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Prediction of gestational diabetes mellitus by multiple biomarkers at early gestation

BMC Pregnancy and Childbirth volume 24, Article number: 601 (2024) Cite this article

Abstract

Background

It remains unclear which early gestational biomarkers can be used in predicting later development of gestational diabetes mellitus (GDM). We sought to identify the optimal combination of early gestational biomarkers in predicting GDM in machine learning (ML) models.

Methods

This was a nested case-control study including 100 pairs of GDM and euglycemic (control) pregnancies in the Early Life Plan cohort in Shanghai, China. High sensitivity C reactive protein, sex hormone binding globulin, insulin-like growth factor I, IGF binding protein 2 (IGFBP-2), total and high molecular weight adiponectin and glycosylated fibronectin concentrations were measured in serum samples at 11–14 weeks of gestation. Routine first-trimester blood test biomarkers included fasting plasma glucose (FPG), serum lipids and thyroid hormones. Five ML models [stepwise logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, support vector machine and k-nearest neighbor] were employed to predict GDM. The study subjects were randomly split into two sets for model development (training set, n = 70 GDM/control pairs) and validation (testing set: n = 30 GDM/control pairs). Model performance was evaluated by the area under the curve (AUC) in receiver operating characteristics.

Results

FPG and IGFBP-2 were consistently selected as predictors of GDM in all ML models. The random forest model including FPG and IGFBP-2 performed the best (AUC 0.80, accuracy 0.72, sensitivity 0.87, specificity 0.57). Adding more predictors did not improve the discriminant power.

Conclusion

The combination of FPG and IGFBP-2 at early gestation (11–14 weeks) could predict later development of GDM with moderate discriminant power. Further validation studies are warranted to assess the utility of this simple combination model in other independent cohorts.

Novelty statements

It remains unclear which early gestational biomarkers can be used in predicting later development of gestational diabetes mellitus.

The present study demonstrates that the combination of fasting plasma glucose and insulin-like growth factor binding protein 2 at early gestation can predict later development of gestational diabetes with moderate discriminant power.

Peer Review reports

Background

Gestational diabetes mellitus (GDM) is characterized by de novo glucose intolerance in the 2nd half of pregnancy affecting 7–15% of mothers worldwide [1, 2]. GDM increases the risks of pre-eclampsia, macrosomia and neonatal hypoglycemia [3, 4], and put both mothers and their offspring at increased risks of obesity, type 2 diabetes and cardiovascular diseases in later life [5,6,7,8]. Early interventions in pregnancy may reduce the risk of GDM and adverse pregnancy outcomes [9, 10]. However, GDM is routinely diagnosed between 24 and 28 gestational weeks by an oral glucose tolerance test (OGTT) [11, 12], missing the optimal window for interventions as fetal and placental development have already occurred [13]. Identifying subjects at early gestation who are destined to develop GDM may inform early intervention strategies in improving maternal and child health.

A number of early gestational biomarkers have been reported in predicting later development of GDM, mostly notably, fasting plasma glucose (FPG) [14, 15], sex hormone-binding globulin (SHBG) [16, 17], adiponectin [18,19,20], insulin-like growth factor I (IGF-I) and IGF binding protein 2 (IGFBP-2) [21, 22], high sensitivity C reactive protein (hs-CRP) [23, 24] and glycosylated fibronectin [25, 26]. However, no single biomarker has demonstrated sufficient discriminant power in clinical utility. It is unclear which combination is the optimal.

Most previous studies in predicting GDM employed conventional logistics regression models [27,28,29]. However, such models tend to ignore non-linear relationships and interactions between predictors, and the predictive performance may be suboptimal [30, 31]. Machine learning (ML) models may be helpful in alleviating these limitations [32]. In the present study, we sought to explore the optimal combination of early gestational biomarkers in predicting later development of GDM in ML models considering six main candidate biomarkers: FBG, SHBG, IGF-I, IGFBP-2, hs-CRP and glycosylated fibronectin.

Materials and methods

Study design and subjects

We conducted a nested matched (1:1) case-control study in the Early Life Plan (ELP) cohort. The ELP cohort was initiated in Xinhua Hospital - a university affiliated maternity and pediatric care hospital in Shanghai for studies on the determinants of pregnancy outcomes and child health. Pregnant women were recruited during routine first trimester antenatal care visits. Eligibility criteria were: (1) age 18 + years; (2) residents in Shanghai; (3) planned to have prenatal care and delivery at Xinhua Hospital. Trained research staff collected data through face-to-face interviews and medical chart reviews using standardized structured questionnaires. The study was approved by the medical research ethical committee of Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University (approval no. XHEC-C-2013-001). Written informed consent was obtained from all study participants. The data collection questionnaire is provided in the appendix - Early Life Plan Cohort Study Questionnaire.

