Development and validation of a clinical model for preconception and early pregnancy risk prediction of gestational diabetes mellitus in nulliparous women

Development and validation of a prediction model for gestational diabetes mellitus based on clinical characteristics and laboratory biomarkers among Chinese women

BACKGROUND AND AIMS

Early detection of gestational diabetes mellitus (GDM) is critical for maternal and child health. Although several prediction models exist, their complexity and reliance on less clinically accessible biomarkers have limited generalizability. This study aimed to develop and validate a clinically practical GDM prediction model.

METHODS AND RESULTS

Based on a retrospective cohort containing 30 480 pregnant women from China (2014-2019), three prediction models (basic, full and optimal) were developed using logistic regression to select predictors. Predictive accuracy of prediction models was evaluated by the area under receiver operating characteristic curve (AUC). The nomogram was established to predict individual probability of GDM, with decision curve analysis (DCA) assessing clinical utility. A total of 8161 (26.8 %) women were diagnosed with GDM. The optimal model, incorporating nine clinical characteristics and biochemical indicators, had a good predictive effect for GDM with AUCs of 0.74 (95 % CI: 0.74-0.75) in the training cohort and 0.74 (0.73-0.76) in the validation cohort. The performance of the optimal model was significantly greater than the basic model (AUC of 0.62) and close to the full model (AUC of 0.75). The calibration curve showed that the established nomogram had good accuracy to predict individual probability of GDM. The DCA showed that the prediction model had a positive net benefit at threshold between 0.1 and 0.8.

CONCLUSION

The nine-item optimal prediction model yielded high predictive accuracy, facilitating the identification of high-risk women, and the refinement of personalized diagnostic and treatment modalities.

The association between inflammatory indices in early pregnancy and the risk of gestational diabetes mellitus in Chinese population

BACKGROUND

The association between inflammatory indices from peripheral blood cell in early pregnancy and the risk of gestational diabetes mellitus (GDM) is unclear.

METHODS

This was a retrospective study involving the medical data of 15,807 pregnant women who gave birth in 2019. Data were collected from the medical records and analyzed. The pregnant women's age, educational level, pre-pregnancy body weight, height, parity, family history of diabetes, lipid profile, blood pressure were recorded during 11 ~ 13+ 6 pregnancy weeks. We collected and measured several easily accessible systemic inflammatory indices from peripheral blood cell count, including Neutrophils, Lymphocytes, Monocytes, MHR (monocyte count/HDL-C), SII (platelet count ×neutrophil count/lymphocyte count ) and SIRI (neutrophil count ×monocyte count/lymphocyte count), and we analyzed their association with the risk of developing GDM.

RESULTS

In the present study, a total of 15,807 women were included, including 2,355 (14.9%) women diagnosed with GDM. Women who were diagnosed with GDM showed markedly lower level of monocyte count and higher level of neutrophil and lymphocyte counts. The GDM group showed relatively lower level of SIRI, while no significant differences were found between GDM group and non-GDM group in MHR or SII. After adjusting for potential confounding factors, we observed a significant association between monocyte counts, MHR and the risk of developing GDM, and the risk tended to decrease with increasing levels of monocyte counts and MHR.

CONCLUSION

The present study revealed that in early pregnancy, monocyte count and MHR have great potential as early diagnostic markers of GDM.

First-Trimester Prediction Models Based on Maternal Characteristics for Adverse Pregnancy Outcomes: A Systematic Review and Meta-Analysis

BACKGROUND

Early risk stratification can facilitate timely interventions for adverse pregnancy outcomes, including preeclampsia (PE), small-for-gestational-age neonates (SGA), spontaneous preterm birth (sPTB) and gestational diabetes mellitus (GDM).

OBJECTIVES

To perform a systematic review and meta-analysis of first-trimester prediction models for adverse pregnancy outcomes.

SEARCH STRATEGY

The PubMed database was searched until 6 June 2024.

SELECTION CRITERIA

First-trimester prediction models based on maternal characteristics were included. Articles reporting on prediction models that comprised biochemical or ultrasound markers were excluded.

DATA COLLECTION AND ANALYSIS

Two authors identified articles, extracted data and assessed risk of bias and applicability using PROBAST.

MAIN RESULTS

A total of 77 articles were included, comprising 30 developed models for PE, 15 for SGA, 11 for sPTB and 35 for GDM. Discriminatory performance in terms of median area under the curve (AUC) of these models was 0.75 [IQR 0.69-0.78] for PE models, 0.62 [0.60-0.71] for SGA models of nulliparous women, 0.74 [0.72-0.74] for SGA models of multiparous women, 0.65 [0.61-0.67] for sPTB models of nulliparous women, 0.71 [0.68-0.74] for sPTB models of multiparous women and 0.71 [0.67-0.76] for GDM models. Internal validation was performed in 40/91 (43.9%) of the models. Model calibration was reported in 21/91 (23.1%) models. External validation was performed a total of 96 times in 45/91 (49.5%) of the models. High risk of bias was observed in 94.5% of the developed models and in 58.3% of the external validations.

