{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T03:27:08Z","timestamp":1784258828266,"version":"3.55.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T00:00:00Z","timestamp":1752796800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T00:00:00Z","timestamp":1752796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001505","name":"Health Research Council of New Zealand","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001505","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001516","name":"National Heart Foundation of New Zealand","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001516","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cardiovasc Diabetol"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Epicardial adipose tissue (EAT) is associated with cardiometabolic risk in type 2 diabetes (T2D), but its spatial distribution and structural alterations remain understudied. We aim to develop a shape-aware, AI-based method for automated segmentation and morphogeometric analysis of EAT in T2D.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>A total of 90 participants (45 with T2D and 45 age-, sex-matched controls) underwent cardiac 3D Dixon MRI, enrolled between 2014 and 2018 as part of the sub-study of the Swedish SCAPIS cohort. We developed EAT-Seg, a multi-modal deep learning model incorporating signed distance maps (SDMs) for shape-aware segmentation. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), the 95% Hausdorff distance (HD95), and the average symmetric surface distance (ASSD). Statistical shape analysis combined with partial least squares discriminant analysis (PLS-DA) was applied to point cloud representations of EAT to capture latent spatial variations between groups. Morphogeometric features, including volume, 3D local thickness map, elongation and fragmentation index, were extracted and correlated with PLS-DA latent variables using Pearson correlation. Features with high-correlation were identified as key differentiators and evaluated using a Random Forest classifier.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>EAT-Seg achieved a DSC of 0.881, a HD95 of 3.213\u00a0mm, and an ASSD of 0.602\u00a0mm. Statistical shape analysis revealed spatial distribution differences in EAT between T2D and control groups. Morphogeometric feature analysis identified volume and thickness gradient-related features as key discriminators (<jats:italic>r<\/jats:italic>\u2009&gt;\u20090.8, <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.05). Random Forest classification achieved an AUC of 0.703.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>This AI-based framework enables accurate segmentation for structurally complex EAT and reveals key morphogeometric differences associated with T2D, supporting its potential as a biomarker for cardiometabolic risk assessment.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Graphical abstract<\/jats:title>\n          <\/jats:sec>","DOI":"10.1186\/s12933-025-02829-y","type":"journal-article","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T14:57:55Z","timestamp":1752850675000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["AI-Driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetes"],"prefix":"10.1186","volume":"24","author":[{"given":"Fan","family":"Feng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdallah I.","family":"Hasaballa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Long","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Fernandez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carl-Johan","family":"Carlh\u00e4ll","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jichao","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,18]]},"reference":[{"key":"2829_CR1","doi-asserted-by":"publisher","unstructured":"Zhang S, Wang S, Liu S, Li P, Huang D, Zeng X et al. Epicardial adipose tissue: a new link between type 2 diabetes and heart failure\u2014a comprehensive review. Heart Fail Rev [Internet]. 