Chronotype Polygenic Score and the Timing and Quality of Workplace Cafeteria Purchases: Secondary Analysis of the ChooseWell 365 Randomized Controlled Trial.

Background: Studies on the link between chronotype (i.e., propensity for morning or evening preference) and dietary intake have relied on self-reported data, estimating consumption, and chronotype from questionnaires. Objectives: This study examined the associations between genetically proxied evening chronotype, objectively estimated workplace dietary choices, and the effectiveness of a behavioral intervention in hospital employees enrolled in the ChooseWell 365 study. Methods: ChooseWell 365 was a randomized trial of a 12-mo automated, personalized intervention to prevent weight gain and improve diet. Cafeteria sales data were used to measure the timing and healthfulness of workplace food purchases of employees during the 12-mo-long baseline, intervention, and postintervention follow-up periods. A genome-wide polygenic score for evening chronotype was calculated for all participants and the population was divided into quartiles; the highest quartile indicated the most evening chronotype. Associations between polygenic score quartiles and workplace purchases at baseline, 12 mo, and 24 mo and changes from baseline at 12 and 24 mo were tested using adjusted multivariable linear regression models. Results: At baseline, the highest chronotype quartile was associated with self-reported breakfast skipping. Over the 24-mo study, the highest quartile was associated with later timing of the first workplace purchase, but not with the healthfulness of purchases. There were no differences by the chronotype quartile in the effectiveness of the ChooseWell 365 intervention in improving employees' healthy food choices at work. Conclusions: A chronotype polygenic score was associated with breakfast skipping and later workplace mealtimes of hospital employees, but not with the nutritional quality of objectively measured workplace food purchases. In addition, employees across the chronotype spectrum benefited from the workplace healthy eating intervention.This trial was registered at clinicaltrials.gov as NCT02660086 (https://clinicaltrials.gov/ct2/show/NCT02660086?cond=NCT02660086&draw=2&rank=1).

Genetic risk for obesity and the effectiveness of the ChooseWell 365 workplace intervention to prevent weight gain and improve dietary choices.

BACKGROUND: It is unknown whether behavioral interventions to improve diet are effective in people with a genetic predisposition to obesity. OBJECTIVES: To examine associations between BMI genetic risk and changes in weight and workplace purchases by employees participating in a randomized controlled trial of an automated behavioral workplace intervention to promote healthy food choices. METHODS: Participants were hospital employees enrolled in a 12-mo intervention followed by a 12-mo follow-up. Hospital cafeterias utilized a traffic-light labeling system (e.g., green = healthy, red = unhealthy) that was used to calculate a validated Healthy Purchasing Score (HPS; higher = healthier). A weighted genome-wide BMI genetic score was generated by summing BMI-increasing alleles. RESULTS: The study included 397 adults of European ancestry (mean age, 44.9 y; 80.9% female). Participants in the highest genetic quartile (Q4) had a lower HPS and higher purchases of red-labeled items relative to participants in the lowest quartile (Q1) at baseline [Q4-Q1 Beta HPS, -4.66 (95% CI, -8.01 to -1.32); red-labeled items, 4.26% (95% CI, 1.45%-7.07%)] and at the 12-mo [HPS, -3.96 (95% CI, -7.5 to -0.41); red-labeled items, 3.20% (95% CI, 0.31%-6.09%)] and 24-mo [HPS, -3.70 (95% CI, -7.40 to 0.00); red-labeled items, 3.48% (95% CI, 0.54%-6.41%)] follow-up periods. In the intervention group, increases in HPS were similar in Q4 and Q1 at 12 mo (Q4-Q1 Beta, 1.04; 95% CI, -2.42 to 4.50). At the 24-mo follow-up, the change in BMI from baseline was similar between Q4 and Q1 (0.17 kg/m2; 95% CI, -0.55 to 0.89 kg/m2) in the intervention group, but higher in Q4 than Q1 (1.20 kg/m2; 95% CI, 0.26-2.13 kg/m2) in the control group. No interaction was evident between the treatment arm and genetic score for BMI or HPS. CONCLUSIONS: Having a high BMI genetic risk was associated with greater increases in BMI and lower quality purchases over 2 y. The 12-mo behavioral intervention improved employees' food choices, regardless of the genetic burden, and may have attenuated weight gain conferred by having the genetic risk.

Habitual Sleep Duration, Daytime Napping, and Dietary Intake: A Mendelian Randomization Study.

BACKGROUND: Chronic inadequate sleep and frequent daytime napping may inflict deleterious health effects including weight gain, cardiometabolic and psychiatric diseases, and cancer. It is plausible that these relations may be partly influenced by the consumption of suboptimal diets. OBJECTIVES: The study aimed to identify potential causal links of genetically proxied longer habitual sleep duration and more frequent daytime napping on 61 dietary variables derived from an FFQ. In addition, the study aimed to assess potential bidirectional causal links between habitual sleep duration or daytime napping and macronutrient composition. METHODS: Genetic variants robustly associated with habitual sleep duration and daytime napping from published genome-wide association analyses were used. Outcomes included 61 dietary variables estimated from FFQs in the UK Biobank (n = 361,194). For bidirectional associations with macronutrient composition, genetic variants associated with percentage of energy from carbohydrate, fat, and protein were used. Two-sample Mendelian randomization (MR) effects were estimated with inverse-variance weighted (IVW) analysis. RESULTS: In 2-sample MR, genetically proxied longer sleep duration was associated with a 0.068 (95% CI: 0.034, 0.103) category increase in salad/raw vegetable intake [P false discovery rate (FDR) = 0.006] per hour of sleep and with "no major dietary changes in the past 5 years" (P FDR = 0.043). No associations were evident for daytime napping on dietary variables (all P FDR > 0.05). In addition, there were no bidirectional associations between habitual sleep duration or daytime napping with the relative intake of carbohydrate, fat, and protein (all P IVW > 0.05). CONCLUSIONS: In this MR study, there was modest evidence for associations between habitual sleep duration with dietary intake and no evidence for associations between daytime napping frequency with dietary intake. These preliminary findings suggest that changes to habitual sleep duration or daytime napping frequency may have limited impact on long-term changes in dietary intake.