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Zhou Y, Xiao C, Yang Y. Pre-pregnancy body mass index combined with peripheral blood PLGF, DCN, LDH, and UA in a risk prediction model for pre-eclampsia. Front Endocrinol (Lausanne) 2024; 14:1297731. [PMID: 38260145 PMCID: PMC10800432 DOI: 10.3389/fendo.2023.1297731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
Objective This study analyzes the levels of peripheral blood placental growth factor (PLGF), body mass index (BMI), decorin (DCN), lactate dehydrogenase (LDH), uric acid (UA), and clinical indicators of patients with preeclampsia (PE), and establishes a predictive risk model of PE, which can provide a reference for early and effective prediction of PE. Methods 81 cases of pregnant women with PE who had regular prenatal checkups and delivered in Jinshan Branch of Shanghai Sixth People's Hospital from June 2020 to December 2022 were analyzed, and 92 pregnant women with normal pregnancies who had their antenatal checkups and delivered at the hospital during the same period were selected as the control group. Clinical data and peripheral blood levels of PLGF, DCN, LDH, and UA were recorded, and the two groups were subjected to univariate screening and multifactorial logistic regression analysis. Based on the screening results, the diagnostic efficacy of PE was evaluated using the receiver operating characteristic (ROC) curve. Risk prediction nomogram model was constructed using R language. The Bootstrap method (self-sampling method) was used to validate and produce calibration plots; the decision curve analysis (DCA) was used to assess the clinical benefit rate of the model. Results There were statistically significant differences in age, pre-pregnancy BMI, gestational weight gain, history of PE or family history, family history of hypertension, gestational diabetes mellitus, and history of renal disease between the two groups (P < 0.05). The results of multifactorial binary logistic stepwise regression revealed that peripheral blood levels of PLGF, DCN, LDH, UA, and pre-pregnancy BMI were independent influences on the occurrence of PE (P < 0.05). The area under the curve of PLGF, DCN, LDH, UA levels and pre-pregnancy BMI in the detection of PE was 0.952, with a sensitivity of 0.901 and a specificity of 0.913, which is better than a single clinical diagnostic indicator. The results of multifactor analysis were constructed as a nomogram model, and the mean absolute error of the calibration curve of the modeling set was 0.023, suggesting that the predictive probability of the model was generally compatible with the actual value. DCA showed the predictive model had a high net benefit in the range of 5% to 85%, suggesting that the model has clinical utility value. Conclusion The occurrence of PE is related to the peripheral blood levels of PLGF, DCN, LDH, UA and pre-pregnancy BMI, and the combination of these indexes has a better clinical diagnostic value than a single index. The nomogram model constructed by using the above indicators can be used for the prediction of PE and has high predictive efficacy.
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Affiliation(s)
- Yanna Zhou
- Department of Obstetrics and Gynecology, Jinshan Branch of Shanghai Sixth People’s Hospital, Shanghai, China
| | - Chunhai Xiao
- Department of Laboratory, Jinshan Branch of Shanghai Sixth People’s Hospital, Shanghai, China
| | - Yiting Yang
- Department of Obstetrics and Gynecology, Jinshan Branch of Shanghai Sixth People’s Hospital, Shanghai, China
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He Q, Lin M, Wu Z, Yu R. Predictive value of first-trimester GPR120 levels in gestational diabetes mellitus. Front Endocrinol (Lausanne) 2023; 14:1220472. [PMID: 37842292 PMCID: PMC10570794 DOI: 10.3389/fendo.2023.1220472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/12/2023] [Indexed: 10/17/2023] Open
Abstract
Background Early diagnosis of gestational diabetes mellitus (GDM) reduces the risk of unfavorable perinatal and maternal consequences. Currently, there are no recognized biomarkers or clinical prediction models for use in clinical practice to diagnosing GDM during early pregnancy. The purpose of this research is to detect the serum G-protein coupled receptor 120 (GPR120) levels during early pregnancy and construct a model for predicting GDM. Methods This prospective cohort study was implemented at the Women's Hospital of Jiangnan University between November 2019 and November 2022. All clinical indicators were assessed at the Hospital Laboratory. GPR120 expression was measured in white blood cells through quantitative PCR. Thereafter, the least absolute shrinkage and selection operator (LASSO) regression analysis technique was employed for optimizing the selection of the variables, while the multivariate logistic regression technique was implemented for constructing the