Prediction Of Gestational Diabetes Mellitus Research Articles

Gestational diabetes mellitus (GDM): diagnosis using biochemical parameters and anthropometric measurements during the first trimester in the Indian population

Abstract Objectives The objective of the study was to use anthropometric measurements (age, BMI, and subcutaneous fat) in conjunction with biochemical parameters (sex hormone-binding globulin (SHBG), homeostasis model assessment-insulin resistance (HOMA-IR), fasting glucose, serum insulin, and total cholesterol) to predict the probability of gestational diabetes mellitus (GDM) in the first trimester. Methods The study enrolled 48 pregnant women with GDM and 64 high-risk pregnant women without GDM. During the first-trimester examination, maternal blood samples were collected to measure SHBG, fasting blood glucose, serum insulin, and total cholesterol levels. Regression model analysis was used to examine the variables that showed statistically significant differences between the groups and were independent predictors of GDM. Receiver operating characteristic (ROC) curve analysis was employed to determine the risk of developing GDM based on cut-off values. Results The levels of SHBG, HOMA-IR, serum insulin, fasting glucose, and total cholesterol were identified as significant independent markers for predicting GDM. Meanwhile, age, body mass index, and subcutaneous fat values were found to be non-independent predictors of GDM. The areas under the ROC curve were calculated to determine the predictive accuracy of total cholesterol, HOMA-IR, SHBG, and subcutaneous fat for developing into GDM, and were 0.869, 0.977, 0.868, and 0.822 respectively. The sensitivities for a false positive rate of 5 % for predicting GDM were 68.7 , 91.67, 91.7, and 97.9 % for total cholesterol, HOMA-IR, SHBG, and subcutaneous fat, respectively. Conclusions The independent predictors for the subsequent development of GDM in high-risk pregnancies are HOMA-IR, SHBG, Total cholesterol, and subcutaneous fat (SC) levels. These parameters can be used to create a regression model to predict the occurrence of GDM.

Predictive Utility of Biochemical Markers for the Diagnosis and Prognosis of Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is a common complication during pregnancy with an increasing prevalence worldwide. Early prediction of GDM and its associated adverse outcomes is crucial for timely intervention and improved maternal and fetal health. The objective of this review is to provide a comprehensive summary of contemporary evidence on biomarkers, focusing on their potential to predict the development of GDM and serve as predictors of maternal, fetal, and neonatal outcomes in women with GDM. A literature search was conducted in the PubMed database using relevant terms. Original research articles published in English between 1 January 2015, and 30 June 2024, were included. A two-stage screening process was employed to identify studies on biomarkers for GDM diagnosis and prognosis and to evaluate the evidence for each biomarker’s diagnostic performance and its potential prognostic correlation with GDM. Various biochemical markers, including adipokines, inflammatory markers, insulin resistance markers, glycemic markers, lipid profile markers, placenta-derived markers, and other related markers, have shown promise in identifying women at risk of developing GDM and predicting adverse pregnancy outcomes. Several promising markers with high predictive performance were identified. However, no single biomarker has demonstrated sufficient accuracy to replace the current diagnostic criteria for GDM. The complexity of multiple pathways in GDM pathogenesis highlights the need for a multi-marker approach to improve risk stratification and guide personalized management strategies. While significant progress has been made in GDM biomarker research, further studies are required to refine and validate these markers for clinical use and to develop a comprehensive, evidence-based approach to GDM prediction and management that can improve maternal and child health outcomes.

