Hassan S Dashti1,2,3, Douglas E Levy4, Marie-France Hivert5,6, Kaitlyn Alimenti1, Jessica L McCurley7, Richa Saxena1,2,3, Anne N Thorndike7. 1. Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. 2. Department of Medical and Population Genetics, Broad Institute, Cambridge, MA, USA. 3. Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. 4. Mongan Institute Health Policy Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. 5. Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA. 6. Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA. 7. Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Abstract
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.
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.
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