nomogram model to anticipate the risk of GDM. The calibration curve analysis, area under the receiver operating characteristic curve (AUC) analysis, and the decision curve analysis (DCA) were conducted for assessing the performance of the constructed nomogram. Results Herein, we included a total of 250 pregnant women (125 with GDM). The results showed that the GDM group showed significantly higher GPR120 expression levels in their first trimester compared to the normal pregnancy group (p < 0.05). LASSO and multivariate regression analyses were carried out to construct a GDM nomogram during the first trimester. The indicators used in the nomogram included fasting plasma glucose, total cholesterol, lipoproteins, and GPR120 levels. The nomogram exhibited good performance in the training (AUC 0.996, 95% confidence interval [CI] = 0.989-0.999) and validation sets (AUC=0.992) for predicting GDM. The Akaike Information Criterion of the nomogram was 37.961. The nomogram showed a cutoff value of 0.714 (sensitivity = 0.989; specificity = 0.977). The nomogram displayed good calibration and discrimination, while the DCA was conducted for validating the clinical applicability of the nomogram. Conclusions The patients in the GDM group showed a high GPR120 expression level during the first trimester. Therefore, GPR120 expression could be used as an effective biomarker for predicting the onset of GDM. The nomogram incorporating GPR120 levels in early pregnancy showed good predictive ability for the onset of GDM.
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Affiliation(s)
- Qingwen He
- Department of Public Health, Women’s Hospital of Jiangnan University, Wuxi, China
| | - Mengyuan Lin
- Center of Reproductive Medicine, Women’s Hospital of Jiangnan University, Wuxi, China
| | - Zhenhong Wu
- Department of Public Health, Women’s Hospital of Jiangnan University, Wuxi, China
| | - Renqiang Yu
- Department of Neonatology, Wuxi Maternity and Child Health Care Hospital, Women’s Hospital of Jiangnan University, Wuxi, China
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Zhu H, Zhao Z, Xu J, Chen Y, Cai J, Shi C, Zhou L, Zhu Q, Ji L. Comprehensive landscape of the T and B-cell repertoires of newly diagnosed gestational diabetes mellitus. Genomics 2023; 115:110681. [PMID: 37453476 DOI: 10.1016/j.ygeno.2023.110681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 06/03/2023] [Accepted: 07/12/2023] [Indexed: 07/18/2023]
Abstract
This study conducted a high-throughput sequencing analysis of the T- and B- repertoires in the newly diagnosed GDM patients and evaluated the association between abnormal adaptive immunity and GDM. The unique TCR CDR3 clonotypes were mildly decreased in GDM patients, and the similarity of TCR V-J distributions was higher in the GDM group. Moreover, the usages of the V gene and V-J pair and the frequency distributions of some CDR3 amino acids (AAs) both in BCR and TCR were significantly different between groups. Moreover, the cytokines including IL-4, IL-6, IFN-γ and IL-17A were synchronously elevated in the GDM cases. Our findings provide a comprehensive view of BCR and TCR repertoires at newly diagnosed GDM patients, revealing the mild reduction in unique TCRB CDR3 sequences and slight alteration of the V gene, V-J combination and CDR3 (AA) usages of BCR and TCR. This work provides deep insight into the mechanism of maternal adaptive immunity in GDM and provides novel diagnostic biomarkers and potential immunotherapy targets for GDM.
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Affiliation(s)
- Hui Zhu
- Department of Internal Medicine, School of Medicine, Ningbo University, Ningbo, Zhejiang 315211, PR China
| | - Zhijia Zhao
- School of Public Health, School of Medicine, Ningbo University, Ningbo, Zhejiang 315211, PR China
| | - Jin Xu
- School of Public Health, School of Medicine, Ningbo University, Ningbo, Zhejiang 315211, PR China; Zhejiang Key Laboratory of Pathophysiology, School of Medicine, Ningbo University, Ningbo, Zhejiang 315211, PR China
| | - Yanming Chen
- School of Public Health, School of Medicine, Ningbo University, Ningbo, Zhejiang 315211, PR China
| | - Jie Cai
- Center for Reproductive Medicine, Ningbo Women and Children's Hospital, Ningbo, Zhejiang 315211, PR China
| | - Chaoyi Shi
- Center for Reproductive Medicine, Ningbo Women and Children's Hospital, Ningbo, Zhejiang 315211, PR China
| | - Liming Zhou
- Center for Reproductive Medicine, Ningbo Women and Children's Hospital, Ningbo, Zhejiang 315211, PR China
| | - Qiong Zhu
- Department of Pediatrics, Affiliated People's Hospital of Ningbo University, Ningbo, Zhejiang 315040, PR China
| | - Lindan Ji
- Zhejiang Key Laboratory of Pathophysiology, School of Medicine, Ningbo University, Ningbo, Zhejiang 315211, PR China; Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, School of Medicine, Ningbo, Zhejiang 315211, PR China.
