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

Biesbroek, Sander; A, Daphne L. van der; Brosens, M; Beulens, Joline W.J.; Verschuren, W M Monique; Schouw, Yvonne T. van der; Boer, Jolanda M. A.

doi:10.3945/ajcn.114.092288

Cited by 36 publications

(44 citation statements)

References 33 publications

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“…Similar comparison studies also showed that in general PCA-derived patterns explain more variance of the food groups but explain less of the variance in response variables (Hoffmann et al 2005;Hoffmann et al 2004). Our first RRR pattern explained 40.5% of the response variable variation, which is in line with results from other studies (Biesbroek et al 2015;Hoffmann et al 2005;Hoffmann et al 2004), but clearly leaves a proportion of the variance unexplained. Other factors, such as socioeconomic status, age, or gender might also impact GHG emissions of the diet and dietary quality.…”

Section: Discussionsupporting

confidence: 80%

Identification of data-driven Dutch dietary patterns that benefit the environment and are healthy

et al. 2018

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More sustainable dietary patterns are needed to mitigate global warming. This study aims to identify data-driven healthy dietary patterns that benefit the environment. In EPIC-NL, diet was assessed using a 178-item FFQ in 36,203 participants aged 20-70 years between 1993 and 1997. The Dutch Healthy Diet index 2015 (DHD15-index) was used to score healthiness of the diet. As proxy for environmental impact, greenhouse gas (GHG) emissions were calculated using life cycle analysis. To determine patterns that are both healthy and environmentally friendly, reduced rank regression was applied. A BPlant-based Pattern^characterized by high consumption of fruits, vegetables, and legumes, and low consumption of fries, red meat, and processed meat and a BDairy-based Pattern^characterized by high consumption of dairy, and nuts and seeds and low consumption of coffee and tea, sugar-containing sodas, lowfiber bread, and savory sauces were derived. At equal energy intake, the diet of adherents (highest quartile) to the BPlant-based Pattern^were significantly healthier (89.8 points on the DHD15-index, p < 0.0001) and more sustainable (3.96 kg C0 2 -eq/day, p < 0.0001) compared to the average diet (76.2 points, 4.06 kg C0 2 -eq/day), whereas the BDairy-based Pattern^was somewhat healthier (77.9 points, p < 0.0001), but less sustainable (4.43 kg C0 2 -eq/day, p < 0.0001). When deriving dietary patterns based on health and environmental aspects of the diets, a BPlant-based^and a BDairy-based^pattern were observed in our study population. Of these, the plant-based diet benefits health as well as the environment.

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Section: Discussionsupporting

confidence: 80%

Identification of data-driven Dutch dietary patterns that benefit the environment and are healthy

et al. 2018

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“…The relationships between selected health outcomes and 'traditional' DP identified in different populations strongly depend on the dietary habits of these populations. For instance, in the European Prospective Investigation into Cancer (EPIC) -Netherlands Study, the 'traditional' DP was significantly associated with coronary heart disease (hazard ratio (HR) 1.25; 95% CI: 1.07-1.47 or 1.29; 95% CI: 1.11-1.50, depending on the statistical method) [26]. In a cross-sectional study performed in Brazil, the 'traditional' DP had a negative effect on obesity indicators, lipids and fasting plasma glucose [27].…”

Section: Discussionmentioning

confidence: 99%

Association between dietary patterns and cardiovascular risk factors in a selected population of Lower Silesia (PURE Study Poland)

Czekajło¹,

Różańska²,

Zatońska³

et al. 2018

Ann Agric Environ Med.

