{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T05:54:44Z","timestamp":1791179684277,"version":"4.1.0"},"reference-count":67,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T00:00:00Z","timestamp":1791158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100020884","name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100020884","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Prediction of gestational diabetes mellitus (GDM) in early pregnancy enables timely, targeted interventions to reduce risks for both the mother and fetus. We aimed to improve early GDM prediction and clinical risk stratification using a novel deep learning (DL) approach that transforms tabular clinical data into images using a genetic algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A dataset of 1,611 pregnancies was used. The dataset contained routinely collected early-pregnancy data, including clinical measurements, blood tests, and medical history. A novel method to transform tabular data into images was applied to enable the use of deep learning models for GDM prediction. A genetic algorithm optimized the spatial arrangement of variables during image generation by determining their spatial positions. Interpretability was assessed using gradient-weighted class activation mapping (Grad-CAM) to identify variables the model preferentially attended to for positive and negative predictions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Multiple model configurations were evaluated by varying the spatial arrangement of variables within the transformation. Prioritizing high sensitivity, one model achieved an accuracy of 0.86, sensitivity of 0.82, specificity of 0.87, and an AUC-ROC of 0.83. Another model achieved an accuracy of 0.79, sensitivity of 0.92, specificity of 0.77, and an AUC-ROC of 0.85. Compared with previously published machine learning models using the same dataset, our approach demonstrated improved performance. Our best models increased specificity by an average of 13.93 percentage points compared with traditional ML models at matched sensitivity levels, thereby reducing false-positive classifications that may lead to additional clinical evaluation. Grad-CAM analysis revealed that attention patterns of the model aligned with established clinical risk factors for GDM, with first trimester fasting glycemia and parity preferentially attended for positive predictions and maternal weight for negative predictions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study demonstrates that transforming tabular clinical data into image representations, optimized using a genetic algorithm, improves early GDM prediction. Representing tabular variables as images enables more effective application of DL models, which have demonstrated enhanced pattern recognition in image-based tasks compared with traditional machine learning methods. Performance improvements over state-of-the-art approaches were achieved. Accurate DL models can be developed using a limited number of input variables, supporting early identification of women at increased risk of GDM and facilitating timely clinical management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1941799","type":"journal-article","created":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T05:33:31Z","timestamp":1791178411000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Early prediction of gestational diabetes mellitus using deep learning applied to routinely collected clinical data"],"prefix":"10.3389","volume":"17","author":[{"given":"Gabriel","family":"Cubillos","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Universidad de Chile","place":["Santiago, Chile"]},{"name":"IMPACT, Center of Interventional Medicine for Precision and Advanced Cellular Therapy","place":["Santiago, Chile"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Max","family":"Monckeberg","sequence":"additional","affiliation":[{"name":"IMPACT, Center of Interventional Medicine for Precision and Advanced Cellular Therapy","place":["Santiago, Chile"]},{"name":"Department of Obstetrics and Gynecology and Laboratory of Reproductive Biology, Faculty of Medicine, Universidad de los Andes","place":["Santiago, Chile"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian E.","family":"Illanes","sequence":"additional","affiliation":[{"name":"IMPACT, Center of Interventional Medicine for Precision and Advanced Cellular Therapy","place":["Santiago, Chile"]},{"name":"Department of Obstetrics and Gynecology and Laboratory of Reproductive Biology, Faculty of Medicine, Universidad de los Andes","place":["Santiago, Chile"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudio A.","family":"Perez","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Universidad de Chile","place":["Santiago, Chile"]},{"name":"IMPACT, Center of Interventional Medicine for Precision and Advanced Cellular Therapy","place":["Santiago, Chile"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,10,5]]},"reference":[{"key":"B1","unstructured":"IDF Diabetes Atlas. 