2024 [cited 2024 Dec 31]; Available from: https:\/\/link.springer.com\/https:\/\/doi.org\/10.1007\/s10741-024-10478-8","DOI":"10.1007\/s10741-024-10478-8"},{"key":"2829_CR2","doi-asserted-by":"publisher","first-page":"2460","DOI":"10.1161\/01.CIR.0000099542.57313.C5","volume":"108","author":"T Mazurek","year":"2003","unstructured":"Mazurek T, Zhang L, Zalewski A, Mannion JD, Diehl JT, Arafat H, et al. Human epicardial adipose tissue is a source of inflammatory mediators. Circulation. 2003;108:2460\u20136.","journal-title":"Circulation"},{"key":"2829_CR3","doi-asserted-by":"publisher","unstructured":"Chong B, Jayabaskaran J, Ruban J, Goh R, Chin YH, Kong G et al. Epicardial Adipose Tissue Assessed by Computed Tomography and Echocardiography Are Associated With Adverse Cardiovascular Outcomes: A Systematic Review and Meta-Analysis. Circ: Cardiovascular Imaging [Internet]. 2023 [cited 2024 Dec 7];16. Available from: https:\/\/www.ahajournals.org\/doi\/https:\/\/doi.org\/10.1161\/CIRCIMAGING.122.015159","DOI":"10.1161\/CIRCIMAGING.122.015159"},{"key":"2829_CR4","doi-asserted-by":"publisher","first-page":"20190770","DOI":"10.1259\/bjr.20190770","volume":"93","author":"CB Monti","year":"2020","unstructured":"Monti CB, Codari M, De Cecco CN, Secchi F, Sardanelli F, Stillman AE. Novel imaging biomarkers: epicardial adipose tissue evaluation. BJR. 2020;93:20190770.","journal-title":"BJR"},{"key":"2829_CR5","doi-asserted-by":"publisher","first-page":"1730","DOI":"10.1016\/j.jacc.2021.08.037","volume":"78","author":"AC Ernault","year":"2021","unstructured":"Ernault AC, Meijborg VMF, Coronel R. Modulation of cardiac arrhythmogenesis by epicardial adipose tissue. J Am Coll Cardiol. 2021;78:1730\u201345.","journal-title":"J Am Coll Cardiol"},{"key":"2829_CR6","doi-asserted-by":"publisher","first-page":"864","DOI":"10.5114\/aoms.2016.63259","volume":"4","author":"E Nagy","year":"2017","unstructured":"Nagy E, Jermendy AL, Merkely B, Maurovich-Horvat P. Clinical importance of epicardial adipose tissue. Aoms. 2017;4:864\u201374.","journal-title":"Aoms"},{"key":"2829_CR7","doi-asserted-by":"publisher","first-page":"2223","DOI":"10.1111\/j.1582-4934.2010.01141.x","volume":"14","author":"DM Ouwens","year":"2010","unstructured":"Ouwens DM, Sell H, Greulich S, Eckel J. The role of epicardial and perivascular adipose tissue in the pathophysiology of cardiovascular disease. J Cell Mol Medi. 2010;14:2223\u201334.","journal-title":"J Cell Mol Medi"},{"key":"2829_CR8","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1007\/s10741-024-10478-8","volume":"30","author":"S Zhang","year":"2024","unstructured":"Zhang S, Wang S, Liu S, Li P, Huang D, Zeng X, et al. Epicardial adipose tissue: a new link between type 2 diabetes and heart failure\u2014a comprehensive review. Heart Fail Rev. 2024;30:477\u201391.","journal-title":"Heart Fail Rev"},{"key":"2829_CR9","doi-asserted-by":"publisher","first-page":"1820","DOI":"10.1161\/ATVBAHA.114.303035","volume":"34","author":"S Lim","year":"2014","unstructured":"Lim S, Meigs JB. Links between ectopic fat and vascular disease in humans. ATVB. 2014;34:1820\u20136.","journal-title":"ATVB"},{"key":"2829_CR10","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1038\/ncpcardio0319","volume":"2","author":"G Iacobellis","year":"2005","unstructured":"Iacobellis G, Corradi D, Sharma AM. Epicardial adipose tissue: anatomic, biomolecular and clinical relationships with the heart. Nat Rev Cardiol. 2005;2:536\u201343.","journal-title":"Nat Rev Cardiol"},{"key":"2829_CR11","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s10554-015-0778-8","volume":"32","author":"R Homsi","year":"2016","unstructured":"Homsi R, Meier-Schroers M, Gieseke J, Dabir D, Luetkens JA, Kuetting DL, et al. 3D-Dixon MRI based volumetry of peri- and epicardial fat. Int J Cardiovasc Imaging. 