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Interaction of obesity polygenic score with lifestyle risk factors in an electronic health record biobank

BACKGROUND: Genetic and lifestyle factors have considerable effects on obesity and related diseases, yet their effects in a clinical cohort are unknown. This study in a patient biobank examined associations of a BMI polygenic risk score (PRS), and its interactions with lifestyle risk factors, with c...

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Autores principales: Dashti, Hassan S., Miranda, Nicole, Cade, Brian E., Huang, Tianyi, Redline, Susan, Karlson, Elizabeth W., Saxena, Richa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8753909/
https://www.ncbi.nlm.nih.gov/pubmed/35016652
http://dx.doi.org/10.1186/s12916-021-02198-9
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author Dashti, Hassan S.
Miranda, Nicole
Cade, Brian E.
Huang, Tianyi
Redline, Susan
Karlson, Elizabeth W.
Saxena, Richa
author_facet Dashti, Hassan S.
Miranda, Nicole
Cade, Brian E.
Huang, Tianyi
Redline, Susan
Karlson, Elizabeth W.
Saxena, Richa
author_sort Dashti, Hassan S.
collection PubMed
description BACKGROUND: Genetic and lifestyle factors have considerable effects on obesity and related diseases, yet their effects in a clinical cohort are unknown. This study in a patient biobank examined associations of a BMI polygenic risk score (PRS), and its interactions with lifestyle risk factors, with clinically measured BMI and clinical phenotypes. METHODS: The Mass General Brigham (MGB) Biobank is a hospital-based cohort with electronic health record, genetic, and lifestyle data. A PRS for obesity was generated using 97 genetic variants for BMI. An obesity lifestyle risk index using survey responses to obesogenic lifestyle risk factors (alcohol, education, exercise, sleep, smoking, and shift work) was used to dichotomize the cohort into high and low obesogenic index based on the population median. Height and weight were measured at a clinical visit. Multivariable linear cross-sectional associations of the PRS with BMI and interactions with the obesity lifestyle risk index were conducted. In phenome-wide association analyses (PheWAS), similar logistic models were conducted for 675 disease outcomes derived from billing codes. RESULTS: Thirty-three thousand five hundred eleven patients were analyzed (53.1% female; age 60.0 years; BMI 28.3 kg/m(2)), of which 17,040 completed the lifestyle survey (57.5% female; age: 60.2; BMI: 28.1 (6.2) kg/m(2)). Each standard deviation increment in the PRS was associated with 0.83 kg/m(2) unit increase in BMI (95% confidence interval (CI) =0.76, 0.90). There was an interaction between the obesity PRS and obesity lifestyle risk index on BMI. The difference in BMI between those with a high and low obesogenic index was 3.18 kg/m(2) in patients in the highest decile of PRS, whereas that difference was only 1.55 kg/m(2) in patients in the lowest decile of PRS. In PheWAS, the obesity PRS was associated with 40 diseases spanning endocrine/metabolic, circulatory, and 8 other disease groups. No interactions were evident between the PRS and the index on disease outcomes. CONCLUSIONS: In this hospital-based clinical biobank, obesity risk conferred by common genetic variants was associated with elevated BMI and this risk was attenuated by a healthier patient lifestyle. Continued consideration of the role of lifestyle in the context of genetic predisposition in healthcare settings is necessary to quantify the extent to which modifiable lifestyle risk factors may moderate genetic predisposition and inform clinical action to achieve personalized medicine. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-021-02198-9.
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spelling pubmed-87539092022-01-18 Interaction of obesity polygenic score with lifestyle risk factors in an electronic health record biobank Dashti, Hassan S. Miranda, Nicole Cade, Brian E. Huang, Tianyi Redline, Susan Karlson, Elizabeth W. Saxena, Richa BMC Med Research Article BACKGROUND: Genetic and lifestyle factors have considerable effects on obesity and related diseases, yet their effects in a clinical cohort are unknown. This study in a patient biobank examined associations of a BMI polygenic risk score (PRS), and its interactions with lifestyle risk factors, with clinically measured BMI and clinical phenotypes. METHODS: The Mass General Brigham (MGB) Biobank is a hospital-based cohort with electronic health record, genetic, and lifestyle data. A PRS for obesity was generated using 97 genetic variants for BMI. An obesity lifestyle risk index using survey responses to obesogenic lifestyle risk factors (alcohol, education, exercise, sleep, smoking, and shift work) was used to dichotomize the cohort into high and low obesogenic index based on the population median. Height and weight were measured at a clinical visit. Multivariable linear cross-sectional associations of the PRS with BMI and interactions with the obesity lifestyle risk index were conducted. In phenome-wide association analyses (PheWAS), similar logistic models were conducted for 675 disease outcomes derived from billing codes. RESULTS: Thirty-three thousand five hundred eleven patients were analyzed (53.1% female; age 60.0 years; BMI 28.3 kg/m(2)), of which 17,040 completed the lifestyle survey (57.5% female; age: 60.2; BMI: 28.1 (6.2) kg/m(2)). Each standard deviation increment in the PRS was associated with 0.83 kg/m(2) unit increase in BMI (95% confidence interval (CI) =0.76, 0.90). There was an interaction between the obesity PRS and obesity lifestyle risk index on BMI. The difference in BMI between those with a high and low obesogenic index was 3.18 kg/m(2) in patients in the highest decile of PRS, whereas that difference was only 1.55 kg/m(2) in patients in the lowest decile of PRS. In PheWAS, the obesity PRS was associated with 40 diseases spanning endocrine/metabolic, circulatory, and 8 other disease groups. No interactions were evident between the PRS and the index on disease outcomes. CONCLUSIONS: In this hospital-based clinical biobank, obesity risk conferred by common genetic variants was associated with elevated BMI and this risk was attenuated by a healthier patient lifestyle. Continued consideration of the role of lifestyle in the context of genetic predisposition in healthcare settings is necessary to quantify the extent to which modifiable lifestyle risk factors may moderate genetic predisposition and inform clinical action to achieve personalized medicine. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-021-02198-9. BioMed Central 2022-01-12 /pmc/articles/PMC8753909/ /pubmed/35016652 http://dx.doi.org/10.1186/s12916-021-02198-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Dashti, Hassan S.
Miranda, Nicole
Cade, Brian E.
Huang, Tianyi
Redline, Susan
Karlson, Elizabeth W.
Saxena, Richa
Interaction of obesity polygenic score with lifestyle risk factors in an electronic health record biobank
title Interaction of obesity polygenic score with lifestyle risk factors in an electronic health record biobank
title_full Interaction of obesity polygenic score with lifestyle risk factors in an electronic health record biobank
title_fullStr Interaction of obesity polygenic score with lifestyle risk factors in an electronic health record biobank
title_full_unstemmed Interaction of obesity polygenic score with lifestyle risk factors in an electronic health record biobank
title_short Interaction of obesity polygenic score with lifestyle risk factors in an electronic health record biobank
title_sort interaction of obesity polygenic score with lifestyle risk factors in an electronic health record biobank
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8753909/
https://www.ncbi.nlm.nih.gov/pubmed/35016652
http://dx.doi.org/10.1186/s12916-021-02198-9
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