Maternal Fasting Plasma Glucose, Age and Body Mass Index as Prediction of Gestational Diabetes Mellitus in Iran

Background: Gestational diabetes mellitus (GDM) is a common complication of pregnancy that can lead to adverse outcomes. In this retrospective cohort study, maternal fasting blood sugar (FBS) in the second trimester of pregnancy, body mass index (BMI), and age were assessed as potential screening indicators of later GDM. Methods: The study population included information on 4007 Iranian pregnant women documented by the Integrated Health Record System (SIB) record system (2019-2020). Results: In the adjusted analysis, FBS maintained a significant relationship with GDM (P ≤0.001). In the simple ROC analysis, the AUC (SE) of FBS for the prediction of GDM was 0.905(0.09), and considering the cut-off point as 85.95, sensitivity (Se) and specificity (Sp) were equal to 0.81 and 0.71, respectively, but by stillbirth, abortion, prematurity, neonatal weight, height, and head circumference not obtained acceptable AUC (≥.60) for detection of FBS cut-off point. The cut-off point of FBS in the presence of maternal age (AUC>0.6) and BMI (AUC>0.6) by GDM was 83.75(Se= 86.4%, Sp= 80.0%). Conclusion: Based on the evidence presented, maternal weight and BMI are important in predicting hyperglycemia leading to GDM. It is necessary to conduct more precise national studies to standardize the FBS cut-off point by controlling age and BMI variables.

Combined detection of serum adiponectin and pregnancy-associated plasma protein A for early prediction of gestational diabetes mellitus.

Early diagnosis of gestational diabetes mellitus (GDM) reduces the risk of adverse perinatal and maternal outcomes. At present, the value of serum adiponectin (ADP) and pregnancy-associated plasma protein A (PAPP-A) in clinical practice for the diagnosis of GDM in early pregnancy is unclear. To investigate the predictive value of serum ADP and PAPP-A in GDM. The electronic medical record data of all pregnant women from Zhongshan People's Hospital from 2018 to 2021 were retrospectively collected and divided into GDM group and control group according to whether GDM occurred. ADP and PAPP-A levels of the 2 groups were detected in early pregnancy, and the related factors of GDM were analyzed by binary logistic regression analysis. Receiver operating characteristic (ROC) curves of ADP and PAPP-A in predicting GDM in the early pregnancy were plotted and their clinical predictive value was analyzed. The significance level for all statistical tests is 0.05. Compared with the non-GDM group, the ADP of the GDM group was significantly lower than that of the non-GDM group [(8.19 ± 2.24) vs. (10.04 ± 2.73)]mg/L, the difference between groups was statistically significant (P < .05), and the multiple of median (MoM) of PAPP-A was significantly lower than that of the non-GDM group (1.13 ± 0.52) versus (1.45 ± 0.61) (P < .05). Binary logistic regression analysis showed that elevated serum ADP and PAPP-A levels were negatively correlated with the subsequent development of GDM [odds ratio (OR) 95% confidence interval (95% CI)] was 0.626 (0.536, 0.816), 0.934 (0.908, 0.961), respectively, P < .05.ROC curve analysis showed that the sensitivity and specificity of ADP and PAPP-A in predicting gestational diabetes were79.1% and 58.6%, respectively, 92.7% and 73.1%, and respectively. The area under curve (AUC) is 0.755 for ADP and 0.770 for PAPP-A. The AUC of the combined detection was 0.867, both of which were higher than that of single index diagnosis, and the sensitivity and specificity of the combined detection were 0.958 and 0.853, respectively. In summary, PAPP-A and ADP levels are independent related factors affecting the occurrence of GDM. The combined detection of PAPP-A and ADP should be utilized in diagnosing GDM to improve pregnancy outcomes for pregnant women.

The role of first-trimester systemic immune-inflammation index for the prediction of gestational diabetes mellitus.