All GDM and controls were from the ELP cohort in subjects recruited between 2016/6 and 2019/02. GDM was diagnosed according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria [33]: if any one of the blood glucose values was at or above the following thresholds in the routine 75 g oral glucose tolerance test (OGTT) at 24–28 weeks of gestation: fasting 5.1 mmol/L, 1-hour 10.0 mmol/L and 2-hour 8.5 mmol/L. Controls were women with a euglycemic pregnancy.

The eligibility criteria for the present study were: (1) Han ethnicity (the majority ethnic group); (2) maternal age 20–45 years; (3) natural conception; (4) singleton pregnancy; (5) no severe pre-pregnancy illnesses (e.g., type 1 or type 2 diabetes) or life-threatening gestational complications (e.g., preeclampsia); (6) serum specimen at 11–14 weeks of gestation available for biomarker assays. All eligible GDM women with complete data and specimen collected by Feb 2019 (n = 100) were included. Controls were randomly sampled among all eligible subjects recruited up to Feb 2019 in the ELP cohort, and matched to cases (1:1) by maternal age (within 1 year), pre-pregnancy BMI (within 1 kg/m2) and gestational age (within 1 week) at blood sampling. Therefore, a total of 200 subjects (100 pairs of GDM/control) constituted the study sample. Figure S1 presents the flowchart in the selection of study subjects.

Blood samples

Maternal fasting blood samples were collected during the first prenatal care visits at 11–14 weeks of gestation. Trained technicians collected blood samples following standardized operating procedures. Fasting morning venous blood samples were collected in multiple tubes for serum (non-coagulant) and plasma (EDTA). The blood samples were centrifuged (Beckman Coulter Allegra X-15R, USA) at 4 °C, 4000 r/min for 10 min. The separated serum and plasma samples were stored at − 80 °C until assays.

Biochemical assays

In all biomarker assays, the lab technicians were blinded to the clinical status (GDM or not) of study subjects. Serum insulin-like growth factor (IGF)-I and insulin-like growth factor binding protein 2 (IGFBP-2) were measured by ELISA kits (Crystal Chem, Lllinois, USA) and the absorbance was determined using a microplate spectrophotometer (Beckman CX7, USA). Serum high sensitivity C reactive protein (hs-CRP) was measured by an ELISA kit (Anogen, Ontario, Canada). Serum HMW and total adiponectin were measured by an ELISA kit (ALPCO, Salem, NH, USA). Serum glycosylated fibronectin was measured by an ELISA kit (Anshlabs, Webster, USA). Serum sex hormone binding globulin (SHBG) was measured by an automated chemiluminescent assay (Kangrun Biotech, Guangzhou, China). The limits of detection were 2 ng/mL for IGF-I, 2 ng/mL for IGFBP-2, 0.03 mg/L for CRP, 0.034 ng/mL for HMW and total adiponectin, 2.85 ng/mL for glycosylated fibronectin, and 0.1 nmol/L for SHBG, respectively. The intra-assay and inter-assay coefficients of variation were in the ranges of 0.8-5.4% for IGF-I, 0.1-3.3% for IGFBP-2, 9.9-14.7% for hs-CRP, 0.5-4.5% for HMW and total adiponectin, 1.3-2.5% for glycosylated fibronectin, and < 8% for SHBG, respectively.

Routine first-trimester prenatal blood test biomarkers included fasting plasma glucose (FPG), serum lipids [triglyceride, total cholesterols (TC)] and thyroid hormones [thyroid stimulating hormone (TSH); free thyroxine (FT4); free triiodothyronine (FT3)], and were measured in the clinical biochemistry laboratory of Xinhua hospital (a nationally accredited clinical biochemistry lab) following standardized operating protocols.

Selection of predictors

Potential predictors included the measured serum biomarkers (hs-CRP, IGFBP-2, IGF-1, SHBG, glycosylated fibronectin, total and HMW adiponectin) and available biomarkers from routine clinical tests (hemoglobin, FPG, TG, TC, TSH, FT4 and FT3) at 11–14 weeks of gestation, and maternal characteristics (parity, family history of diabetes or hypertension, systolic blood pressure (SBP), diastolic blood pressure (DBP)).

