Identifying cardiovascular risk factor-related dietary patterns with reduced rank regression and random forest in the EPIC-NL cohort

被引:29
作者
Biesbroek, Sander [1 ]
van der A, Daphne L. [1 ]
Brosens, Marinka C. [1 ]
Beulens, Joline W. J. [2 ]
Verschuren, W. M. Monique [1 ]
van der Schouw, Yvonne T. [2 ]
Boer, Jolanda M. A. [1 ]
机构
[1] Natl Inst Publ Hlth & Environm RIVM, Bilthoven, Netherlands
[2] Univ Med Ctr Utrecht, Julius Ctr, Utrecht, Netherlands
关键词
cardiovascular diseases; dietary patterns; principal component analysis; random forest; reduced rank regression; CORONARY-HEART-DISEASE; PHYSICAL-ACTIVITY; PUBLIC-HEALTH; CLASSIFICATION; NUTRITION; CANCER; REPRODUCIBILITY; COMPONENTS; VALIDITY; PROFILE;
D O I
10.3945/ajcn.114.092288
中图分类号
R15 [营养卫生、食品卫生]; TS201 [基础科学];
学科分类号
100403 ;
摘要
Background: Several methods are used to determine dietary patterns. Hybrid methods incorporate information on nutrient intake or biological factors to extract patterns relevant to disease etiology. Objective: We explore differences between patterns derived with 2 hybrid methods with those obtained by a posteriori methods and compare associations of these patterns with coronary artery disease (CAD) and stroke risk. Design: Food-frequency questionnaires were used to estimate dietary intake in 34,644 participants of European Prospective Investigation into Cancer Netherlands at baseline (1993-1997). Follow-up was complete until 31 December 2007. Hybrid methods to determine dietary patterns were reduced rank regression (RRR) and random forest with classification tree analysis (RF-CTA). Included risk factors were body mass index, total:high-density lipoprotein cholesterol ratio, and systolic blood pressure. Results were compared with those from principal component analysis (PCA) and k-means cluster analysis (KCA), respectively. Results: Both RRR and PCA derived a "Western," "prudent," and "traditional pattern." All RRR patterns were significantly associated with CAD risk [highest vs. lowest quartile factor score; HR: 1.45(95% CI: 1.25, 1.69), 0.86 (0.74, 0.99), and 1.25 (1.07, 1.47), respectively]. Only the prudent RRR factor was statistically significant associated with stroke (HR: 0.76; 95% CI: 0.59, 0.97). From the PCA patterns, only the traditional pattern was associated with CAD (HR: 1.29; 95% Cl: 1.11, 1.50). RF-CTA derived 7 dietary patterns that could be categorized as "Western-like," "prudent-like," and "traditional-like." KCA established a prudent and Western cluster. Compared with the RF-CTA "prudent-like l" pattern, only the "traditional-like l" pattern was associated with CAD (HR: 1.36; 955 CI: 1.12, 1.65). None of the RF-CTA groups were associated with stroke. Compared with the Western KCA cluster, the prudent cluster was not associated with CAD or stroke. Conclusion: Including risk factors in RRR and RF-CTA resulted in small differences in food groups, contributing to similar patterns that showed in general stronger associations with CAD than PCA and KCA, respectively.
引用
收藏
页码:146 / 154
页数:9
相关论文
共 36 条
[1]  
[Anonymous], 2005, International Journal of Advance Research in Computer Science and Management Studies
[2]   Healthy indexes in public health practice and research: A review [J].
Arvaniti, Fotini ;
Panagiotakos, Demosthenes B. .
CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION, 2008, 48 (04) :317-327
[3]   Dietary patterns and the risk of type 2 diabetes in overweight and obese individuals [J].
Bauer, Florianne ;
Beulens, Joline W. J. ;
van der A, Daphne L. ;
Wijmenga, Cisca ;
Grobbee, Diederick E. ;
Spijkerman, Annemieke M. W. ;
van der Schouw, Yvonne T. ;
Onland-Moret, N. Charlotte .
EUROPEAN JOURNAL OF NUTRITION, 2013, 52 (03) :1127-1134
[4]   Cohort Profile: The EPIC-NL study [J].
Beulens, Joline W. J. ;
Monninkhof, Evelyn M. ;
Verschuren, W. M. Monique ;
van der Schouw, Yvonne T. - ;
Smit, Jet ;
Ocke, Marga C. ;
Jansen, Eugene H. J. M. ;
van Dieren, Susan ;
Grobbee, Diederick E. ;
Peeters, Petra H. M. ;
Bueno-de-Mesquita, H. Bas .
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 2010, 39 (05) :1170-1178
[5]  
Blokstra A, 2005, MONITORING RISK FACT
[6]   Prospect-EPIC Utrecht: Study design and characteristics of the cohort population [J].
Boker, LK ;
van Noord, PAH ;
van der Schouw, YT ;
Koot, NVCM ;
de Mesquita, HBB ;
Riboli, E ;
Grobbee, DE ;
Peeters, PHM .
EUROPEAN JOURNAL OF EPIDEMIOLOGY, 2001, 17 (11) :1047-1053
[7]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[8]   Identifying SNPs predictive of phenotype using random forests [J].
Bureau, A ;
Dupuis, J ;
Falls, K ;
Lunetta, KL ;
Hayward, B ;
Keith, TP ;
Van Eerdewegh, P .
GENETIC EPIDEMIOLOGY, 2005, 28 (02) :171-182
[9]   Gene selection and classification of microarray data using random forest -: art. no. 3 [J].
Díaz-Uriarte, R ;
de Andrés, SA .
BMC BIOINFORMATICS, 2006, 7 (1)
[10]   Comparison of 3 Methods for Identifying Dietary Patterns Associated With Risk of Disease [J].
DiBello, Julia R. ;
Kraft, Peter ;
McGarvey, Stephen T. ;
Goldberg, Robert ;
Campos, Hannia ;
Baylin, Ana .
AMERICAN JOURNAL OF EPIDEMIOLOGY, 2008, 168 (12) :1433-1443