Meal timing, distribution of macronutrients, and inflammation among African-American women: A cross-sectional study.
Samantha C TrumanMichael D WirthSwann Arp AdamsGabrielle M Turner-McGrievyKelly E ReissJames R HébertPublished in: Chronobiology international (2022)
Chronic low-grade inflammation is an underlying risk factor for numerous chronic diseases, including cancer. Eating earlier in the day has been associated with a reduction in levels of inflammatory markers and inflammation-related health outcomes (e.g., obesity, metabolic disorders). This cross-sectional study of 249 obese African-American women examined the effect of various mealtime-related factors associated with macronutrient consumption in relation to chronic inflammation and Breast Imaging Reporting and Data System (BI-RAD) readings. During 2011 and 2013, a single 24-hour dietary recall was administered, blood samples were assayed for c-reactive protein (CRP) and interleukin-6 (IL-6), and BI-RAD ratings were assessed to determine the influence of mealtime on chronic inflammation and breast cancer risk score. Multiple linear and logistic regression models were used to assess these relationships. Higher carbohydrate consumption at breakfast was associated with a significantly lower CRP vs. higher carbohydrate consumption at dinner (6.99, vs. 9.56 mg/L, respectively, p = .03). Additionally, every 1-unit increase in percent energy consumed after 5PM resulted in a BI-RAD reading indicating a possibly suspicious abnormality (OR: 1.053, 95% CI: 1.003-1.105), suggesting an increase in breast cancer risk. Timing of energy and macronutrient intake may have important implications for reducing the risk of diseases associated with chronic inflammation.
Keyphrases
- african american
- oxidative stress
- low grade
- breast cancer risk
- dna damage
- metabolic syndrome
- weight loss
- dna repair
- blood pressure
- high grade
- squamous cell carcinoma
- drug induced
- air pollution
- insulin resistance
- high resolution
- electronic health record
- pregnant women
- young adults
- body mass index
- papillary thyroid
- weight gain
- artificial intelligence
- pregnancy outcomes
- deep learning
- adverse drug
- neural network