Prediction of low birth weight with hypoglycemia in glucose tolerance test.
Ontology highlight
ABSTRACT: Objective
Determine the value of the combination of fasting glucose less than the 10th percentile (FG < p10) during 75 gram oral glucose tolerance test (75g OGTT) with maternal characteristics to predict low birth weight (LBW) established by Intergrowth-21st tables.Methods
Prospective cohort study of pregnant women who was underwent 75g OGTT between 24 and 28.6 weeks. The 10th percentile fasting glucose of the population was determined at 65 mg/dL and women with risk factors that could modify fetal weight, including those related to intrauterine growth restriction, were excluded. Two groups were formed: group FG < p10 and group with normal fasting glucose. The main finding was the diagnosis of LBW. The association between FG < p10, maternal characteristics and LBW was established by multivariate logistic regression. The predictive performance of the models constructed was evaluated by receiver operating characteristic (ROC) curve and area under the curve (AUC) analysis.Results
349 women were eligible for study, of whom 66 (18.91%) had FG < p10; neonates in this group had lower birth weights (2947.28 g and 3138.26 g, p = 0.001), higher frequencies of LBW (25% and 6.81%, p < 0.001) and of weights < 2500 g in term births (8.6% and 2.3%, p = 0.034). The basal prediction model consisted of nulliparity by achieving an AUC of 60%, while the addition of FG < p10 resulted in the significant improvement of the previous model (AUC 72%, DeLong: p = 0.005).Conclusions
In pregnant women without factors that could modify fetal weight, the predictive model created by combining FG < p10 during 75g OGTT with nulliparity was significantly associated with increased risk of LBW.Registration
ClinicalTrials.gov: NCT04144595.
SUBMITTER: Hernandez-Castro F
PROVIDER: S-EPMC8102028 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Impaired glucose tolerance (IGT) is diagnosed by a standardized oral glucose tolerance test (OGTT). However, the OGTT is laborious, and when not performed, glucose tolerance cannot be determined from fasting samples retrospectively. We tested if glucose tolerance status is reasonably predictable from a combination of demographic, anthropometric, and laboratory data assessed at one time point in a fasting state.Given a set of 22 variables selected upon clinical feasibility such as sex, age, height, weight, waist circumference, blood pressure, fasting glucose, HbA1c, hemoglobin, mean corpuscular volume, serum potassium, fasting levels of insulin, C-peptide, triglyceride, non-esterified fatty acids (NEFA), proinsulin, prolactin, cholesterol, low-density lipoprotein, HDL, uric acid, liver transaminases, and ferritin, we used supervised machine learning to estimate glucose tolerance status in 2,337 participants of the TUEF study who were recruited before 2012. We tested the performance of 10 different machine learning classifiers on data from 929 participants in the test set who were recruited after 2012. In addition, reproducibility of IGT was analyzed in 78 participants who had 2 repeated OGTTs within 1?year.The most accurate prediction of IGT was reached with the recursive partitioning method (accuracy?=?0.78). For all classifiers, mean accuracy was 0.73?±?0.04. The most important model variable was fasting glucose in all models. Using mean variable importance across all models, fasting glucose was followed by NEFA, triglycerides, HbA1c, and C-peptide. The accuracy of predicting IGT from a previous OGTT was 0.77.Machine learning methods yield moderate accuracy in predicting glucose tolerance from a wide set of clinical and laboratory variables. A substitution of OGTT does not currently seem to be feasible. An important constraint could be the limited reproducibility of glucose tolerance status during a subsequent OGTT.
