Metabolomics to Understand Alterations Induced by Physical Activity during Pregnancy.
Ana Carolina Rosa da SilvaAnahita YadegariVelislava TzanevaTarushika VasanthanKatarina LaketicJane ShearerShannon A BainbridgeCory S HarrisKristi B AdamoPublished in: Metabolites (2023)
Physical activity (PA) and exercise have been associated with a reduced risk of cancer, obesity, and diabetes. In the context of pregnancy, maintaining an active lifestyle has been shown to decrease gestational weight gain (GWG) and lower the risk of gestational diabetes mellitus (GDM), hypertension, and macrosomia in offspring. The main pathways activated by PA include BCAAs, lipids, and bile acid metabolism, thereby improving insulin resistance in pregnant individuals. Despite these known benefits, the underlying metabolites and biological mechanisms affected by PA remain poorly understood, highlighting the need for further investigation. Metabolomics, a comprehensive study of metabolite classes, offers valuable insights into the widespread metabolic changes induced by PA. This narrative review focuses on PA metabolomics research using different analytical platforms to analyze pregnant individuals. Existing studies support the hypothesis that exercise behaviour can influence the metabolism of different populations, including pregnant individuals and their offspring. While PA has shown considerable promise in maintaining metabolic health in non-pregnant populations, our comprehension of metabolic changes in the context of a healthy pregnancy remains limited. As a result, further investigation is necessary to clarify the metabolic impact of PA within this unique group, often excluded from physiological research.
Keyphrases
- weight gain
- physical activity
- pregnant women
- body mass index
- mass spectrometry
- weight loss
- birth weight
- pregnancy outcomes
- type diabetes
- cardiovascular disease
- healthcare
- public health
- metabolic syndrome
- high fat diet
- blood pressure
- preterm birth
- mental health
- ms ms
- insulin resistance
- depressive symptoms
- risk assessment
- body composition
- fatty acid
- health information
- deep learning
- big data
- gestational age
- arterial hypertension