JOURNAL ARTICLE
Effect of fish-oil supplementation on the glycemic and lipidemic profiles of pregnant women: a systematic review and meta-analysis.
Published In: Nutrition Reviews, 2024, v. 82, n. 12. P. 1756 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Silveira, Julie M; Ribeiro, Thassia S; Guilarducci, Mariana J; Reis, Marcela Gomes; Vieira, Renata A L; Guimarães, Nathalia S; Gomes, Júnia M G 3 of 3
Abstract
This article systematically reviews the effects of fish-oil-capsule supplementation, containing eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), on the glycemic and lipid profiles of pregnant women. Analyzing 15 randomized controlled trials involving 4,494 pregnant women, the meta-analysis found no overall significant impact of fish-oil supplementation on fasting glucose, insulin, insulin resistance (homeostasis model assessment of insulin resistance, HOMA-IR), oral-glucose-tolerance test (OGTT), or lipid parameters including total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides. However, subgroup analyses indicated that fish-oil supplementation may reduce insulin levels and HOMA-IR in pregnant women with diabetes mellitus and that EPA doses below 200 mg/day were associated with improved insulin resistance. The review highlights the need for further research to clarify optimal dosing and efficacy of fish-oil supplementation as an adjuvant therapy for metabolic control during pregnancy, especially in women with preexisting metabolic disorders.
Additional Information
- Source:Nutrition Reviews. 2024/12, Vol. 82, Issue 12, p1756
- Document Type:Article
- Subject Area:Complementary and Alternative Medicine
- Publication Date:2024
- ISSN:0029-6643
- DOI:10.1093/nutrit/nuad158
- Accession Number:180861914
- Copyright Statement:Copyright of Nutrition Reviews is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.