Validating Multicenter Cohort Circular RNA Model for Early Screening and Diagnosis of Gestational Diabetes Mellitus
Background Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Metho...
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Korean Diabetes Association
2025-05-01
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| Series: | Diabetes & Metabolism Journal |
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| Online Access: | http://e-dmj.org/upload/pdf/dmj-2024-0205.pdf |
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| author | Shuo Ma Yaya Chen Zhexi Gu Jiwei Wang Fengfeng Zhao Yuming Yao Gulinaizhaer Abudushalamu Shijie Cai Xiaobo Fan Miao Miao Xun Gao Chen Zhang Guoqiu Wu |
| author_facet | Shuo Ma Yaya Chen Zhexi Gu Jiwei Wang Fengfeng Zhao Yuming Yao Gulinaizhaer Abudushalamu Shijie Cai Xiaobo Fan Miao Miao Xun Gao Chen Zhang Guoqiu Wu |
| author_sort | Shuo Ma |
| collection | DOAJ |
| description | Background Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or mid-term diagnosis of GDM, offering clinical tools for early GDM screening. |
| format | Article |
| id | doaj-art-7e8aa52ea32043e6bbd896b67b62355f |
| institution | OA Journals |
| issn | 2233-6079 2233-6087 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Korean Diabetes Association |
| record_format | Article |
| series | Diabetes & Metabolism Journal |
| spelling | doaj-art-7e8aa52ea32043e6bbd896b67b62355f2025-08-20T02:31:24ZengKorean Diabetes AssociationDiabetes & Metabolism Journal2233-60792233-60872025-05-0149346247410.4093/dmj.2024.02052922Validating Multicenter Cohort Circular RNA Model for Early Screening and Diagnosis of Gestational Diabetes MellitusShuo Ma0Yaya Chen1Zhexi Gu2Jiwei Wang3Fengfeng Zhao4Yuming Yao5Gulinaizhaer Abudushalamu6Shijie Cai7Xiaobo Fan8Miao Miao9Xun Gao10Chen Zhang11Guoqiu Wu12 Center of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, China Center of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, China Center of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, China Center of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, China Center of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, China Center of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, China Center of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, China Center of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, China Department of Laboratory Medicine, Medical School of Southeast University, Nanjing, China Women’s Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing, China Center of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, China Center of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, China Center of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, ChinaBackground Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or mid-term diagnosis of GDM, offering clinical tools for early GDM screening.http://e-dmj.org/upload/pdf/dmj-2024-0205.pdfbiomarkerscohort studiesdiabetes, gestationalpredictive learning modelsroc curve |
| spellingShingle | Shuo Ma Yaya Chen Zhexi Gu Jiwei Wang Fengfeng Zhao Yuming Yao Gulinaizhaer Abudushalamu Shijie Cai Xiaobo Fan Miao Miao Xun Gao Chen Zhang Guoqiu Wu Validating Multicenter Cohort Circular RNA Model for Early Screening and Diagnosis of Gestational Diabetes Mellitus Diabetes & Metabolism Journal biomarkers cohort studies diabetes, gestational predictive learning models roc curve |
| title | Validating Multicenter Cohort Circular RNA Model for Early Screening and Diagnosis of Gestational Diabetes Mellitus |
| title_full | Validating Multicenter Cohort Circular RNA Model for Early Screening and Diagnosis of Gestational Diabetes Mellitus |
| title_fullStr | Validating Multicenter Cohort Circular RNA Model for Early Screening and Diagnosis of Gestational Diabetes Mellitus |
| title_full_unstemmed | Validating Multicenter Cohort Circular RNA Model for Early Screening and Diagnosis of Gestational Diabetes Mellitus |
| title_short | Validating Multicenter Cohort Circular RNA Model for Early Screening and Diagnosis of Gestational Diabetes Mellitus |
| title_sort | validating multicenter cohort circular rna model for early screening and diagnosis of gestational diabetes mellitus |
| topic | biomarkers cohort studies diabetes, gestational predictive learning models roc curve |
| url | http://e-dmj.org/upload/pdf/dmj-2024-0205.pdf |
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