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Huang QF, Hu YC, Wang CK, Huang J, Shen MD, Ren LH. Clinical First-Trimester Prediction Models for Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis. Biol Res Nurs 2023; 25:185-197. [PMID: 36218132 DOI: 10.1177/10998004221131993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Gestational diabetes mellitus (GDM) is a common pregnancy complication that negatively impacts the health of both the mother and child. Early prediction of the risk of GDM may permit prompt and effective interventions. This systematic review and meta-analysis aimed to summarize the study characteristics, methodological quality, and model performance of first-trimester prediction model studies for GDM. METHODS Five electronic databases, one clinical trial register, and gray literature were searched from the inception date to March 19, 2022. Studies developing or validating a first-trimester prediction model for GDM were included. Two reviewers independently extracted data according to an established checklist and assessed the risk of bias by the Prediction Model Risk of Bias Assessment Tool (PROBAST). We used a random-effects model to perform a quantitative meta-analysis of the predictive power of models that were externally validated at least three times. RESULTS We identified 43 model development studies, six model development and external validation studies, and five external validation-only studies. Body mass index, maternal age, and fasting plasma glucose were the most commonly included predictors across all models. Multiple estimates of performance measures were available for eight of the models. Summary estimates range from 0.68 to 0.78 (I2 ranged from 0% to 97%). CONCLUSION Most studies were assessed as having a high overall risk of bias. Only eight prediction models for GDM have been externally validated at least three times. Future research needs to focus on updating and externally validating existing models.
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Affiliation(s)
- Qi-Fang Huang
- School of Nursing, 33133Peking University, Beijing, China
| | - Yin-Chu Hu
- School of Nursing, 33133Peking University, Beijing, China
| | - Chong-Kun Wang
- School of Nursing, 33133Peking University, Beijing, China
| | - Jing Huang
- Florence Nightingale School of Nursing, 4616King's College London, London, UK
| | - Mei-Di Shen
- School of Nursing, 33133Peking University, Beijing, China
| | - Li-Hua Ren
- School of Nursing, 33133Peking University, Beijing, China
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Li F, Zhang Y, Peng Z, Wang Y, Zeng Z, Tang Z. Diagnostic, clustering, and immune cell infiltration analysis of m6A regulators in patients with sepsis. Sci Rep 2023; 13:2532. [PMID: 36781867 PMCID: PMC9925440 DOI: 10.1038/s41598-022-27039-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 12/23/2022] [Indexed: 02/15/2023] Open
Abstract
RNA N6-methladenosine (m6A) regulators are required for a variety of biological processes, including immune responses, and increasing evidence indicates that their dysregulation is closely associated with many diseases. However, the potential roles of m6A regulators in sepsis remain unknown. We comprehensively analyzed the transcriptional variations in and interactions of 26 m6A regulators in sepsis based on the Gene Expression Omnibus (GEO) database. A random forest (RF) model and nomogram were established to predict the occurrence and risk of sepsis in patients. Then, two different m6A subtypes were defined by consensus clustering analysis, and we explored the correlation between the subtypes and immune cells. We found that 17 of the 26 m6A regulators were significantly differentially expressed between patients with and without sepsis, and strong correlations among these 17 m6A regulators were revealed. Compared with the support vector machine (SVM) model, the RF model had better predictive ability, and therefore was used to construct a reliable nomogram containing 10 candidate m6A regulators to predict the risk of sepsis in patients. In addition, a consensus clustering algorithm was used to identify two different subtypes of m6A, which helped us distinguish different levels of immune cell infiltration and inflammation in patients with sepsis. Comprehensive analysis of m6A regulators in sepsis revealed their potential roles in sepsis occurrence, immune cell infiltration and inflammation in patients with sepsis. This study may contribute to the development of follow-up treatment strategies for sepsis.
