquisition.Supplementary Table S2. EPICHEART study participants’ characteristics (n=574)Supplementary Table S3: Distribution of EAT volume and EAT volume indexed to body surface area, according to cardiovascular risk factors, conventional cardiovascular medications, significant CAD and CCS score-groups.Supplementary Table S4: Clinical characteristics of patients with and without CAD included in comparative proteomics analysis between EAT, MAT and SAT samplesSupplementary Table S5: Proteins differently expressed in non-CAD patients according to fat depots (EAT+MAT vs. SAT) (VIP>1.0).Supplementary Table S6: Proteins differently expressed in CAD patients according to fat depots (EAT vs. MAT or SAT) (VIP>1.0).Supplementary Table S7: Clinical characteristics of patients with obstructive and non-obstructive CAD whose EAT samples were included in the overall proteomics analysis.Supplementary Table S8: Proteins differently expressed in EAT according to CAD (VIP>1.0).Supplementary Table S1: Windows used in SWATH acquisition.WindowMass Range (m/z)Window Width (Da)Collision Energy Spread1349.5-358.69.152357.6-366.79.153365.7-374.38.654373.3-381.58.255380.5-388.78.256387.7-395.98.257394.9-402.77.858401.7-409.98.259408.9-416.67.7510415.6-423.88.2511422.8-430.67.8512429.6-437.88.2513436.8-4458.2514444-452.28.2515451.2-459.48.2516458.4-466.68.2517465.6-473.88.2518472.8-480.57.7519479.5-487.37.8520486.3-493.67.3521492.6-499.97.3522498.9-505.76.8523504.7-511.66.9524510.6-517.46.8525516.4-523.36.9526522.3-529.16.8527528.1-534.56.4528533.5-539.96.4529538.9-544.96530543.9-550.36.4531549.3-555.76.4532554.7-561.66.9533560.6-5676.4534566-572.86.8535571.8-578.76.9536577.7-584.16.4537583.1-589.96.8538588.9-595.86.9539594.8-602.17.3540601.1-608.87.7541607.8-615.67.8542614.6-622.88.2543621.8-630.48.6544629.4-637.68.2545636.6-645.79.1546644.7-654.710547653.7-664.210.5548663.2-673.210549672.2-683.110.9550682.1-694.312.2551693.3-706.913.6552705.9-720.414.5553719.4-737.117.7554736.1-759.123555758.1-790.232.1556789.2-834.745.5857833.7-901.367.6858900.3-983.783.4859982.7-1090.8108.110601089.8-1250.5160.710Supplementary Table S2. EPICHEART study participants’ characteristics (n=574)DemographicAge (yrs)76±18.5Male (%)281 (48.9)Clinical historyEuroscore II (%)*1.4 (1.1; 2.1)BMI (Kg/m2)28.3±4.7Hypertension (%)462 (80.5)Hyperlipidemia (%)392 (68.2)Diabetes mellitus (%)213 (37.1)Former smoker (%)57 (9.9)NYHA class III/IV (%)62 (10.8)AF (%)116 (20.2)Prior percutaneous coronary intervention (%)45 (7.8)Peripheral arterial disease (%)27 (4.7)Ischemic stroke (%)45 (7.8)Glomerular filtration rate (ml/min)81.8±25.7Statin use (%)287 (50)Insulin therapy (%)34 (5.9)Oral antidiabetic (%)103 (17.9)ACEi (%)142 (24.7)ARA (%)109 (18.9)Beta-blocker (%)147 (25.6)Antiplatelet use (%)195 (33.9)Transthoracic echocardiographyAVAi (cm2/m2)0.43±0.09Peack velocity (cm/s)434±62.2LVEF (%)65.3±7.9Indexed