CONCLUSIONS

Multiple first-trimester prediction models are available, but almost all suffer from high risk of bias, and internal and external validations were often not performed. Hence, methodological quality improvement and assessment of the clinical utility are needed.

Development and validation of a risk prediction model for gestational diabetes mellitus in women of advanced maternal age during the first trimester

With the global rise in advanced maternal age (AMA) pregnancies, the risk of gestational diabetes mellitus (GDM) increases. However, few GDM prediction models are tailored for AMA women. This study aims to develop a practical risk prediction model for GDM in AMA women. Data were obtained from a prospective observational cohort of AMA pregnant women from the Obstetrics and Gynecology Hospital in Shanghai, China. Singleton pregnancies with complete OGTT results at 24-28 weeks were selected and divided into training (70%) and validation (30%) sets. First-trimester predictors, including demographic, metabolic parameters, and clinical history, were evaluated for statistical significance. A multivariate logistic regression model was developed, with performance evaluated using receiver operating characteristic (ROC) curves and calibration plots. Predictors were primarily incorporated as categorical variables in a nomogram to enhance model convenience. A model using continuous predictors was also tested for comparison. A total of 1904 AMA women were included, with GDM incidence rates of 18.3% (243/1333) in the training set and 19.3% (110/571) in the validation set. Significant predictors for GDM diagnosis at 24-28 weeks included maternal age, GDM history, first-trimester fasting plasma glucose, mean arterial pressure, and triglyceride levels. The categorical model achieved an area under the ROC curve of 0.717 (95% CI: 0.682-0.753) in the training set and 0.702 (95% CI: 0.645-0.758) in the validation set. The Hosmer-Lemeshow test indicated good calibration (p = .97 in the training set; p = .66 in the validation set). The model with category and continuous predictors exhibited similar performance. This study developed and validated a practical early risk prediction nomogram for GDM in AMA women, using commonly available clinical data. The model shows good predictive performance and is resource-efficient, making it suitable for real-world clinical implementation.

Identifying Predictor Variables for a Composite Risk Prediction Tool for Gestational Diabetes and Hypertensive Disorders of Pregnancy: A Modified Delphi Study

A composite cardiometabolic risk prediction tool will support the systematic identification of women at increased cardiometabolic risk during pregnancy to enable early screening and intervention. This study aims to identify and select predictor variables for a composite risk prediction tool for cardiometabolic risk (gestational diabetes mellitus and/or hypertensive disorders of pregnancy) for use in the first trimester. A two-round modified online Delphi study was undertaken. A prior systematic literature review generated fifteen potential predictor variables for inclusion in the tool. Multidisciplinary experts (n = 31) rated the clinical importance of variables in an online survey and nominated additional variables for consideration (Round One). An online meeting (n = 14) was held to deliberate the importance, feasibility and acceptability of collecting variables in early pregnancy. Consensus was reached in a second online survey (Round Two). Overall, 24 variables were considered; 9 were eliminated, and 15 were selected for inclusion in the tool. The final 15 predictor variables related to maternal demographics (age, ethnicity/race), pre-pregnancy history (body mass index, height, history of chronic kidney disease/polycystic ovarian syndrome, family history of diabetes, pre-existing diabetes/hypertension), obstetric history (parity, history of macrosomia/pre-eclampsia/gestational diabetes mellitus), biochemical measures (blood glucose levels), hemodynamic measures (systolic blood pressure). Variables will inform the development of a cardiometabolic risk prediction tool in subsequent research. Evidence-based, clinically relevant and routinely collected variables were selected for a composite cardiometabolic risk prediction tool for early pregnancy.

Design, rationale and protocol for Glycemic Observation and Metabolic Outcomes in Mothers and Offspring (GO MOMs): an observational cohort study

INTRODUCTION

Given the increasing prevalence of both obesity and pre-diabetes in pregnant adults, there is growing interest in identifying hyperglycaemia in early pregnancy to optimise maternal and perinatal outcomes. Multiple organisations recommend first-trimester diabetes screening for individuals with risk factors; however, the benefits and drawbacks of detecting glucose abnormalities more mild than overt diabetes in early gestation and the best screening method to detect such abnormalities remain unclear.

METHODS AND ANALYSIS

The goal of the Glycemic Observation and Metabolic Outcomes in Mothers and Offspring study (GO MOMs) is to evaluate how early pregnancy glycaemia, measured using continuous glucose monitoring and oral glucose tolerance testing, relates to the diagnosis of gestational diabetes (GDM) at 24-28 weeks' gestation (maternal primary outcome) and large-for-gestational-age birth weight (newborn primary outcome). Secondary objectives include relating early pregnancy glycaemia to other adverse pregnancy outcomes and comprehensively detailing longitudinal changes in glucose over the course of pregnancy. GO MOMs enrolment began in April 2021 and will continue for 3.5 years with a target sample size of 2150 participants.