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Affiliation(s)
- Fenghui Li
- Intensive Care Unit, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, 528000, Guangdong Province, China
| | - Yuan Zhang
- Intensive Care Unit, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, 528000, Guangdong Province, China
| | - Zhiyun Peng
- Intensive Care Unit, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, 528000, Guangdong Province, China
| | - Yingjing Wang
- Intensive Care Unit, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, 528000, Guangdong Province, China
| | - Zhaoshang Zeng
- Intensive Care Unit, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, 528000, Guangdong Province, China
| | - Zhongxiang Tang
- Intensive Care Unit, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, 528000, Guangdong Province, China.
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Yarşılıkal Güleroğlu F, Ekmez M, Ekmez F, Karacabey S, Çetin A. Second-trimester Uterine Artery Doppler Parameters but not Triple Test Analytes, May Predict Gestational Diabetes Mellitus. ISTANBUL MEDICAL JOURNAL 2023. [DOI: 10.4274/imj.galenos.2022.58046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023] Open
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Hu X, Hu X, Yu Y, Wang J. Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm. Front Endocrinol (Lausanne) 2023; 14:1105062. [PMID: 36967760 PMCID: PMC10034315 DOI: 10.3389/fendo.2023.1105062] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 01/30/2023] [Indexed: 03/29/2023] Open
Abstract
OBJECTIVE To develop the extreme gradient boosting (XG Boost) machine learning (ML) model for predicting gestational diabetes mellitus (GDM) compared with a model using the traditional logistic regression (LR) method. METHODS A case-control study was carried out among pregnant women, who were assigned to either the training set (these women were recruited from August 2019 to November 2019) or the testing set (these women were recruited in August 2020). We applied the XG Boost ML model approach to identify the best set of predictors out of a set of 33 variables. The performance of the prediction model was determined by using the area under the receiver operating characteristic (ROC) curve (AUC) to assess discrimination, and the Hosmer-Lemeshow (HL) test and calibration plots to assess calibration. Decision curve analysis (DCA) was introduced to evaluate the clinical use of each of the models. RESULTS A total of 735 and 190 pregnant women were included in the training and testing sets, respectively. The XG Boost ML model, which included 20 predictors, resulted in an AUC of 0.946 and yielded a predictive accuracy of 0.875, whereas the model using a traditional LR included four predictors and presented an AUC of 0.752 and yielded a predictive accuracy of 0.786. The HL test and calibration plots show that the two models have good calibration. DCA indicated that treating only those women whom the XG Boost ML model predicts are at risk of GDM confers a net benefit compared with treating all women or treating none. CONCLUSIONS The established model using XG Boost ML showed better predictive ability than the traditional LR model in terms of discrimination. The calibration performance of both models was good.
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Affiliation(s)
- Xiaoqi Hu
- Department of Nursing, Yantian District People's Hospital, Shenzhen, Guangdong, China
| | - Xiaolin Hu
- School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
| | - Ya Yu
- Department of Nursing, Guangzhou First People's Hospital, Guangzhou, Guangdong, China
| | - Jia Wang
- Department of Nursing, Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong, China
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Wei Y, He A, Tang C, Liu H, Li L, Yang X, Wang X, Shen F, Liu J, Li J, Li R. Risk prediction models of gestational diabetes mellitus before 16 gestational weeks. BMC Pregnancy Childbirth 2022; 22:889. [PMID: 36456970 PMCID: PMC9714187 DOI: 10.1186/s12884-022-05219-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 11/15/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Gestational diabetes mellitus (GDM) can lead to adverse maternal and fetal outcomes, and early prevention is particularly important for their health, but there is no widely accepted approach to predict it in the early pregnancy. The aim of the present study is to build and evaluate predictive models for GDM using routine indexes, including maternal clinical characteristics and laboratory biomarkers, before 16 gestational weeks. METHODS A total of 2895 pregnant women were recruited and maternal clinical characteristics and laboratory biomarkers before 16 weeks of gestation were collected from two hospitals. All participants were randomly stratified into the training cohort and the internal validation cohort by the ratio of 7:3. Using multivariable logistic regression analysis, two nomogram models, including a basic model and an extended model, were built. The discrimination, calibration, and clinical validity were used to evaluate the models in the internal validation cohort. RESULTS The area under the receiver operating characteristic curve of the basic and the extended model was 0.736 and 0.756 in the training cohort, and was 0.736 and 0.763 in the validation cohort, respectively. The calibration curve analysis showed that the predicted values of the two models were not significantly different from the actual observations (p = 0.289 and 0.636 in the training cohort, p = 0.684 and 0.635 in the internal validation cohort, respectively). The decision-curve analysis showed a good clinical application value of the models. CONCLUSIONS The present study built simple and effective models, indicating that routine clinical and laboratory parameters can be used to predict the risk of GDM in the early pregnancy, and providing a novel reference for studying the prediction of GDM.