| S-EPMC5868129 | biostudies-literature
Project description:ObjectivesHypoglycemia is frequent in very low birth weight (VLBW) neonates and compromises their neurological outcome. The aim of this study was to compare real-time continuous glucose monitoring system (RT-CGMS) to standard methods by intermittent capillary blood glucose testing in detecting and managing hypoglycemia.Study designForty-eight VLBW neonates were enrolled in this prospective study. During their 3 first days of life, their glucose level was monitored either by RT-CGMS (CGM-group), or by intermittent capillary glucose testing (IGM-group) associated with a blind-CGMS to detect retrospectively missed hypoglycemia. Outcomes were the number and duration of hypoglycemic (≤ 50 mg/dl) episodes per patient detected by CGMS.ResultsForty-three monitorings were analyzed (IGM n = 21, CGM n = 22), with a median recording time of 72 hours. In the IGM group, blind-CGMS revealed a significantly higher number of hypoglycemia episodes than capillary blood glucose testing (1.2 ± 0.4 vs 0.4 ± 0.2 episode/patient, p<0.01). In the CGM-group, the use of RT-CGMS made it possible (i) to detect the same number of hypoglycemia episodes as blind-CGMS (1.2 ± 0.4 episode/patient), (ii) to adapt the glucose supply in neonates with hypoglycemia (increased supply during days 1 and 2), and (iii) to significantly reduce the duration of hypoglycemia episodes per patient (CGM 44[10-140] min versus IGM 95[15-520] min, p<0.05). Furthermore, it reduced the number of blood samples (CGM 16.9 ± 1.0 vs IGM 21.9 ± 1.0 blood sample/patient, p<0.001).ConclusionRT-CGMS played a beneficial role in managing hypoglycemia in VLBW neonates by adjusting the carbohydrate supply to the individual needs and by reducing the duration of hypoglycemia episodes. The clinical significance of the biological differences observed in our study need to be explored.
| S-EPMC4295867 | biostudies-literature
Project description:Low Birth weight (LBW) infants pose a serious public health concern worldwide in both the short and long term for infants and their mothers. Infant weight prediction prior to birth can help to identify risk factors and reduce the risk of infant morbidity and mortality. Although many Machine Learning (ML) algorithms have been proposed for LBW prediction using maternal features and produced considerable model performance, their performance needs to be improved so that they can be adapted in real-world clinical settings. Existing algorithms used for LBW classification often fail to capture structural information from the tabular dataset of patients with different complications. Therefore, to improve the LBW classification performance, we propose a solution by transforming the tabular data into a knowledge graph with the aim that patients from the same class (normal or LBW) exhibit similar patterns in the graphs. To achieve this, several features related to each node are extracted such as node embedding using node2vec algorithm, node degree, node similarity, nearest neighbors, etc. Our method is evaluated on a real-life dataset obtained from a large cohort study in the United Arab Emirates which contains data from 3453 patients. Multiple experiments were performed using the seven most commonly used ML models on the original dataset, graph features, and a combination of features, respectively. Experimental results show that our proposed method achieved the best performance with an area under the curve of 0.834 which is over 6% improvement compared to using the original risk factors without transforming them into knowledge graphs. Furthermore, we provide the clinical relevance of the proposed model that are important for the model to be adapted in clinical settings.
| S-EPMC9859143 | biostudies-literature
Project description:OBJECTIVE:Predictive models for preterm infant mortality have been developed internationally, albeit not valid for all populations. This study aimed to develop and validate different mortality predictive models, using Spanish data, to be applicable to centers with similar morbidity and mortality. METHODS:Infants born alive, admitted to NICU (BW<1500 g or GA<30 w), and registered in the SEN1500 database, were included. There were two time periods; development of the predictive models (2009-2012) and validation (2013-2015). Three models were produced; prenatal (1), first 24 hours of life (2), and whilst admitted (3). For the statistical analysis, hospital mortality was the dependent variable. Significant variables were used in multivariable regression models. Specificity, sensitivity, accuracy, and area under the curve (AUC), for all models, were calculated. RESULTS:Out of 14953 included newborns, 2015 died; 373 (18.5%) in their first 24 hours, 1315 (65.3%) during the first month, and 327 (16.2%) thereafter, before discharge. In the development stage, mortality prediction AUC was 0.834 (95% CI: 0.822-0.846) (p<0.001) in model 1 and 0.872 (95% CI: 0.860-0.884) (p<0.001) in model 2. Model 3's AUC was 0.989 (95% CI: 0.983-0.996) (p<0.001) and 0.942 (95% CI: 0.929-0.956) (p<0.001) during the 0-30 and >30 days of life, respectively. During validation, models 1 and 2 showed moderate concordance, whilst that of model 3 was good. CONCLUSION:Using dynamic models to predict individual mortality can improve outcome estimations. Development of models in the prenatal period, first 24 hours, and during hospital admission, cover key stages of mortality prediction in preterm infants.