LV mass (g/m2)123±30.7Cardiac computed tomographyBody fat EAT volume (ml)109.7±55.9VAT(cm2)164.8±83.6CCS537.8 (126.0; 1424.2) CCS=021 (5.7) CCS>0<10060 (16.2) CCS≥100<40086 (23.2) CCS≥400204 (55.0)Invasive coronary angiography≥1 stenosis ≥50%276 (48.1) 1 vessel (%)128 (22.3) 2 vessels (%)77 (13.4) 3 vessels (%)71 (12.4) LM (%)35 (6.1) LAD (%)189 (32.9) RCA (%)75 (13.1)Severe stenosis178 (31.0)Values are n (%). mean±SD or median (quartile 1; quartile 3)*(due to the skewed distribution).ACEi, angiotensin converting enzyme inhibitor. ARA, angiotensin receptor antagonist. AF, atrial fibrillation. AVAi, aortic valve area index. BMI, body mass index. CCS, coronary calcium score. COPD, chronic obstructive pulmonary disease. EAT, epicardial adipose tissue. NYHA, New York Heart Association. LAD, left anterior descending artery. LM, left main coronary artery. LVEF, left ventricular ejection fraction. SAT, subcutaneous adipose tissue. VAT, visceral abdominal adipose tissue.Supplementary Table S3: Distribution of EAT volume and EAT volume indexed to body surface area, according to cardiovascular risk factors, conventional cardiovascular medications, significant CAD and CCS score-groups.GroupnEAT volume (mL) Median [Q1; Q3] P value*EAT volume index (mL/m2) Median [Q1; Q3] P value*All patients574106.9 [75.6; 132.4]59.7 [44.3; 74.9]Age. years <74136111.3 [86.9; 143.7]58.1 [44.8; 77.8] ≥74 <79137111.1 [83.4; 144.2]0.9661.1 [48.4; 74.3]0.37 ≥79 <84157118.3 [93.5; 149.4]0.2561.3 [45.4; 78.7]0.33 ≥8414496.2 [70.1; 130.9]0.0351.5 [41.3; 63.3]0.03Sex Female294103.4 [70.9; 128.7]55.4 [41.4; 71.7] Male281118.4 [94.8; 160.4]<0.0159.3 [47.9; 80.7]0.12BMI Normal weight18786.4 [59.3; 109.3]48.1 [35.0; 61.1] Overweight232115.2 [90.6; 146.6]<0.00160.4 [47.9; 77.7]<0.001 Obese155124.6 [104.2; 169.4]<0.00166.3 [52.9; 87.3]<0.001Hypertension No11296.7 [64.0; 125.9]53.4 [39.7; 65.3] Yes462112.4 [86.9; 144.7]0.0358.4 [45.4; 78.7]0.13Dyslipidemia No182104.0 [78.8; 128.0]54.9 [41.1; 68.7] Yes392114.6 [87.7; 149.0]0.0859.1 [45.7; 78.7]0.09Diabetes No361104.8 [81.4; 137.2]55.9 [43.3; 72.1] Yes213120.0 [95.6; 148.0]<0.00161.4 [48.6; 77.5]0.02Smoking No510107.8 [83.2; 137.6]60.8 [47.4; 79.7] Yes64117.9 [86.8; 169.6]0.2161.4 [48.6; 77.5]0.47ACEi use No465105.2 [82.2; 137.0]59.3 [46.9; 77.6] Yes109116.7 [92.3; 148.9]0.0365.4 [50.6; 83.9]0.09ARA use No432109.3 [83.3; 143.8]60.3 [47.6; 80.2] Yes142109.1 [85.4; 142.9]0.9763.0 [50.0; 81.9]0.36ARA or ACEi use No325103.7 [75.6; 132.6]56.3 [43.1; 73.1] Yes249115.1 [87.9; 146.9]0.0364.5 [50.2; 83.2]<0.01BB use No427104.9 [79.7; 138.7]58.4 [46.8; 80.1] Yes147115.0 [93.7; 144.7]0.0964.2 [51.3; 81.4]0.08Statin use No28797.2 [74.5; 126.1]56.7 [43.1; 68.8] Yes287114.8 [90.8; 148.5]<0.00163.6 [50.0; 81.6]0.03Insulin therapy No540108.4 [83.4; 139.9]60.6 [48.1; 79.9] Yes34124.6 [62.8; 156.1]0.0972.8 [39.6; 85.4]0.02Oral antidiabetic use