Four models were employed in the selection of predictors/features that distinguish GDM from euglycemic pregnancies, including stepwise logistic regression (LR), support vector machine-recursive feature elimination (SVM-RFE), the least absolute shrinkage and selection operator (LASSO) and random forest. The MASS package in R was used for stepwise LR. SVM-RFE is a ML technique that trains a subset of features from different categories to shrink the feature set and find the most predictive features [34]. The SVM-RFE was applied in the selection of predictive features via a 10-fold cross-validation, using the e1071 package in R. LASSO is a regression method that performs both feature selection and regularization to enhance the prediction accuracy [35]. LASSO regression with a 10-fold cross-validation was performed to determine the optimal lambda using the glmnet package in R. The random forest method is a common method for ranking variable importance based on prediction performance [36]. For each trained tree, out-of-bag prediction performance was calculated before and after permutation of the values of each predictor variable, and the differences were then averaged over all trees. The randomForest package in R was used to implement the random forest algorithm. Ultimately, features that were selected by all algorithms were retained in the ML prediction models; other features that were selected by at least 2 algorithms were assessed on whether their additions could improve the predictions.

Prediction models

Conventional LR and five ML models (ML-LR, SVM, k-nearest neighbor (KNN), LASSO and random forest) were employed in predicting GDM. ML-LR iteratively identifies the strongest combination of variables with the greatest probability of detecting the observed outcomes. The SVM aims to create a decision boundary between two classes that enables the prediction from one or more feature vectors. KNN algorithm is a nonparametric approach for classification, and it integrates the information about the K neighbor points for the classification of subjects. The random forest model is a regression tree technique using bootstrapped samples and a random subset of features to achieve a high degree of predictive accuracy. In all ML models, we tuned the hyperparameters to select the optimal combination with the highest mean AUC using a 10-fold cross validation via GridSearchCV function in the “sklearn” package in Python: ML-LR (hyperparameters: penalty “L2”, solver “liblinear”, C “0.001”), KNN (hyperparameters: number of neighbors to use “14”, weight function “uniform”, algorithm used to predict the nearest neighbors “auto”, leaf size “30”), SVM (kernel parameter “linear”, C “1”, gamma “scale”), and random forest (hyperparameters: the number of trees in the forest “256”, criterion to measure the quality of a split “gini”, the maximum depth of the tree “3”), LASSO (hyperparameters: penalty”L1”, solver “liblinear”, C ”0.5”).

Of the 100 pairs of GDM/control subjects, 70 pairs (70%) were randomly selected as the training set, and the remaining 30 pairs (30%) were used as the testing set to evaluate the performance of the prediction models. To evaluate the diagnostic performance of each model, six metrics including area under the curve (AUC) in receiver-operating-characteristics (ROC), accuracy, precision, sensitivity, specificity and F1 score, were measured in the testing set. AUC (range 0 to 1) is a widely used index to describe a model’s ability to predict outcomes. True positive (TP), true negative (TN), false positive (FP), and false negative (FN) numbers were used in calculating the accuracy, precision, sensitivity, specificity and F1 score: accuracy = (TP + TN)/(TP + FP + TN + FN), precision = TP/(TP + FP), sensitivity = TP/(TP + FN), specificity = TN/(TN + FP), the F1 score is the harmonic mean of precision and sensitivity, F1 = 2TP/(2TP + FN + FP) (range 0–1).

Statistical analysis

Mean ± SD was presented for continuous variables, and frequency (%) was presented for categorical variables. Paired t-test and Chi-square test were used to examine the differences between GDM and control groups in continuous and categorical variables respectively. Generalized linear models were used to comparing circulating concentrations of biomarkers between GDM and control subjects adjusting for maternal and pregnancy characteristics. Logistic regression was used to explore the associations of GDM with biomarkers. A stepwise regression was used in the selection of the co-variables in final regression models (retaining co-variables with P < 0.2 only).

ML prediction models were fitted using R packages (MASS, e1071, glmnet, randomForest) in R studio and Python (Sklearn). Missing data were rare in all biomarkers (0–4%), and were imputed/replaced by the respective mean values to maintain consistent sample sizes in all models. All data analyses were performed using R Studio (version 4.2) and Python (version 3.10.9). P value < 0.05 (two-tailed) was considered statistically significant.

