PMID- 33521815
OWN - NLM
STAT- MEDLINE
DCOM- 20210831
LR  - 20220202
IS  - 1476-6256 (Electronic)
IS  - 0002-9262 (Print)
IS  - 0002-9262 (Linking)
VI  - 190
IP  - 7
DP  - 2021 Jul 1
TI  - Joint Associations of Multiple Dietary Components With Cardiovascular Disease 
      Risk: A Machine-Learning Approach.
PG  - 1353-1365
LID - 10.1093/aje/kwab004 [doi]
AB  - 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.
CI  - (c) The Author(s) 2021. Published by Oxford University Press on behalf of the Johns 
      Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, 
      please e-mail: journals.permissions@oup.com.
FAU - Zhao, Yi
AU  - Zhao Y
FAU - Naumova, Elena N
AU  - Naumova EN
FAU - Bobb, Jennifer F
AU  - Bobb JF
FAU - Claus Henn, Birgit
AU  - Claus Henn B
FAU - Singh, Gitanjali M
AU  - Singh GM
LA  - eng
GR  - R00 HL124321/HL/NHLBI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PL  - United States
TA  - Am J Epidemiol
JT  - American journal of epidemiology
JID - 7910653
SB  - IM
MH  - Adult
MH  - Bayes Theorem
MH  - Cardiovascular Diseases/*epidemiology/etiology
MH  - Diet/adverse effects/*statistics & numerical data
MH  - Diet Surveys
MH  - Female
MH  - Follow-Up Studies
MH  - Heart Disease Risk Factors
MH  - Humans
MH  - Linear Models
MH  - Longitudinal Studies
MH  - *Machine Learning
MH  - Male
MH  - Middle Aged
MH  - Prospective Studies
MH  - Risk Assessment
MH  - United States/epidemiology
PMC - PMC8245893
OTO - NOTNLM
OT  - cardiovascular diseases
OT  - complex mixtures
OT  - machine learning
EDAT- 2021/02/02 06:00
MHDA- 2021/09/01 06:00
PMCR- 2022/02/01
CRDT- 2021/02/01 06:04
PHST- 2019/12/20 00:00 [received]
PHST- 2021/01/06 00:00 [revised]
PHST- 2021/01/07 00:00 [accepted]
PHST- 2021/02/02 06:00 [pubmed]
PHST- 2021/09/01 06:00 [medline]
PHST- 2021/02/01 06:04 [entrez]
PHST- 2022/02/01 00:00 [pmc-release]
AID - 6123947 [pii]
AID - kwab004 [pii]
AID - 10.1093/aje/kwab004 [doi]
PST - ppublish
SO  - Am J Epidemiol. 2021 Jul 1;190(7):1353-1365. doi: 10.1093/aje/kwab004.