No471105.3 [81.5; 137.6]59.3 [46.6; 79.9] Yes103121.3 [99.5; 149.1]<0.0165.2 [52.8; 81.4]0.09Antiplatetet use No379104.5 [75.6; 133.7]59.9 [46.4; 76.9] Yes195117.1 [86.2; 143.9]0.02363.2 [50.2; 81.4]0.30CAC score** CACS=02194.5 [72.6; 113.1]53.2 [46.9; 64.5] CAC >0<10060112.8 [97.6; 146.8]0.1564.3 [53.4; 81.4]0.10 CAC ≥100<40086107.5 [74.5; 135.2]0.2862.5 [46.4; 79.5]0.16 CAC≥400204109.9 [87.1; 148.1]0.1862.5 [49.9; 81.4]0.12CAD (≥1 stenosis ≥50%) No298108.3 [83.6; 144.4]61.4 [48.2; 81.6] Yes276108.9 [86.2; 143.9]0.9762.3 [52.3; 80.6]0.63ACEi= angiotension converting enzyme inhibitor. ARA= angiotensin receptor antagonist. BB= beta-blocker. BMI= body mass index. CAC= coronary artery calcification score. CAD= coronary artery disease*P-value calculated by Kruskal-Wallis ANOVASupplementary Table S4: Clinical characteristics of patients with and without CAD included in comparative proteomics analysis between EAT, MAT and SAT samples All (n=574)Non-obstructive CAD (n=5)Obstructive CAD (n=5)P-valueAge, yrs79 (11)61 (14)73 (8.5)0.22Male281 (48.9)5 (100)5 (100)1BMI, Kg/m227.1 (5.7)28.2 (3.2)25.8 (3.2)0.41Hypertension462 (80.5)4 (80)4 (80)1Hyperlipidaemia392 (68.2)2 (40)2 (40)1Diabetes mellitus213 (37.1)2 (40)2 (40)1Former smoker57 (9.9)01 (20)1Statin use287 (50)4 (80)4 (80)1Insulin therapy34 (5.9)01 (20)1Oral antidiabetic103 (17.9)2 (40)1 (20)1ACEi142 (24.7)02 (40)0.44ARA109 (18.9)3 (60)2 (40)1BB147 (25.6)1 (20)3 (40)1CAC score537.8 (1298)37 (679.3)948 (1711.8)0.11ACEi=angiotension converting enzyme inhibitor. ARA=angiotensin receptor antagonist. BMI=body mass index. CAC=coronary artery calcification. CAD=coronary artery disease. BB=beta-blocker. EAT=epicardial adipose tissue. MAT: mediastinal adipose tissue. SAT: subcutaneous adipose tissue. Values are n (%) or median (Interquartile range)* (due to the small sample size). Differences between groups (Obstructive vs. Non-obstructive CAD) were analysed using Fisher exact-test to compare categorical variables and Mann-Whitney-U test to compare continuous variables. Supplementary Table S5: Proteins differently expressed in non-CAD patients according to fat depots (EAT+MAT vs. SAT) (VIP>1.0).Protein IDGeneDescriptionAverage MagnitudeP-valueUp-regulated in EAT+MAT and down-regulated in SATQ5VTE0|EF1A3 EEF1A1P5Putative elongation factor 1-alpha-like 31.590.004P01024|CO3 C3Complement C31.600.002P01011|AACT SERPINA3Alpha-1-antichymotrypsin1.440.008P25705|ATPA ATP5A1ATP synthase subunit alpha. Mitochondrial1.410.005P40925|MDHC MDH1Malate dehydrogenase. Cytoplasmic1.310.011Q13228|SBP1 SELENBP1Selenium-binding protein 10.880.052P02671|FIBA FGAFibrinogen alpha chain0.860.065P00751|CFAB CFBComplement factor B1.020.055Q9BQE3|TBA1C TUBA1CTubulin alpha-1C chain0.860.052P62937|PPIA PPIAPeptidyl-prolyl cis-trans isomerase A0.810.157P01743|HV102 IGHV1-46Immunoglobulin heavy variable 1-460.890.046P15090|FABP4 