โก Quick Summary
This study examined maternal lipid levels in relation to preterm and small-for-gestational-age (SGA) newborns, analyzing data from 641 lipids and 639 metabolites across three low- and middle-income countries. The findings revealed a significant lipid imbalance, with increased triglycerides linked to preterm births and decreased levels associated with SGA births.
๐ Key Details
- ๐ Dataset: Plasma samples from women by 24 weeks of pregnancy, totaling 641 lipids and 639 metabolites.
- ๐ Locations: Bangladesh, Zimbabwe, and Kenya.
- โ๏ธ Technology: Machine learning model for predicting preterm birth.
- ๐ Performance: AUC for preterm birth prediction: 0.69; AUC for SGA prediction: 0.64.
๐ Key Takeaways
- ๐ฌ Maternal lipidomic analysis provides insights into fetal growth and development.
- ๐ Increased triglycerides containing proinflammatory omega-6 PUFAs were linked to preterm births.
- ๐ Decreased lipid levels were observed in pregnancies resulting in SGA infants.
- ๐ค Machine learning models showed modest predictive performance for preterm and SGA births.
- ๐ก Dietary implications suggest potential benefits of long-chain fatty acid supplementation.
- ๐ Study highlights the importance of maternal nutrition in low- and middle-income countries.
- ๐ Research conducted on samples collected by 24 weeks of gestation.
๐ Background
Maternal nutrition plays a crucial role in fetal development, particularly in low- and middle-income countries where access to adequate dietary resources may be limited. Understanding the dynamic changes in lipid levels during pregnancy can provide valuable insights into the health of both mothers and their infants, especially in the context of preterm and SGA births.
๐๏ธ Study
The study involved a comprehensive analysis of maternal plasma samples collected from women in Bangladesh, Zimbabwe, and Kenya by 24 weeks of pregnancy. Researchers aimed to identify lipidomic signatures that could differentiate between pregnancies resulting in preterm births and those leading to SGA infants. By analyzing a wide array of lipids and metabolites, the study sought to uncover critical insights into maternal health and fetal growth.
๐ Results
The analysis revealed a significant lipid imbalance, with increased triglycerides linked to preterm births and decreased lipid levels associated with SGA births. The machine learning model developed for predicting preterm birth achieved an AUC of 0.69, while the prediction for SGA births had an AUC of 0.64. These findings underscore the potential of lipidomic profiling in understanding pregnancy outcomes.
๐ Impact and Implications
The implications of this study are profound, particularly for maternal and child health in low- and middle-income countries. By identifying lipidomic signatures associated with adverse pregnancy outcomes, healthcare providers can better understand the nutritional needs of pregnant women. This research also supports the idea of dietary supplementation with long-chain fatty acids, which may improve pregnancy outcomes and fetal health.
๐ฎ Conclusion
This study highlights the importance of maternal lipidomic profiling in predicting preterm and SGA births. The findings suggest that monitoring lipid levels could be a valuable tool in improving maternal and fetal health outcomes. As we continue to explore the relationship between maternal nutrition and pregnancy outcomes, further research is essential to validate these findings and develop effective interventions.
๐ฌ Your comments
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Maternal lipidomic signatures of preterm and small-for-gestational-age newborn infants in low- and middle-income countries.
Abstract
Maternal lipid levels change dynamically during gestation to support normal fetal growth. To obtain a detailed footprint of these changes and their differences in pregnancies with preterm or small-for-gestational-age (SGA) neonates, we analyzed 641 lipids and 639 metabolites in plasma from women by 24 weeks of pregnancy from three cohorts from low- and middle-income countries: Bangladesh, Zimbabwe, and Kenya. We consistently found a significant lipid imbalance with increased lipid levels that preceded preterm birth and decreased levels that preceded SGA births. Changes were most pronounced in triglycerides, including triglycerides containing proinflammatory omega-6 polyunsaturated fatty acids (PUFAs) in pregnancies with preterm infants. A machine learning model for prediction of preterm birth had modest performance [area under the receiver operator curve (AUC)ย =ย 0.69, 95% confidence interval (CI)ย =ย (0.68, 0.70)] and lower performance for predicting SGA [AUCย =ย 0.64, CI 95%ย =ย (0.62, 0.65)]. Increased triglycerides containing proinflammatory PUFAs provide further evidence in favor of a previously considered dietary supplementation with the long-chain fatty acids.
Author: [‘Mariฤ I’, ‘Mahzarnia A’, ‘Mujuru HA’, ‘Chimhini G’, ‘Saha SK’, ‘Shameem Hassan M’, ‘Otieno NA’, ‘Hawken S’, ‘Wilson K’, ‘Shen X’, ‘Lancaster S’, ‘Wong RJ’, ‘Reiss JD’, ‘Kerner J’, ‘Snyder MP’, ‘Hay W’, ‘Shaw GM’, ‘Stevenson DK’, ‘Ward V’, ‘Darmstadt GL’]
Journal: Sci Adv
Citation: Mariฤ I, et al. Maternal lipidomic signatures of preterm and small-for-gestational-age newborn infants in low- and middle-income countries. Maternal lipidomic signatures of preterm and small-for-gestational-age newborn infants in low- and middle-income countries. 2025; 11:eadu9145. doi: 10.1126/sciadv.adu9145