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Introduction. Dietary pattern analysis is used to describe the dietary habits of a selected population. In many studies, dietary patterns (DPs) have been associated with risk factors for cardiovascular disease (CVD). The aim of the study was to assess the association between dietary patterns identified in the population of Lower Silesia, Poland, with anthropometric and biochemical risk factors for CVD. Materials and method. The study group included 2,025 participants of the Prospective Urban Rural Epidemiological (PURE) Study. Dietary intake was evaluated based on data from the Food Frequency Questionnaire (FFQ). Dietary patterns were derived using principal component analysis (PCA). The relationship between DPs and body mass index (BMI), waist circumference, waist-hip ratio, blood pressure, total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides and fasting glucose level, was assessed. Results. Three dietary patterns identified in the study explained 35.6% of total variance. The 'fruit, vegetables & dairy' DP, characterized by a high intake of vegetables, fruits, nuts, seeds, raisins, milk and low-fat dairy, was associated with improved lipid profile and anthropometric measures, lower diastolic blood pressure and lower fasting glucose concentration. 'Traditional' and 'fat & sugar' DPs were unfavourably associated with most of the risk factors for CVD presented in this study. Conclusions. Dietary patterns identified in this study were differently related to selected anthropometric and biochemical risk factors for CVD. 'Fruit, vegetables & dairy' DP was favourably associated with the biochemical and anthropometric CVD risk factors, and was characterized by higher nutritional value in comparison with 'traditional' and 'fat & sugar' DPs.

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“…As a result, in contrast to purely data-driven approaches, such as PCA, RRR can explain variation in nutrient intakes by linear functions of food intakes ( 5 ) . Taken together, RRR generates DP that may be more appropriate for the diseases of interest ( 7 ) . Although DP generated from RRR are specific to the dietary intakes of the cohort, and are thus criticised for their generalisability when compared with diet quality scores, RRR has the advantage of being less sensitive to violations of assumptions concerning directional relations because it does not fix expected directions of effects when determining response variables ( 5 ) .…”

Section: Discussionmentioning

confidence: 99%

“…The latter offers the advantage of considering the correlation structure of food intakes, yet often fails to predict disease outcomes ( 4 ) . Reduced rank regression (RRR) is a combined approach that utilises both exploratory, data-driven analyses and a priori knowledge of a disease ( 5 ) , which has been shown to derive DP that predict disease outcomes ( 6 , 7 ) . Moreover, unlike purely data-driven approaches such as principal component analysis (PCA), RRR provides a valuable link between nutrient and food-based approaches ( 8 ) .…”

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confidence: 99%

Dietary patterns by reduced rank regression are associated with obesity and hypertension in Australian adults

Livingstone

McNaughton

2017

Br J Nutr

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Evidence linking dietary patterns (DP) and obesity and hypertension prevalence is inconsistent. We aimed to identify DP derived from energy density, fibre and sugar intakes, as well as Na, K, fibre, SFA and PUFA, and investigate associations with obesity and hypertension. Adults (n 4908) were included from the cross-sectional Australian Health Survey 2011-2013. Two 24-h dietary recalls estimated food and nutrient intakes. Reduced rank regression derived DP with dietary energy density (DED), fibre density and total sugar intake as response variables for obesity and Na:K, SFA:PUFA and fibre density as variables for hypertension. Poisson regression investigated relationships between DP and prevalence ratios (PR) of overweight/obesity (BMI≥25 kg/m2) and hypertension (blood pressure≥140/90 mmHg). Obesity-DP1 was positively correlated with fibre density and sugars and inversely with DED. Obesity-DP2 was positively correlated with sugars and inversely with fibre density. Individuals in the highest tertile of Obesity-DP1 and Obesity-DP2, compared with the lowest, had lower (PR 0·88; 95 % CI 0·81, 0·95) and higher (PR 1·09; 95 % CI 1·01, 1·18) prevalence of obesity, respectively. Na:K and SFA:PUFA were positively correlated with Hypertension-DP1 and inversely correlated with Hypertension-DP2, respectively. There was a trend towards higher hypertension prevalence in the highest tertile of Hypertension-DP1 compared with the lowest (PR 1·18; 95 % CI 0·99, 1·41). Hypertension-DP2 was not associated with hypertension. Obesity prevalence was inversely associated with low-DED, high-fibre and high-sugar (natural sugars) diets and positively associated with low-fibre and high-sugar (added sugars) diets. Hypertension prevalence was higher on low-fibre and high-Na and SFA diets.

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

Abstract: 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.

Cited by 36 publications

References 33 publications

Identification of data-driven Dutch dietary patterns that benefit the environment and are healthy

Identification of data-driven Dutch dietary patterns that benefit the environment and are healthy

Association between dietary patterns and cardiovascular risk factors in a selected population of Lower Silesia (PURE Study Poland)

Dietary patterns by reduced rank regression are associated with obesity and hypertension in Australian adults

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