11th Ed\n          \n          \n          Brussels, Belgium\n          International Diabetes Federation\n          \n          2025"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2018.05.010","article-title":"Prevalence of gestational diabetes and subsequent type 2 diabetes among U.S. women","volume":"141","author":"Casagrande","year":"2018","journal-title":"Diabetes Res Clin Pract"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.2337\/dc11-1687","article-title":"Hyperglycemia and adverse pregnancy outcome (HAPO) study: Associations of maternal A1C and glucose with pregnancy outcomes","volume":"35","author":"Lowe","year":"2012","journal-title":"Diabetes Care"},{"key":"B4","doi-asserted-by":"publisher","first-page":"S8","DOI":"10.2337\/dc15-S005","article-title":"2. Classification and diagnosis of diabetes","volume":"38","year":"2014","journal-title":"Diabetes Care"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.3390\/ijms19113342","article-title":"The pathophysiology of gestational diabetes mellitus","volume":"19","author":"Plows","year":"2018","journal-title":"Int J Mol Sci"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2022.101294","article-title":"The life course perspective of gestational diabetes: An opportunity for the prevention of diabetes and heart disease in women","volume":"45","author":"Fu","year":"2022","journal-title":"EClinicalMedicine"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1210\/endrev\/bnac003","article-title":"A clinical update on gestational diabetes mellitus","volume":"43","author":"Sweeting","year":"2022","journal-title":"Endocr Rev"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.2337\/dc09-1848","article-title":"International Association of Diabetes and Pregnancy Study Groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy","volume":"33","year":"2010","journal-title":"Diabetes Care"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2023.3312433","article-title":"Reinforcement learning-based false data injection attacks detector for modular multilevel converters","volume":"71","author":"Gallardo","year":"2024","journal-title":"IEEE Trans Ind Electron"},{"key":"B10","doi-asserted-by":"publisher","first-page":"101994","DOI":"10.1016\/j.ijinfomgt.2019.08.002","article-title":"Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy","volume":"57","author":"Dwivedi","year":"2021","journal-title":"Int J Inf Manage"},{"key":"B11","doi-asserted-by":"publisher","first-page":"956","DOI":"10.1186\/s12884-026-09411-8","article-title":"Machine learning to improve the prediction of large for gestational age (LGA) neonates: A cohort study","volume":"26","author":"Cubillos","year":"2026","journal-title":"BMC Pregnancy Childbirth"},{"key":"B12","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1186\/s40001-025-02680-7","article-title":"Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: A comprehensive review of machine learning and deep learning approaches","volume":"30","author":"Sadr","year":"2025","journal-title":"Eur J Med Res"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0233678","article-title":"Deep learning-based survival prediction for multiple cancer types using histopathology images","volume":"15","author":"Wulczyn","year":"2020","journal-title":"PloS One"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2023.105228","article-title":"Comparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population; the Monash GDM machine learning model","volume":"179","author":"Belsti","year":"2023","journal-title":"Int J Med Inform"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2024.105533","article-title":"External validation of risk prediction model for gestational diabetes: Individual participant data meta-analysis of randomized trials","volume":"190","author":"Ranasinha","year":"2024","journal-title":"Int J Med Inform"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.850191","article-title":"Application value of predictive model based on maternal coagulation function and glycolipid metabolism indicators in early diagnosis of gestational diabetes mellitus","author":"Zheng","year":"2022","journal-title":"Front Public Health"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2024.1294588","article-title":"Relationships between triglyceride-glucose index and incident gestational diabetes mellitus: A prospective cohort study of a Korean population using publicly available data","author":"Mo","year":"2024","journal-title":"Front Public Health"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1210\/clinem\/dgaa899","article-title":"Early prediction of gestational diabetes mellitus in the Chinese population via advanced machine learning","volume":"106","author":"Wu","year":"2021","journal-title":"J Clin Endocrinol Metab"},{"key":"B19","doi-asserted-by":"publisher","first-page":"1998","DOI":"10.1210\/clinem\/dgad052","article-title":"A prediction model of gestational diabetes mellitus based on OGTT in early pregnancy: A prospective cohort study","volume":"108","author":"Wu","year":"2023","journal-title":"J Clin Endocrinol Metab"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1111\/1471-0528.18110","article-title":"First-trimester prediction of gestational diabetes mellitus based on maternal risk factors","volume":"132","author":"Syngelaki","year":"2025","journal-title":"BJOG"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2022.1017448","article-title":"AST-to-ALT ratio in the first trimester and the risk of gestational diabetes mellitus","author":"An","year":"2022","journal-title":"Front Endocrinol (Lausanne)"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2023.1087994","article-title":"Establishment and validation of a predictive nomogram for gestational diabetes mellitus during early pregnancy term: A retrospective study","volume":"14","author":"Li","year":"2023","journal-title":"Front Endocrinol (Lausanne)"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2023.1105062","article-title":"Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm","author":"Hu","year":"2023","journal-title":"Front Endocrinol (Lausanne)"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2023.1216832","article-title":"Integration of clinical demographics and routine laboratory analysis parameters for early prediction of gestational diabetes mellitus in the