| S-EPMC7347394 | biostudies-literature
Project description:BACKGROUND:Higher birth weight is an important adverse outcome associated with hyperglycemia in pregnancy. Recent studies suggest that phthalate exposure is associated with elevated glucose levels in pregnant women, with implications for higher birth weight in the offspring. No study to date has investigated the association between prenatal phthalate exposure on infant high birth weight accounting for the range of pregnancy glucose levels. METHODS:A total of 350 women participating in an ongoing pregnancy cohort had data available on urinary phthalate metabolite concentrations at up to four time points across pregnancy. Urinary phthalate metabolites were averaged across pregnancy and log-transformed, specific gravity-adjusted and analyzed in quartiles. Birth weight was examined continuously (in grams), as well as dichotomized as large for gestational age (>90th percentile). Glucose levels were assessed based on Results from 50-g glucose challenge tests as a part of screening for gestational diabetes conducted at 24-28 weeks gestation, and grouped into 3 categories <120?mg/dL, 120-<140?mg/dL and ?140?mg/dL. Multivariable linear regression was performed, adjusting for potential confounders in the overall population and stratified by pregnancy glucose levels. RESULTS:Approximately 20% of infants born to women with glucose levels ?140?mg/dL were large for gestational age. Average mono-ethyl phthalate (MEP) concentrations were higher among women who had glucose levels ?140?mg/dL (geometric mean 140.9??g/L; 95% CI: 91.6-216.8); however, higher MEP concentrations were not associated with higher birth weight. When stratified by maternal glucose levels, there was a suggestive association between higher concentrations of mono-(3-carboxypropyl) phthalate (MCPP) and higher birth weight among women with glucose levels ?140?mg/dL (adj. birth weight: 569.2?g; 95% CI: 14.1, 1178.2). CONCLUSIONS:Higher urinary phthalate metabolite concentrations were not significantly associated with higher birth weight. Counter to our hypothesis, women with higher glucose levels and higher urinary phthalate metabolites did not deliver babies with higher birth weight.
| S-EPMC7193985 | biostudies-literature
Project description:Exercise is a well-established tool for cardiovascular risk reduction. Particularly eccentric exercise, which essentially means walking downwards could favour more people becoming physically active. With the present controlled study, we tested the hypothesis that eccentric exercise can improve insulin sensitivity, triglyceride handling, body mass index, glucose tolerance and inflammation. We allocated 127 healthy sedentary individuals to one of two groups: (i) an active group of 102 individuals walking downwards a predefined route three to five times per week over two months, covering a difference in altitude of 540 m; for the upward route a cable car was used, for which adherence was recorded electronically and (ii) a matched control group of 25 individuals who stayed sedentary. Fasting and postprandial metabolic profiles were obtained at baseline and after two months. Compared to baseline, eccentric exercise significantly improved HOMA insulin resistance (1.94 ± 1.65 vs. 1.71 ± 1.36 (µU-1 ml) × ((mmol/l)-122.5); p = 0.038) and resulted in a decrease in fasting glucose (97 ± 15 vs. 94 ± 9 mg dl-1; p = 0.025) and glucose tolerance (238 ± 50 vs. 217 ± 47 mg dl-1 h-1; p < 0.001), whereas these parameters did not change significantly in the control group. Eccentric exercise significantly improved triglyceride tolerance (1923 ± 1295 vs. 1670 ± 1085 mg dl-1 h-1; p = 0.003), whereas triglyceride tolerance remained unchanged in the control group (p = 0.819). Furthermore, body mass index (27.7 ± 4.3 vs. 27.4 ± 4.3 kg m-2; p = 0.003) and C-reactive protein (0.27 ± 0.42 vs. 0.23 ± 0.25 mg dl-1; p = 0.031) were significantly lowered in the eccentric exercise group but not in the control group. Downhill walking, a type of exercise is a promising unusual exercise modality with favorable effects on body mass index, insulin action, on postprandial glucose and triglyceride handling and on C-reactive protein.ClinicalTrials.gov Identifier: NCT00386854.
