Maternal Obesity: A Focus on Maternal Interventions to Improve Health of Offspring

Author(s)/Creator(s)

Latha RamalingamFollow

ORCID

Latha Ramalingam: 0000-0002-4856-7327

Document Type

Article

Date

7-2021

Keywords

animal models, high fat diet, maternal obesity, nutritional interventions, physical activity

Disciplines

Molecular, Genetic, and Biochemical Nutrition | Other Nutrition

Description/Abstract

Maternal obesity has many implications for offspring health that persist throughout their lifespan that include obesity and cardiovascular complications. Several different factors contribute to obesity and they encompass interplay between genetics and environment. In the prenatal period, untreated obesity establishes a foundation for a myriad of symptoms and negative delivery experiences, including gestational hypertensive disorders, gestational diabetes, macrosomia, and labor complications. However, data across human and animal studies show promise that nutritional interventions and physical activity may rescue much of the adverse effects of obesity on offspring metabolic health. Further, these maternal interventions improve the health of the offspring by reducing weight gain, cardiovascular disorders, and improving glucose tolerance. Mechanisms from animal studies have also been proposed to elucidate the signaling pathways that regulate inflammation, lipid metabolism, and oxidative capacity of the tissue, ultimately providing potential specific courses of treatment. This review aims to pinpoint the risks of maternal obesity and provide plausible intervention strategies. We delve into recent research involving both animal and human studies with maternal interventions. With the increasing concerning of obesity rates witnessed in the United States, it is imperative to acknowledge the long-term effects posed on future generations and specifically modify maternal nutrition and care to mitigate these adverse outcomes.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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