Results

Table 1 presents the characteristics of study subjects. There were no differences between GDM and control subjects in maternal age, primiparity, pre-pregnancy BMI, family history of diabetes or hypertension. FPG, SBP and DBP at 11–14 weeks of gestation were all higher in GDM vs. controls (all p < 0.05), while there were no differences in serum levels of TSH, FT3, FT4, TG and TC. Comparing subjects in the training (70 pairs) vs. testing sets for ML models (Table S1), there were no differences in all maternal characteristics and biomarkers, except for that average FPG tended to be slightly higher in the training set (5.0 ± 0.4 vs. 4.8 ± 0.4 mmol/L, P = 0.027).

Table 1 Characteristics of study subjects in GDM and euglycemic (control) singleton pregnancies
Full size table

Adjusting for family history of diabetes and gestational age at blood sampling (other covariates did not affect the comparisons), serum concentrations at early gestation (11–14 weeks) were lower in GDM vs. euglycemic pregnancies for IGFBP-2 (74.7 ± 25.8 vs. 87.2 ± 32 ng/ml, P = 0.005), HMW adiponectin (2147.7 ± 1434.1 vs. 2480.5 ± 1309.3 ng/ml, p = 0.03) and SHBG (157.0 ± 42.6 vs. 168.5 ± 40.3 nmol/L, P = 0.05) (Table 2), but higher for hs-CRP (5.54 ± 5.25 vs. 4.60 ± 4.87 mg/L, P = 0.03) and IGF-I/IGFBP-2 ratios (0.8 ± 0.6 vs. 0.6 ± 0.4, p = 0.03). There were no differences in serum glycosylated fibronectin (381.1 ± 211.5 vs. 375.7 ± 193.8 μg/mL, P = 0.80), IGF-I (52.2 ± 25.4 vs. 48.8 ± 19.8 ng/ml, P = 0.48) and total adiponectin (5489.8 ± 1989.4 vs. 6107.4 ± 2437.5 ng/ml, P = 0.16) concentrations in GDM and euglycemic pregnancies.

Table 2 Serum biomarkers in the first trimester (11–14 weeks) in GDM and euglycemic (control) pregnancies
Full size table

Higher serum IGFBP-2 and SHBG concentrations were associated with lower odds of GDM, while higher FPG, IGF-1/IGFBP-2 ratio, SBP and DBP were associated with higher odds of GDM (Table S2). Other observed biomarkers were not associated with GDM.

The four ML algorithms selected different unique sets of predictors. The LR identified 5 predictors (FPG, IGFBP-2, TSH, FT3, hemoglobin), the LASSO identified 2 predictors (FPG, IGFBP-2), SVM-RFE identified 5 predictors (FPG, IGFBP-2, TSH, Hs-CRP, hemoglobin), and random forest identified 5 predictors (FPG, IGFBP-2, Hs-CRP, IGF-I/IGFBP-2 ratio, HMW adiponectin) (Table S3). Two predictors (FPG, IGFBP-2) were selected in all the four ML methods. TSH, Hs-CRP and hemoglobin were each selected twice in the four ML models.

Prediction performance metrics were assessed in the training and testing sets in 4 different models: Model 1: FPG, IGFBP-2; Model 2: FPG, IGFBP-2, TSH; Model 3: FPG, IGFBP-2, hs-CRP; and Model 4: each ML algorithm selected set of predictors (Table 3). In the testing sets of all models, the combination of FPG and IGFBP-2 only (model 1) outperformed other combinations of features (Models 2, 3 and 4). The random forest model using FPG and IGFBP-2 was the optimal model with the highest AUC (0.8) among all the models with sensitivity = 87%. Other predictive performance metrics for these models are shown in Table 4. The addition of other predictors did not further improve the prediction performance.

Table 3 AUC and accuracy of conventional logistic regression (LR) and 5 machine learning (ML) models in predicting GDM by early gestational biomarkers
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Table 4 The performance of logistic regression and 5 machine learning models in predicting GDM by early gestational biomarkers
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The feature importance of variables for predicting gestational diabetes in random forest models is presented in Figure S2, and the SHAP (SHapley Additive exPlanations) summary plots illustrating the importance and impact of each feature in predicting gestational diabetes is presented in Figure S3. The FPG was the most important and impactful predictor, and IGFBP-2 ranked the second.