The aim of this study was to investigate the role of systemic immune-inflammation index, neutrophil-lymphocyte ratio, lymphocyte-monocyte ratio, and platelet-lymphocyte ratios calculated in the first trimester as inflammatory markers in predicting gestational diabetes mellitus diagnosis. This study was conducted retrospectively at a tertiary center between January 2020 and June 2023. A total of 111 pregnant women with gestational diabetes and 378 pregnant women in the control group were included in the study. Systemic immune-inflammation index, neutrophil-lymphocyte ratio, lymphocyte-monocyte ratio, and platelet-lymphocyte ratios values were compared between the gestational diabetes mellitus group patients and the healthy group. Receiver operating characteristic analysis curve was used for predicting gestational diabetes mellitus using systemic immune-inflammation index and lymphocyte-monocyte ratio. In pregnant women in the first trimester, systemic immune-inflammation index and lymphocyte-monocyte ratio values based on routine complete blood count parameters were found to be statistically significantly higher in gestational diabetes mellitus patients compared to healthy patients, while neutrophil-lymphocyte ratio and platelet-lymphocyte ratios values were found to be similar (p=0.033, p=0.005, p=0.211, and p=0.989). For predicting gestational diabetes mellitus, a cut-off value of 655.75 for systemic immune-inflammation index resulted in 80.2% sensitivity and 34.4% specificity, and a cut-off value of 3.62 for lymphocyte-monocyte ratio resulted in 56.8% sensitivity and 63.2% specificity, indicating good discriminatory ability. We believe that systemic immune-inflammation index and lymphocyte-monocyte ratio values measured in the first-trimester complete blood count parameters are effective in predicting gestational diabetes mellitus but are not effective in determining insulin requirement.

Application of machine learning algorithm incorporating dietary intake in prediction of gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) significantly affects pregnancy outcomes. Therefore, it is crucial to develop prediction models since they can guide timely interventions to reduce the incidence of GDM and its associated adverse effects. A total of 554 pregnant women were selected and their sociodemographic characteristics, clinical data and dietary data were collected. Dietary data was investigated by a validated semi-quantitative food frequency questionnaire (FFQ). We applied random forest mean decrease impurity for feature selection and the models are built using Logistic Regression, XGBoost, and LightGBM algorithms. The prediction performance of different models was compared by Accuracy, Sensitivity, Specificity, Area Under Curve (AUC) and Hosmer-Lemeshow test. Blood glucose, age, pre-pregnancy body mass index (BMI), triglycerides and high-density lipoprotein cholesterol (HDL) were the top five features according to the feature selection. Among the three algorithms, XGBoost performed best with an AUC of 0.788, LightGBM came second (AUC = 0.749), and Logistic Regression performed the worst (AUC = 0.712). In addition, XGBoost and LightGBM both achieved a fairly good performance when dietary information was included, surpassing their performance on the non-dietary dataset (0.788 vs. 0.718 in XGBoost; 0.749 vs. 0.726 in LightGBM). XGBoost and LightGBM algorithms outperform Logistic Regression in predicting GDM among the Chinese pregnant women. In addition, dietary data may have a positive effect on improving model performance, which deserves more in-depth investigation with larger sample size.

Seroconversion Toxoplasmosis as a predictor for gestational diabetes mellitus among pregnant ladies in Iraq

Background. Toxoplasma gondii (T.g.) can reach many host organs including the pancreas and pancreatic tissue may be directly attacked and compromised by the organisms. Aim. This study aimed to evaluate the risk of toxoplasmosis infestation as a predictor of gestational diabetes mellitus (GDM) among pregnant ladies in Thi-Qar, Southern Iraq. Methodology. A case-control study was conducted on 151 pregnant ladies classified (61) with GDM and (89) healthy pregnant ladies attending a tertiary Endocrine Center. All patient data were gathered from direct interviews and the digital records of the tertiary center, which used an internal network system and Microsoft Access program. Each pregnant woman was diagnosed with new GDM according to the American Diabetes Association (ADA) definition criteria. Results. The mean ages of the whole participants were 28 ±6 years old and more than half of them were in the second decade of life, their mean weight was 75.38±13.29) kg and their body mass index was 29.82 ± 5.23 (Kg/m2). Family history of DM, history of toxoplasmosis infestation, and positive serological test of IgG for toxoplasmosis was significantly predominant among women with GDM rather than those without (p-value= 0.007, 0.002, 0.004 respectively). Conclusions. The second decade of age is the independent risk factor for developing new GDM, while toxoplasmosis infestation and positive IGG were additive-dependent risk factors for developing new GDM in collaboration with other patients' profiles. Pregnant women with GDM were significantly older, heavier in body weight, and obese than those without GDM making them at higher risk factors for getting hyperglycemia.

