An early model to predict the risk of gestational diabetes mellitus in the absence of blood examination indexes: application in primary health care centres

Enhancing gestational diabetes mellitus risk assessment and treatment through GDMPredictor: a machine learning approach

BACKGROUND

Gestational diabetes mellitus (GDM) is a serious health concern that affects pregnant women worldwide and can lead to adverse pregnancy outcomes. Early detection of high-risk individuals and the implementation of appropriate treatment can enhance these outcomes.

METHODS

We conducted a study on a cohort of 3467 pregnant women during their pregnancy, with a total of 5649 clinical and biochemical records collected. We utilized this dataset as our training dataset to develop a web server called GDMPredictor. The GDMPredictor utilizes advanced machine learning techniques to predict the risk of GDM in pregnant women. We also personalize treatment recommendations based on essential biochemical indicators, such as A1MG, BMG, CysC, CO2, TBA, FPG, and CREA. Our assessment of GDMPredictor's effectiveness involved training it on the dataset of 3467 pregnant women and measuring its ability to predict GDM risk using an AUC and auPRC.

RESULTS

GDMPredictor demonstrated an impressive level of precision by achieving an AUC score of 0.967. To tailor our treatment recommendations, we use the GDM risk level to identify higher risk candidates who require more intensive care. The GDMPredictor can accept biochemical indicators for predicting the risk of GDM at any period from 1 to 24 weeks, providing healthcare professionals with an intuitive interface to identify high-risk patients and give optimal treatment recommendations.

CONCLUSIONS

The GDMPredictor presents a valuable asset for clinical practice, with the potential to change the management of GDM in pregnant women. Its high accuracy and efficiency make it a reliable tool for doctors to improve patient outcomes. Early identification of high-risk individuals and tailored treatment can improve maternal and fetal health outcomes http://www.bioinfogenetics.info/GDM/ .

Low muscle mass index is associated with type 2 diabetes risk in a Latin-American population: a cross-sectional study

Objective

Diabetes mellitus is a growing disease with severe complications. Various scores predict the risk of developing this pathology. The amount of muscle mass is associated with insulin resistance, yet there is no established evidence linking muscle mass with diabetes risk. This work aims to study that relationship.

Research methods and procedures

This cross-sectional study included 1,388 employees. The FINDRISC score was used to assess type 2 diabetes risk, and bioimpedance was used for body composition analysis. Appendicular skeletal muscle mass adjusted by body mass index (ASM/BMI) was analyzed. Sociodemographic, clinical and anthropometric measures were evaluated, logistic regression models with sex stratification were conducted and ROC curves were calculated to determine the ability of ASM/BMI index to predict T2D risk.

Results

It was observed that patients with higher ASM/BMI had a lower FINDRISC score in both men and women (p < 0.001). A logistic regression model showed and association between ASM/BMI and diabetes risk in women [OR: 0.000 (0.000-0.900), p = 0.048], but not in men [OR: 0.267 (0.038-1.878), p = 0.185]. However, when the body mass index variable was excluded from the model, an association was found between muscle mass adjusted to BMI and diabetes risk in both men [OR: 0.000 (0.000-0.016), p < 0.001], and women [OR:0.001 (0.000-0.034), p < 0.001]. Other risk factors were having a low level of physical activity, waist circumference, age and sedentary lifestyle. A ROC curve was built and the optimal ASM/BMI cut-of value for predicting T2D risk was 0.82 with a sensitivity of 53.71% and specificity of 69.3% [AUC of 0.665 (0.64-0.69; p < 0.0001)].

Conclusion

When quantifying the risk of type 2 diabetes in both women and men, assessing muscle mass can help detect adult individuals with a high risk of developing type 2 diabetes.