ETHICS AND DISSEMINATION

GO MOMs is centrally overseen by Vanderbilt University's Institutional Review Board and an Observational Study Monitoring Board appointed by National Institute of Diabetes and Digestive and Kidney Diseases. GO MOMs has potential to yield data that will improve understanding of hyperglycaemia in pregnancy, elucidate better approaches for early pregnancy GDM screening, and inform future clinical trials of early GDM treatment.

TRIAL REGISTRATION NUMBER

NCT04860336.

Early prediction of gestational diabetes mellitus using maternal demographic and clinical risk factors

OBJECTIVE

To build and validate an early risk prediction model for gestational diabetes mellitus (GDM) based on first-trimester electronic medical records including maternal demographic and clinical risk factors.

METHODS

To develop and validate a GDM prediction model, two datasets were used in this retrospective study. One included data of 14,015 pregnant women from Máxima Medical Center (MMC) in the Netherlands. The other was from an open-source database nuMoM2b including data of 10,038 nulliparous pregnant women, collected in the USA. Widely used maternal demographic and clinical risk factors were considered for modeling. A GDM prediction model based on elastic net logistic regression was trained from a subset of the MMC data. Internal validation was performed on the remaining MMC data to evaluate the model performance. For external validation, the prediction model was tested on an external test set from the nuMoM2b dataset.

RESULTS

An area under the receiver-operating-characteristic curve (AUC) of 0.81 was achieved for early prediction of GDM on the MMC test data, comparable to the performance reported in previous studies. While the performance markedly decreased to an AUC of 0.69 when testing the MMC-based model on the external nuMoM2b test data, close to the performance trained and tested on the nuMoM2b dataset only (AUC = 0.70).

Development and validation of a clinical model to predict preconception risk of gestational diabetes mellitus in nulliparous women: A retrospective cohort study

OBJECTIVE

To develop and validate a model to predict the preconception risk of gestational diabetes mellitus (GDM) in nulliparous women.

METHODS

This was a retrospective cohort study. A total of 1565 women in early pregnancy who underwent preconception health examinations in the Women and Children's Hospital of Chongqing Medical University between January 2020 and June 2021 were invited to participate in a questionnaire survey. Logistic regression analysis was performed to determine the preconception risk factors for GDM. These factors were used to construct a model to predict GDM risk in nulliparous women. Then, the model was used to assess the preconception risk of GDM in 1060 nulliparous women.

RESULTS

Independent preconception risk factors for GDM included the following: age 35 years or greater, diastolic blood pressure 80 mm Hg or greater, fasting plasma glucose 5.1 mmol/L or greater, body mass index (BMI, calculated as weight in kilograms divided by the square of height in meters) 24 or greater, weight gain 10 kg or greater in the year before pregnancy, age of menarche 15 years or greater, three or more previous pregnancies, daily staple food intake 300 g or greater, fondness for sweets, and family history of diabetes. BMI less than 18.5, daily physical activity duration 1 h or greater, and high-intensity physical activity were protective factors. These factors were used to construct a model to predict GDM risk in nulliparous women, and the incidence of GDM significantly increased as the risk score increased. The area under the curve of the prediction model was 0.82 (95% confidence interval 0.80-0.85).

CONCLUSION

The preconception GDM risk prediction model demonstrated good predictive efficacy and can be used to identify populations at high risk of GDM before pregnancy, which provides the possibility for preconception intervention.

Study of How Adiposity in Pregnancy has an Effect on outcomeS (SHAPES): protocol for a prospective cohort study

INTRODUCTION

Maternal obesity increases the risk of multiple maternal and infant pregnancy complications, such as gestational diabetes and pre-eclampsia. Current UK guidelines use body mass index (BMI) to identify which women require additional care due to increased risk of complications. However, BMI may not accurately predict which women will develop complications during pregnancy as it does not determine amount and distribution of adipose tissue. Some adiposity measures (eg, waist circumference, ultrasound measures of abdominal visceral fat) can better identify where body fat is stored, which may be useful in predicting those women who need additional care.

METHODS AND ANALYSIS

This prospective cohort study (SHAPES, Study of How Adiposity in Pregnancy has an Effect on outcomeS) aims to evaluate the prognostic performance of adiposity measures (either alone or in combination with other adiposity, sociodemographic or clinical measures) to estimate risk of adverse pregnancy outcomes. Pregnant women (n=1400) will be recruited at their first trimester ultrasound scan (11+2-14+1 weeks') at Newcastle upon Tyne National Health Service Foundation Trust, UK. Early pregnancy adiposity measures and clinical and sociodemographic data will be collected. Routine data on maternal and infant pregnancy outcomes will be collected from routine hospital records. Regression methods will be used to compare the different adiposity measures with BMI in terms of their ability to predict pregnancy complications. If no individual measure performs better than BMI, multivariable models will be developed and evaluated to identify the most parsimonious model. The apparent performance of the developed model will be summarised using calibration, discrimination and internal validation analyses.