| S-EPMC8282605 | biostudies-literature
Project description:Thirty-six VLBW infants who underwent F-18 fluorodeoxyglucose (F-18 FDG) brain PET and MRI were prospectively enrolled, while infants with evidence of parenchymal brain injury on MRI were excluded. The regional glucose metabolic ratio and asymmetry index were calculated. The asymmetry index more than 10% (right > left asymmetry) or less than -10% (left > right asymmetry) were defined as abnormal. Regional cerebral glucose metabolism were compared between right and left cerebral hemispheres, and between the following subgroups: multiple gestations, premature rupture of membrane, bronchopulmonary dysplasia, and low-grade intraventricular hemorrhage.In the individual analysis, 21 (58.3%) of 36 VLBW infants exhibited asymmetric cerebral glucose metabolism. Fifteen infants (41.7%) exhibited right > left asymmetry, while six (16.7%) exhibited left > right asymmetry. In the regional analysis, right > left asymmetry was more extensive than left > right asymmetry. The metabolic ratio in the right frontal, temporal, and occipital cortices and right thalamus were significantly higher than those in the corresponding left regions. In the subgroup analyses, the cerebral glucose metabolism in infants with multiple gestations, premature rupture of membrane, bronchopulmonary dysplasia, or low-grade intraventricular hemorrhage were significantly lower than those in infants without these.VLBW infants without structural abnormalities have asymmetry of cerebral glucose metabolism. Decreased cerebral glucose metabolism are noted in infants with neurodevelopmental risk factors. F-18 FDG PET could show microstructural abnormalities not detected by MRI in VLBW infants.
| S-EPMC5667759 | biostudies-literature
Project description:Low birth weight neonates with 2000g or less birth weight constitute about 10% of live births with perinatal mortality as high as 32.4%. Perinatal morbidity is 19.3% with asphyxia neonatorum and neonatal jaundice heading the list. Epidemiological maternal factors include extremes of age and parity, lack of antenatal care, low socioeconomic status, illiteracy and underweight short women. Etiologic factors are obstetric complications, hypertensive disorders, systemic diseases or idiopathic. The scope of preventive measures include improvement of economic status and education about health and safe pregnancy. Proper antenatal care for early detection of high risk cases, adequate and timely management of complications and adequate facilities for neonatal care can reduce the perinatal morbidity and mortality.
| S-EPMC5532125 | biostudies-other
Project description:Sub-optimal fetal development is associated with an increased risk of developing cardiovascular disease, type 2 diabetes (T2D) and adiposity later in life. However, definitions of intrauterine growth restriction (IUGR) and small for gestational age (SGA) are based on simple statistical approaches that may misclassify infants with a normal developmental profile and vice versa. We used an unbiased global profiling approach to identify gene expression patterns in umbilical cord tissue from 38 infants and identified a set of 466 genes which separated the subjects into 2 distinct groups – one biased towards lower birth weight and one biased towards normal birth weight. The data suggest that approximately 30% of children of normal size have a molecular profile more typical of impaired fetal development and who may be on a programmed trajectory. Differences in expression between the two groups encompassed 384 upregulated and 82 downregulated genes. Molecular profiling at birth may have utility in identifying markers that potentially reflect antenatal developmental and may be predictive of future phenotypic development after birth. Importantly, it may provide an alternative to the current classification of infants using birth weights.
2011-10-21 | GSE24818 | GEO
Project description:BACKGROUND:Extremely-low-birth-weight (ELBW; ?1,000 g) infants are at high risk for neurodevelopmental impairments. Conventional brain MRI at term-equivalent age is increasingly used for prediction of outcomes. However, optimal prediction models remain to be determined, especially for cognitive outcomes. OBJECTIVE:The aim was to evaluate the accuracy of a data-driven MRI scoring system to predict neurodevelopmental impairments. METHODS:122 ELBW infants had a brain MRI performed at term-equivalent age. Conventional MRI findings were scored with a standardized algorithm and tested using a multivariable regression model to predict neurodevelopmental impairment, defined as one or more of the following at 18-24 months' corrected age: cerebral palsy, bilateral blindness, bilateral deafness requiring amplification, and/or cognitive/language delay. Results were compared with a commonly cited scoring system. RESULTS:In multivariable analyses, only moderate-to-severe gyral maturational delay was a significant predictor of overall neurodevelopmental impairment (OR: 12.6, 95% CI: 2.6, 62.0; p < 0.001). Moderate-to-severe gyral maturational delay also predicted cognitive delay, cognitive delay/death, and neurodevelopmental impairment/death. Diffuse cystic abnormality was a significant predictor of cerebral palsy (OR: 33.6, 95% CI: 4.9, 229.7; p < 0.001). These predictors exhibited high specificity (range: 94-99%) but low sensitivity (30-67%) for the above outcomes. White or gray matter scores, determined using a commonly cited scoring system, did not show significant association with neurodevelopmental impairment. CONCLUSIONS:In our cohort, conventional MRI at term-equivalent age exhibited high specificity in predicting neurodevelopmental outcomes. However, sensitivity was suboptimal, suggesting additional clinical factors and biomarkers are needed to enable accurate prognostication.
| S-EPMC5768198 | biostudies-literature