Prediction of gestational diabetes mellitus by multiple biomarkers at early gestation

BackgroundIt remains unclear which early gestational biomarkers can be used in predicting later development of gestational diabetes mellitus (GDM). We sought to identify the optimal combination of early gestational biomarkers in predicting GDM in machine learning (ML) models.MethodsThis was a nested case-control study including 100 pairs of GDM and euglycemic (control) pregnancies in the Early Life Plan cohort in Shanghai, China. High sensitivity C reactive protein, sex hormone binding globulin, insulin-like growth factor I, IGF binding protein 2 (IGFBP-2), total and high molecular weight adiponectin and glycosylated fibronectin concentrations were measured in serum samples at 11–14 weeks of gestation. Routine first-trimester blood test biomarkers included fasting plasma glucose (FPG), serum lipids and thyroid hormones. Five ML models [stepwise logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, support vector machine and k-nearest neighbor] were employed to predict GDM. The study subjects were randomly split into two sets for model development (training set, n = 70 GDM/control pairs) and validation (testing set: n = 30 GDM/control pairs). Model performance was evaluated by the area under the curve (AUC) in receiver operating characteristics.ResultsFPG and IGFBP-2 were consistently selected as predictors of GDM in all ML models. The random forest model including FPG and IGFBP-2 performed the best (AUC 0.80, accuracy 0.72, sensitivity 0.87, specificity 0.57). Adding more predictors did not improve the discriminant power.ConclusionThe combination of FPG and IGFBP-2 at early gestation (11–14 weeks) could predict later development of GDM with moderate discriminant power. Further validation studies are warranted to assess the utility of this simple combination model in other independent cohorts.

Clinical Risk Factors and First Gestational 75 g OGTT May Predict Recurrent and New-Onset Gestational Diabetes in Multiparous Women.

Background: Women who experience gestational diabetes mellitus (GDM) during their first pregnancy are at a high risk of developing GDM again in subsequent pregnancies. Even mothers with no previous history of GDM may develop the condition in a new pregnancy. Methods: In this retrospective cross-sectional observational study, 759 multiparous women tested for GDM in two successive pregnancies using the 75 g OGTT (IADPSG criteria) were enrolled. The OGTT was performed at 24-28 weeks' gestation or earlier if there was a history of GDM. Participants were categorized into four groups: women with normal glucose tolerance (NGT) in both pregnancies (n = 493), women with a first occurrence of GDM in their second pregnancy (n = 74), women with non-recurrent GDM in their second pregnancy (n = 92), and women with recurrent GDM in their second pregnancy (n = 100). Results: Intergroup comparisons revealed clinical predictors of GDM in the first pregnancy (family history of type 2 diabetes, PCOS, advanced maternal age, pregravid obesity) and in the second pregnancy (interpregnancy BMI gain), as well as predictors of recurrent GDM (pregravid obesity, PCOS). A positive correlation was observed between the OGTT glucose levels of consecutive pregnancies. Adjusted logistic regression indicated that a higher 1-h post-load glucose level (≥130 mg/dL) during the first pregnancy significantly increased the likelihood of new-onset GDM in the second pregnancy (OR: 2.496), whereas a higher 2-h post-load glucose level (≥153 mg/dL) at the first diagnostic OGTT increased the likelihood of recurrent GDM (OR: 2.214). Conclusions: Clinical risk factors and post-load glucose levels during the first gestational 75 g OGTT can help predict new-onset or recurrent GDM in multiparous women.

The early prediction of gestational diabetes mellitus by machine learning models

BackgroundWe 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.MethodsWe 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.Results97 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.DiscussionThe 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.

Correlation Between the First Trimester Two-Hour Postprandial Blood Glucose Greater Than 110 mg/dL for the Prediction of Gestational Diabetes Mellitus.

