Polygenic risk score for obesity and the quality, quantity, and timing of workplace food purchases: A secondary analysis from the ChooseWell 365 randomized trial
Autoři:
Hassan S. Dashti aff001; Marie-France Hivert aff004; Douglas E. Levy aff006; Jessica L. McCurley aff007; Richa Saxena aff001; Anne N. Thorndike aff007
Působiště autorů:
Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
aff001; Broad Institute, Cambridge, Massachusetts, United States of America
aff002; Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
aff003; Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, Massachusetts, United States of America
aff004; Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
aff005; Mongan Institute Health Policy Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
aff006; Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
aff007
Vyšlo v časopise:
Polygenic risk score for obesity and the quality, quantity, and timing of workplace food purchases: A secondary analysis from the ChooseWell 365 randomized trial. PLoS Med 17(7): e32767. doi:10.1371/journal.pmed.1003219
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pmed.1003219
Souhrn
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 (BMIGPS) 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) BMI97 (97 loci), (2) BMICNS (54 loci near genes related to central nervous system [CNS]), and (3) BMInon-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 BMIGPS 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 BMIGPS quartile. Compared with the lowest quartile, the highest BMICNS quartile was associated with fewer items purchased (P = 0.04), and the highest BMInon-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.
Klíčová slova:
Body Mass Index – Central nervous system – Decision making – Employment – Food – Genetic loci – Obesity – Weight gain
Zdroje
1. Loos RJ. The genetics of adiposity. Current Opinion in Genetics and Development. Elsevier; 2018. pp. 86–95. doi: 10.1016/j.gde.2018.02.009 29529423
2. Elks CE, den Hoed M, Zhao JH, Sharp SJ, Wareham NJ, Loos RJF, et al. Variability in the heritability of body mass index: a systematic review and meta-regression. Front Endocrinol (Lausanne). 2012;3: 29. doi: 10.3389/fendo.2012.00029 22645519
3. de Lauzon-Guillain B, Clifton EA, Day FR, Clément K, Brage S, Forouhi NG, et al. Mediation and modification of genetic susceptibility to obesity by eating behaviors. Am J Clin Nutr. 2017;106: 996–1004. doi: 10.3945/ajcn.117.157396 28814400
4. Cornelis MC, Rimm EB, Curhan GC, Kraft P, Hunter DJ, Hu FB, et al. Obesity susceptibility loci and uncontrolled eating, emotional eating and cognitive restraint behaviors in men and women. Obesity. 2014;22: E135–E141. doi: 10.1002/oby.20592 23929626
5. Tanofsky-Kraff M, Han JC, Anandalingam K, Shomaker LB, Columbo KM, Wolkoff LE, et al. The FTO gene rs9939609 obesity-risk allele and loss of control over eating. Am J Clin Nutr. 2009;90: 1483–1488. doi: 10.3945/ajcn.2009.28439 19828706
6. Merino J, Dashti HS, Li SX, Sarnowski C, Justice AE, Graff M, et al. Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium. Mol Psychiatry. 2018 [cited 30 Jul 2018]. doi: 10.1038/s41380-018-0079-4 29988085
7. Althubaiti A. Information bias in health research: Definition, pitfalls, and adjustment methods. Journal of Multidisciplinary Healthcare. 2016;9: 211–217. doi: 10.2147/JMDH.S104807 27217764
8. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518: 197–206. doi: 10.1038/nature14177 25673413
9. Berthoud H-R, Morrison C. The brain, appetite, and obesity. Annu Rev Psychol. 2008;59: 55–92. doi: 10.1146/annurev.psych.59.103006.093551 18154499
10. Qi Q, Chu AY, Kang JH, Jensen MK, Curhan GC, Pasquale LR, et al. Sugar-sweetened beverages and genetic risk of obesity. N Engl J Med. 2012;367: 1387–96. doi: 10.1056/NEJMoa1203039 22998338
11. Ding M, Ellervik C, Huang T, Jensen MK, Curhan GC, Pasquale LR, et al. Diet quality and genetic association with body mass index: results from 3 observational studies. Am J Clin Nutr. 2018;108: 1291–1300. doi: 10.1093/ajcn/nqy203 30351367