FABP4Fatty acid-binding protein. Adipocyte0.610.343P00918|CAH2 CA2Carbonic anhydrase 20.280.499P02545|LMNA LMNAPrelamin-A/C0.320.477P00738|HPT HPHaptoglobin0.010.953Down-regulated in EAT+MAT and up-regulated in SATP07585|PGS2 DCNDecorin-1.970.055Q03135|CAV1 CAV1Caveolin-1-2.080.031P02452|CO1A1 COL1A1Collagen alpha-1(I) chain-1.910.076P08123|CO1A2 COL1A2Collagen alpha-2(I) chain-1.720.098P02042|HBD HBDHemoglobin subunit delta-1.580.081P01834|IGKC IGKCImmunoglobulin kappa constant-1.440.067P02774|VTDB GCVitamin D-binding protein-1.040.145Q16853|AOC3 AOC3Membrane primary amine oxidase-0.810.225CAD: coronary artery disease; EAT: epicardial adipose tissue; MAT: mediastinal adipose tissue; SAT: subcutaneous adipose tissue.Supplementary Table S6: Proteins differently expressed in CAD patients according to fat depots (EAT vs. MAT or SAT) (VIP>1.0).Protein IDGeneDescriptionAverage MagnitudeP-valueAverage MagnitudeP-valueUp-regulated in EATEAT vs MATEAT vs SATQ5VTE0|EF1A3 EEF1A1P5Putative elongation factor 1-alpha-like 32.320.0083.300.006P01011|AACT SERPINA3Alpha-1-antichymotrypsin2.180.0231.730.028P07237|PDIA1 PPIAPeptidyl-prolyl cis-trans isomerase A1.850.0192.010.029P02763|A1AG1 ORM1Alpha-1-acid glycoprotein 11.940.0201.090.123P06396|GELS GSNGelsolin2.150.0100.730.286O60240|PLIN1 PLIN1Perilipin-11.500.0730.800.252Q16555|DPYL2 DPYSL2Dihydropyrimidinase-related protein 21.940.0150.730.283P21695|GPDA GPD1Glycerol-3-phosphate dehydrogenase [NAD(+)]. cytoplasmic1.630.0340.440.509P07355|ANXA2 ANXA2Annexin A21.230.1050.380.574Down-regulated in EATEAT vs MATEAT vs SATQ03135|CAV1 CAV1Caveolin-1-3.440.003-2.000.032P32119|PRDX2 PRDX2Peroxiredoxin-2-2.610.007-1.130.147P04040|CATA CATCatalase-0.870.216-1.010.095Q99880|H2B1L HIST1H2BLHistone H2B type 1-L-0.830.0950.110.874Q9BQE3|TBA1C TUBA1CTubulin alpha-1C chain-0.860.2200.970.175P02452|CO1A1 COL1A1Collagen alpha-1(I) chain0.080.904-0.880.209Up-regulated in MATMAT vs EAT MAT vs SAT Q9BQE3|TBA1C TUBA1CTubulin alpha-1C chain0.860.2201.510.066Q99880|H2B1L HIST1H2BLHistone H2B type 1-L0.830.0950.960.200Down-regulated in MATMAT vs EAT MAT vs SAT P07237|PDIA1 PPIAPeptidyl-prolyl cis-trans isomerase A-1.850.019-0.450.509P06396|GELS GSNGelsolin-2.150.010-1.090.126P21695|GPDA GPD1Glycerol-3-phosphate dehydrogenase [NAD(+)]. cytoplasmic-1.630.034-1.310.073Q16555|DPYL2 DPYSL2Dihydropyrimidinase-related protein 2-1.940.015-0.830.231P02763|A1AG1 ORM1Alpha-1-acid glycoprotein 1-1.940.020-0.490.467P07355|ANXA2 ANXA2Annexin A2-1.230.105-1.610.034O60240|PLIN1 PLIN1Perilipin-1-1.500.073-1.040.163Up-regulated in SATSAT vs EAT SAT vs MATQ03135|CAV1 CAV1Caveolin-12.000.0320.700.319P02452|CO1A1 COL1A1Collagen alpha-1(I) chain0.880.2091.140.112Down-regulated in SATSAT vs EATSAT vs MATP01024|CO3 C3Complement C3-1.900.028-4.800.000Q5VTE0|EF1A3 EEF1A1P5Putative elongation factor 1-alpha-like 3-3.300.006-1.910.008P25705|ATPA ATP5A1ATP