A stepwise prediction and interpretation of gestational diabetes mellitus: Foster the practical application of machine learning in clinical decision

Background

Machine learning has shown to be an effective method for early prediction and intervention of Gestational diabetes mellitus (GDM), which greatly decreases GDM incidence, reduces maternal and infant complications and improves the prognosis. However, there is still much room for improvement in data quality, feature dimension, and accuracy. The contributions and mechanism explanations of clinical data at different pregnancy stages to the prediction accuracy are still lacking. More importantly, current models still face notable obstacles in practical applications due to the complex and diverse input features and difficulties in redeployment. As a result, a simple, practical but accurate enough model is urgently needed.

Design and methods

In this study, 2309 samples from two public hospitals in Shenzhen, China were collected for analysis. Different algorithms were systematically compared to build a robust and stepwise prediction system (level A to C) based on advanced machine learning, and models under different levels were interpreted.

Results

XGBoost reported the best performance with ACC of 0.922, 0.859 and 0.850, AUC of 0.974, 0.924 and 0.913 for the selected level A to C models in the test set, respectively. Tree-based feature importance and SHAP method successfully identified the commonly recognized risk factors, while indicated new inconsistent impact trends for GDM in different stages of pregnancy.

Conclusion

A stepwise prediction system was successfully established. A practical tool that enables a quick prediction of GDM was released at https://github.com/ifyoungnet/MedGDM.This study is expected to provide a more detailed profiling of GDM risk and lay the foundation for the application of the model in practice.

A Theoretical Exploration of Artificial Intelligence's Impact on Feto-Maternal Health from Conception to Delivery

The implementation of Artificial Intelligence (AI) in healthcare is enhancing diagnostic accuracy in clinical setups. The use of AI in healthcare is steadily increasing with advancing technology, extending beyond disease diagnosis to encompass roles in feto-maternal health. AI harnesses Machine Learning (ML), Natural Language Processing (NLP), Artificial Neural Networks (ANN), and computer vision to analyze data and draw conclusions. Considering maternal health, ML analyzes vast datasets to predict maternal and fetal health outcomes, while NLP interprets medical texts and patient records to assist in diagnosis and treatment decisions. ANN models identify patterns in complex feto-maternal medical data, aiding in risk assessment and intervention planning whereas, computer vision enables the analysis of medical images for early detection of feto-maternal complications. AI facilitates early pregnancy detection, genetic screening, and continuous monitoring of maternal health parameters, providing real-time alerts for deviations, while also playing a crucial role in the early detection of fetal abnormalities through enhanced ultrasound imaging, contributing to informed decision-making. This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health. Additionally, it examines potential future directions and challenges associated with these applications.

Combined general and central obesity indices to predict gestational diabetes

OBJECTIVE

To investigate the relationship between general and central obesity in the first trimester of pregnancy and gestational diabetes and its predicted value.

MATERIALS AND METHODS

We recruited 813 women who registered at 6-12 weeks of gestation. Anthropometric measurements were done at the first antenatal visit. At 24-28 weeks of pregnancy, gestational diabetes was diagnosed using the 75 g oral glucose tolerance test. Binary logistic regression was used to determine odds ratios and 95% confidence intervals. The receiver-operating characteristic curve was used to evaluate the ability of obesity indices to predict the risk of gestational diabetes.

RESULTS

Odds ratios (95% confidence intervals) of gestational diabetes across increasing quartiles of waist-to-hip ratio were 1.00, 1.54 (0.65-3.66), 2.63 (1.18-5.85), and 4.96 (2.27-10.85), respectively (p < .001), while those for waist-to-height ratio were 1.00, 1.21 (0.47-3.08), 2.99 (1.26-7.10), and 4.01 (1.57-10.19), respectively (p < .001). Areas under the curve for general and central obesity were similar. However, the area under the curve of body mass index combined with the waist-to-hip ratio was the biggest.

CONCLUSION

Higher waist-to-hip ratio and waist-to-height ratio in the first trimester of pregnancy are associated with increased risks of gestational diabetes in Chinese women. The combination of body mass index and waist-to-hip ratio in the first trimester of pregnancy is a good predictor for gestational diabetes.