Discussion

Main findings

Our results suggest that at early gestation (11–14 weeks), a combination of FPG and IGFBP-2 could predict GDM with moderate discriminant power.

Most predictive biomarkers

ML model analyses showed that FPG and IGFBP-2 at early gestation were always selected in all ML models, and represented the minimal set of biomarkers in predicting GDM. FPG in the first-trimester is a known biomarker strongly predictive of GDM [14, 37, 38]. A prospective cohort study (n = 450) reported an AUC of 0.61 for FPG [23], while in a large retrospective cohort (n = 48,444), the AUC was 0.77 for first-trimester FPG in predicting GDM [15]. These consistent findings highlighted the importance of first trimester FPG in predicting GDM.

Circulating IGFBP-2 levels are lower in GDM vs. euglycemic pregnancies throughout the gestation [21, 39, 40]. Consistent with our results, low IGFBP-2 in early gestation was observed to be strongly predictive of later development of GDM in a previous study [21]. IGFBP-2 overexpression has been associated with reduced susceptibility to obesity and diabetes via inhibition of adipogenesis and stimulation of insulin sensitivity in mice [41, 42], suggesting a critical role of IGFBP-2 in metabolic homeostasis. There is a lack of post-prandial fluctuations in IGFBP-2 concentrations [43], rendering IGFBP-2 a promising early gestational biomarker of GDM.

Prediction models

In our study, the best prediction model was a combination of FPG and IGFBP-2, with moderate discriminant power (AUC = 0.80) in identifying GDM. Some other observed biomarkers and features, although were associated with GDM, but their additions could not improve the predictions or discriminant power.

A number of studies have reported that biomarkers in early pregnancy may improve the accuracy in predicting GDM upon routine clinical risk factors. In a large multiethnic cohort, a model combining clinical risk factors (previous GDM, family history of diabetes, South/East Asian ethnicity, parity, maternal age, and BMI) and biomarkers (pregnancy-associated plasma protein A, uterine artery pulsatility index, MAP, and free β-human chorionic gonadotropin) achieved an AUC of 0.91 in predicting GDM [44]. In a large retrospective Chinese cohort, a 7-variables model (maternal age, family history of diabetes, multiple pregnancy, GDM history, FPG, HbA1c and triglyceride) achieved an AUC of 0.77 [45]. In a nested case-control study (GDM 80, control 300), the addition of SHBG and adiponectin might improve prediction beyond clinical risk factors (AUC: 0. 84 vs. 0.79) [46]. The AUC of a model including clinical factors (age, gestational age at sampling, BMI, ethnicity, family history of diabetes, and prior GDM) plus HDL cholesterol and tissue plasminogen activator was reported to be 0.86 in predicting GDM [47]. In a study of 1843 Belgian women, the AUC was 0.76 in a model including clinical factors, fasting plasma glucose, triglycerides and HbA1c [48]. A small study (n = 20 GDM cases) reported that the combination of soluble CD163, insulin, tumor-necrosis factor alpha, placental protein 13, and pregnancy associated plasma protein A at the first trimester could predict DM with AUC = 0.94 [49]. In a nested case-control study, the AUC was 0.70 in a model including chemerin, leptin, secreted frizzled-related protein 4 and adiponectin [50]. The AUC was 0.87 in predicting GDM in a model including total cholesterol, triglycerides, insulin, homeostatic model assessment, low density lipoprotein and tissue plasminogen activator in another small study (16 GDM cases) [51]. These variable findings may be partly attributable to the inclusion of different candidate biomarkers. In contrast, our model is much simpler, with 2-biomarkers only achieving an AUC of 0.80. We have considered the most promising candidate biomarkers according to up-to-date literature, and our study demonstrates the potential usefulness of a parsimonious model in predicting GDM using two biomarkers only (FPG and IGFBP-2).

Conventional LR vs. ML models

A meta-analysis showed that LR was the most commonly used model in predicting GDM: 25 studies using conventional LR, vs. 5 studies using ML with an overall pooled AUC of 0.89 [29]. Our ML model requires the smallest number of predictors (only two). In a large prospective cohort, ML method (XGBoost) achieved a higher AUC than conventional LR in predicting GDM [52]. On the other hand, two other studies indicated that ML did not outperform conventional LR in predicting GDM [45, 53]. In our study, all ML models achieved higher AUC than conventional LR. ML models may perform differently in different datasets and algorithms [53,54,55,56]. We tested five common ML algorithms, and the random forest was identified as the best model with the highest AUC.