ETHICS AND DISSEMINATION

Ethical favourable opinion has been obtained from the North East: Newcastle & North Tyneside 1 Research Ethics Committee (REC reference: 22/NE/0035). All participants provide informed consent to take part in SHAPES. Planned dissemination includes peer-reviewed publications and additional dissemination appropriate to target audiences, including policy briefs for policymakers, media/social-media coverage for public and conferences for research TRIAL REGISTRATION NUMBER: ISRCTN82185177.

MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women

Given the barriers to early detection of gestational diabetes mellitus (GDM), this study aimed to develop an artificial intelligence (AI)-based prediction model for GDM in pregnant Mexican women. Data were retrieved from 1709 pregnant women who participated in the multicenter prospective cohort study 'Cuido mi embarazo'. A machine-learning-driven method was used to select the best predictive variables for GDM risk: age, family history of type 2 diabetes, previous diagnosis of hypertension, pregestational body mass index, gestational week, parity, birth weight of last child, and random capillary glucose. An artificial neural network approach was then used to build the model, which achieved a high level of accuracy (70.3%) and sensitivity (83.3%) for identifying women at high risk of developing GDM. This AI-based model will be applied throughout Mexico to improve the timing and quality of GDM interventions. Given the ease of obtaining the model variables, this model is expected to be clinically strategic, allowing prioritization of preventative treatment and promising a paradigm shift in prevention and primary healthcare during pregnancy. This AI model uses variables that are easily collected to identify pregnant women at risk of developing GDM with a high level of accuracy and precision.

Clinical First-Trimester Prediction Models for Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis

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.

First-trimester glycosylated hemoglobin (HbA1c) and maternal characteristics in the prediction of gestational diabetes: An observational cohort study

INTRODUCTION

This study aimed to investigate the extent to which gestational diabetes mellitus (GDM) can be predicted in the first trimester by combining a marker of growing interest, glycosylated hemoglobin A1c (HbA1c), and maternal characteristics.

MATERIAL AND METHODS

This observational study was conducted in the outpatient obstetric department of our institution. The values of HbA1c and venous random plasma glucose were prospectively assessed in the first trimester of pregnancy. We determined maternal characteristics that were independent predictors from the regression analysis and calculated areas under the receiver-operating curves by combining the maternal age, body mass index, previous history of GDM, and first-degree family history for diabetes mellitus. Moreover we investigated the predictive capability of HbA1c to exclude GDM. Patients with a first-trimester HbA1c level of 6.5% (48 mmol/mol) or more were excluded. The study was registered at ClinicalTrials.gov ID: NCT02139254.

RESULTS

We included 785 cases with complete dataset. The prevalence of GDM was 14.7% (115/785). Those who developed GDM had significantly higher HbA1c and random plasma glucose values (p < 0.0001 and p = 0.0002, respectively). In addition, they had a higher body mass index, were more likely to have a history of GDM and/or a first-degree family history of diabetes. When these maternal characteristics were combined with the first-trimester HbA1c and random plasma glucose the combined area under the receiver operating characteristics curve was 0.76 (95% CI 0.70-0.81).

CONCLUSIONS

Our results indicate that HbA1c and random plasma glucose values combined with age, body mass index, and personal and family history, allow the identification of women in the first trimester who are at increased risk of developing GDM.

Prediction of recurrent gestational diabetes mellitus: a retrospective cohort study

BACKGROUND

Women after gestational diabetes mellitus (GDM) are at increased risk for development of GDM recurrence. It was the aim of our study to evaluate factors for prediction of risk of recurrence.

METHODS

In this retrospective cohort study we included 159 women with GDM and a subsequent pregnancy. Putative risk factors for GDM recurrence were analyzed by logistic regression models. Results were compared to a cohort of age-matched women without GDM as controls (n = 318).

RESULTS

The overall risk of GDM recurrence was 72.3% (115/159). Risk factors of recurrence were a body mass index (BMI) ≥ 30 kg/m2 before the index pregnancy (odds ratio (OR) 2.8 [95% CI 1.3-6.2], p = 0,008), a BMI ≥ 25 kg/m2 before the subsequent pregnancy (OR 2.7 [95% CI 1.3-5.8]. p = 0.008), a positive family history (OR 4.3 [95% CI 1.2-15.4], p = 0.016) and insulin treatment during the index pregnancy (OR 2.3 [95% CI 1.1-4.6], p = 0.023). Delivery by caesarean section (index pregnancy) was of borderline significance (OR 2.2 [95% CI 0.9-5.2], p = 0.069). Interpregnancy weight gain, excessive weight gain during the index pregnancy and fetal outcome where not predictive for GDM recurrence. Neonates after GDM revealed a higher frequency of transfer to intensive care unit compared to healthy controls (OR 2.3 [95% CI 1.1-4.6], p = 0.0225). The best combined risk model for prediction of GDM recurrence including positive family history and a BMI ≥ 25 kg/m2 before the subsequent pregnancy revealed moderate test characteristics (positive likelihood ratio 7.8 [95% CI 1.1-54.7] and negative likelihood ratio 0.7 [95% CI 0.6-0.9]) with a positive predictive value of 96.6% in our cohort.