12. Moon J-Y, Wang T, Sofer T, North KE, Isasi CR, Cai J, et al. Objectively Measured Physical Activity, Sedentary Behavior, and Genetic Predisposition to Obesity in U.S. Hispanics/Latinos: Results From the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Diabetes. 2017;66: 3001–3012. doi: 10.2337/db17-0573 28986399
13. Robino A, Concas MP, Catamo E, Gasparini P. A Brief Review of Genetic Approaches to the Study of Food Preferences: Current Knowledge and Future Directions. Nutrients. 2019;11: 1735. doi: 10.3390/nu11081735 31357559
14. Ranzenhofer LM, Mayer LES, Davis HA, Mielke‐Maday HK, McInerney H, Korn R, et al. The FTO Gene and Measured Food Intake in 5‐ to 10‐Year‐Old Children Without Obesity. Obesity. 2019;27: 1023–1029. doi: 10.1002/oby.22464 31119882
15. van der Klaauw AA, Keogh JM, Henning E, Stephenson C, Kelway S, Trowse VM, et al. Divergent effects of central melanocortin signalling on fat and sucrose preference in humans. Nat Commun. 2016;7: 13055. doi: 10.1038/ncomms13055 27701398
16. McCurley JL, Levy DE, Rimm EB, Gelsomin ED, Anderson EM, Sanford JM, et al. Association of Worksite Food Purchases and Employees’ Overall Dietary Quality and Health. Am J Prev Med. 2019;57: 87–94. doi: 10.1016/j.amepre.2019.02.020 31128960
17. Levy DE, Gelsomin ED, Rimm EB, Pachucki M, Sanford J, Anderson E, et al. Design of ChooseWell 365: Randomized controlled trial of an automated, personalized worksite intervention to promote healthy food choices and prevent weight gain. Contemp Clin Trials. 2018;75: 78–86. doi: 10.1016/j.cct.2018.11.004 30414448
18. Thorndike AN, Sonnenberg L, Riis J, Barraclough S, Levy DE. A 2-phase labeling and choice architecture intervention to improve healthy food and beverage choices. Am J Public Health. 2012;102: 527–33. doi: 10.2105/AJPH.2011.300391 22390518
19. Frankenfeld CL, Poudrier JK, Waters NM, Gillevet PM, Xu Y. Dietary Intake Measured from a Self-Administered, Online 24-Hour Recall System Compared with 4-Day Diet Records in an Adult US Population. J Acad Nutr Diet. 2012;112: 1642–1647. doi: 10.1016/j.jand.2012.06.003 22878341
20. Subar AF, Kirkpatrick SI, Mittl B, Zimmerman TP, Thompson FE, Bingley C, et al. The Automated Self-Administered 24-hour dietary recall (ASA24): a resource for researchers, clinicians, and educators from the National Cancer Institute. J Acad Nutr Diet. 2012;112: 1134–7. doi: 10.1016/j.jand.2012.04.016 22704899
21. McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016;48: 1279–83. doi: 10.1038/ng.3643 27548312
22. Loh PR, Danecek P, Palamara PF, Fuchsberger C, Reshef YA, Finucane HK, et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat Genet. 2016;48: 1443–1448. doi: 10.1038/ng.3679 27694958
23. Wang C, Zhan X, Liang L, Abecasis GR, Lin X. Improved ancestry estimation for both genotyping and sequencing data using projection procrustes analysis and genotype imputation. Am J Hum Genet. 2015;96: 926–37. doi: 10.1016/j.ajhg.2015.04.018 26027497
24. Cann HM, de Toma C, Cazes L, Legrand M-F, Morel V, Piouffre L, et al. A human genome diversity cell line panel. Science. 2002;296: 261–2. Available from: http://www.ncbi.nlm.nih.gov/pubmed/11954565 doi: 10.1126/science.296.5566.261b 11954565
25. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81: 559–75. doi: 10.1086/519795 17701901
26. Qi Q, Chu AY, Kang JH, Huang J, Rose LM, Jensen MK, et al. Fried food consumption, genetic risk, and body mass index: gene-diet interaction analysis in three US cohort studies. BMJ. 2014;348: g1610. doi: 10.1136/bmj.g1610 24646652
27. Euesden J, Lewis CM, O’Reilly PF. PRSice: Polygenic Risk Score software. Bioinformatics. 2015;31: 1466–8. doi: 10.1093/bioinformatics/btu848 25550326
28. Stolwijk AM, Straatman H, Zielhuis GA. Studying seasonality by using sine and cosine functions in regression analysis. J Epidemiol Community Health. 1999;53: 235–8. doi: 10.1136/jech.53.4.235 10396550