To determine the correlation between first-trimester two-hour postprandial blood glucose (PPBG) > 110 mg/dL for predicting gestational diabetes mellitus (GDM). This prospective cohort study enrolled 200 women between 8 and 10 weeks of gestation from February 2022 to February 2024. All recruited pregnant women underwent testing for two-hour PPBS at 8-10 weeks and were followed up till delivery. GDM screening was done during 14-16, 24-28, and 32-34 weeks of gestation. Amongst women having PPBS > 110 mg/dL, 95.9% developed GDM, while in the group with PPBS < 110 mg/dL, only 4% developed GDM. In the PPBS > 110 mg/dL group, a significantly higher number of women were in the older age group (p < 0.049), had a higher BMI (p < 0.001), a family history of diabetes (p < 0.001), and previous history of abortion (p < 0.001). Women with PPBS > 110 mg/dL had significantly higher rates of cesarean section (p < 0.01), preterm delivery (p < 0.001), and macrosomia (p < 0.001). A positive correlation (r = 0.677; p < 0.001) was observed between first trimester two-hour PPBS and cord blood glucose levels. Similarly, a positive correlation (r = 0.465; p < 0.001) was present between insulin levels measured during the first trimester with cord blood insulin. The area under the curve (AUC) for PPBS was 0.969 (p < 0.001) with 95% CI: 0.933-0.988. PPBS > 110 mg/dL has a sensitivity of 95.9%, specificity of 95.6%, positive predictive value (PPV) of 95.9%, negative predictive value (NPV) of 95.7%, and diagnostic accuracy of 95.77% to predict GDM. PPBS > 110 mg/dL at two hours exhibits high levels of diagnostic accuracy for the prediction of GDM and is associated with adverse fetomaternal outcomes. PPBG is a superior, physiologic, and low-cost option compared to HbA1c for early prediction of GDM and can also be performed as a simple point-of-care test with a glucometer at home or in the periphery by healthcare workers (HCW) and in wellness centers.

Functional genetic variants and susceptibility and prediction of gestational diabetes mellitus

The aetiological mechanism of gestational diabetes mellitus (GDM) has still not been fully understood. The aim of this study was to explore the associations between functional genetic variants screened from a genome-wide association study (GWAS) and GDM risk among 554 GDM patients and 641 healthy controls in China. Functional analysis of single nucleotide polymorphisms (SNPs) positively associated with GDM was further performed. Univariate regression and multivariate logistic regression analyses were used to screen clinical risk factors, and a predictive nomogram model was established. After adjusting for age and prepregnancy BMI, rs9283638 was significantly associated with GDM susceptibility (P < 0.05). Moreover, an obvious interaction between rs9283638 and clinical variables was detected (Pinteraction < 0.05). Functional analysis confirmed that rs9283638 can regulate not only target gene transcription factor binding, but it also regulates the mRNA levels of SAMD7 (P < 0.05). The nomogram model constructed with the factors of age, FPG, 1hPG, 2hPG, HbA1c, TG and rs9283638 revealed an area under the ROC curve of 0.920 (95% CI 0.902–0.939). Decision curve analysis (DCA) suggested that the model had greater net clinical benefit. Conclusively, genetic variants can alter women's susceptibility to GDM by affecting the transcription of target genes. The predictive nomogram model constructed based on genetic and clinical variables can effectively distinguish individuals with different GDM risk factors.

