Joint Associations of Multiple Dietary Components With Cardiovascular Disease Risk: A Machine-Learning Approach. Issue 7 (1st February 2021)
- Record Type:
- Journal Article
- Title:
- Joint Associations of Multiple Dietary Components With Cardiovascular Disease Risk: A Machine-Learning Approach. Issue 7 (1st February 2021)
- Main Title:
- Joint Associations of Multiple Dietary Components With Cardiovascular Disease Risk: A Machine-Learning Approach
- Authors:
- Zhao, Yi
Naumova, Elena N
Bobb, Jennifer F
Claus Henn, Birgit
Singh, Gitanjali M - Abstract:
- Abstract: The human diet consists of a complex mixture of components. To realistically assess dietary impacts on health, new statistical tools that can better address nonlinear, collinear, and interactive relationships are necessary. Using data from 1, 928 healthy participants in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort (1985–2006), we explored the association between 12 dietary factors and 10-year predicted risk of atherosclerotic cardiovascular disease (ASCVD) using an innovative approach, Bayesian kernel machine regression (BKMR). Employing BKMR, we found that among women, unprocessed red meat was most strongly related to the outcome: An interquartile range increase in unprocessed red meat consumption was associated with a 0.07-unit (95% credible interval: 0.01, 0.13) increase in ASCVD risk when intakes of other dietary components were fixed at their median values (similar results were obtained when other components were fixed at their 25th and 75th percentile values). Among men, fruits had the strongest association: An interquartile range increase in fruit consumption was associated with −0.09-unit (95% credible interval (CrI): −0.16, −0.02), −0.10-unit (95% CrI: −0.16, −0.03), and −0.11-unit (95% CrI: −0.18, −0.04) lower ASCVD risk when other dietary components were fixed at their 25th, 50th (median), and 75th percentile values, respectively. Using BKMR to explore the complex structure of the total diet, we found distinct sex-specificAbstract: The human diet consists of a complex mixture of components. To realistically assess dietary impacts on health, new statistical tools that can better address nonlinear, collinear, and interactive relationships are necessary. Using data from 1, 928 healthy participants in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort (1985–2006), we explored the association between 12 dietary factors and 10-year predicted risk of atherosclerotic cardiovascular disease (ASCVD) using an innovative approach, Bayesian kernel machine regression (BKMR). Employing BKMR, we found that among women, unprocessed red meat was most strongly related to the outcome: An interquartile range increase in unprocessed red meat consumption was associated with a 0.07-unit (95% credible interval: 0.01, 0.13) increase in ASCVD risk when intakes of other dietary components were fixed at their median values (similar results were obtained when other components were fixed at their 25th and 75th percentile values). Among men, fruits had the strongest association: An interquartile range increase in fruit consumption was associated with −0.09-unit (95% credible interval (CrI): −0.16, −0.02), −0.10-unit (95% CrI: −0.16, −0.03), and −0.11-unit (95% CrI: −0.18, −0.04) lower ASCVD risk when other dietary components were fixed at their 25th, 50th (median), and 75th percentile values, respectively. Using BKMR to explore the complex structure of the total diet, we found distinct sex-specific diet-ASCVD relationships and synergistic interaction between whole grain and fruit consumption. … (more)
- Is Part Of:
- American journal of epidemiology. Volume 190:Issue 7(2021)
- Journal:
- American journal of epidemiology
- Issue:
- Volume 190:Issue 7(2021)
- Issue Display:
- Volume 190, Issue 7 (2021)
- Year:
- 2021
- Volume:
- 190
- Issue:
- 7
- Issue Sort Value:
- 2021-0190-0007-0000
- Page Start:
- 1353
- Page End:
- 1365
- Publication Date:
- 2021-02-01
- Subjects:
- cardiovascular diseases -- complex mixtures -- machine learning
Epidemiology -- Periodicals
Public health -- Periodicals
614.4 - Journal URLs:
- http://aje.oxfordjournals.org/ ↗
http://ukcatalogue.oup.com/ ↗ - DOI:
- 10.1093/aje/kwab004 ↗
- Languages:
- English
- ISSNs:
- 0002-9262
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0824.600000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 24965.xml