2016;32:291\u20139.","journal-title":"Int J Cardiovasc Imaging"},{"key":"2829_CR12","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1186\/s12880-020-00478-z","volume":"20","author":"M Henningsson","year":"2020","unstructured":"Henningsson M, Brundin M, Scheffel T, Edin C, Viola F, Carlh\u00e4ll C-J. Quantification of epicardial fat using 3D cine Dixon MRI. BMC Med Imaging. 2020;20:80.","journal-title":"BMC Med Imaging"},{"key":"2829_CR13","first-page":"800","volume":"16","author":"HW West","year":"2023","unstructured":"West HW, Siddique M, Williams MC, Volpe L, Desai R, Lyasheva M, et al. Deep-Learning for epicardial adipose tissue assessment with computed tomography. JACC: Cardiovasc Imaging. 2023;16:800\u201316.","journal-title":"JACC: Cardiovasc Imaging"},{"key":"2829_CR14","doi-asserted-by":"publisher","first-page":"128032","DOI":"10.1109\/ACCESS.2020.3008190","volume":"8","author":"Q Zhang","year":"2020","unstructured":"Zhang Q, Zhou J, Zhang B, Jia W, Wu E. Automatic epicardial fat segmentation and quantification of CT scans using dual U-Nets with a morphological processing layer. IEEE Access. 2020;8:128032\u201341.","journal-title":"IEEE Access"},{"key":"2829_CR15","doi-asserted-by":"crossref","unstructured":"Commandeur F, Goeller M, Razipour A, Cadet S, Hell MM, Kwiecinski J et al. Fully Automated CT Quantification of Epicardial Adipose Tissue by Deep Learning: A Multicenter Study. Radiology: Artificial Intelligence. 2019;1:e190045.","DOI":"10.1148\/ryai.2019190045"},{"key":"2829_CR16","doi-asserted-by":"publisher","first-page":"1392","DOI":"10.1007\/s12559-022-10036-0","volume":"14","author":"J Qu","year":"2022","unstructured":"Qu J, Chang Y, Sun L, Li Y, Si Q, Yang M-F, et al. Deep Learning-Based approach for the automatic quantification of epicardial adipose tissue from Non-Contrast CT. Cogn Comput. 2022;14:1392\u2013404.","journal-title":"Cogn Comput"},{"key":"2829_CR17","doi-asserted-by":"publisher","first-page":"126","DOI":"10.3390\/diagnostics12010126","volume":"12","author":"P Daud\u00e9","year":"2022","unstructured":"Daud\u00e9 P, Ancel P, Confort Gouny S, Jacquier A, Kober F, Dutour A, et al. Deep-Learning segmentation of epicardial adipose tissue using Four-Chamber cardiac magnetic resonance imaging. Diagnostics. 2022;12:126.","journal-title":"Diagnostics"},{"key":"2829_CR18","doi-asserted-by":"publisher","first-page":"117549","DOI":"10.1016\/j.atherosclerosis.2024.117549","volume":"397","author":"M Guglielmo","year":"2024","unstructured":"Guglielmo M, Penso M, Carerj ML, Giacari CM, Volpe A, Fusini L, et al. DEep LearnIng-based quantification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR. Atherosclerosis. 2024;397:117549.","journal-title":"Atherosclerosis"},{"key":"2829_CR19","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.1109\/TMI.2018.2804799","volume":"37","author":"F Commandeur","year":"2018","unstructured":"Commandeur F, Goeller M, Betancur J, Cadet S, Doris M, Chen X, et al. Deep learning for quantification of epicardial and thoracic adipose tissue from Non-Contrast CT. IEEE Trans Med Imaging. 2018;37:1835\u201346.","journal-title":"IEEE Trans Med Imaging"},{"key":"2829_CR20","doi-asserted-by":"crossref","unstructured":"Zhao K, Liu Z, Liu J, Zhou J, Liao B, Tang H et al. Uncertainty-driven and Adversarial Calibration Learning for Epicardial Adipose Tissue Segmentation [Internet]. arXiv; 2024 [cited 2024 Oct 31]. Available from: http:\/\/arxiv.org\/abs\/2402.14349","DOI":"10.1109\/AINIT61980.2024.10581609"},{"key":"2829_CR21","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1186\/s12933-024-02474-x","volume":"23","author":"\u0141 Nogajski","year":"2024","unstructured":"Nogajski \u0141, Mazuruk M, Kacperska M, Kurpias M, M\u0105czewski M, Nowakowski M, et al. Epicardial fat density obtained with computed tomography imaging - more important than volume? Cardiovasc Diabetol. 2024;23:389.","journal-title":"Cardiovasc Diabetol"},{"key":"2829_CR22","doi-asserted-by":"publisher","first-page":"110157","DOI":"10.1016\/j.ejrad.2022.110157","volume":"148","author":"V Brandt","year":"2022","unstructured":"Brandt V, Bekeredjian R, Schoepf UJ, Varga-Szemes A, Emrich T, Aquino GJ, et al. Prognostic value of epicardial adipose tissue volume in combination with coronary plaque and flow assessment for the prediction of major adverse cardiac events. Eur J Radiol. 2022;148:110157.","journal-title":"Eur J Radiol"},{"key":"2829_CR23","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1186\/s12933-024-02376-y","volume":"23","author":"CD Yang","year":"2024","unstructured":"Yang CD, Quan JW, Tay GP, Feng S, Yuan H, Amuti A, et al. Epicardial adipose tissue volume and density are associated with heart failure with improved ejection fraction. Cardiovasc Diabetol. 2024;23:283.","journal-title":"Cardiovasc Diabetol"},{"key":"2829_CR24","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.jcct.2017.11.007","volume":"12","author":"M Goeller","year":"2018","unstructured":"Goeller M, Achenbach S, Marwan M, Doris MK, Cadet S, Commandeur F, et al. Epicardial adipose tissue density and volume are related to subclinical atherosclerosis, inflammation and major adverse cardiac events in asymptomatic subjects. J Cardiovasc Comput Tomogr. 2018;12:67\u201373.","journal-title":"J Cardiovasc Comput Tomogr"},{"key":"2829_CR25","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1016\/j.acra.2011.03.011","volume":"18","author":"F Saremi","year":"2011","unstructured":"Saremi F, Mekhail S, Sefidbakht S, Thonar B, Malik S, Sarlaty T. Quantification of epicardial adipose tissue. Acad Radiol. 2011;18:977\u201383.","journal-title":"Acad Radiol"},{"key":"2829_CR26","doi-asserted-by":"publisher","first-page":"388","DOI":"10.6061\/clinics\/2014(06)04","volume":"69","author":"MB Demircelik","year":"2014","unstructured":"Demircelik MB, Yilmaz OC, Gurel OM, Selcoki Y, Atar IA, Bozkurt A, et al. Epicardial adipose tissue and pericoronary fat thickness measured with 64-multidetector computed tomography: potential predictors of the severity of coronary artery disease. Clinics. 2014;69:388\u201392.","journal-title":"Clinics"},{"key":"2829_CR27","doi-asserted-by":"publisher","first-page":"405","DOI":"10.4093\/dmj.2015.39.5.405","volume":"39","author":"DK Song","year":"2015","unstructured":"Song DK, Hong YS, Lee H, Oh J-Y, Sung Y-A, Kim Y. Increased epicardial adipose tissue thickness in type 2 diabetes mellitus and obesity. Diabetes Metab J. 2015;39:405.","journal-title":"Diabetes Metab J"},{"key":"2829_CR28","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1007\/s12265-024-10505-x","volume":"17","author":"RC Brigham","year":"2024","unstructured":"Brigham RC, Mattson AR, Iaizzo PA. Ventricular epicardial adipose distribution on human hearts: 3-Dimensional reconstructions and quantitative assessments. J Cardiovasc Trans Res. 2024;17:959\u201368.","journal-title":"J Cardiovasc Trans Res"},{"key":"2829_CR29","doi-asserted-by":"publisher","first-page":"e1008851","DOI":"10.1371\/journal.pcbi.1008851","volume":"17","author":"C Rodero","year":"2021","unstructured":"Rodero C, Strocchi M, Marciniak M, Longobardi S, Whitaker J, O\u2019Neill MD, et al. Linking statistical shape models and simulated function in the healthy adult human heart. Beard DA, editor. PLoS Comput Biol. 2021;17:e1008851.","journal-title":"PLoS Comput Biol"},{"key":"2829_CR30","doi-asserted-by":"publisher","first-page":"813427","DOI":"10.3389\/fcvm.2022.813427","volume":"9","author":"C Edin","year":"2022","unstructured":"Edin C, Ekstedt M, Scheffel T, Karlsson M, Swahn E, \u00d6stgren CJ, et al. Ectopic fat is associated with