Longitudinal integrative cell-free DNA analysis in gestational diabetes mellitus

Gestational diabetes mellitus (GDM) presents varied manifestations throughout pregnancy and poses a complex clinical challenge. High-depth cell-free DNA (cfDNA) sequencing analysis holds promise in advancing our understanding of GDM pathogenesis and prediction. In 299 women with GDM and 299 matched healthy pregnant women, distinct cfDNA fragment characteristics associated with GDM are identified throughout pregnancy. Integrating cfDNA profiles with lipidomic and single-cell transcriptomic data elucidates functional changes linked to altered lipid metabolism processes in GDM. Transcription start site (TSS) scores in 50 feature genes are used as the cfDNA signature to distinguish GDM cases from controls effectively. Notably, differential coverage of the islet acinar marker gene PRSS1 emerges as a valuable biomarker for GDM. A specialized neural network model is developed, predicting GDM occurrence and validated across two independent cohorts. This research underscores the high-depth cfDNA early prediction and characterization of GDM, offering insights into its molecular underpinnings and potential clinical applications.

Continuous Glucose Monitoring Prediction of Gestational Diabetes Mellitus and Perinatal Complications.

Objective: To assess the performance of continuous glucose monitoring (CGM)-measured glycemic metrics in predicting development of gestational diabetes mellitus (GDM) and select perinatal complications. Research Methods: In a prospective observational study, CGM data were collected from 760 pregnant females throughout gestation after study enrollment. GDM was diagnosed using the oral glucose tolerance test (OGTT) at 24-34 weeks of gestation. Predictive models were built using logistic and elastic net regression. Predictive performance was assessed by the area under the receiver-operating characteristic (AUROC) curve. Results: The AUROCs of using second trimester percent time >140 mg/dL (TA140) and week 13-14 TA140 in predicting GDM were 0.81 and 0.74, respectively. The AUROCs for predicting large-for-gestational-age (LGA) births and hypertensive disorders of pregnancy (HDP) using second trimester TA140 were both 0.58. When matching the specificity of OGTT, a model using TA140 in weeks 13-14 achieved similar sensitivity to OGTT in predicting HDP (13% vs. 10%, respectively) and LGA (6% for both methods). Elastic net also demonstrated similar AUROC and diagnostic performance with no meaningful improvement by using multiple predictors. Conclusion: CGM-measured hyperglycemic metrics such as TA140 predicted GDM with high AUROCs as early as 13-14 weeks of gestation. These metrics were also similar statistically to the OGTT at 24-34 weeks in predicting perinatal complications, although sensitivity was low for both. CGM could potentially be used as an early screening tool for elevated hyperglycemia during gestation, which could be used in addition to or instead of the OGTT.

Development and validation of a Non-INvaSive Pregnancy RIsk ScoRE (INSPIRE) for the screening of high-risk pregnant women for gestational diabetes mellitus in Pakistan

IntroductionThe prevalence of gestational diabetes mellitus (GDM) is on the rise in low-income and middle-income countries, such as Pakistan. Therefore, the development of a risk score that is simple, affordable...

Association between atherogenic index of plasma and gestational diabetes mellitus: a prospective cohort study based on the Korean population

BackgroundAtherogenic index of plasma (AIP) is a non-traditional lipid parameter that can reflect the burden of atherosclerosis. A lipid profile resembling atherosclerosis emerged during pregnancy. Although lipid metabolism is pivotal in diabetes pathogenesis, there is no evidence linking AIP to gestational diabetes mellitus (GDM). Therefore, our objective was to explore the relationship between AIP and GDM and assess AIP's predictive capability for GDM.MethodsThis was a secondary analysis based on data from a prospective cohort study in Korea involving 585 single pregnant women. AIP was calculated as log10 (TG/HDL). We examined the relationship between AIP and GDM using logistic regression models, curve fitting, sensitivity analyses, and subgroup analyses. Receiver operating characteristic (ROC) analysis was also used to determine the ability of AIP to predict GDM.ResultsThe average age of the participants was 32.06 ± 3.76 years. The AIP was 0.24 ± 0.20 on average. The GDM incidence was 6.15%. After adjustment for potentially confounding variables, AIP showed a positive linear relationship with GDM (P for non-linearity: 0.801, OR 1.58, 95% CI 1.27–1.97). The robustness of the connection between AIP and GDM was demonstrated by sensitivity analyses and subgroup analyses. An area under the ROC curve of 0.7879 (95% CI 0.7087–0.8671) indicates that AIP is an excellent predictor of GDM. With a specificity of 75.41% and sensitivity of 72.22%, the ideal AIP cut-off value for identifying GDM was 0.3557.ConclusionsThis study revealed that the AIP at 10–14 weeks of gestation was independently and positively correlated with GDM risk. AIP could serve as an early screening and monitoring tool for pregnant women at high risk of GDM, thereby optimizing GDM prevention strategies.Trial registrationClinicalTrials.gov registration no. NCT02276144.Graphical abstract