CONCLUSIONS

A positive family history of diabetes mellitus in combination with overweight or obesity were strongly associated with recurrence of a GDM in the subsequent pregnancy. Normalization of the pregravid BMI should be an effective approach for reducing the risk of GDM recurrence.

Metabolic Profiling of Pregnant Women with Obesity: An Exploratory Study in Women at Greater Risk of Gestational Diabetes

Gestational diabetes mellitus (GDM) is one of the most prevalent obstetric conditions, particularly among women with obesity. Pathways to hyperglycaemia remain obscure and a better understanding of the pathophysiology would facilitate early detection and targeted intervention. Among obese women from the UK Pregnancies Better Eating and Activity Trial (UPBEAT), we aimed to compare metabolic profiles early and mid-pregnancy in women identified as high-risk of developing GDM, stratified by GDM diagnosis. Using a GDM prediction model combining maternal age, mid-arm circumference, systolic blood pressure, glucose, triglycerides and HbA1c, 231 women were identified as being at higher-risk, of whom 119 women developed GDM. Analyte data (nuclear magnetic resonance and conventional) were compared between higher-risk women who developed GDM and those who did not at timepoint 1 (15+0−18+6 weeks) and at timepoint 2 (23+2−30+0 weeks). The adjusted regression analyses revealed some differences in the early second trimester between those who developed GDM and those who did not, including lower adiponectin and glutamine concentrations, and higher C-peptide concentrations (FDR-adjusted p < 0.005, < 0.05, < 0.05 respectively). More differences were evident at the time of GDM diagnosis (timepoint 2) including greater impairment in β-cell function (as assessed by HOMA2-%B), an increase in the glycolysis-intermediate pyruvate (FDR-adjusted p < 0.001, < 0.05 respectively) and differing lipid profiles. The liver function marker γ-glutamyl transferase was higher at both timepoints (FDR-adjusted p < 0.05). This exploratory study underlines the difficulty in early prediction of GDM development in high-risk women but adds to the evidence that among pregnant women with obesity, insulin secretory dysfunction may be an important discriminator for those who develop GDM.

Prevention of gestational diabetes in pregnant women with obesity: protocol for a pilot randomised controlled trial

BACKGROUND

Obesity in pregnancy increases the risk of gestational diabetes mellitus (GDM) and associated adverse outcomes. Despite metabolic differences, all pregnant women with obesity are considered to have the same risk of developing GDM. Improved risk stratification is required to enable targeted intervention in women with obesity who would benefit the most. The aim of this study is to identify pregnant women with obesity at higher risk of developing GDM and, in a pilot randomised controlled trial (RCT), test feasibility and assess the efficacy of a lifestyle intervention and/or metformin to improve glycaemic control.

METHODS

Women aged 18 years or older with a singleton pregnancy and body mass index (BMI) ≥ 30kg/m² will be recruited from one maternity unit in London, UK. The risk of GDM will be assessed using a multivariable GDM prediction model combining maternal age, mid-arm circumference, systolic blood pressure, glucose, triglycerides and HbA1c. Women identified at a higher risk of developing GDM will be randomly allocated to one of two intervention groups (lifestyle advice with or without metformin) or standard antenatal care. The primary feasibility outcomes are study recruitment, retention rate and intervention adherence and to collect information needed for the sample size calculation for the definitive trial. A process evaluation will assess the acceptability of study processes and procedures to women. Secondary patient-centred outcomes include a reduction in mean glucose/24h of 0.5mmol/l as assessed by continuous glucose monitoring and changes in a targeted maternal metabolome, dietary intake and physical activity. A sample of 60 high-risk women is required.

DISCUSSION

Early risk stratification of GDM in pregnant women with obesity and targeted intervention using lifestyle advice with or without metformin could improve glucose tolerance compared to standard antenatal care. The results from this feasibility study will inform a larger adequately powered RCT should the intervention show trends for potential effectiveness.

TRIAL REGISTRATION

This study has been approved by the NHS Research Ethics Committee (UK IRAS integrated research application system; reference 18/LO/1500). EudraCT number 2018-000003-16 .