Chinese population","author":"Zhang","year":"2023","journal-title":"Front Endocrinol (Lausanne)"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2021.109001","article-title":"A risk prediction model of gestational diabetes mellitus before 16 gestational weeks in Chinese pregnant women","volume":"179","author":"Wu","year":"2021","journal-title":"Diabetes Res Clin Pract"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-019-0724-8","article-title":"Prediction of gestational diabetes based on nationwide electronic health records","volume":"26","author":"Artzi","year":"2020","journal-title":"Nat Med"},{"key":"B27","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1186\/s12884-019-2374-8","article-title":"A simple model to predict risk of gestational diabetes mellitus from 8 to 20\u2009weeks of gestation in Chinese women","volume":"19","author":"Zheng","year":"2019","journal-title":"BMC Pregnancy Childbirth"},{"key":"B28","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/s12884-019-2703-y","article-title":"Nomogram for prediction of gestational diabetes mellitus in urban, Chinese, pregnant women","volume":"20","author":"Guo","year":"2020","journal-title":"BMC Pregnancy Childbirth"},{"key":"B29","doi-asserted-by":"publisher","first-page":"814","DOI":"10.1186\/s12884-021-04295-2","article-title":"An early model to predict the risk of gestational diabetes mellitus in the absence of blood examination indexes: Application in primary health care centres","volume":"21","author":"Wang","year":"2021","journal-title":"BMC Pregnancy Childbirth"},{"key":"B30","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s12884-021-04348-6","article-title":"Diagnostic accuracy of first and early second trimester multiple biomarkers for prediction of gestational diabetes mellitus: A multivariate longitudinal approach","volume":"22","author":"Shaarbaf Eidgahi","year":"2022","journal-title":"BMC Pregnancy Childbirth"},{"key":"B31","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1186\/s12884-022-05219-4","article-title":"Risk prediction models of gestational diabetes mellitus before 16 gestational weeks","volume":"22","author":"Wei","year":"2022","journal-title":"BMC Pregnancy Childbirth"},{"key":"B32","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1186\/s12884-024-06783-7","article-title":"The early prediction of gestational diabetes mellitus by machine learning models","volume":"24","author":"Kaya","year":"2024","journal-title":"BMC Pregnancy Childbirth"},{"key":"B33","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1186\/s12884-024-06651-4","article-title":"Prediction of gestational diabetes mellitus by multiple biomarkers at early gestation","volume":"24","author":"Yang","year":"2024","journal-title":"BMC Pregnancy Childbirth"},{"key":"B34","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1186\/s12884-024-07079-6","article-title":"Prediction of gestational diabetes mellitus using early-pregnancy data: A secondary analysis from a prospective cohort study in Iran","volume":"24","author":"Parsaei","year":"2024","journal-title":"BMC Pregnancy Childbirth"},{"key":"B35","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1186\/s12884-025-07442-1","article-title":"Development and validation of a prediction model for gestational diabetes mellitus risk among women from 8 to 14 weeks of gestation in Western China","volume":"25","author":"Zhang","year":"2025","journal-title":"BMC Pregnancy Childbirth"},{"key":"B36","doi-asserted-by":"publisher","first-page":"21223","DOI":"10.1038\/s41598-020-78164-x","article-title":"Risk prediction model of gestational diabetes mellitus based on nomogram in a Chinese population cohort study","volume":"10","author":"Zhang","year":"2020","journal-title":"Sci Rep"},{"key":"B37","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1038\/s41598-022-05112-2","article-title":"An explainable machine learning-based clinical decision support system for prediction of gestational diabetes mellitus","volume":"12","author":"Du","year":"2022","journal-title":"Sci Rep"},{"key":"B38","doi-asserted-by":"publisher","first-page":"13356","DOI":"10.1038\/s41598-023-39680-8","article-title":"Prediction of gestational diabetes mellitus in Asian women using machine learning algorithms","volume":"13","author":"Kang","year":"2023","journal-title":"Sci Rep"},{"key":"B39","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1186\/s12916-025-04258-w","article-title":"Early prediction of gestational diabetes mellitus based on systematically selected multi-panel biomarkers and clinical accessibility\u2014a longitudinal study of a multi-racial pregnant cohort","volume":"23","author":"Yang","year":"2025","journal-title":"BMC Med"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2025.1687146","article-title":"Early prediction of gestational diabetes mellitus using machine learning-integrated metabolomic and clinical features","author":"Ji","year":"2025","journal-title":"Front Endocrinol (Lausanne)"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2010.04.006","article-title":"The hyperglycemia and adverse pregnancy outcome (HAPO) study: Paving the way for new diagnostic criteria for gestational diabetes mellitus","volume":"202","author":"Coustan","year":"2010","journal-title":"Am J Obstet Gynecol"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2013.10.012","article-title":"Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy: A World Health Organization guideline","volume":"103","year":"2014","journal-title":"Diabetes Res Clin Pract"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J Big Data"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2021.100379","article-title":"A