Association of Clinical Factors with the Development of GDM in the Hoosier Mom’s Cohort

Introduction: Gestational diabetes mellitus (GDM) develops during pregnancy, raising the risk of adverse perinatal outcomes. The objective of this study was to investigate potential therapeutic targets for prepregnancy and early pregnancy interventions to decrease the impact of GDM on health during and after pregnancy. Methods: The Hoosier Mom’s Cohort was a prospective pregnancy cohort designed to assess for predictors of GDM. Inclusion criteria were gestational age &lt;20 weeks, a singleton pregnancy, and being at least 18 years old. Individuals with a pregestational diabetes diagnosis, HbA1c at screening of ≥6.5, or abnormal 3-hour oral glucose tolerance test before 20 weeks of gestation were excluded. Study visits with survey completion and biospecimen collection occurred, one at enrollment, and one in the early third trimester. All participants were given a Garmin activity tracker to collect objective physical activity and sleep data. Sociodemographic, medical, behavioral, and psychosocial characteristics were obtained, and obstetric outcomes were abstracted after delivery by certified chart abstractors. The primary outcome was development of GDM. Associations between categorical variables and GDM were made with Chi-square testing, and those between continuous variables and GDM were done with t-test. Results: Of 411 participants recruited into the Hoosier Moms Cohort, complete data was available for 391 (95.1%). Participants had a mean age of 30 years, a BMI of 28, and 17% were of Hispanic ethnicity. Study participants self-identified mostly as White (71%) or Black (16%). 66% of participants were nulliparous. Additionally, 54% reported a family history of diabetes, with 4% of patients also reporting a prior history of GDM. A total of 39 participants (10.0%) developed GDM. Participants who developed GDM had significantly higher BMI at study entry (31.6 ±8.5 vs 27.2 ±6.5, p=0.003), lower probability of being nulliparous (15.38% vs 33.61%, p=0.02), higher baseline HbA1c values (5.24 ±0.3 vs 5.07 ±0.3, p&lt;0.001), higher triglycerides levels (156.85 ±62 vs 134.16 ±54.5, p=0.022), and higher blood glucose values (85.90 ±14.4 vs 79.96 ±17.7, p=0.025) at first study visit. Additionally, 28% of participants who developed GDM had a prior history of GDM, compared to 2% of participants who did not (p&lt;0.0001). Those who developed GDM had higher rates of chronic hypertension (12.82% vs 1.96%, p=0.0034) and higher insomnia scores (9.62 ±5.1 vs 7.80 ±4.7, p=0.028). Conclusion/ Implications: Multiple clinical factors were found to be statistically associated with the development of GDM. These characteristics will be utilized in a comprehensive predictive model using machine learning methodologies to guide clinical decisions early in pregnancy.

Longitudinal observation of tRNA-derived fragments profiles in gestational diabetes mellitus and its diagnostic value.

Gestational Diabetes Mellitus (GDM) poses significant risks to maternal and fetal health. Current diagnostic methods based on glucose tolerance tests have limitations for early detection. tRNA-derived small RNAs (tsRNAs) have emerged as potential molecular regulators in various diseases, including metabolic disorders. However, the diagnostic value of tsRNAs in plasma for early GDM or postpartum remains unclear. This longitudinal study profiled the expression of tsRNAs across different gestational stages and postpartum in women with GDM (n = 40) and healthy control gestational women (HCs, n = 40). High-throughput small RNA sequencing identified candidate tsRNAs, which were then validated and correlated with clinical biochemical markers such as fasting blood glucose (FBG), HOMA-IR, and GHbA1c. tRF-1:32-Val-AAC-1-M6, tRF-1:31-Glu-CTC-1-M2, and tRF-1:30-Gly-CCC-1-M4 were consistently upregulated in the GDM group compared to HCs during the second trimester (p < 0.05). Only tRF-1:31-Glu-CTC-1-M2 was highly expressed during the first trimester, and tRF-1:30-Gly-CCC-1-M4 increased during postpartum. tRF-1:31-Glu-CTC-1-M2 showed a significant correlation with FBG levels in the first trimester (R = 0.317, p = 0.047). The expression of tRF-1:30-Gly-CCC-1-M4 was significantly correlated with HOMA-IR (r = 0.65, p < 0.001) and GHBA1c (r = 0.33, p = 0.037) during postpartum. A joint diagnostic model incorporating tsRNAs expression and clinical markers demonstrated enhanced predictive power for GDM (ROC AUC = 0.768). Our results revealed distinct expression patterns of specific tsRNAs in GDM, showcasing their correlation with key metabolic parameters. This underscores their promising role as biomarkers for early prediction and diagnosis of GDM. The integration of tRFs into a composite biomarker panel holds the potential to improve clinical outcomes by enabling personalized risk assessment and targeted interventions.

1968-LB: Early Prediction of Gestational Diabetes Mellitus Using Electronic Health Records and Machine Learning

1968-LB: Early Prediction of Gestational Diabetes Mellitus Using Electronic Health Records and Machine Learning

Effect of interpregnancy interval on the risk of gestational diabetes mellitus during a second pregnancy

BackgroundInterpregnancy interval (IPI) is associated with the risk of GDM in a second pregnancy. However, an optimal IPI is still need to be determined based on the characteristics of the population. This study aimed to analyze the effect of interpregnancy interval (IPI) on the risk of gestational diabetes mellitus (GDM) in the Chinese population.MethodsWe conducted a retrospective cohort study on female participants who had consecutive deliveries at Peking University Shenzhen Hospital from 2013 to 2021. The IPI was categorized into 7 groups and included into the multivariate logistic regression model with other confound factors. Analysis was also stratified based on age of first pregnancy, BMI, and history of GDM. Adjusted OR values (aOR) and 95% confidence intervals (CI) calculated. The regression coefficient of IPI months on GDM prediction risk was analyzed using a linear regression model.ResultsA total of 2,392 participants were enrolled. The IPI of the GDM group was significantly greater than that of the non-GDM group (P < 0.05). Compared with the 18–24 months IPI category, participants with longer IPIs (24–36 months, 36–48 months, 48–60 months, and ≥ 60 months) had a higher risk of GDM (aOR:1.585, 2.381, 2.488, and 2.565; 95% CI: 1.021–2.462, 1.489–3.809, 1.441–4.298, and 1.294–5.087, respectively). For participants aged < 30 years or ≥ 30 years or without GDM history, all longer IPIs (≥ 36 months) were all significantly associated with the GDM risk in the second pregnancy (P < 0.05), while any shorter IPIs (< 18 months) was not significantly associated with GDM risk (P > 0.05). For participants with GDM history, IPI 12–18 months, 24–36 months, 36–48 months, and ≥ 60 months were all significantly associated with the GDM risk (aOR: 2.619, 3.747, 4.356, and 5.373; 95% CI: 1.074–6.386, 1.652–8.499, 1.724–11.005, and 1.078–26.793, respectively), and the slope value of linear regression (0.5161) was significantly higher compared to participants without a history of GDM (0.1891) (F = 284.168, P < 0.001).ConclusionsLong IPI increases the risk of GDM in a second pregnancy, but this risk is independent of maternal age. The risk of developing GDM in a second pregnancy for women with GDM history is more significantly affected by IPI.