An effective integrated machine learning approach for detecting diabetic retinopathy

Millions of people across the world are suffering from diabetic retinopathy. This disease majorly affects the retina of the eye, and if not identified priorly causes permanent blindness. Hence, detecting diabetic retinopathy at an early stage is very important to safeguard people from blindness. Several machine learning (ML) algorithms are implemented on the dataset of diabetic retinopathy available in the UCI ML repository to detect the symptoms of diabetic retinopathy. But, most of those algorithms are implemented individually. Hence, this article proposes an effective integrated ML approach that uses the support vector machine (SVM), principal component analysis (PCA), and moth-flame optimization techniques. Initially, the ML algorithms decision tree (DT), SVM, random forest (RF), and Naïve Bayes (NB) are applied to the diabetic retinopathy dataset. Among these, the SVM algorithm is outperformed with an average of 76.96% performance. Later, all the aforementioned ML algorithms are implemented by integrating the PCA technique to reduce the dimensions of the dataset. After integrating PCA, it is noticed that the performance of the algorithms NB, RF, and SVM is reduced dramatically; on the contrary, the performance of DT is increased. To improve the performance of ML algorithms, the moth-flame optimization technique is integrated with SVM and PCA. This proposed approach is outperformed with an average of 85.61% performance among all the other considered ML algorithms, and the classification of class labels is achieved correctly.

Optimising Cardiometabolic Risk Factors in Pregnancy: A Review of Risk Prediction Models Targeting Gestational Diabetes and Hypertensive Disorders

Cardiovascular disease, especially coronary heart disease and cerebrovascular disease, is a leading cause of mortality and morbidity in women globally. The development of cardiometabolic conditions in pregnancy, such as gestational diabetes mellitus and hypertensive disorders of pregnancy, portend an increased risk of future cardiovascular disease in women. Pregnancy therefore represents a unique opportunity to detect and manage risk factors, prior to the development of cardiovascular sequelae. Risk prediction models for gestational diabetes mellitus and hypertensive disorders of pregnancy can help identify at-risk women in early pregnancy, allowing timely intervention to mitigate both short- and long-term adverse outcomes. In this narrative review, we outline the shared pathophysiological pathways for gestational diabetes mellitus and hypertensive disorders of pregnancy, summarise contemporary risk prediction models and candidate predictors for these conditions, and discuss the utility of these models in clinical application.

Early-pregnancy maternal heart rate is related to gestational diabetes mellitus (GDM)

OBJECTIVE

The study examined the association between resting heart rate (RHR) of early pregnancy and risk of gestational diabetes mellitus (GDM) in Chinese population.

METHODS

As retrospective study, medical data of 15,092 pregnant women gave birth in 2019 was collected and analyzed. The pregnant women's age, educational level, pre-pregnancy body weight, height, parity, family history of diabetes, lipid profile, blood pressure and RHR were recorded during 11 ∼ 13⁺⁶ weeks. Multivariate logistic regression analysis was used to estimate the association between maternal characteristics and RHR and GDM. And we further evaluated the predictive roll of RHR in different sub-groups defined by their body mass index (BMI), age, fasting plasma glucose (FPG), total cholesterol and triglyceride.

RESULTS

2313 women (15.33%) were diagnosed as GDM according to 75 g OGTT. According to the quartile value of RHR, the subjects were divided into four groups. Risk of GDM increased significantly as RHR increased. In the fully adjusted model, ORs(95%CI) for the lowest vs highest quartiles of heart rate were 1(as reference), 1.14(0.97 ∼ 1.33), 1.25(1.05 ∼ 1.40), 1.41(1.21 ∼ 1.62), respectively. In the subgroup's analysis, we found the relationship between RHR and risk of GDM was evident in women with low and normal BMI; with normal fasting plasma; and normal serum lipid level.

CONCLUSION

The current study shows early-pregnancy maternal RHR is associated with potential risk of developing GDM.

Development and Validation of an Obstetric Comorbidity Risk Score for Clinical Use

Background: A comorbidity summary score may support early and systematic identification of women at high risk for adverse obstetric outcomes. The objective of this study was to conduct the initial development and validation of an obstetrics comorbidity risk score for automated implementation in the electronic health record (EHR) for clinical use. Methods: The score was developed and validated using EHR data for a retrospective cohort of pregnancies with delivery between 2010 and 2018 at Kaiser Permanente Northern California, an integrated health care system. The outcome used for model development consisted of adverse obstetric events from delivery hospitalization (e.g., eclampsia, hemorrhage, death). Candidate predictors included maternal age, parity, multiple gestation, and any maternal diagnoses assigned in health care encounters in the 12 months before admission for delivery. We used penalized regression for variable selection, logistic regression to fit the model, and internal validation for model evaluation. We also evaluated prenatal model performance at 18 weeks of pregnancy. Results: The development cohort (n = 227,405 pregnancies) had an outcome rate of 3.8% and the validation cohort (n = 41,683) had an outcome rate of 2.9%. Of 276 candidate predictors, 37 were included in the final model. The final model had a validation c-statistic of 0.72 (95% confidence interval [CI] 0.70-0.73). When evaluated at 18 weeks of pregnancy, discrimination was modestly diminished (c-statistic 0.68 [95% CI 0.67-0.70]). Conclusions: The obstetric comorbidity score demonstrated good discrimination for adverse obstetric outcomes. After additional appropriate validation, the score can be automated in the EHR to support early identification of high-risk women and assist efforts to ensure risk-appropriate maternal care.