synthase subunit alpha. mitochondrial-1.540.072-3.680.004P01011|AACT SERPINA3Alpha-1-antichymotrypsin-1.730.0280.100.881CAD: coronary artery disease; EAT: epicardial adipose tissue; MAT: mediastinal adipose tissue; SAT: subcutaneous adipose tissue.Supplementary Table S7: Clinical characteristics of patients with obstructive and non-obstructive CAD whose EAT samples were included in the overall proteomics analysis. All(n=574)Non-obstructiveCAD(n=14)ObstructiveCAD(n=7)pAge, yrs79 (73; 84)69 (60; 77)74 (66; 76)0.439Male281 (49)8 (64)8 (89)0.340BMI, kg/m227 (24; 30)29 (26; 30)29 (26; 30)0.688Hypertension462 (80)7 (78)9 (61,3)0.657Hyperlipidaemia392 (68)9 (89)10 (71)0.611Diabetes mellitus213 (37)5 (56)3 (21,4)0.179Former smoker57 (10)1 (11)1 (7)1Statin use287 (50)8 (88)12 (85)1Insulin therapy34 (6)02 (22)0.142Oral antidiabetic103 (17)2 (14)3 (33)0.343ACEi142 (24)3 (21)3 (33)0.643ARA109 (19)3 (21)4 (44)0.363BB147 (25)3 (21)4 (44)0.363EAT volume, mL107 (76; 132)97 (76; 152)110 (87; 135)0.734CAC score538 (126; 1424)9 (0; 98)840 (260; 1831)0.0001ACEi=angiotensin converting enzyme inhibitor, ARA=angiotensin receptor antagonist, BMI=body mass index, CAC=coronary artery calcification, CAD=coronary artery disease, BB=beta-blocker, EAT=epicardial adipose tissue. Values are n (%) or median (Interquartile range)* (due to the small sample size). Differences between groups (Obstructive vs. Non-obstructive CAD) were analysed using Fisher exact-test to compare categorical variables and Mann-Whitney-U test to compare continuous variables.Supplementary Table S8: Proteins differently expressed in EAT according to CAD (VIP>1.0).Upregulated proteins in CAD groupAccessionProteinGeneDescriptionAverage MagnitudeP-valueP07355ANXA2ANXA2Annexin A22.080.008P15090FABP4FABP4Fatty acid-binding protein. adipocyte1.060.037P33121ACSL1ACSL1Long-chain-fatty-acid--CoA ligase 11.190.024Q16853AOC3AOC3Membrane primary amine oxidase0.970.067P25705ATPAATP5A1ATP synthase subunit alpha. mitochondrial1.180.046Q5VTE0EF1A3EEF1A1P5Putative elongation factor 1-alpha-like 31.320.031P06733ENOAENO1Alpha-enolase0.780.081Q13228SBP1SELENBP1Selenium-binding protein 10.690.275P04075ALDOAALDOAFructose-bisphosphate aldolase A0.950.158P01011AACTSERPINA3Alpha-1-antichymotrypsin0.720.115P09382LEG1LGALS1Galectin-10.510.817P07195LDHBLDHBL-lactate dehydrogenase B chain0.530.699P14625ENPLHSP90B1Endoplasmin0.561.000P02652APOA2APOA2Apolipoprotein A-II0.470.485P62873GBB1GNB1Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-10.220.275P40925MDHCMDH1Malate dehydrogenase. cytoplasmic0.260.629P01871IGHMIGHMImmunoglobulin heavy constant mu0.430.343P07585PGS2DCNDecorin0.600.197P21695GPDAGPD1Glycerol-3-phosphate dehydrogenase [NAD(+)]. cytoplasmic0.250.183Downregulated proteins in CAD groupAccessionProteinGeneDescriptionAverage MagnitudeP-valueP02774VTDBGCvitamin D-binding protein-1.290.003P02730B3ATSLC4A1Band 3 