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Affiliation(s)
- Yiling Wei
- grid.412601.00000 0004 1760 3828Department of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630 China
| | - Andong He
- grid.412601.00000 0004 1760 3828Department of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630 China
| | - Chaoping Tang
- grid.417009.b0000 0004 1758 4591Department of Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150 China
| | - Haixia Liu
- grid.412601.00000 0004 1760 3828Department of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630 China
| | - Ling Li
- Department of Obstetrics and Gynecology, Jiangmen Maternity and Child Health Care Hospital, Jiangmen, 529000 China
| | - Xiaofeng Yang
- grid.412601.00000 0004 1760 3828Department of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630 China
| | - Xiufang Wang
- grid.412601.00000 0004 1760 3828Department of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630 China
| | - Fei Shen
- Department of Obstetrics and Gynecology, Jiangmen Maternity and Child Health Care Hospital, Jiangmen, 529000 China
| | - Jia Liu
- grid.412601.00000 0004 1760 3828Department of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630 China
| | - Jing Li
- Department of Obstetrics and Gynecology, Jiangmen Maternity and Child Health Care Hospital, Jiangmen, 529000 China
| | - Ruiman Li
- grid.412601.00000 0004 1760 3828Department of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630 China
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Wang N, Guo H, Jing Y, Song L, Chen H, Wang M, Gao L, Huang L, Song Y, Sun B, Cui W, Xu J. Development and Validation of Risk Prediction Models for Gestational Diabetes Mellitus Using Four Different Methods. Metabolites 2022; 12:1040. [PMID: 36355123 PMCID: PMC9697464 DOI: 10.3390/metabo12111040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 09/26/2022] [Accepted: 10/25/2022] [Indexed: 09/21/2023] Open
Abstract
Gestational diabetes mellitus (GDM), a common perinatal disease, is related to increased risks of maternal and neonatal adverse perinatal outcomes. We aimed to establish GDM risk prediction models that can be widely used in the first trimester using four different methods, including a score-scaled model derived from a meta-analysis using 42 studies, a logistic regression model, and two machine learning models (decision tree and random forest algorithms). The score-scaled model (seven variables) was established via a meta-analysis and a stratified cohort of 1075 Chinese pregnant women from the Northwest Women's and Children's Hospital (NWCH) and showed an area under the curve (AUC) of 0.772. The logistic regression model (seven variables) was established and validated using the above cohort and showed AUCs of 0.799 and 0.834 for the training and validation sets, respectively. Another two models were established using the decision tree (DT) and random forest (RF) algorithms and showed corresponding AUCs of 0.825 and 0.823 for the training set, and 0.816 and 0.827 for the validation set. The validation of the developed models suggested good performance in a cohort derived from another period. The score-scaled GDM prediction model, the logistic regression GDM prediction model, and the two machine learning GDM prediction models could be employed to identify pregnant women with a high risk of GDM using common clinical indicators, and interventions can be sought promptly.