Prevention of Gestational Diabetes: The Role of Dietary Intake, Physical Activity, and Weight before, during, and between Pregnancies

Gestational diabetes mellitus (GDM) is the most common complication of pregnancy and a significant clinical and public health problem with lifelong and intergenerational adverse health consequences for mothers and their offspring. The preconception, early pregnancy, and interconception periods represent opportune windows to engage women in preventive and health promotion interventions. This review provides an overview of findings from observational and intervention studies on the role of diet, physical activity, and weight (change) during these periods in the primary prevention of GDM. Current evidence suggests that supporting women to increase physical activity and achieve appropriate weight gain during early pregnancy and enabling women to optimize their weight and health behaviors prior to and between pregnancies have the potential to reduce rates of GDM. Translation of current evidence into practice requires further development and evaluation of co-designed interventions across community, health service, and policy levels to determine how women can be reached and supported to optimize their health behaviors before, during, and between pregnancies to reduce GDM risk.

Spectrochemical differentiation in gestational diabetes mellitus based on attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and multivariate analysis

Gestational diabetes mellitus (GDM) is a hyperglycaemic imbalance first recognized during pregnancy, and affects up to 22% of pregnancies worldwide, bringing negative maternal–fetal consequences in the short- and long-term. In order to better characterize GDM in pregnant women, 100 blood plasma samples (50 GDM and 50 healthy pregnant control group) were submitted Attenuated Total Reflection Fourier-transform infrared (ATR-FTIR) spectroscopy, using chemometric approaches, including feature selection algorithms associated with discriminant analysis, such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machines (SVM), analyzed in the biofingerprint region between 1800 and 900 cm⁻¹ followed by Savitzky–Golay smoothing, baseline correction and normalization to Amide-I band (~ 1650 cm⁻¹). An initial exploratory analysis of the data by Principal Component Analysis (PCA) showed a separation tendency between the two groups, which were then classified by supervised algorithms. Overall, the results obtained by Genetic Algorithm Linear Discriminant Analysis (GA-LDA) were the most satisfactory, with an accuracy, sensitivity and specificity of 100%. The spectral features responsible for group differentiation were attributed mainly to the lipid/protein regions (1462–1747 cm⁻¹). These findings demonstrate, for the first time, the potential of ATR-FTIR spectroscopy combined with multivariate analysis as a screening tool for fast and low-cost GDM detection.

Pathophysiology of gestational diabetes mellitus in lean Japanese pregnant women in relation to insulin secretion or insulin resistance

To determine the pathophysiology of gestational diabetes (GDM) in lean Japanese pregnant women in relation to insulin secretion or insulin resistance. The 75-g oral glucose tolerance test (OGTT) was performed in case of positive results of universal screening of a 50-g glucose challenge test at 24-28 weeks' gestation in Japanese pregnant women. These women were treated in our hospital between 2012 and 2016. Among these women, 30 with a body mass index of < 18.5 kg/m² were selected as lean subjects. Nine women were diagnosed with GDM (GDM group) and the remaining 21 had normal glucose tolerance (control group). For evaluating insulin secretion or resistance, the following parameters were compared between the two groups together with a family history of diabetes mellitus (DM) among first-degree relatives: (1) plasma glucose and immnunoreactive insulin (IRI) levels after glucose loading, (2) insulinogenic index (I.I), (3) homeostasis model assessment of β-cell function (HOMA-β), (4) homeostasis model assessment of insulin resistance (HOMA-IR), and (5) insulin sensitivity index (ISI) composite. The percentage of having a family history of DM was significantly higher in the GDM group (3/9, 33.3%) than in the control group (0/21, 0.0%, P < 0.001). Serum glucose levels at 30, 60, and 120 min after glucose loading were significantly higher in the GDM group than in the control group (all P < 0.05). IRI levels at 60 and 120 min were significantly higher in the GDM group than in the control group (both P < 0.05), and they showed persistent insulin secretion patterns. Values of the I.I. and ISI composite were significantly lower in the GDM group than in the control group (both P < 0.05), with no differences in HOMA-β, HOMA-IR and HbA1c levels between the groups. Lean Japanese pregnant women with GDM have impaired β-cell function, which is in part associated with hereditary traits.

Adipokines in early and mid-pregnancy and subsequent risk of gestational diabetes: a longitudinal study in a multiracial cohort

INTRODUCTION

Several adipokines are implicated in the pathophysiology of gestational diabetes mellitus (GDM), however, longitudinal data in early pregnancy on many adipokines are lacking. We prospectively investigated the association of a panel of adipokines in early and mid-pregnancy with GDM risk.

RESEARCH DESIGN AND METHODS

Within the National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies-Singletons cohort (n=2802), a panel of 10 adipokines (plasma fatty acid binding protein-4 (FABP4), chemerin, interleukin-6 (IL-6), leptin, soluble leptin receptor (sOB-R), adiponectin, omentin-1, vaspin, and retinol binding protein-4) were measured at gestational weeks (GWs) 10-14, 15-26, 23-31, and 33-39 among 107 GDM cases (ascertained on average at GW 27) and 214 non-GDM controls. Conditional logistic regression was used to estimate ORs of each adipokine and GDM, controlling for known GDM risk factors including pre-pregnancy body mass index.