Progress of the application clinical prediction model in polycystic ovary syndrome

Clinical prediction models play an important role in the field of medicine. These can help predict the probability of an individual suffering from disease, complications, and treatment outcomes by applying specific methodologies. Polycystic ovary syndrome (PCOS) is a common disease with a high incidence rate, huge heterogeneity, short- and long-term complications, and complex treatments. In this systematic review study, we reviewed the progress of clinical prediction models in PCOS patients, including diagnosis and prediction models for PCOS complications and treatment outcomes. We aimed to provide ideas for medical researchers and clues for the management of PCOS. In the future, models with poor accuracy can be greatly improved by adding well-known parameters and validations, which will further expand our understanding of PCOS in terms of precision medicine. By developing a series of predictive models, we can make the definition of PCOS more accurate, which can improve the diagnosis of PCOS and reduce the likelihood of false positives and false negatives. It will also help discover complications earlier and treatment outcomes being known earlier, which can result in better outcomes for women with PCOS.

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models' development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer's disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.

Development of machine learning models to predict gestational diabetes risk in the first half of pregnancy

BACKGROUND

Early prediction of Gestational Diabetes Mellitus (GDM) risk is of particular importance as it may enable more efficacious interventions and reduce cumulative injury to mother and fetus. The aim of this study is to develop machine learning (ML) models, for the early prediction of GDM using widely available variables, facilitating early intervention, and making possible to apply the prediction models in places where there is no access to more complex examinations.

METHODS

The dataset used in this study includes registries from 1,611 pregnancies. Twelve different ML models and their hyperparameters were optimized to achieve early and high prediction performance of GDM. A data augmentation method was used in training to improve prediction results. Three methods were used to select the most relevant variables for GDM prediction. After training, the models ranked with the highest Area under the Receiver Operating Characteristic Curve (AUCROC), were assessed on the validation set. Models with the best results were assessed in the test set as a measure of generalization performance.

RESULTS

Our method allows identifying many possible models for various levels of sensitivity and specificity. Four models achieved a high sensitivity of 0.82, a specificity in the range 0.72-0.74, accuracy between 0.73-0.75, and AUCROC of 0.81. These models required between 7 and 12 input variables. Another possible choice could be a model with sensitivity of 0.89 that requires just 5 variables reaching an accuracy of 0.65, a specificity of 0.62, and AUCROC of 0.82.

CONCLUSIONS

The principal findings of our study are: Early prediction of GDM within early stages of pregnancy using regular examinations/exams; the development and optimization of twelve different ML models and their hyperparameters to achieve the highest prediction performance; a novel data augmentation method is proposed to allow reaching excellent GDM prediction results with various models.

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.

Development and validation of a multivariable genotype-informed gestational diabetes prediction algorithm for clinical use in the Mexican population: insights into susceptibility mechanisms

INTRODUCTION

Gestational diabetes mellitus (GDM) is underdiagnosed in Mexico. Early GDM risk stratification through prediction modeling is expected to improve preventative care. We developed a GDM risk assessment model that integrates both genetic and clinical variables.

RESEARCH DESIGN AND METHODS

Data from pregnant Mexican women enrolled in the 'Cuido mi Embarazo' (CME) cohort were used for development (107 cases, 469 controls) and data from the 'Mónica Pretelini Sáenz' Maternal Perinatal Hospital (HMPMPS) cohort were used for external validation (32 cases, 199 controls). A 2-hour oral glucose tolerance test (OGTT) with 75 g glucose performed at 24-28 gestational weeks was used to diagnose GDM. A total of 114 single-nucleotide polymorphisms (SNPs) with reported predictive power were selected for evaluation. Blood samples collected during the OGTT were used for SNP analysis. The CME cohort was randomly divided into training (70% of the cohort) and testing datasets (30% of the cohort). The training dataset was divided into 10 groups, 9 to build the predictive model and 1 for validation. The model was further validated using the testing dataset and the HMPMPS cohort.

