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

Insights into the Global and Mexican Context of Placental-Derived Pregnancy Complications

Placental-derived pregnancy complications encompass a range of disorders that hinder optimal fetal development, significantly impacting maternal and neonatal health outcomes. Key conditions include placental insufficiency, preeclampsia, fetal growth restriction (FGR) or intrauterine growth restriction (IUGR), fetal overgrowth, and gestational diabetes mellitus (GDM), which together contribute to a heightened risk of preterm birth, perinatal mortality, and long-term developmental challenges in affected infants. These complications are particularly notable because they generate approximately 80% of pregnancy disorders and pose significant public health concerns across diverse global contexts. Their management continues to face challenges, including a lack of consensus on diagnostic criteria and varied implementation of care standards. While imaging techniques like magnetic resonance imaging (MRI) and Doppler ultrasound have emerged as critical tools in clinical assessment, disparities in access to such technologies exacerbate existing inequalities in maternal and fetal health outcomes. Maternal and pregnancy care is a broad range of services aimed at promoting the well-being of women throughout the perinatal period. However, access to these services is often limited by economic, geographical, and sociocultural barriers, particularly for marginalized groups and women in low- and middle-income countries (LMICs). The implementation of targeted interventions designed to address specific obstacles faced by disadvantaged populations is a crucial component of bridging the gap in health equity in maternal care. Public health authorities and policymakers strive to develop evidence-based strategies that address the interplay between healthcare access, socioeconomic factors, and effective interventions in order to mitigate the adverse effects of placental-derived pregnancy complications. Continued research and data collection are essential to inform future policies and practices to improve outcomes for mothers and infants.

Association between cardiometabolic index and gestational diabetes mellitus: a cross-sectional study

BACKGROUND

Cardiometabolic index (CMI) is a novel marker of diabetes mellitus. However, few studies have examined its association with gestational diabetes mellitus (GDM) risk. This study aimed to explore the association between CMI and GDM risk among pregnant women in the United States.

METHODS

We performed a cross-sectional study utilizing data recorded in the National Health and Nutrition Examination Survey database from 1999 to 2018. Univariate and multivariate logistic regression, restricted cubic splines (RCS), sensitivity, and subgroup analyses were performed to clarify the relationship between CMI and GDM risk.

RESULTS

A total of 710 pregnant women were recruited, among whom 113 were diagnosed with GDM based on established criteria. This population showed a significant association between a higher CMI value and GDM (odds ratio: 1.75, 95% confidence interval: 1.03-2.99, P = 0.038). RCS regression analysis identified a linear relationship between CMI and GDM (P-value < 0.001, P-nonlinear = 0.702). Sensitivity analysis further confirmed the validity of this relationship. Subgroup analysis indicated a positive association between CMI and GDM among women who drink or smoke and Mexican Americans.

CONCLUSION

This study demonstrates a significant positive association between CMI and GDM risk, suggesting that a higher CMI predicts GDM incidence during pregnancy. Further research is required to investigate the CMI index as an early predictor of GDM.

Innovations in Diabetes Management for Pregnant Women: Artificial Intelligence and the Internet of Medical Things

Pregnancies impacted by diabetes face the compounded challenge of strict glycemic control with mounting insulin resistance as the pregnancy progresses. New technological advances, including artificial intelligence (AI) and the Internet of Medical Things (IoMT), are revolutionizing health care delivery by providing innovative solutions for diabetes care during pregnancy. Together, AI and the IoMT are a multibillion-dollar industry that integrates advanced medical devices and sensors into a connected network that enables continuous monitoring of glucose levels. AI-driven clinical decision support systems (CDSSs) can predict glucose trends and provide tailored evidence-based treatments with real-time adjustments as insulin resistance changes with placental growth. Additionally, mobile health (mHealth) applications facilitate patient education and self-management through real-time tracking of diet, physical activity, and glucose levels. Remote monitoring capabilities are particularly beneficial for pregnant persons with diabetes as they extend quality care to underserved populations and reduce the need for frequent in-person visits. This high-resolution monitoring allows physicians and patients access to an unprecedented wealth of data to make more informed decisions based on real-time data, reducing complications for both the mother and fetus. These technologies can potentially improve maternal and fetal outcomes by enabling timely, individualized interventions based on personalized health data. While AI and IoMT offer significant promise in enhancing diabetes care for improved maternal and fetal outcomes, their implementation must address challenges such as data security, cost-effectiveness, and preserving the essential patient-provider relationship. KEY POINTS: · The IoMT expands how patients interact with their health care.. · AI has widespread application in the care of pregnancies complicated by diabetes.. · A need for validation and black-box methodologies challenges the application of AI-based tools.. · As research in AI grows, considerations for data privacy and ethical dilemmas will be required..

