Predicting Postprandial Oxidative Stress Using Serum Triglycerides Following Oral Fat Tolerance Testing
Journal Title: Journal of Advances in Medicine and Medical Research - Year 2015, Vol 5, Issue 12
Abstract
Background: The topic of postprandial oxidative stress continues to receive considerable attention, with elevations in oxidative stress biomarkers associated with human disease (e.g., insulin resistance, atherosclerosis). The predictable rise in serum triglyceride (TAG) following high fat meal ingestion is strongly correlated to the increase in circulating oxidative stress biomarkers. Our intent with the present study was two-fold: 1) To further characterize the postprandial response to high fat feeding and 2) to develop regression models that could be used to predict the oxidative stress response to high fat feeding. Methods: 154 men and women reported to the lab in the morning hours following an overnight fast and consumed a high fat liquid meal. Blood samples were collected before meal ingestion and at 2 and 4 hours following meal ingestion. Samples were analyzed for TAG and oxidative stress biomarkers (hydrogen peroxide [H2O2], malondialdehyde [MDA], advanced oxidation protein products [AOPP]). Simple linear regression analyses were used for model development to predict the oxidative stress response to feeding. In addition, biochemical variables were analyzed using an analysis of variance (ANOVA) to present response to feeding data across time. Results: In all of the regression models, TAG explained a significant portion of the outcome variables (H2O2, MDA, and AOPP) and predictor models were created for each variable. In addition, values for TAG and all oxidative stress biomarkers increased following meal ingestion. A time effect was noted where all values were higher post meal ingestion as compared to pre meal for all biochemical variables (p<0.05). Conclusion: Obtaining serum TAG values in response to a high fat meal challenge may provide investigators with the ability to predict postprandial oxidative stress using regression equations.
Authors and Affiliations
Richard J. Bloomer, Innocence C. Harvey, Sang-Rok Lee, Michelle B. Stockton
Keywords
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- EP ID EP345020
- DOI 10.9734/BJMMR/2015/13816
- Views 57
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