survey on deep learning and its applications","volume":"40","author":"Dong","year":"2021","journal-title":"Comput Sci Rev"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3229161","article-title":"Deep neural networks and tabular data: A survey","volume":"35","author":"Borisov","year":"2024","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"B46","doi-asserted-by":"publisher","first-page":"11399","DOI":"10.1038\/s41598-019-47765-6","article-title":"DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture","volume":"9","author":"Sharma","year":"2019","journal-title":"Sci Rep"},{"key":"B47","doi-asserted-by":"publisher","first-page":"4391","DOI":"10.1038\/s41467-020-18197-y","article-title":"Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks","volume":"11","author":"Bazgir","year":"2020","journal-title":"Nat Commun"},{"key":"B48","doi-asserted-by":"publisher","first-page":"11325","DOI":"10.1038\/s41598-021-90923-y","article-title":"Converting tabular data into images for deep learning with convolutional neural networks","volume":"11","author":"Zhu","year":"2021","journal-title":"Sci Rep"},{"key":"B49","doi-asserted-by":"publisher","first-page":"21857","DOI":"10.1038\/s41598-022-26378-6","article-title":"Classification with 2-D convolutional neural networks for breast cancer diagnosis","volume":"12","author":"Sharma","year":"2022","journal-title":"Sci Rep"},{"key":"B50","doi-asserted-by":"publisher","first-page":"12851","DOI":"10.1038\/s41598-024-63630-7","article-title":"Enhanced analysis of tabular data through multi-representation DeepInsight","volume":"14","author":"Sharma","year":"2024","journal-title":"Sci Rep"},{"key":"B51","doi-asserted-by":"publisher","first-page":"17004","DOI":"10.1038\/s41598-025-01568-0","article-title":"Transforming tabular data into images via enhanced spatial relationships for CNN processing","volume":"15","author":"Alenizy","year":"2025","journal-title":"Sci Rep"},{"key":"B52","doi-asserted-by":"publisher","first-page":"28024","DOI":"10.1038\/s41598-025-12900-z","article-title":"Development of a novel deep learning method that transforms tabular input variables into images for the prediction of SLD","volume":"15","author":"Cubillos","year":"2025","journal-title":"Sci Rep"},{"key":"B53","doi-asserted-by":"publisher","first-page":"17320","DOI":"10.1038\/s41598-023-44634-1","article-title":"A multimodal deep learning model for predicting severe hemorrhage in placenta previa","volume":"13","author":"Akazawa","year":"2023","journal-title":"Sci Rep"},{"key":"B54","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1186\/s12884-023-05766-4","article-title":"Development of machine learning models to predict gestational diabetes risk in the first half of pregnancy","volume":"23","author":"Cubillos","year":"2023","journal-title":"BMC Pregnancy Childbirth"},{"key":"B55","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2016.90","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int J Comput Vis"},{"key":"B57","article-title":"PyTorch: an imperative style, high-performance deep learning library","author":"Paszke","year":"2019"},{"key":"B58","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV.2017.74","article-title":"Grad-CAM: Visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017"},{"key":"B59","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1201.0490","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"B60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5555\/3122009.3122026","article-title":"Imbalanced-learn: A Python toolbox to tackle the curse of imbalanced datasets in machine learning","volume":"18","author":"Lema\u00eetre","year":"2017","journal-title":"J Mach Learn Res"},{"key":"B61","doi-asserted-by":"crossref","DOI":"10.1145\/2939672.2939785","article-title":"XGBoost","author":"Chen","year":"2016"},{"key":"B62","article-title":"LightGBM: A highly efficient gradient boosting decision tree","author":"Ke","year":"2017"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3111175","article-title":"Two-level genetic algorithm for evolving convolutional neural networks for pattern recognition","volume":"9","author":"Montecino","year":"2021","journal-title":"IEEE Access"},{"key":"B64","unstructured":"A Healthy Lifestyle - WHO Recommendations\n          \n          \n          \n          2010"},{"key":"B65","doi-asserted-by":"publisher","DOI":"10.1177\/0272989X06295361","article-title":"Decision curve analysis: A novel method for evaluating prediction models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med Decision Making"},{"key":"B66","doi-asserted-by":"publisher","DOI":"10.2337\/dc09-0688","article-title":"First-trimester fasting hyperglycemia and adverse pregnancy outcomes","volume":"32","author":"Riskin-Mashiah","year":"2009","journal-title":"Diabetes Care"},{"key":"B67","doi-asserted-by":"publisher","DOI":"10.1111\/obr.12442","article-title":"Lifestyle intervention can reduce the risk of gestational diabetes: a meta-analysis of randomized controlled trials","volume":"17","author":"Song","year":"2016","journal-title":"Obes Rev"}],"container-title":["Frontiers in Endocrinology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2026.1941799\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T05:33:38Z","timestamp":1791178418000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2026.1941799\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,5]]},"references-count":67,"alternative-id":["10.3389\/fendo.2026.1941799"],"URL":"https:\/\/doi.org\/10.3389\/fendo.2026.1941799","relation":{},"ISSN":["1664-2392"],"issn-type":[{"value":"1664-2392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,5]]},"article-number":"1941799"}}