anion transport protein-1.110.019P02765FETUAAHSGAlpha-2-HS-glycoprotein-0.630.034P21333FLNAFLNAFilamin-A-0.630.111Q09666AHNKAHNAKNeuroblast differentiation-associated protein AHNAK-0.340.475P04792HSPB1HSPB1Heat shock protein beta-1-0.590.241P02545LMNALMNAPrelamin-A/C-0.430.588P16403H12HIST1H1CHistone H1.2-0.460.410P63267ACTHACTG2Actin. gamma-enteric smooth muscle-0.510.241P01743HV102IGHV1-46Immunoglobulin heavy variable 1-46-0.470.344P62805H4HIST1H4AHistone H4-0.430.183Q6NZI2PTRFCAVIN1Caveolae-associated protein 1-0.360.699CAD: coronary artery disease; EAT: epicardial adipose tissue.SUPPLEMENTAL REFERENCES ADDIN EN.REFLIST 1.Lang, R.M., et al., Recommendations for Cardiac Chamber Quantification by Echocardiography in Adults: An Update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. 2015.2.Leivseth, L., et al., GOLD classifications and mortality in chronic obstructive pulmonary disease: the HUNT Study, Norway. Thorax, 2013. 68(10): p. 914-21.3.Watanabe, Y., et al., Comparison of Results of Transcatheter Aortic Valve Implantation in Patients With Versus Without Active Cancer. Am J Cardiol, 2016. 118(4): p. 572-7.4.James, P.A., et al., 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). Jama, 2014. 311(5): p. 507-20.5.Stone, N.J., et al., Treatment of blood cholesterol to reduce atherosclerotic cardiovascular disease risk in adults: synopsis of the 2013 American College of Cardiology/American Heart Association cholesterol guideline. Ann Intern Med, 2014. 160(5): p. 339-43.6.Diagnosis and classification of diabetes mellitus. Diabetes Care, 2014. 37 Suppl 1: p. S81-90.7.Mancio, J., et al., Gender differences in the association of epicardial adipose tissue and coronary artery calcification: EPICHEART study: EAT and coronary calcification by gender. International Journal of Cardiology, 2017. 249: p. 419-425.8.Anjo, S.I., C. Santa, and B. Manadas, Short GeLC-SWATH: a fast and reliable quantitative approach for proteomic screenings. Proteomics, 2015. 15(4): p. 757-62.9.Tang, W.H., I.V. Shilov, and S.L. Seymour, Nonlinear fitting method for determining local false discovery rates from decoy database searches. J Proteome Res, 2008. 7(9): p. 3661-7.10.Sennels, L., J.C. Bukowski-Wills, and J. Rappsilber, Improved results in proteomics by use of local and peptide-class specific false discovery rates. BMC Bioinformatics, 2009. 10: p. 179.11.Lambert, J.P., et al., Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition. Nat Methods, 2013. 10(12): p. 1239-45.12.Collins, B.C., et al., Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system. Nat Methods, 2013. 10(12): p. 1246-53.13.Mancio, J., et al., Epicardial adipose tissue volume assessed by computed tomography and coronary artery disease: a systematic review and meta-analysis. Eur Heart J Cardiovasc Imaging, 2017. ................
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