Aims: The aim of this study was to determine if maternal nutritional status, as defined by body composition, leptin, and insulin-like growth factor (IGF)-I levels, relates to foetal growth.
Methods: In this prospective study, mothers of foetuses with foetal growth restriction (FGR; cases; n = 46) and mothers of appropriate-for-gestational-age (AGA) foetuses (controls; n = 81) were consecutively recruited over a 14- month period. A maternal blood sample was obtained during the third trimester (between 32 and 34 weeks of gestation) for the assessment of IGF-I and leptin. Body composition was assessed by dual-energy X-ray absorptiometry within the first 15 days after delivery. The study used the SPSS-PC statistical package, version 19.0, and p < 0.05 was considered statistically significant.
Results: Mean serum IGF-I levels were lower in the cases than in the controls (p < 0.05), whereas leptin concentrations were higher in the cases after adjusting for age, body mass index and cigarette consumption (p < 0.05). Cases had less lean and fat tissue than controls (p < 0.05) but a relatively higher fat percentage.
Conclusions: The mothers of foetuses with FGR have a body composition pattern characterized by a slightly increased fraction of fat mass, lower IGF-I concentrations, and increased serum leptin levels. Optimization of maternal nutritional status should be considered, as the nutritional status may be involved in the pathogenesis of FGR.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1159/000371761 | DOI Listing |
Front Nutr
January 2025
Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Background: Few studies have explored the link between nutritional status and prognosis in patients with epithelial ovarian cancer (EOC), and existing findings are controversial. Thus, this study aimed to explore the effects of pre-treatment nutrition-related indicators on the prognosis of patients with newly diagnosed EOC.
Methods: In this ambispective cohort study, 1,020 patients with EOC diagnosed by pathology examination were enrolled and followed-up until December 31, 2023.
Pan Afr Med J
January 2025
Department of Nutrition, Dietetics and Food Sciences, University of Zimbabwe, P.O Box MP 167, Mt Pleasant, Harare, Zimbabwe.
family-led mid-upper arm circumference (FL-MUAC) is a community-based acute malnutrition screening approach that is centered on training the mother or caregiver to use colour-coded MUAC tapes to screen children for malnutrition. A scoping review was conducted to summarise available evidence and evaluate the use of the FL-MUAC approach in the screening for acute malnutrition in Africa. A systematic literature search was performed using electronic databases to identify relevant research documents investigating the FL-MUAC approach.
View Article and Find Full Text PDFAquac Nutr
January 2025
State Key Laboratory of Fresh Water Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, Hubei, China.
This study was carried out to search for the protein requirement of a new strain of preponderant amphitriploid clone, which integrated genomes partly from white crucian carp (). Seven groups of fish (body weight: 9.73 ± 0.
View Article and Find Full Text PDFBMC Nutr
January 2025
School of Public Health, Collage of Health and Medical Sciences, Haramaya University, Harar, Ethiopia.
Background: Human immunodeficiency virus continues to be a major global public health issue. Body mass index is a general indicator of nutritional status and has emerged as a powerful predictor of morbidity and mortality among adult PLHIV initiating antiretroviral therapy in resource-limited settings. However, there is a dearth of information regarding longitudinal changes in body mass index and its predictors among adult PLHIV in Ethiopia, particularly in the study area.
View Article and Find Full Text PDFBioData Min
January 2025
Department of Statistics, College of Science, Bahir Dar University, P.O. Box 79, Bahir Dar, Ethiopia.
Background: This study employs a LSTM-FC neural networks to address the critical public health issue of child undernutrition in Ethiopia. By employing this method, the study aims classify children's nutritional status and predict transitions between different undernutrition states over time. This analysis is based on longitudinal data extracted from the Young Lives cohort study, which tracked 1,997 Ethiopian children across five survey rounds conducted from 2002 to 2016.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!