{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T00:29:17Z","timestamp":1767054557794,"version":"3.48.0"},"reference-count":41,"publisher":"Korean Endocrine Society","issue":"6","license":[{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2020R1C1C1010723"],"award-info":[{"award-number":["2020R1C1C1010723"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003625","name":"Ministry of Health and Welfare","doi-asserted-by":"publisher","award":["HI21C0032"],"award-info":[{"award-number":["HI21C0032"]}],"id":[{"id":"10.13039\/501100003625","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003625","name":"Ministry of Health and Welfare","doi-asserted-by":"publisher","award":["HI22C0049"],"award-info":[{"award-number":["HI22C0049"]}],"id":[{"id":"10.13039\/501100003625","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["e-enm.org"],"crossmark-restriction":true},"short-container-title":["Endocrinol Metab"],"published-print":{"date-parts":[[2025,12,31]]},"abstract":"<jats:p>Background: The long-term association between adrenal gland volume (AGV) and type 2 diabetes (T2D) remains unclear. We aimed to determine the association between deep learning-based AGV and current glycemic status and incident T2D.Methods: In this observational study, adults who underwent abdominopelvic computed tomography (CT) for health checkups (2011\u20132012), but had no adrenal nodules, were included. AGV was measured from CT images using a three-dimensional nnU-Net deep learning algorithm. We assessed the association between AGV and T2D using a cross-sectional and longitudinal design.Results: We used 500 CT scans (median age, 52.3 years; 253 men) for model development and a Multi-Atlas Labeling Beyond the Cranial Vault dataset for external testing. A clinical cohort included a total of 9708 adults (median age, 52.0 years; 5,769 men). The deep learning model demonstrated a dice coefficient of 0.71\u00b10.11 for adrenal segmentation and a mean volume difference of 0.6\u00b1 0.9 mL in the external dataset. Participants with T2D at baseline had a larger AGV than those without (7.3 cm&lt;sup&gt;3&lt;\/sup&gt; vs. 6.7 cm&lt;sup&gt;3&lt;\/sup&gt; and 6.3 cm&lt;sup&gt;3&lt;\/sup&gt; vs. 5.5 cm&lt;sup&gt;3&lt;\/sup&gt; for men and women, respectively, all &lt;i&gt;P&lt;\/i&gt;&lt;0.05). The optimal AGV cutoff values for predicting T2D were 7.2 cm&lt;sup&gt;3&lt;\/sup&gt; in men and 5.5 cm&lt;sup&gt;3&lt;\/sup&gt; in women. Over a median 7.0-year follow-up, T2D developed in 938 participants. Cumulative T2D risk was accentuated with high AGV compared with low AGV (adjusted hazard ratio, 1.27; 95% confidence interval, 1.11 to 1.46).Conclusion: AGV, measured using deep learning algorithms, is associated with current glycemic status and can significantly predict the development of T2D.<\/jats:p>","DOI":"10.3803\/enm.2025.2336","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:11:37Z","timestamp":1750209097000},"page":"991-1001","update-policy":"https:\/\/doi.org\/10.3803\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Based Adrenal Gland Volumetry for the Prediction of Diabetes"],"prefix":"10.3803","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5533-4989","authenticated-orcid":true,"given":"Eu Jeong","family":"Ku","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3700-0165","authenticated-orcid":true,"given":"Soon Ho","family":"Yoon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5719-1539","authenticated-orcid":true,"given":"Seung Shin","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9003-0614","authenticated-orcid":true,"given":"Ji Won","family":"Yoon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1932-0234","authenticated-orcid":true,"given":"Jung Hee","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2119","reference":[{"key":"ref1","first-page":"S19","volume-title":"2","author":"ElSayed","year":"2023"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.4093\/dmj.2022.0106"},{"volume-title":"IDF Diabetes Atlas 2025 [Internet]","year":"2025","key":"ref3"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3390\/ijms23158178"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1210\/en.2004-0607"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.2337\/diacare.10.5.662"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/s0140-6736(64)90616-6"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s11695-020-04593-2"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsbmb.2019.04.006"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2006-2540"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1210\/jc.83.6.1853"},{"key":"ref12","first-page":"345","volume-title":"Hyperactivity of the hypothalamo-pituitary-adrenal axis in obesity: a study of ACTH, AVP, beta-lipotrophin and cortisol responses to insulin-induced hypoglycaemia","author":"Weaver","year":"1993"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.2147\/dmsott.s9089"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/7297419"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/s2213-8587(16)00086-3"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.psyneuen.2015.04.008"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104749"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.220101"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-022-09347-5"},{"volume-title":"Deep learning-based adrenal gland volumetry for the prediction of diabetes mellitus: an observational study. 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