Dataset Information

Analysis of Amino Acid Patterns With Nutrition Regimens in Preterm Infants With Extrauterine Growth Retardation.

ABSTRACT: Background: Amino acid (AA) metabolic patterns have emerged as an analytical technique to characterize biomarkers compromising normal growth and elucidate underlying nutritional exposure. This study aimed to identify AA metabolites most likely associated with poor growth and examine the association between AA metabolites and nutrition regimens in preterm infants during transition from parenteral nutrition (PN) to enteral nutrition (EN), using gas chromatography-mass spectrometry (GC-MS). Methods: This observational cohort study was conducted in infants born at <32 weeks' gestation with birth weight of <1,500 g. The outcome of extrauterine growth retardation (EUGR) based on whether the weight was <10th percentile for post-menstrual age, was evaluated when full EN reached. Samples were collected at four sampling points according to nutritional status. AA profiles in dried sampling point spots (DBS) were quantified using GC-MS; and were compared simultaneously. The correlation of AA concentration with growth and nutritional parameters was examined using multivariate analysis. Results: We identified 40 eligible infants: 20 in the EUGR group and 20 in the non-EUGR group. AA deficiency progressively emerged during the transition. Lower concentrations of four AAs, including citrulline (Cit), were associated with increased risk of EUGR when adjusted for gestational age, birth weight z-score, age when trophic EN was started, as well as average energy and protein intakes in synchronous nutritional period. Moreover, a lower Cit concentration was positively correlated with the compromised protein and energy deficits in EN during early transition. Conclusion: A low Cit concentration during transition from PN to full EN should be noticed by the clinician to more closely examine nutrition practices to prevent EUGR.

SUBMITTER: Wang L

PROVIDER: S-EPMC7212428 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Analysis of Amino Acid Patterns With Nutrition Regimens in Preterm Infants With Extrauterine Growth Retardation.

Wang Li L Liu Danyang D Shen Haiqing H Wang Ying Y Han Lianshu L He Zhenjuan Z

Frontiers in pediatrics 20200428

<b>Background:</b> Amino acid (AA) metabolic patterns have emerged as an analytical technique to characterize biomarkers compromising normal growth and elucidate underlying nutritional exposure. This study aimed to identify AA metabolites most likely associated with poor growth and examine the association between AA metabolites and nutrition regimens in preterm infants during transition from parenteral nutrition (PN) to enteral nutrition (EN), using gas chromatography-mass spectrometry (GC-MS). ...[more]

PMID: 32426308

Similar Datasets

Project description:BackgroundNutritional support of preterm infants remains a field of debate in the literature and clinical practice varies significantly. Adequate nutrition should promote growth and aim for optimal later neurodevelopment. However, it is often impaired by prematurity-associated morbidity and the physiologic immaturity of preterm infants. This study assessed the impact of energy and macronutrient provision on growth velocity and outcome and explored differences attributed to the heterogeneity of the preterm population.MethodsWe retrospectively collected clinical and nutritional data from neonates hospitalized in two separate Neonatal Intensive Care Units (NICUs). Estimated energy and protein balance were calculated based on the ESPGHAN guidelines and their association with the growth outcome was explored. Growth assessment was based on somatometry Delta (Δ) z-scores at discharge.ResultsIn total, 174 neonates were included in the study. By day 14, most preterm infants were exclusively enterally fed, whereas there were infants in the <28 and 28-31+6 subgroups fed exclusively parenterally. Energy balance was positive for all gestational age (GA) subgroups except for those born <28 weeks. Protein balance was consistently positive for extremely premature but negative for late preterms. Cumulative substrates provisions were strong predictors of a positive energy or protein balance in the <34 weeks GA preterms on days 14 (ROC analyses, p < 0.001) and 7 (p < 0.05). A higher GA (p = 0.013) and enteral nutrition (p = 0.005) were additional predictors of a positive energy balance. All GA subgroups had a negative Δ z-score of weight at discharge. In the <34 GA subcohorts, a positive protein balance on day 14 (p = 0.009) and a short time to regain birth weight (exp(B) 3.1 (p = 0.004)) were independently associated with a positive Δ z-score of weight at discharge.ConclusionsEarly achievement of a positive energy and protein balance, based on the ESPGHAN guidelines, is crucial to ensure optimal postnatal growth and prevent extrauterine growth restriction, a relatively common occurrence in preterm infants.