RESULTS

Throughout pregnancy changes in chemerin, sOB-R, adiponectin, and high-molecular-weight adiponectin (HMW-adiponectin) concentrations from 10-14 to 15-26 GWs were significantly different among GDM cases compared with non-GDM controls. In early and mid-pregnancy, FABP4, chemerin, IL-6 and leptin were positively associated with increased GDM risk. For instance, at 10-14 GWs, the OR comparing the highest versus lowest quartile (ORQ4-Q1) of FABP4 was 3.79 (95% CI 1.63 to 8.85). In contrast, in both early and mid-pregnancy adiponectin (eg, ORQ4-Q1 0.14 (0.05, 0.34) during 10-14 GWs) and sOB-R (ORQ4-Q1 0.23 (0.11, 0.50) during 10-14 GWs) were inversely related to GDM risk. At 10-14 GWs a model that included conventional GDM risk factors and FABP4, chemerin, sOB-R, and HMW-adiponectin improved the estimated prediction (area under the curve) from 0.71 (95% CI 0.66 to 0.77) to 0.77 (95% CI 0.72 to 0.82).

CONCLUSIONS

A panel of understudied adipokines including FABP4, chemerin, and sOB-R may be implicated in the pathogenesis of GDM with significant associations detected approximately 10-18 weeks before typical GDM screening.

Early pregnancy prediction of gestational diabetes mellitus risk using prenatal screening biomarkers in nulliparous women

AIMS

To evaluate the clinical utility of first and second trimester prenatal screening biomarkers for early pregnancy prediction of gestational diabetes mellitus (GDM) risk in nulliparous women.

METHODS

We conducted a population-based cohort study of nulliparous women participating in the California Prenatal Screening Program from 2009 to 2011 (n = 105,379). GDM was ascertained from hospital discharge records or birth certificates. Models including maternal characteristics and prenatal screening biomarkers were developed and validated. Risk stratification and reclassification were performed to assess clinical utility of the biomarkers.

RESULTS

Decreased levels of first trimester pregnancy-associated plasma protein A (PAPP-A) and increased levels of second trimester unconjugated estriol (uE₃) and dimeric inhibin A (INH) were associated with GDM. The addition of PAPP-A only and PAPP-A, uE₃, and INH to maternal characteristics resulted in small, yet significant, increases in area under the receiver operating characteristic curve (AUC) (maternal characteristics only: AUC 0.714 (95% CI 0.703-0.724), maternal characteristics + PAPP-A: AUC 0.718 (95% CI 0.707-0.728), maternal characteristics + PAPP-A, uE₃, and INH: AUC 0.722 (0.712-0.733)); however, no net improvement in classification was observed.

CONCLUSIONS

PAPP-A, uE₃, and INH have limited clinical utility for prediction of GDM risk in nulliparous women. Utility of other readily accessible clinical biomarkers in predicting GDM risk warrants further investigation.

Development and validation of an early pregnancy risk score for the prediction of gestational diabetes mellitus in Chinese pregnant women

OBJECTIVE

To develop and validate a set of risk scores for the prediction of gestational diabetes mellitus (GDM) before the 15th gestational week using an established population-based prospective cohort.

METHODS

From October 2010 to August 2012, 19 331 eligible pregnant women were registered in the three-tiered antenatal care network in Tianjin, China, to receive their antenatal care and a two-step GDM screening. The whole dataset was randomly divided into a training dataset (for development of the risk score) and a test dataset (for validation of performance of the risk score). Logistic regression was performed to obtain coefficients of selected predictors for GDM in the training dataset. Calibration was estimated using Hosmer-Lemeshow test, while discrimination was checked using area under the receiver operating characteristic curve (AUC) in the test dataset.

RESULTS

In the training dataset (total=12 887, GDM=979 or 7.6%), two risk scores were developed, one only including predictors collected at the first antenatal care visit for early prediction of GDM, like maternal age, body mass index, height, family history of diabetes, systolic blood pressure, and alanine aminotransferase; and the other also including predictors collected during pregnancy, that is, at the time of GDM screening, like physical activity, sitting time at home, passive smoking, and weight gain, for maximum performance. In the test dataset (total=6444, GDM=506 or 7.9%), the calibrations of both risk scores were acceptable (both p for Hosmer-Lemeshow test >0.25). The AUCs of the first and second risk scores were 0.710 (95% CI: 0.680 to 0.741) and 0.712 (95% CI: 0.682 to 0.743), respectively (p for difference: 0.9273).

CONCLUSION

Both developed risk scores had adequate performance for the prediction of GDM in Chinese pregnant women in Tianjin, China. Further validations are needed to evaluate their performance in other populations and using different methods to identify GDM cases.