A Probabilistic Approach to Extract Qualitative Knowledge for Early Prediction of Gestational Diabetes.
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ABSTRACT:
SUBMITTER: Karanam A
PROVIDER: S-EPMC8274548 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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.
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Project description:Gestational diabetes mellitus (GDM) is conventionally confirmed with oral glucose tolerance test (OGTT) in 24 to 28 weeks of gestation, but it is still uncertain whether it can be predicted with secondary use of electronic health records (EHRs) in early pregnancy. To this purpose, the cost-sensitive hybrid model (CSHM) and five conventional machine learning methods are used to construct the predictive models, capturing the future risks of GDM in the temporally aggregated EHRs. The experimental data sources from a nested case-control study cohort, containing 33,935 gestational women in West China Second Hospital. After data cleaning, 4,378 cases and 50 attributes are stored and collected for the data set. Through selecting the most feasible method, the cost parameter of CSHM is adapted to deal with imbalance of the dataset. In the experiment, 3940 samples are used for training and the rest 438 samples for testing. Although the accuracy of positive samples is barely acceptable (62.16%), the results suggest that the vast majority (98.4%) of those predicted positive instances are real positives. To our knowledge, this is the first study to apply machine learning models with EHRs to predict GDM, which will facilitate personalized medicine in maternal health management in the future.
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Project description:AimsThe heterogeneity in Gestational Diabetes Mellitus (GDM) risk factors among different populations impose challenges in developing a generic prediction model. This study evaluates the predictive ability of existing UK NICE guidelines for assessing GDM risk in Singaporean women, and used machine learning to develop a non-invasive predictive model.MethodsData from 909 pregnancies in Singapore's most deeply phenotyped mother-offspring cohort study, Growing Up in Singapore Towards healthy Outcomes (GUSTO), was used for predictive modeling. We used a CatBoost gradient boosting algorithm, and the Shapley feature attribution framework for model building and interpretation of GDM risk attributes.ResultsUK NICE guidelines showed poor predictability in Singaporean women [AUC:0.60 (95% CI 0.51, 0.70)]. The non-invasive predictive model comprising of 4 non-invasive factors: mean arterial blood pressure in first trimester, age, ethnicity and previous history of GDM, greatly outperformed [AUC:0.82 (95% CI 0.71, 0.93)] the UK NICE guidelines.ConclusionsThe UK NICE guidelines may be insufficient to assess GDM risk in Asian women. Our non-invasive predictive model outperforms the current state-of-the-art machine learning models to predict GDM, is easily accessible and can be an effective approach to minimize the economic burden of universal testing & GDM associated healthcare in Asian populations.
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Project description:All obese women are categorised as being of equally high risk of gestational diabetes (GDM) whereas the majority do not develop the disorder. Lifestyle and pharmacological interventions in unselected obese pregnant women have been unsuccessful in preventing GDM. Our aim was to develop a prediction tool for early identification of obese women at high risk of GDM to facilitate targeted interventions in those most likely to benefit. Clinical and anthropometric data and non-fasting blood samples were obtained at 15+0-18+6 weeks' gestation in 1303 obese pregnant women from UPBEAT, a randomised controlled trial of a behavioural intervention. Twenty one candidate biomarkers associated with insulin resistance, and a targeted nuclear magnetic resonance (NMR) metabolome were measured. Prediction models were constructed using stepwise logistic regression. Twenty six percent of women (n = 337) developed GDM (International Association of Diabetes and Pregnancy Study Groups criteria). A model based on clinical and anthropometric variables (age, previous GDM, family history of type 2 diabetes, systolic blood pressure, sum of skinfold thicknesses, waist:height and neck:thigh ratios) provided an area under the curve of 0.71 (95%CI 0.68-0.74). This increased to 0.77 (95%CI 0.73-0.80) with addition of candidate biomarkers (random glucose, haemoglobin A1c (HbA1c), fructosamine, adiponectin, sex hormone binding globulin, triglycerides), but was not improved by addition of NMR metabolites (0.77; 95%CI 0.74-0.81). Clinically translatable models for GDM prediction including readily measurable variables e.g. mid-arm circumference, age, systolic blood pressure, HbA1c and adiponectin are described. Using a ≥35% risk threshold, all models identified a group of high risk obese women of whom approximately 50% (positive predictive value) later developed GDM, with a negative predictive value of 80%. Tools for early pregnancy identification of obese women at risk of GDM are described which could enable targeted interventions for GDM prevention in women who will benefit the most.
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Project description:BackgroundOur study aimed to reveal the relationship of maternal pentraxin 3 (PTX3)'s serum concentrations in early pregnancy with gestational diabetes mellitus (GDM) and to explore its potential in the prediction of GDM.MethodsTotally 824 pregnant women were enrolled and divided into a GDM group and a normal glucose tolerance (NGT) group, whose maternal fasting serum PTX3 levels, plasma glucose and insulin were collected. The beta cell function index and quantitative insulin sensitivity check index (QUICKI) was calculated and a homeostatic model assessment of insulin resistance (HOMA-IR) was used with SPSS 22 software used for statistical analysis.ResultsOf all subjects, 13.59% developed GDM. Compared to the NGT group, the PTX3 level was increased in the GDM group (1.48 vs. 1.52 ng/mL, P<0.05), and independently associated with the prediction of GDM (4.209, 95% CI, 1.756-10.091) (P=0.001). The area under receiver operating characteristic curve (AUROC) of the combined screening of PTX3 for GDM was incremented to 0.657 by the addition of maternal characteristics, and it reached a maximum of 0.743 in further combination with biochemical markers.ConclusionsSerum PTX3 levels in early pregnancy may provide a useful approach for early prediction of GDM.