Project description:IntroductionPreterm birth is a leading cause of infant mortality and morbidity. Despite the improvement in the overall mortality in premature infants, the intact survival of these infants remains a significant challenge. Screening the physical growth of infants is fundamental to potentially reducing the escalation of this disorder. Recently, machine learning models have been used to predict the growth restrictions of infants; however, they frequently rely on conventional risk factors and cross-sectional data and do not leverage the longitudinal database associated with medical data from laboratory tests.MethodsThis study aimed to present an automated interpretable ML-based approach for the prediction and classification of short-term growth outcomes in preterm infants. We prepared four datasets based on weight and length including weight baseline, length baseline, weight follow-up, and length follow-up. The CHA Bundang Medical Center Neonatal Intensive Care Unit dataset was classified using two well-known supervised machine learning algorithms, namely support vector machine (SVM) and logistic regression (LR). A five-fold cross-validation, and several performance measures, including accuracy, precision, recall and F1-score were used to compare classifier performances. We further illustrated the models' trustworthiness using calibration and cumulative curves. The visualized global interpretations using Shapley additive explanation (SHAP) is provided for analyzing variables' contribution to final prediction.ResultsBased on the experimental results with area under the curve, the discrimination ability of the SVM algorithm was found to better than that of the LR model on three of the four datasets with 81%, 76% and 72% in weight follow-up, length baseline and length follow-up dataset respectively. The LR classifier achieved a better ROC score only on the weight baseline dataset with 83%. The global interpretability results revealed that pregnancy-induced hypertension, gestational age, twin birth, birth weight, antenatal corticosteroid use, premature rupture of membranes, sex, and birth length were consistently ranked as important variables in both the baseline and follow-up datasets.DiscussionThe application of machine learning models to the early detection and automated classification of short-term growth outcomes in preterm infants achieved high accuracy and may provide an efficient framework for clinical decision systems enabling more effective monitoring and facilitating timely intervention.

Project description:Preterm birth survivors are at a higher risk of growth and developmental disabilities compared to their term counterparts. Development of strategies to lower the complications of preterm birth forms the rising need of the hour. Appropriate nutrition is essential for the growth and development of preterm infants. Early administration of optimal nutrition to preterm birth survivors lowers the risk of adverse health outcomes and improves cognition in adulthood. A group of neonatologists, pediatricians, and nutrition experts convened to discuss and frame evidence-based recommendations for optimizing nutrition in preterm low birth weight (LBW) infants. The following were the primary recommendations of the panel: (1) enteral feeding is safe and may be preferred to parenteral nutrition due to the complications associated with the latter; however, parenteral nutrition may be a useful adjunct to enteral feeding in some critical cases; (2) early, fast, or continuous enteral feeding yields better outcomes compared to late, slow, or intermittent feeding, respectively; (3) routine use of nasogastric tubes is not advisable; (4) preterm infants can be fed while on ventilator or continuous positive airway pressure; (5) routine evaluation of gastric residuals and abdominal girth should be avoided; (6) expressed breast milk (EBM) is the first choice for feeding preterm infants due to its beneficial effects on cardiovascular, neurological, bone health, and growth outcomes; the second choice is donor pasteurized human milk; (7) EBM or donor milk may be fortified with human milk fortifiers, without increasing the osmolality of the milk, to meet the high protein requirements of preterm infants; (8) standard fortification is effective and safe but does not fulfill the high protein needs; (9) use of targeted and adjustable fortification, where possible, helps provide optimal nutrition; (10) optimizing weight gain in preterm infants prevents long-term cardiovascular complications; (11) checking for optimal weight and sucking/swallowing ability is essential prior to discharge of preterm infants; and (12) appropriate counseling and regular follow-up and monitoring after discharge will help achieve better long-term health outcomes. This consensus summary serves as a useful guide to clinicians in addressing the challenges and providing optimal nutrition to preterm LBW infants.

Dataset Information

Analysis of Amino Acid Patterns With Nutrition Regimens in Preterm Infants With Extrauterine Growth Retardation.

Publications

Analysis of Amino Acid Patterns With Nutrition Regimens in Preterm Infants With Extrauterine Growth Retardation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets