{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T03:57:52Z","timestamp":1786679872192,"version":"build-2736575974"},"reference-count":24,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Diabetes Sci Technol"],"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Gestational diabetes mellitus (GDM) is a frequent metabolic complication during pregnancy that significantly impacts both maternal and neonatal health outcomes regularly resulting in NH. Exploring the interactions between maternal characteristics, neonatal outcomes, and data collected from wearable technologies, such as continuous glucose monitoring (CGM) could potentially enable the development of predictive models and support personalized care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>\n                      This study employed probabilistic modeling, using Bayesian networks (BNs), to analyze data from the STEADY SUGAR clinical trial (\n                      <jats:italic toggle=\"yes\">N<\/jats:italic>\n                      = 118 women with GDM) with the aim of discovering interactions between maternal characteristics, CGM-derived features calculated in the 90 days preceding delivery, and neonatal outcomes, particularly NH. The final BN returns a graph and conditional probability tables between inputs and outputs, whose statistical relevance has been quantified via odds ratios (ORs).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Direct associations were identified between NH and maternal hypertension (OR: 2.13 [1.02, 4.46]), family history for diabetes (OR: 1.43 [0.57, 3.57]), and elevated maternal body mass index (BMI) (OR: 3.59 [1.42, 9.08] comparing lower vs higher BMI categories). Cesarean delivery also influenced NH risk (OR: 2.05 [0.98, 4.28]). Indirect associations involving medication regimens and delivery type were significant. Ethnic disparities emerged, notably higher hyperglycemia among Afro-American patients (OR: 2.91 [1.19, 7.11]), highlighting ethnicity-related variations in glycemic control. Notably, CGM-derived metrics were associated with multiple neonatal outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>Bayesian network allowed to explore the complex interactions between variables in pregnancies affected by GDM. This framework will be extended with wider data sets to provide valuable insights for clinical decision-making able to mitigate maternal and neonatal risks.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/19322968251388107","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T12:55:01Z","timestamp":1762779301000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Exploring Relationships Between Maternal Characteristics, Continuous Glucose Monitoring Data, and Neonatal Hypoglycemia in Gestational Diabetes Pregnancies Using Probabilistic Modeling"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4358-9268","authenticated-orcid":false,"given":"Giacomo","family":"Cappon","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Padova, Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Catanuso","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Padova, Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erica","family":"Tavazzi","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Padova, Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karen","family":"Elkind-Hirsch","sequence":"additional","affiliation":[{"name":"Dexcom Inc., San Diego, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8041-2280","authenticated-orcid":false,"given":"Andrea","family":"Facchinetti","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Padova, Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"e_1_3_3_2_2","article-title":"Summary and recommendations of the Fourth International Workshop-Conference on Gestational Diabetes Mellitus","volume":"21","author":"Metzger BE","year":"1998","unstructured":"Metzger BE, Coustan DR. Summary and recommendations of the Fourth International Workshop-Conference on Gestational Diabetes Mellitus. Diabetes Care. 1998;21(suppl 2):B161-B167.","journal-title":"Diabetes Care"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopha.2021.112183"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1111\/dme.12382"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrendo.2012.96"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.297.6659.1304"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.3389\/fped.2024.1432113"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph18073402"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2024.01.010"},{"key":"e_1_3_3_10_2","unstructured":"Milic MK Sinanovi\u0107 Kilibarda \u0160T Prodovi\u0107 T Vladimir J. Supporting Decision-Making Through Bayesian Networks in Predicting Outcomes of Chronic Diseases. CERN Zenodo Repository; 2024. https:\/\/zenodo.org\/records\/14642037. Accessed October 7 2025."},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-61135-x"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-19810-7_36"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICHI52183.2021.00018"},{"key":"e_1_3_3_14_2","unstructured":"Dexcom Inc. Dexcom G6 CGM system. https:\/\/www.dexcom.com\/it-CH\/it-ch-dexcom-g6-cgm-system. 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