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Project description:The knowledge of the tertiary structure of RNA loops is important for understanding their functions. In this work we develop an efficient approach named RNApps, specifically designed for predicting the tertiary structure of RNA loops, including hairpin loops, internal loops, and multi-way junction loops. It includes a probabilistic coarse-grained RNA model, an all-atom statistical energy function, a sequential Monte Carlo growth algorithm, and a simulated annealing procedure. The approach is tested with a dataset including nine RNA loops, a 23S ribosomal RNA, and a large dataset containing 876 RNAs. The performance is evaluated and compared with a homology modeling based predictor and an ab initio predictor. It is found that RNApps has comparable performance with the former one and outdoes the latter in terms of structure predictions. The approach holds great promise for accurate and efficient RNA tertiary structure prediction.
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Project description:BackgroundGestational diabetes mellitus (GDM) can lead to adverse maternal and fetal outcomes, and early prevention is particularly important for their health, but there is no widely accepted approach to predict it in the early pregnancy. The aim of the present study is to build and evaluate predictive models for GDM using routine indexes, including maternal clinical characteristics and laboratory biomarkers, before 16 gestational weeks.MethodsA total of 2895 pregnant women were recruited and maternal clinical characteristics and laboratory biomarkers before 16 weeks of gestation were collected from two hospitals. All participants were randomly stratified into the training cohort and the internal validation cohort by the ratio of 7:3. Using multivariable logistic regression analysis, two nomogram models, including a basic model and an extended model, were built. The discrimination, calibration, and clinical validity were used to evaluate the models in the internal validation cohort.ResultsThe area under the receiver operating characteristic curve of the basic and the extended model was 0.736 and 0.756 in the training cohort, and was 0.736 and 0.763 in the validation cohort, respectively. The calibration curve analysis showed that the predicted values of the two models were not significantly different from the actual observations (p = 0.289 and 0.636 in the training cohort, p = 0.684 and 0.635 in the internal validation cohort, respectively). The decision-curve analysis showed a good clinical application value of the models.ConclusionsThe present study built simple and effective models, indicating that routine clinical and laboratory parameters can be used to predict the risk of GDM in the early pregnancy, and providing a novel reference for studying the prediction of GDM.
| S-EPMC9714187 | biostudies-literature
Project description:AimsTo 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.MethodsWe 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.ResultsDecreased 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.ConclusionsPAPP-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.
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Project description:Background/aimsTo develop an early prediction model for gestational diabetes mellitus (GDM) using machine learning and to evaluate whether the inclusion of nonalcoholic fatty liver disease (NAFLD)-associated variables increases the performance of model.MethodsThis prospective cohort study evaluated pregnant women for NAFLD using ultrasound at 10-14 weeks and screened them for GDM at 24-28 weeks of gestation. The clinical variables before 14 weeks were used to develop prediction models for GDM (setting 1, conventional risk factors; setting 2, addition of new risk factors in recent guidelines; setting 3, addition of routine clinical variables; setting 4, addition of NALFD-associated variables, including the presence of NAFLD and laboratory results; and setting 5, top 11 variables identified from a stepwise variable selection method). The predictive models were constructed using machine learning methods, including logistic regression, random forest, support vector machine, and deep neural networks.ResultsAmong 1,443 women, 86 (6.0%) were diagnosed with GDM. The highest performing prediction model among settings 1-4 was setting 4, which included both clinical and NAFLD-associated variables (area under the receiver operating characteristic curve [AUC] 0.563-0.697 in settings 1-3 vs. 0.740-0.781 in setting 4). Setting 5, with top 11 variables (which included NAFLD and hepatic steatosis index), showed similar predictive power to setting 4 (AUC 0.719-0.819 in setting 5, P=not significant between settings 4 and 5).ConclusionWe developed an early prediction model for GDM using machine learning. The inclusion of NAFLDassociated variables significantly improved the performance of GDM prediction. (ClinicalTrials.gov Identifier: NCT02276144).
| S-EPMC8755469 | biostudies-literature
Project description:AIM:To identify differentially expressed proteins (DEPs) in 1st trimester maternal plasma between pregnant women at risk for gestational diabetes mellitus (GDM) and uncomplicated controls. MATERIALS AND METHODS:First-trimester plasma from five women who developed GDM and five from non-diabetic ones were analyzed using isobaric tag for relative and absolute quantitation - labeled proteomics. Enzyme-linked immunosorbent assay was further applied in an independent cohort of 25 GDM cases and 25 controls for verification. RESULTS:Prenylcysteine oxidase 1 (PCYOX1), beta-ala-his dipeptidase (CNDP1), extracellular matrix protein 1 (ECM1), basement membrane-specific heparan sulfate proteoglycan core protein (HSPG2), thrombospondin-4 (TSP-4) demonstrated significant differences in expression between the two groups (p<0.05). DEPs are mainly associated with complement and coagulation cascades. CONCLUSION:The reported plasma proteomic changes represent potential biomarkers for the early identification of women at risk for GDM. Future studies using larger and more diverse cohorts are necessary to assess the clinical utility of these findings.
| S-EPMC7157887 | biostudies-literature