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Affiliation(s)
- Ning Wang
- Department of Endocrinology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, China
- International Center for Obesity and Metabolic Disease Research of Xi’an Jiaotong University, Xi’an 710061, China
| | - Haonan Guo
- Department of Endocrinology and Second Department of Geriatrics, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Yingyu Jing
- Department of Endocrinology and Second Department of Geriatrics, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Lin Song
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China
| | - Huan Chen
- Department of Endocrinology and Second Department of Geriatrics, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Mengjun Wang
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China
- Department of Endocrinology, 521 Hospital of Norinco Group, Xi’an 710065, China
| | - Lei Gao
- Department of Endocrinology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, China
| | - Lili Huang
- Department of Medical Ultrasound, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, China
| | - Yanan Song
- Department of Endocrinology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, China
| | - Bo Sun
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China
| | - Wei Cui
- International Center for Obesity and Metabolic Disease Research of Xi’an Jiaotong University, Xi’an 710061, China
- Department of Endocrinology and Second Department of Geriatrics, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Jing Xu
- Department of Endocrinology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, China
- International Center for Obesity and Metabolic Disease Research of Xi’an Jiaotong University, Xi’an 710061, China
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Cardiovascular Disease-Associated MicroRNAs as Novel Biomarkers of First-Trimester Screening for Gestational Diabetes Mellitus in the Absence of Other Pregnancy-Related Complications. Int J Mol Sci 2022; 23:ijms231810635. [PMID: 36142536 PMCID: PMC9501303 DOI: 10.3390/ijms231810635] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/08/2022] [Accepted: 09/09/2022] [Indexed: 11/25/2022] Open
Abstract
We assessed the diagnostic potential of cardiovascular disease-associated microRNAs for the early prediction of gestational diabetes mellitus (GDM) in singleton pregnancies of Caucasian descent in the absence of other pregnancy-related complications. Whole peripheral venous blood samples were collected within 10 to 13 weeks of gestation. This retrospective study involved all pregnancies diagnosed with only GDM (n = 121) and 80 normal term pregnancies selected with regard to equality of sample storage time. Gene expression of 29 microRNAs was assessed using real-time RT-PCR. Upregulation of 11 microRNAs (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-23a-3p, miR-100-5p, miR-125b-5p, miR-126-3p, miR-181a-5p, miR-195-5p, miR-499a-5p, and miR-574-3p) was observed in pregnancies destinated to develop GDM. Combined screening of all 11 dysregulated microRNAs showed the highest accuracy for the early identification of pregnancies destinated to develop GDM. This screening identified 47.93% of GDM pregnancies at a 10.0% false positive rate (FPR). The predictive model for GDM based on aberrant microRNA expression profile was further improved via the implementation of clinical characteristics (maternal age and BMI at early stages of gestation and an infertility treatment by assisted reproductive technology). Following this, 69.17% of GDM pregnancies were identified at a 10.0% FPR. The effective prediction model specifically for severe GDM requiring administration of therapy involved using a combination of these three clinical characteristics and three microRNA biomarkers (miR-20a-5p, miR-20b-5p, and miR-195-5p). This model identified 78.95% of cases at a 10.0% FPR. The effective prediction model for GDM managed by diet only required the involvement of these three clinical characteristics and eight microRNA biomarkers (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-100-5p, miR-125b-5p, miR-195-5p, miR-499a-5p, and miR-574-3p). With this, the model identified 50.50% of GDM pregnancies managed by diet only at a 10.0% FPR. When other clinical variables such as history of miscarriage, the presence of trombophilic gene mutations, positive first-trimester screening for preeclampsia and/or fetal growth restriction by the Fetal Medicine Foundation algorithm, and family history of diabetes mellitus in first-degree relatives were included in the GDM prediction model, the predictive power was further increased at a 10.0% FPR (72.50% GDM in total, 89.47% GDM requiring therapy, and 56.44% GDM managed by diet only). Cardiovascular disease-associated microRNAs represent promising early biomarkers to be implemented into routine first-trimester screening programs with a very good predictive potential for GDM.