cardiac remodeling\u2014A comprehensive assessment of regional fat depots in type 2 diabetes using multi-parametric MRI. Front Cardiovasc Med. 2022;9:813427.","journal-title":"Front Cardiovasc Med"},{"key":"2829_CR31","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1111\/joim.12384","volume":"278","author":"G Bergstr\u00f6m","year":"2015","unstructured":"Bergstr\u00f6m G, Berglund G, Blomberg A, Brandberg J, Engstr\u00f6m G, Engvall J, et al. The Swedish cardiopulmonary bioimage study: objectives and design. J Intern Med. 2015;278:645\u201359.","journal-title":"J Intern Med"},{"key":"2829_CR32","unstructured":"Gu A, Dao T, Mamba. Linear-Time Sequence Modeling with Selective State Spaces [Internet]. arXiv; 2024 [cited 2025 Feb 24]. Available from: http:\/\/arxiv.org\/abs\/2312.00752"},{"key":"2829_CR33","unstructured":"Ma J, Li F, Wang B. U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation [Internet]. arXiv; 2024 [cited 2025 Feb 24]. Available from: http:\/\/arxiv.org\/abs\/2401.04722"},{"key":"2829_CR34","doi-asserted-by":"crossref","unstructured":"Dahl VA, Dahl AB. Fast local thickness. 2023. pp. 4336\u201344.","DOI":"10.1109\/CVPRW59228.2023.00456"},{"key":"2829_CR35","doi-asserted-by":"publisher","unstructured":"Kerfoot E, Clough J, Oksuz I, Lee J, King AP, Schnabel JA et al. Left-Ventricle Quantification Using Residual U-Net. In: Pop M, Sermesant M, Zhao J, Li S, McLeod K, Young A, editors. Statistical Atlases and Computational Models of the Heart Atrial Segmentation and LV Quantification Challenges [Internet]. Cham: Springer International Publishing; 2019 [cited 2025 Feb 19]. pp. 371\u201380. Available from: https:\/\/doi.org\/10.1007\/978-3-030-12029-0_40","DOI":"10.1007\/978-3-030-12029-0_40"},{"key":"2829_CR36","doi-asserted-by":"publisher","unstructured":"Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J, UNet++: et al. A Nested U-Net Architecture for Medical Image Segmentation. In: Stoyanov D, Taylor Z, Carneiro G, Syeda-Mahmood T, Martel A, Maier-Hein L, editors. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support [Internet]. Cham: Springer International Publishing; 2018 [cited 2025 Feb 19]. pp. 3\u201311. Available from: https:\/\/link.springer.com\/https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"2829_CR37","doi-asserted-by":"publisher","first-page":"2360","DOI":"10.1016\/j.jacc.2018.03.509","volume":"71","author":"M Packer","year":"2018","unstructured":"Packer M. Epicardial adipose tissue May mediate deleterious effects of obesity and inflammation on the myocardium. J Am Coll Cardiol. 2018;71:2360\u201372.","journal-title":"J Am Coll Cardiol"},{"key":"2829_CR38","doi-asserted-by":"publisher","first-page":"6300","DOI":"10.1210\/jc.2005-1087","volume":"90","author":"G Iacobellis","year":"2005","unstructured":"Iacobellis G, Leonetti F. Epicardial adipose tissue and insulin resistance in obese subjects. J Clin Endocrinol Metabolism. 2005;90:6300\u20132.","journal-title":"J Clin Endocrinol Metabolism"},{"key":"2829_CR39","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1038\/s41569-022-00679-9","volume":"19","author":"G Iacobellis","year":"2022","unstructured":"Iacobellis G. Epicardial adipose tissue in contemporary cardiology. Nat Rev Cardiol. 2022;19:593\u2013606.","journal-title":"Nat Rev Cardiol"},{"key":"2829_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1475-2840-5-1","volume":"5","author":"AR Baker","year":"2006","unstructured":"Baker AR, Da Silva NF, Quinn DW, Harte AL, Pagano D, Bonser RS, et al. Human epicardial adipose tissue expresses a pathogenic profile of adipocytokines in patients with cardiovascular disease. Cardiovasc Diabetol. 