29. Hagströmer M, Oja P, Sjöström M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr. 2006;9: 755–762. doi: 10.1079/phn2005898 16925881
30. Wang T, Heianza Y, Sun D, Huang T, Ma W, Rimm EB, et al. Improving adherence to healthy dietary patterns, genetic risk, and long term weight gain: gene-diet interaction analysis in two prospective cohort studies. BMJ. 2018;360: j5644. doi: 10.1136/bmj.j5644 29321156
31. Khera A V, Emdin CA, Kathiresan S. Genetic Risk, Lifestyle, and Coronary Artery Disease. N Engl J Med. 2017;376: 1192–1195. doi: 10.1056/NEJMc1700362 28332385
32. Meyre D, Mohamed S, Gray JC, Weafer J, MacKillop J, de Wit H. Association between impulsivity traits and body mass index at the observational and genetic epidemiology level. Sci Rep. 2019;9: 17583. doi: 10.1038/s41598-019-53922-8 31772290
33. Roberts SB, Das SK, Suen VMM, Pihlajamäki J, Kuriyan R, Steiner-Asiedu M, et al. Measured energy content of frequently purchased restaurant meals: multi-country cross sectional study. BMJ. 2018;363: k4864. doi: 10.1136/bmj.k4864 30541752
34. McCaffery JM, Papandonatos GD, Peter I, Huggins GS, Raynor HA, Delahanty LM, et al. Obesity susceptibility loci and dietary intake in the Look AHEAD Trial. Am J Clin Nutr. 2012;95: 1477–86. doi: 10.3945/ajcn.111.026955 22513296
35. Rapuano KM, Zieselman AL, Kelley WM, Sargent JD, Heatherton TF, Gilbert-Diamond D. Genetic risk for obesity predicts nucleus accumbens size and responsivity to real-world food cues. Proc Natl Acad Sci U S A. 2017;114: 160–165. doi: 10.1073/pnas.1605548113 27994159
36. Rios M. Special issue: neural control of appetite BDNF and the central control of feeding: accidental bystander or essential player? Trends Neurosci. 2013;36: 83–90. doi: 10.1016/j.tins.2012.12.009 23333344
37. Meddens SFW, de Vlaming R, Bowers P, Burik CAP, Linnér RK, Lee C, et al. Genomic analysis of diet composition finds novel loci and associations with health and lifestyle. Mol Psychiatry. 2020. doi: 10.1038/s41380-020-0697-5 32393786
38. Buzaglo-Azriel L, Kuperman Y, Tsoory M, Haran M, Vernochet C, Gross Correspondence A, et al. Loss of Muscle MTCH2 Increases Whole-Body Energy Utilization and Protects from Diet-Induced Obesity. CellReports. 2016;14: 1602–1610. doi: 10.1016/j.celrep.2016.01.046 26876167
39. Tumin R, Anderson SE. Television, Home-Cooked Meals, and Family Meal Frequency: Associations with Adult Obesity. J Acad Nutr Diet. 2017;117: 937–945. doi: 10.1016/j.jand.2017.01.009 28242429
40. Baron KG, Reid KJ, Kern AS, Zee PC. Role of Sleep Timing in Caloric Intake and BMI. Obesity. 2011;19: 1374–1381. doi: 10.1038/oby.2011.100 21527892
41. Garaulet M, Gómez-Abellán P, Alburquerque-Béjar JJ, Lee Y-C, Ordovás JM, Scheer FAJL. Timing of food intake predicts weight loss effectiveness. Int J Obes (Lond). 2013;37: 604–11. doi: 10.1038/ijo.2012.229 23357955
42. Udler MS, Kim J, von Grotthuss M, Bonàs-Guarch S, Cole JB, Chiou J, et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis. PLoS Med. 2018;15: e1002654. doi: 10.1371/journal.pmed.1002654 30240442
Článek vyšel v časopise
PLOS Medicine
2020 Číslo 7
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Je libo čepici místo mozkového implantátu?
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- AI může chirurgům poskytnout cenná data i zpětnou vazbu v reálném čase
- Nová metoda odlišení nádorové tkáně může zpřesnit resekci glioblastomů
Nejčtenější v tomto čísle
- Obesity, clinical, and genetic predictors for glycemic progression in Chinese patients with type 2 diabetes: A cohort study using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank
- Participation in adherence clubs and on-time drug pickup among HIV-infected adults in Zambia: A matched-pair cluster randomized trial
- Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe
- Neonatal outcome in 29 pregnant women with COVID-19: A retrospective study in Wuhan, China