Limitations

The study was based on a large pregnancy cohort. The proposed ML prediction model requires two predictors only, and that adding various clinical features or other biomarkers did not improve the predictions. All study subjects are Chinese. More studies in other ethnicity groups are required to understand the generalizability of the study findings.

In summary, the combination of FPG and IGFBP-2 at early gestation can predict later development of GDM with moderate discriminant power. Further validation studies are warranted to assess the utility of this simple combination model in other independent cohorts in the quest for a potentially useful clinical risk monitoring tool.

Data availability

Access to the de-identified participant research data must be approved by the research ethics board on a case-by-case basis, please contact the corresponding authors (zc.luo@utoronto.ca; ouyangfengxiu@xinhuamed.com.cn; junjimzhang@sjtu.edu.cn) for assistance in data access request.

Abbreviations

GDM:

Gestational diabetes mellitus

IGFBP-2:

Insulin-like growth factor binding protein 2

OGTT:

Oral glucose tolerance test

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Acknowledgements

We gratefully acknowledged all research staff who had contributed to patient recruitment and data collection in the Early Life Plan cohort in Shanghai.

Funding

Supported by research grants from the Ministry of Science and Technology of China (2019YFA0802501), the Shanghai Municipal Science and Technology Commission (21410713500), the Shanghai Municipal Health Commission (2020CXJQ01), the National Natural Science Foundation of China (82073570, 81930095, 82125032 and 81961128023), and the Canadian Institutes of Health Research (155955, 158616). The funders have no role in all aspects of the study, including study design, data collection and analysis, the preparation of the manuscript and the decision for publication.

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Author notes

  1. Meng-Nan Yang, Lin Zhang and Wen-Juan Wang contributed equally to this work.

Authors and Affiliations

  1. Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Department of Pediatrics, Xinhua Hospital, Early Life Health Institute, Shanghai Jiao-Tong University School of Medicine, Kong-Jiang Road, Shanghai, 200092, China

    Meng-Nan Yang, Wen-Juan Wang, Hua He, Fang Fang, Fei Li, Fengxiu Ouyang, Jiong Li, Jun Zhang & Zhong-Cheng Luo

  2. Prosserman Centre for Population Health Research, Department of Obstetrics and Gynecology, Mount Sinai Hospital, Faculty of Medicine, Lunenfeld-Tanenbaum Research Institute, University of Toronto, L5-240, Murray Street 60, Toronto, ON, M5T 3H7, Canada

    Meng-Nan Yang, Rong Huang, Justin Cheng & Zhong-Cheng Luo

  3. Obstetrics and Gynecology, International Peace Maternity and Child Health Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200030, China

    Lin Zhang

  4. Clinical Skills Center, School of Clinical Medicine, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China

    Wen-Juan Wang

  5. Obstetrics and Gynecology, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200092, China

    Tao Zheng

  6. Department of Clinical Assay Laboratory, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200092, China

    Guang-Hui Zhang

  7. State Key Laboratory of Reproductive Medicine, Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China

    Jiong Li

Authors
  1. Meng-Nan Yang
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Contributions

LZ, FL, FO, JL, JZ and ZCL conceived the study. MNY, LZ, WJW, HH, TZ, GHZ, FF, RH, FL, FO, JZ and ZCL contributed to the acquisition of research data. MNY, LZ, RH, VM and JC contributed to the literature review. MNY, WJW and RH conducted the data analysis. MNY, WJW and LZ drafted the manuscript. All authors contributed in revising the article critically for important intellectual content, and approved the final version for publication. ZCL is the guarantor of the manuscript.

Corresponding authors

Correspondence to Fengxiu Ouyang, Jun Zhang or Zhong-Cheng Luo.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the research ethics committee of Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University (reference number XHEC-C-2013-001). Written informed consent was obtained from all study participants.

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Not applicable.

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The authors declare no competing interests.

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Yang, MN., Zhang, L., Wang, WJ. et al. Prediction of gestational diabetes mellitus by multiple biomarkers at early gestation. BMC Pregnancy Childbirth 24, 601 (2024). https://doi.org/10.1186/s12884-024-06651-4

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BMC Pregnancy and Childbirth

ISSN: 1471-2393

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