Variability in Daily Eating Patterns and Eating Jetlag Are Associated With Worsened Cardiometabolic Risk Profiles in the American Heart Association Go Red for Women Strategically Focused Research Network

Background

Sleep variability and social jetlag are associated with adverse cardiometabolic outcomes via circadian disruption. Variable eating patterns also lead to circadian disruption, but associations with cardiometabolic health are unknown.

Methods and Results

Women (n=115, mean age: 33±12 years) completed a 1‐week food record using the Automated Self‐Administered 24‐Hour Dietary Assessment Tool at baseline and 1 year. Timing of first and last eating occasions, nightly fasting duration, and %kcal consumed after 5 pm (%kcal 5 pm) and 8 pm (%kcal 8 pm) were estimated. Day‐to‐day eating variability was assessed from the SD of these variables. Eating jetlag was defined as weekday‐weekend differences in these metrics. Multivariable‐adjusted linear models examined cross‐sectional and longitudinal associations of day‐to‐day variability and eating jetlag metrics with cardiometabolic risk. Greater jetlag in eating start time, nightly fasting duration, and %kcal 8 pm related to higher body mass index and waist circumference at baseline (P<0.05). In longitudinal analyses, a 10% increase in %kcal 8 pm SD predicted increased body mass index (β, 0.52; 95% CI, 0.23–0.81) and waist circumference (β, 1.73; 95% CI, 0.58–2.87); greater %kcal 8 pm weekday‐weekend differences predicted higher body mass index (β, 0.25; 95% CI, 0.07–0.43). Every 30‐minute increase in nightly fasting duration SD predicted increased diastolic blood pressure (β, 0.95; 95% CI, 0.40–1.50); an equivalent increase in nightly fasting duration weekday‐weekend differences predicted higher systolic blood pressure (β, 0.58; 95% CI, 0.11–1.05) and diastolic blood pressure (β, 0.45; 95% CI, 0.10–0.80). Per 10% increase in %kcal 5 pm SD, there were 2.98 mm Hg (95% CI, 0.04–5.92) and 2.37mm Hg (95% CI, 0.19–4.55) increases in systolic blood pressure and diastolic blood pressure; greater %kcal 5 pm weekday‐weekend differences predicted increased systolic blood pressure (β, 1.83; 95% CI, 0.30–3.36). For hemoglobin A1c, every 30‐minute increase in eating start and end time SD and 10% increase in %kcal 5 pm SD predicted 0.09% (95% CI, 0.03–0.15), 0.06% (95% CI, 0.001–0.12), and 0.23% (95% CI, 0.07–0.39) increases, respectively.

Conclusions

Variable eating patterns predicted increased blood pressure and adiposity and worse glycemic control. Findings warrant confirmation in population‐based cohorts and intervention studies.