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Polygenic risk score for obesity and the quality, quantity, and timing of workplace food purchases: A secondary analysis from the ChooseWell 365 randomized trial

BACKGROUND: The influence of genetic risk for obesity on food choice behaviors is unknown and may be in the causal pathway between genetic risk and weight gain. The aim of this study was to examine associations between genetic risk for obesity and food choice behaviors using objectively assessed wor...

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Autores principales: Dashti, Hassan S., Hivert, Marie-France, Levy, Douglas E., McCurley, Jessica L., Saxena, Richa, Thorndike, Anne N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7373257/
https://www.ncbi.nlm.nih.gov/pubmed/32692747
http://dx.doi.org/10.1371/journal.pmed.1003219
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author Dashti, Hassan S.
Hivert, Marie-France
Levy, Douglas E.
McCurley, Jessica L.
Saxena, Richa
Thorndike, Anne N.
author_facet Dashti, Hassan S.
Hivert, Marie-France
Levy, Douglas E.
McCurley, Jessica L.
Saxena, Richa
Thorndike, Anne N.
author_sort Dashti, Hassan S.
collection PubMed
description BACKGROUND: The influence of genetic risk for obesity on food choice behaviors is unknown and may be in the causal pathway between genetic risk and weight gain. The aim of this study was to examine associations between genetic risk for obesity and food choice behaviors using objectively assessed workplace food purchases. METHODS AND FINDINGS: This study is a secondary analysis of baseline data collected prior to the start of the “ChooseWell 365” health-promotion intervention randomized control trial. Participants were employees of a large hospital in Boston, MA, who enrolled in the study between September 2016 and February 2018. Cafeteria sales data, collected retrospectively for 3 months prior to enrollment, were used to track the quantity (number of items per 3 months) and timing (median time of day) of purchases, and participant surveys provided self-reported behaviors, including skipping meals and preparing meals at home. A previously validated Healthy Purchasing Score was calculated using the cafeteria traffic-light labeling system (i.e., green = healthy, yellow = less healthy, red = unhealthy) to estimate the healthfulness (quality) of employees’ purchases (range, 0%–100% healthy). DNA was extracted and genotyped from blood samples. A body mass index (BMI) genome-wide polygenic score (BMI(GPS)) was generated by summing BMI-increasing risk alleles across the genome. Additionally, 3 polygenic risk scores (PRSs) were generated with 97 BMI variants previously identified at the genome-wide significance level (P < 5 × 10(−8)): (1) BMI(97) (97 loci), (2) BMI(CNS) (54 loci near genes related to central nervous system [CNS]), and (3) BMI(non-CNS) (43 loci not related to CNS). Multivariable linear and logistic regression tested associations of genetic risk score quartiles with workplace purchases, adjusted for age, sex, seasonality, and population structure. Associations were considered significant at P < 0.05. In 397 participants, mean age was 44.9 years, and 80.9% were female. Higher genetic risk scores were associated with higher BMI. The highest quartile of BMI(GPS) was associated with lower Healthy Purchasing Score (−4.8 percentage points [95% CI −8.6 to −1.0]; P = 0.02), higher quantity of food purchases (14.4 more items [95% CI −0.1 to 29.0]; P = 0.03), later time of breakfast purchases (15.0 minutes later [95% CI 1.5–28.5]; P = 0.03), and lower likelihood of preparing dinner at home (Q4 odds ratio [OR] = 0.3 [95% CI 0.1–0.9]; P = 0.03) relative to the lowest BMI(GPS) quartile. Compared with the lowest quartile, the highest BMI(CNS) quartile was associated with fewer items purchased (P = 0.04), and the highest BMI(non-CNS) quartile was associated with purchasing breakfast at a later time (P = 0.01), skipping breakfast (P = 0.03), and not preparing breakfast (P = 0.04) or lunch (P = 0.01) at home. A limitation of this study is our data come from a relatively small sample of healthy working adults of European ancestry who volunteered to enroll in a health-promotion study, which may limit generalizability. CONCLUSIONS: In this study, genetic risk for obesity was associated with the quality, quantity, and timing of objectively measured workplace food purchases. These findings suggest that genetic risk for obesity may influence eating behaviors that contribute to weight and could be targeted in personalized workplace wellness programs in the future. TRIAL REGISTRATION: Clinicaltrials.gov NCT02660086.