Innovative development of territories based on the integrated use of social, resource and environmental potential

This article investigates the interrelationships and interdependencies inherent in the integrated use of the territory's social, resource, and environmental potential to achieve its sustainable development. The basic hypotheses formulated during this study were as follows: Sustainable development of the territory's resource and ecological potential cannot be achieved if its social potential is reduced; Sustainable development of the territory's ecological and social potential can be achieved only if it is economically feasible, that is if it contributes to the sustainable development of the territory's resource potential; Sustainable development of the territory's resource and social potential cannot be achieved while its ecological potential deteriorates; Ensured sustainable development of territories in today's world is only possible with simultaneous integrated use of the territory's social, resource, and environmental potential. The aim of this research is to investigate the interrelationships among these three components and to determine their impact on the overall development of the regions. A comprehensive multi-stage research project based on the analysis of statistical information and survey results was designed and applied to meet the research objective. Methodologically, the study was based on a quantitative approach, which led to the integrated use of economic-statistical and econometric data processing methods. All these hypotheses were tested and found to be true in the Kizilsu Kyrgyz Autonomous Prefecture of the People's Republic of China. The results indicate a direct and stable relationship between the development of resource potential, ecological potential, and social potential of the territories. Specifically, regions with high resource potential exhibit better social indicators, while ecological investments contribute to the improvement of public health and quality of life. The originality of this study lies in its comprehensive approach to examining the interconnections among resources, social aspects, and ecological sustainability, thereby contributing to the international literature in the field of sustainable development. These results can be of use to state and municipal government experts when planning territorial development, as well as to academic researchers, including to identify promising avenues for later research.

Artificial Neural Networks to Predict Metabolic Syndrome without Invasive Methods in Adolescents

Background/Objectives: The prevalence of metabolic syndrome (MetS) is increasing worldwide, and an increasing number of cases are diagnosed in younger age groups. This study aimed to propose predictive models based on demographic, anthropometric, and non-invasive clinical variables to predict MetS in adolescents. Methods: A total of 2064 adolescents aged 18-19 from São Luís-Maranhão, Brazil were enrolled. Demographic, anthropometric, and clinical variables were considered, and three criteria for diagnosing MetS were employed: Cook et al., De Ferranti et al. and the International Diabetes Federation (IDF). A feed-forward artificial neural network (ANN) was trained to predict MetS. Accuracy, sensitivity, and specificity were calculated to assess the ANN's performance. The ROC curve was constructed, and the area under the curve was analyzed to assess the discriminatory power of the networks. Results: The prevalence of MetS in adolescents ranged from 5.7% to 12.3%. The ANN that used the Cook et al. criterion performed best in predicting MetS. ANN 5, which included age, sex, waist circumference, weight, and systolic and diastolic blood pressure, showed the best performance and discriminatory power (sensitivity, 89.8%; accuracy, 86.8%). ANN 3 considered the same variables, except for weight, and exhibited good sensitivity (89.0%) and accuracy (87.0%). Conclusions: Using non-invasive measures allows for predicting MetS in adolescents, thereby guiding the flow of care in primary healthcare and optimizing the management of public resources.

The early prediction of gestational diabetes mellitus by machine learning models

BACKGROUND

We aimed to determine the best-performing machine learning (ML)-based algorithm for predicting gestational diabetes mellitus (GDM) with sociodemographic and obstetrics features in the pre-conceptional period.

METHODS

We collected the data of pregnant women who were admitted to the obstetric clinic in the first trimester. The maternal age, body mass index, gravida, parity, previous birth weight, smoking status, the first-visit venous plasma glucose level, the family history of diabetes mellitus, and the results of an oral glucose tolerance test of the patients were evaluated. The women were categorized into groups based on having and not having a GDM diagnosis and also as being nulliparous or primiparous. 7 common ML algorithms were employed to construct the predictive model.

RESULTS

97 mothers were included in the study. 19 and 26 nulliparous were with and without GDM, respectively. 29 and 23 primiparous were with and without GDM, respectively. It was found that the greatest feature importance variables were the venous plasma glucose level, maternal BMI, and the family history of diabetes mellitus. The eXtreme Gradient Boosting (XGB) Classifier had the best predictive value for the two models with the accuracy of 66.7% and 72.7%, respectively.

DISCUSSION

The XGB classifier model constructed with maternal sociodemographic findings and the obstetric history could be used as an early prediction model for GDM especially in low-income countries.