RESULTS

Nineteen attributes (14 SNPs and 5 clinical variables) were significantly associated with the outcome; 11 SNPs and 4 clinical variables were included in the GDM prediction regression model and applied to the training dataset. The algorithm was highly predictive, with an area under the curve (AUC) of 0.7507, 79% sensitivity, and 71% specificity and adequately powered to discriminate between cases and controls. On further validation, the training dataset and HMPMPS cohort had AUCs of 0.8256 and 0.8001, respectively.

CONCLUSIONS

We developed a predictive model using both genetic and clinical factors to identify Mexican women at risk of developing GDM. These findings may contribute to a greater understanding of metabolic functions that underlie elevated GDM risk and support personalized patient recommendations.

A machine learning approach for early prediction of gestational diabetes mellitus using elemental contents in fingernails

The aim of this pilot study was to predict the risk of gestational diabetes mellitus (GDM) by the elemental content in fingernails and urine with machine learning analysis. Sixty seven pregnant women (34 control and 33 GDM patient) were included. Fingernails and urine were collected in the first and second trimesters, respectively. The concentrations of elements were determined by inductively coupled plasma-mass spectrometry. Logistic regression model was applied to estimate the adjusted odd ratios and 95% confidence intervals. The predictive performances of multiple machine learning algorithms were evaluated, and an ensemble model was built to predict the risk for GDM based on the elemental contents in the fingernails. Beryllium, selenium, tin and copper were positively associated with the risk of GDM while nickel and mercury showed opposite result. The trained ensemble model showed larger area under curve (AUC) of receiver operating characteristic curve (0.81) using fingernail Ni, Cu and Se concentrations. The model was validated by external data set with AUC = 0.71. In summary, the results of the present study highlight the potential of fingernails, as an alternative sample, together with machine learning in human biomonitoring studies.

Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes

Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes and its complications. The aim of this commentary article is to provide a general perspective and present recent advances on how artificial intelligence can be applied to improve the prediction and diagnosis of six significant complications of diabetes including (1) gestational diabetes, (2) hypoglycemia in the hospital, (3) diabetic retinopathy, (4) diabetic foot ulcers, (5) diabetic peripheral neuropathy, and (6) diabetic nephropathy.

Stacking Ensemble Method for Gestational Diabetes Mellitus Prediction in Chinese Pregnant Women: A Prospective Cohort Study

Gestational diabetes mellitus (GDM) is closely related to adverse pregnancy outcomes and other diseases. Early intervention in pregnant women who are at high risk of developing GDM could help prevent adverse health consequences. The study aims to develop a simple model using the stacking ensemble method to predict GDM for women in the first trimester based on easily available factors. We used the data from the Chinese Pregnant Women Cohort Study from July 2017 to November 2018. A total of 6,848 pregnant women in the first trimester were included in the analysis. Logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) were considered as base learners. Optimal feature subsets for each learner were chosen by using recursive feature elimination cross-validation. Then, we built a pipeline to process imbalance data, tune hyperparameters, and evaluate model performance. The learners with the best hyperparameters were employed in the first layer of the proposed stacking method. Their predictions were obtained using optimal feature subsets and served as meta-learner's inputs. Another LR was used as a meta-learner to obtain the final prediction results. Accuracy, specificity, error rate, and other metrics were calculated to evaluate the performance of the models. A paired samples t-test was performed to compare the model performance. In total, 967 (14.12%) women developed GDM. For base learners, the RF model had the highest accuracy (0.638 (95% confidence interval (CI) 0.628-0.648)) and specificity (0.683 (0.669-0.698)) and lowest error rate (0.362 (0.352-0.372)). The stacking method effectively improved the accuracy (0.666 (95% CI 0.663-0.670)) and specificity (0.725 (0.721-0.729)) and decreased the error rate (0.333 (0.330-0.337)). The differences in the performance between the stacking method and RF were statistically significant. Our proposed stacking method based on easily available factors has better performance than other learners such as RF.