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spelling pubmed-73732572020-07-29 Polygenic risk score for obesity and the quality, quantity, and timing of workplace food purchases: A secondary analysis from the ChooseWell 365 randomized trial Dashti, Hassan S. Hivert, Marie-France Levy, Douglas E. McCurley, Jessica L. Saxena, Richa Thorndike, Anne N. PLoS Med Research Article BACKGROUND: The influence of genetic risk for obesity on food choice behaviors is unknown and may be in the causal pathway between genetic risk and weight gain. The aim of this study was to examine associations between genetic risk for obesity and food choice behaviors using objectively assessed workplace food purchases. METHODS AND FINDINGS: This study is a secondary analysis of baseline data collected prior to the start of the “ChooseWell 365” health-promotion intervention randomized control trial. Participants were employees of a large hospital in Boston, MA, who enrolled in the study between September 2016 and February 2018. Cafeteria sales data, collected retrospectively for 3 months prior to enrollment, were used to track the quantity (number of items per 3 months) and timing (median time of day) of purchases, and participant surveys provided self-reported behaviors, including skipping meals and preparing meals at home. A previously validated Healthy Purchasing Score was calculated using the cafeteria traffic-light labeling system (i.e., green = healthy, yellow = less healthy, red = unhealthy) to estimate the healthfulness (quality) of employees’ purchases (range, 0%–100% healthy). DNA was extracted and genotyped from blood samples. A body mass index (BMI) genome-wide polygenic score (BMI(GPS)) was generated by summing BMI-increasing risk alleles across the genome. Additionally, 3 polygenic risk scores (PRSs) were generated with 97 BMI variants previously identified at the genome-wide significance level (P < 5 × 10(−8)): (1) BMI(97) (97 loci), (2) BMI(CNS) (54 loci near genes related to central nervous system [CNS]), and (3) BMI(non-CNS) (43 loci not related to CNS). Multivariable linear and logistic regression tested associations of genetic risk score quartiles with workplace purchases, adjusted for age, sex, seasonality, and population structure. Associations were considered significant at P < 0.05. In 397 participants, mean age was 44.9 years, and 80.9% were female. Higher genetic risk scores were associated with higher BMI. The highest quartile of BMI(GPS) was associated with lower Healthy Purchasing Score (−4.8 percentage points [95% CI −8.6 to −1.0]; P = 0.02), higher quantity of food purchases (14.4 more items [95% CI −0.1 to 29.0]; P = 0.03), later time of breakfast purchases (15.0 minutes later [95% CI 1.5–28.5]; P = 0.03), and lower likelihood of preparing dinner at home (Q4 odds ratio [OR] = 0.3 [95% CI 0.1–0.9]; P = 0.03) relative to the lowest BMI(GPS) quartile. Compared with the lowest quartile, the highest BMI(CNS) quartile was associated with fewer items purchased (P = 0.04), and the highest BMI(non-CNS) quartile was associated with purchasing breakfast at a later time (P = 0.01), skipping breakfast (P = 0.03), and not preparing breakfast (P = 0.04) or lunch (P = 0.01) at home. A limitation of this study is our data come from a relatively small sample of healthy working adults of European ancestry who volunteered to enroll in a health-promotion study, which may limit generalizability. CONCLUSIONS: In this study, genetic risk for obesity was associated with the quality, quantity, and timing of objectively measured workplace food purchases. These findings suggest that genetic risk for obesity may influence eating behaviors that contribute to weight and could be targeted in personalized workplace wellness programs in the future. TRIAL REGISTRATION: Clinicaltrials.gov NCT02660086. Public Library of Science 2020-07-21 /pmc/articles/PMC7373257/ /pubmed/32692747 http://dx.doi.org/10.1371/journal.pmed.1003219 Text en © 2020 Dashti et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Dashti, Hassan S.
Hivert, Marie-France
Levy, Douglas E.
McCurley, Jessica L.
Saxena, Richa
Thorndike, Anne N.
Polygenic risk score for obesity and the quality, quantity, and timing of workplace food purchases: A secondary analysis from the ChooseWell 365 randomized trial
title Polygenic risk score for obesity and the quality, quantity, and timing of workplace food purchases: A secondary analysis from the ChooseWell 365 randomized trial
title_full Polygenic risk score for obesity and the quality, quantity, and timing of workplace food purchases: A secondary analysis from the ChooseWell 365 randomized trial
title_fullStr Polygenic risk score for obesity and the quality, quantity, and timing of workplace food purchases: A secondary analysis from the ChooseWell 365 randomized trial
title_full_unstemmed Polygenic risk score for obesity and the quality, quantity, and timing of workplace food purchases: A secondary analysis from the ChooseWell 365 randomized trial
title_short Polygenic risk score for obesity and the quality, quantity, and timing of workplace food purchases: A secondary analysis from the ChooseWell 365 randomized trial
title_sort polygenic risk score for obesity and the quality, quantity, and timing of workplace food purchases: a secondary analysis from the choosewell 365 randomized trial
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7373257/
https://www.ncbi.nlm.nih.gov/pubmed/32692747
http://dx.doi.org/10.1371/journal.pmed.1003219
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