The role of machine learning algorithms in detection of gestational diabetes; a narrative review of current evidence

Gestational Diabetes Mellitus (GDM) poses significant health risks to mothers and infants. Early prediction and effective management are crucial to improving outcomes. Machine learning techniques have emerged as powerful tools for GDM prediction. This review compiles and analyses the available studies to highlight key findings and trends in the application of machine learning for GDM prediction. A comprehensive search of relevant studies published between 2000 and September 2023 was conducted. Fourteen studies were selected based on their focus on machine learning for GDM prediction. These studies were subjected to rigorous analysis to identify common themes and trends. The review revealed several key themes. Models capable of predicting GDM risk during the early stages of pregnancy were identified from the studies reviewed. Several studies underscored the necessity of tailoring predictive models to specific populations and demographic groups. These findings highlighted the limitations of uniform guidelines for diverse populations. Moreover, studies emphasised the value of integrating clinical data into GDM prediction models. This integration improved the treatment and care delivery for individuals diagnosed with GDM. While different machine learning models showed promise, selecting and weighing variables remains complex. The reviewed studies offer valuable insights into the complexities and potential solutions in GDM prediction using machine learning. The pursuit of accurate, early prediction models, the consideration of diverse populations, clinical data, and emerging data sources underscore the commitment of researchers to improve healthcare outcomes for pregnant individuals at risk of GDM.

Gestational Diabetes Mellitus Subtypes Classified by Oral Glucose Tolerance Test and Maternal and Perinatal Outcomes: Results of a Mexican Multicenter Prospective Cohort Study "Cuido Mi Embarazo"

Purpose

This study explores the impact of gestational diabetes mellitus (GDM) subtypes classified by oral glucose tolerance test (OGTT) values on maternal and perinatal outcomes.

Patients and Methods

This multicenter prospective cohort study (May 2019-December 2022) included participants from the Mexican multicenter cohort study Cuido mi Embarazo (CME). Women were classified into four groups per 75-g 2-h OGTT: 1) normal glucose tolerance (normal OGTT), 2) GDM-Sensitivity (isolated abnormal fasting or abnormal fasting in combination with 1-h or 2-h abnormal results), 3) GDM-Secretion (isolated abnormal values at 1-h or 2-h or their combination), and 4) GDM-Mixed (three abnormal values). Cesarean delivery, neonates large for gestational age (LGA), and pre-term birth rates were among the outcomes compared. Between-group comparisons were analyzed using either the t-test, chi-square test, or Fisher's exact test.

Results

Of 2,056 Mexican pregnant women in the CME cohort, 294 (14.3%) had GDM; 53.7%, 34.4%, and 11.9% were classified as GDM-Sensitivity, GDM-Secretion, and GDM-Mixed subtypes, respectively. Women with GDM were older (p = 0.0001) and more often multiparous (p = 0.119) vs without GDM. Cesarean delivery (63.3%; p = 0.02) and neonate LGA (10.7%; p = 0.078) were higher in the GDM-Mixed group than the overall GDM group (55.6% and 8.4%, respectively). Pre-term birth was more common in the GDM-Sensitivity group than in the overall GDM group (10.2% vs 8.5%, respectively; p=0.022). At 6 months postpartum, prediabetes was more frequent in the GDM-Sensitivity group than in the overall GDM group (31.6% vs 25.5%). Type 2 diabetes was more common in the GDM-Mixed group than in the overall GDM group (10.0% vs 3.3%).

Conclusion

GDM subtypes effectively stratified maternal and perinatal risks. GDM-Mixed subtype increased the risk of cesarean delivery, LGA, and type 2 diabetes postpartum. GDM subtypes may help personalize clinical interventions and optimize maternal and perinatal outcomes.

Review of Genetic and Artificial Intelligence approaches to improving Gestational Diabetes Mellitus Screening and Diagnosis in sub-Saharan Africa

Background: Adverse outcomes from gestational diabetes mellitus (GDM) in the mother and newborn are well established. Genetic variants may predict GDM and Artificial Intelligence (AI) can potentially assist with improved screening and early identification in lower resource settings. There is limited information on genetic variants associated with GDM in sub-Saharan Africa and the implementation of AI in GDM screening in sub-Saharan Africa is largely unknown. Methods: We reviewed the literature on what is known about genetic predictors of GDM in sub-Saharan African women. We searched PubMed and Google Scholar for single nucleotide polymorphisms (SNPs) involved in GDM predisposition in a sub-Saharan African population. We report on barriers that limit the implementation of AI that could assist with GDM screening and offer possible solutions. Results: In a Black South African cohort, the minor allele of the SNP rs4581569 existing in the PDX1 gene was significantly associated with GDM. We were not able to find any published literature on the implementation of AI to identify women at risk of GDM before second trimester of pregnancy in sub-Saharan Africa. Barriers to successful integration of AI into healthcare systems are broad but solutions exist. Conclusions: More research is needed to identify SNPs associated with GDM in sub-Saharan Africa. The implementation of AI and its applications in the field of healthcare in the sub-Saharan African region is a significant opportunity to positively impact early identification of GDM.