Cardiovascular Disease-Associated MicroRNAs as Novel Biomarkers of First-Trimester Screening for Gestational Diabetes Mellitus in the Absence of Other Pregnancy-Related Complications

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.

Prediction of gestational diabetes mellitus by different obesity indices

Background

The incidence rates of obesity and gestational diabetes mellitus (GDM) are increasing in parallel. This study aimed to evaluate the relationship between different obesity indices, including prepregnancy body mass index (preBMI), the first-trimester abdominal circumference (AC), and first-trimester abdominal circumference/height ratio (ACHtR), and GDM, and the efficacy of these three indices in predicting GDM was assessed.

Methods

A total of 15,472 pregnant women gave birth to a singleton at the Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China. Prepregnancy weight was self-reported by study participants, body height and AC were measured by nurses at the first prenatal visit during weeks 11 to 13⁺⁶ of pregnancy. GDM was diagnosed through a 75-g oral glucose tolerance test at 24–28 gestational weeks. Using receiver operator characteristic (ROC) curve analysis, we evaluated the association between obesity indices and GDM.

Results

A total of 1912 women (12.4%) were diagnosed with GDM. Logistic regression analysis showed that AC, ACHtR, and preBMI (P < 0.001) were all independent risk factors for the development of GDM. In the normal BMI population, the higher the AC or ACHtR was, the more likely the pregnant woman was to develop GDM. The area under the ROC curve (AUC) was 0.63 (95% CI: 0.62–0.64) for the AC, 0.64 (95% CI: 0.62–0.65) for the ACHtR and 0.63 (95% CI: 0.62–0.64) for the preBMI. An AC ≥ 80.3 cm (sensitivity: 61.6%; specificity: 57.9%), an ACHtR of ≥ 0.49 (sensitivity: 67.3%; specificity: 54.0%), and a preBMI ≥ 22.7 (sensitivity: 48.4%; specificity: 71.8%) were determined to be the best cut-off levels for identifying subjects with GDM.

Conclusions

An increase in ACHtR may be an independent risk factor for GDM in the first trimester of pregnancy. Even in the normal BMI population, the higher the AC and ACHtR are, the more likely a pregnant woman is to develop GDM. AC, ACHtR in the first trimester and preBMI might be anthropometric indices for predicting GDM, but a single obesity index had limited predictive value for GDM.

Stem Cell Transplantation in the Treatment of Type 1 Diabetes Mellitus: From Insulin Replacement to Beta-Cell Replacement

Type 1 diabetes mellitus (T1DM) is an autoimmune disease that attacks pancreatic β-cells, leading to the destruction of insulitis-related islet β-cells. Islet β-cell transplantation has been proven as a curative measure in T1DM. However, a logarithmic increase in the global population with diabetes, limited donor supply, and the need for lifelong immunosuppression restrict the widespread use of β-cell transplantation. Numerous therapeutic approaches have been taken to search for substitutes of β-cells, among which stem cell transplantation is one of the most promising alternatives. Stem cells have demonstrated the potential efficacy to treat T1DM by reconstitution of immunotolerance and preservation of islet β-cell function in recent research. cGMP-grade stem cell products have been used in human clinical trials, showing that stem cell transplantation has beneficial effects on T1DM, with no obvious adverse reactions. To better achieve remission of T1DM by stem cell transplantation, in this work, we explain the progression of stem cell transplantation such as mesenchymal stem cells (MSCs), human embryonic stem cells (hESCs), and bone marrow hematopoietic stem cells (BM-HSCs) to restore the immunotolerance and preserve the islet β-cell function of T1DM in recent years. This review article provides evidence of the clinical applications of stem cell therapy in the treatment of T1DM.