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Machine learning-based models for gestational diabetes mellitus prediction before 24–28 weeks of pregnancy: A review. Artif Intell Med 2022; 132:102378. [DOI: 10.1016/j.artmed.2022.102378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/21/2022] [Accepted: 08/18/2022] [Indexed: 11/21/2022]
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Lu L, Li C, Deng J, Luo J, Huang C. Maternal serum NGAL in the first trimester of pregnancy is a potential biomarker for the prediction of gestational diabetes mellitus. Front Endocrinol (Lausanne) 2022; 13:977254. [PMID: 36465627 PMCID: PMC9708734 DOI: 10.3389/fendo.2022.977254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 10/31/2022] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE Gestational diabetes mellitus (GDM) has adverse effects on the health of mothers and their offspring. Currently, no known biomarker has been proven to have sufficient validity for the prediction of GDM in the first trimester of pregnancy. The aim of this study was to investigate the potential relationship between serum neutrophil gelatinase-associated lipocalin (NGAL) levels in the first trimester of pregnancy and later GDM risk and to evaluate the performance of serum NGAL as a biomarker for the prediction of GDM. METHODS The study was conducted by recruiting participants at 8-13 weeks of gestation from The First Affiliated Hospital of Chengdu Medical College between January and June 2021; participants were followed up for oral glucose tolerance test (OGTT) screening at 24-28 gestational weeks. We examined the serum NGAL levels of all subjects in the first trimester who met the inclusion and exclusion criteria. Anthropometric, clinical, and laboratory parameters of the study subjects were obtained during the same study period. A logistic regression model was carried out to investigate the potential relationship between serum NGAL levels in the first trimester of pregnancy and later GDM risk. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess the discrimination and calibration of serum NGAL as a biomarker for the prediction of GDM in the first trimester of pregnancy. RESULTS Serum NGAL levels in the first trimester of pregnancy were significantly higher in women who later developed GDM than in those who did not develop GDM. Serum NGAL levels in the first trimester of pregnancy were positively associated with an increased risk of GDM after adjustment for potential confounding factors. The risk prediction model for GDM constructed by using serum NGAL levels in the first trimester of pregnancy achieved excellent performance. CONCLUSIONS Maternal serum NGAL in the first trimester of pregnancy is a potential biomarker for the prediction of GDM, which could help guide the clinical practice of antenatal care.
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Affiliation(s)
| | | | | | - Jianbo Luo
- *Correspondence: Chaolin Huang, ; Jianbo Luo,
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Wang Y, Zhao S, Peng W, Chen Y, Chi J, Che K, Wang Y. The Role of Slit-2 in Gestational Diabetes Mellitus and Its Effect on Pregnancy Outcome. Front Endocrinol (Lausanne) 2022; 13:889505. [PMID: 35813663 PMCID: PMC9261261 DOI: 10.3389/fendo.2022.889505] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/19/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Slit guidance ligand 2 (Slit-2), as a member of the Slit family, can regulate the inflammatory response and glucose metabolism. The purpose of this study was to explore the expression of Slit-2 in maternal peripheral blood and neonatal cord blood of gestational diabetes mellitus (GDM) patients and its potential importance in disease progression. METHODS This study included 57 healthy pregnant women and 61 GDM patients. The levels of Slit-2, C-reactive protein (CRP), monocyte chemoattractant protein-1 (MCP-1), C-peptide (C-P), galectin-3(Gal-3), HbA1c, fasting blood glucose (FBG) and fasting insulin (FINS) in maternal peripheral blood and neonatal cord blood were detected by ELISA. Spearman's rank correlation test was used to assess the association between peripheral Slit-2 and inflammatory indicators, insulin resistance, and pregnancy outcomes. Logistic regression analysis was used to analyze the risk factors of GDM. RESULTS Slit-2 levels in maternal peripheral blood and neonatal cord blood of the GDM patients were higher than those of the HC. Slit-2 levels in maternal peripheral blood and neonatal cord blood of the GDM patients were positively correlated with inflammatory factors CRP and MCP-1 levels. The level of Slit-2 in the maternal peripheral blood of the GDM patients was positively correlated with the level of homeostasis model assessment insulin resistance (HOMA-IR) and HbA1c in maternal peripheral blood, but was negatively correlated with the level of homeostasis model assessment -β (HOMA-β). We also found that the Slit-2 level in the maternal peripheral blood of the GDM patients was negatively correlated with neonatal blood glucose, positively correlated with neonatal weight and independent of neonatal total bilirubin. CONCLUSION Our study suggests that the abnormal increase in Slit-2 in GDM may be related to its pathogenesis, and it was correlated with neonatal blood glucose and weight in patients with GDM, suggesting that Slit-2 may be a potential biomarker of GDM.
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Affiliation(s)
- Yan Wang
- Department of Endocrinology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Shihua Zhao
- Department of Endocrinology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wei Peng
- Department of Obstetrics and Gynecology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ying Chen
- Department of Endocrinology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jingwei Chi
- Qingdao Key Laboratory of Thyroid Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Kui Che
- Qingdao Key Laboratory of Thyroid Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yangang Wang
- Department of Endocrinology, The Affiliated Hospital of Qingdao University, Qingdao, China
- *Correspondence: Yangang Wang,
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