Dietary Patterns of Adults in Québec and their Nutritional Adequacy

Beaudry, Madeleine; Galibois, Isabelle; Chaumette, Pascale

doi:10.1007/bf03404489

Cited by 42 publications

(26 citation statements)

References 8 publications

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“…However, our lack of findings is in line with Wolff et al ,26 Fung et al 27 and Beaudry et al ,28 who all used factor analysis to detect dietary patterns. Wolff et al 26 found seven independent dietary patterns, but none was associated with weight gain.…”

Section: Discussionsupporting

confidence: 87%

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Relation between dietary pattern analysis (principal component analysis) and body mass index: a 5-year follow-up study in a Belgian military population

Mullie

Clarys

2015

J R Army Med Corps

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Section: Discussionsupporting

confidence: 87%

“…Wolff et al 26 found seven independent dietary patterns, but none was associated with weight gain. In a Canadian study, Beaudry et al 28 detected following three factors: high-energy density, traditional and health-conscious. No relation was found between these factors and BMI change.…”

Section: Discussionmentioning

confidence: 99%

Relation between dietary pattern analysis (principal component analysis) and body mass index: a 5-year follow-up study in a Belgian military population

Mullie

Clarys

2015

J R Army Med Corps

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“…Dietary patterns similar to our healthy dietary pattern, with positive loadings on foods generally considered healthy such as fruit and vegetables, fish, whole-grain cereals, vegetable oil, etc., have been identified in other populations (5,(22)(23)(24)(25)(26) . These patterns are often named 'healthy', 'prudent' or 'vegetable' patterns.…”

Section: Dietary Patterns In Different Populationsmentioning

confidence: 93%

Dietary patterns in Swedish adults; results from a national dietary survey

Lemming

Becker

et al. 2015

Br J Nutr

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Dietary patterns derived by statistical procedures is a way to identify overall dietary habits in specific populations. The aim of this study was to identify and characterise dietary patterns in Swedish adults using data from the national dietary survey Riksmaten adults 2010-11 (952 women, 788 men). Principal component analyses were used and two patterns were identified in both sexes: a healthy pattern loading positively on vegetables, fruits, fish and seafood, and vegetable oils, and negatively on refined bread and fast food, and a Swedish traditional pattern loading positively on potatoes, meat and processed meat, full-fat milk products, sweet bakery products, sweet condiments and margarine. In addition, a light-meal pattern was identified in women with positive loadings on fibre-rich bread, cheese, rice, pasta and food grain dishes, substitute products for meat and dairy products, candies and tea. The healthy pattern was positively correlated to dietary fibre (r 0·51-0·58) and n-3 (r 0·25-0·31) (all P < 0·0001), and had a higher nutrient density of folate, vitamin D and Se. The Swedish traditional and the light-meal pattern were positively correlated to added sugar (r 0·20-0·25) and the Swedish traditional also to SFA (r 0·13-0·21) (all P < 0·0001); both patterns were in general negatively correlated to micronutrients. Dietary pattern scores were associated with, for example, age, physical activity, education and income. In conclusion, we identified three major dietary patterns among Swedish adults. The patterns can be further used for examining the association between whole diet and health outcomes. Dietary pattern analysis has become a popular method for studying overall diet and associations between diet and disease risk. Pre-specified indices are used to measure adherence to recommendations or diets with hypothesised health implications, whereas data-driven methods, often factor or cluster analysis, are applied to identify underlying food patterns in a population empirically (1,2) . Participants' adherence to the dietary patterns can then serve as a holistic measure of their diet in assessment of diet-disease relationships. Such a holistic approach has several methodological advantages, as it includes unexplored dietary factors and nutrient interactions and increases the chance of detecting small but meaningful associations as they add up in the context of a whole diet, and at the same time not impeded by the disadvantages of correlated intakes (1,3) . Indeed, throughout the past decades, dietary patterns have been related to multiple health outcomes, mainly morbidity and mortality from CVD and cancer, longevity and neurodegenerative diseases (4)(5)(6)(7)(8)(9)(10)(11) . From the perspective of public health promotion, dietary patterns reflect the foods commonly consumed together. Hence, promoting healthy dietary patterns, in addition to targeted food-based dietary guidelines, might be a useful approach in order to improve diet and health in a population. Understanding the underlying dietary habi...

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“…Likewise, many studies show that eating patterns are associated with other characteristics, including sex, age, socioeconomic status, and general health habits (e.g., smoking, drinking). For example, more women than men have been shown to have a healthier eating pattern 29,59,63,71,75,86,90,106,108 and socioeconomic associations were in the expected direction (e.g., income and education were highest in the Healthy cluster). 59,86,103 Age has been directly 29,79,82 and inversely 63,86 associated with a healthier pattern.…”

Section: Major Findingsmentioning

confidence: 98%

Empirically Derived Eating Patterns Using Factor or Cluster Analysis: A Review

Newby¹,

Tucker²

2004

Nut. Rev.

428

633

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This paper reviews studies performed to date that have employed cluster or factor analysis to empirically derive eating patterns. Since 1980, at least 93 studies were published that used cluster or factor analysis to define dietary exposures, of which 65 were used to test hypotheses or examine associations between patterns and disease outcomes or biomarkers. Studies were conducted in diverse populations across many countries and continents and suggest that patterns are associated with many different biomarkers and disease outcomes, whether measured by cluster or factor analysis. Despite clear differences in approaches and interpretations, there is some evidence that underlying eating patterns are revealed by either method. Although the research considered herein has created a meaningful body of literature, refining both the factor and cluster analysis methods will help to further establish eating patterns as a sound dietary assessment method.

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Dietary Patterns of Adults in Québec and their Nutritional Adequacy

Cited by 42 publications

References 8 publications

Relation between dietary pattern analysis (principal component analysis) and body mass index: a 5-year follow-up study in a Belgian military population

Relation between dietary pattern analysis (principal component analysis) and body mass index: a 5-year follow-up study in a Belgian military population

Dietary patterns in Swedish adults; results from a national dietary survey

Empirically Derived Eating Patterns Using Factor or Cluster Analysis: A Review

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