2006;5:1.","journal-title":"Cardiovasc Diabetol"},{"key":"2829_CR41","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s12933-023-01738-2","volume":"22","author":"VA Myasoedova","year":"2023","unstructured":"Myasoedova VA, Parisi V, Moschetta D, Valerio V, Conte M, Massaiu I, et al. Efficacy of cardiometabolic drugs in reduction of epicardial adipose tissue: a systematic review and meta-analysis. Cardiovasc Diabetol. 2023;22:23.","journal-title":"Cardiovasc Diabetol"},{"key":"2829_CR42","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1016\/j.tem.2011.07.003","volume":"22","author":"G Iacobellis","year":"2011","unstructured":"Iacobellis G, Bianco AC. Epicardial adipose tissue: emerging physiological, pathophysiological and clinical features. Trends Endocrinol Metabolism. 2011;22:450\u20137.","journal-title":"Trends Endocrinol Metabolism"},{"key":"2829_CR43","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s12933-021-01225-6","volume":"20","author":"E Cosson","year":"2021","unstructured":"Cosson E, Nguyen MT, Rezgani I, Tatulashvili S, Sal M, Berkane N, et al. Epicardial adipose tissue volume and coronary calcification among people living with diabetes: a cross-sectional study. Cardiovasc Diabetol. 2021;20:35.","journal-title":"Cardiovasc Diabetol"},{"key":"2829_CR44","doi-asserted-by":"publisher","first-page":"204201882092882","DOI":"10.1177\/2042018820928824","volume":"11","author":"RH Christensen","year":"2020","unstructured":"Christensen RH, Von Scholten BJ, Lehrskov LL, Rossing P, J\u00f8rgensen PG. Epicardial adipose tissue: an emerging biomarker of cardiovascular complications in type 2 diabetes? Therapeutic Adv Endocrinol. 2020;11:2042018820928824.","journal-title":"Therapeutic Adv Endocrinol"},{"key":"2829_CR45","first-page":"101188","volume":"3","author":"T Hu","year":"2024","unstructured":"Hu T, Freeze J, Singh P, Kim J, Song Y, Wu H, et al. Artificial intelligence prediction of cardiovascular events using opportunistic epicardial adipose tissue assessments from computed tomography calcium score. JACC: Adv. 2024;3:101188.","journal-title":"JACC: Adv"},{"key":"2829_CR46","doi-asserted-by":"publisher","unstructured":"Wang T-D, Lee W-J. Epicardial Adipose Tissue Measured by Multidetector Computed Tomography: Practical Tips and Clinical Implications. In: Preedy VR, editor. Handbook of Anthropometry [Internet]. New York, NY: Springer New York; 2012 [cited 2025 May 25]. pp. 955\u201372. Available from: https:\/\/link.springer.com\/https:\/\/doi.org\/10.1007\/978-1-4419-1788-1_57","DOI":"10.1007\/978-1-4419-1788-1_57"},{"key":"2829_CR47","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.ejrad.2005.12.030","volume":"57","author":"S Abbara","year":"2006","unstructured":"Abbara S, Desai JC, Cury RC, Butler J, Nieman K, Reddy V. Mapping epicardial fat with multi-detector computed tomography to facilitate percutaneous transepicardial arrhythmia ablation. Eur J Radiol. 2006;57:417\u201322.","journal-title":"Eur J Radiol"}],"container-title":["Cardiovascular Diabetology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12933-025-02829-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12933-025-02829-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12933-025-02829-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T14:31:06Z","timestamp":1757255466000},"score":1,"resource":{"primary":{"URL":"https:\/\/cardiab.biomedcentral.com\/articles\/10.1186\/s12933-025-02829-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,18]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2829"],"URL":"https:\/\/doi.org\/10.1186\/s12933-025-02829-y","relation":{},"ISSN":["1475-2840"],"issn-type":[{"value":"1475-2840","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,18]]},"assertion":[{"value":"6 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study complied with the Declaration of Helsinki and was approved by the Link\u00f6ping Ethical Review Board (Dnr 2023-02089-02). Informed consent was obtained from all participants.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"294"}}