Skip to content

Advertisement

Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Comparison in dietary patterns derived for the Canadian Newfoundland and Labrador population through two time-separated studies

  • Zhi Chen1,
  • Peizhong Peter Wang1Email author,
  • Lian Shi1,
  • Yun Zhu1,
  • Lin Liu1,
  • Zhiwei Gao2,
  • Janine Woodrow1 and
  • Barbara Roebothan1
Nutrition Journal201514:75

https://doi.org/10.1186/s12937-015-0064-6

Received: 2 February 2015

Accepted: 27 July 2015

Published: 1 August 2015

Abstract

Background

While a dietary pattern is often believed to be stable in a population, there is limited research assessing its stability over time. The objective of this study is to explore and compare major dietary patterns derived for the Canadian subpopulation residing in Newfoundland and Labrador (NL), through two time-separated studies using an identical method.

Methods

In this study, we derived and compared the major dietary patterns derived from two independent studies in the NL adult population. The first study was based on the healthy controls from a large population-based case–control study (CCS) in 2005. The second was from a food-frequency questionnaire validation project (FFQVP) conducted in 2012. In both studies, participants were recruited in the same manner and dietary information was collected by an identical self-administered food-frequency questionnaire (FFQ). Exploratory common factor analysis was conducted to identify major dietary patterns. A comparison was conducted between the two study populations.

Results

Four major dietary patterns were identified: Meat, Vegetables/fruits, Fish, and Grains explaining 22 %, 20 %, 12 % and 9 % variance respectively, with a total variance of 63 %. Three major dietary patterns were derived for the controls of the CCS: Meat, Plant-based diet, and Fish explaining 24 %, 20 %, and 10 % variance respectively, with a total variance of 54 %. As the Plant-based diet pattern derived for the CCS was a combination of the Vegetables/fruits and Grains patterns derived for the FFQVP, no considerable difference in dietary patterns was found between the two studies.

Conclusion

A comparison between two time-separated studies suggests that dietary patterns of the NL adult population have remained reasonably stable over almost a decade.

Keywords

Dietary habitsDietary patternsNutrition epidemiologyNewfoundland and Labrador population

Introduction

Most nutritional epidemiological literature addresses the use of nutrients or individual food items to assess possible associations between diet and health. There are several limitations of the single-nutrient approach: people eat meals consisting of a variety of foods rather than isolated nutrients; single-nutrient analysis does not account for complicated interactions among nutrients [1]; and nutrient effects are difficult to examine individually by single-nutrient analysis due in part to high levels of nutrient-nutrient interactions (for example, potassium and magnesium) [2]. Moreover, single-nutrient analysis may be confounded by each individual’s dietary pattern which is commonly associated with nutrient intakes [3, 4]. In order to overcome these limitations, an emerging approach in nutritional epidemiology is to use dietary patterns rather than isolated nutrients. Compared to the traditional approaches used in previous nutritional epidemiology, dietary pattern analysis considers how food and nutrients are consumed in combination and could therefore provide a more accurate and comprehensive description of dietary exposure in a certain population [46].

Support for the use of dietary pattern analysis has been growing. People’s eating habits usually remain relatively stable unless they experience such major changes in their personal circumstances as getting married, changing geographic location or receiving a serious warning from a health professional that their current diets have significant and negative impacts on their health. Many factors influence food choice including family income, food prices, individual preferences and beliefs, cultural traditions and customs, as well as geographic and environmental factors [7, 8]. While a dietary pattern is often believed to be relatively stable in a population, limited research has assessed its degree of stability over time. The objectives of this study were 1) to identify the major dietary patterns of an NL population from two time-separated studies using identical methods and 2) to explore whether there were differences in these dietary patterns between these two studies conducted several years apart.

Our large and multidisciplinary research team, including more than 40 researchers residing in the provinces of Ontario (ON) and NL, Canada, has published several journal articles on diet of the NL adult population using both nutrient and dietary pattern methods [913]. Using exploratory common factor analysis, this study derived and compared major dietary patterns from dietary data collected by use of a food-frequency questionnaire (FFQ) in two projects conducted with the NL population, a population-based case–control study (CCS), 2001 – 2005 [14, 15], and a food-frequency questionnaire validation project (FFQVP), 2012 [16].

Methods

Study participants

The CCS was conducted from 2001 to 2005 and a detailed description of selecting the population controls can be found elsewhere [17]. Briefly, eligible cases were newly diagnosed colorectal cancer patients. Controls were frequency-matched with cases by sex and age on 5-year strata. Both cases and controls were selected from NL residents, aged from 20 to 74 years. They were identified through random digit dialing using phone numbers provided by a NL phone company (Aliant). By July 2005, a total of 2168 eligible controls were contacted for further survey and 1603 controls agreed to participate. Those persons who agreed to participate were sent a survey package, comprised of a written consent form, a personal history questionnaire (PHQ), and a food-frequency questionnaire (FFQ). Of these, 717 participants completed and returned the survey package with a response rate of 44.7 %. Current study participants were part of the population controls from the CCS project, aged from 35 to 70 years.

The FFQVP was conducted between February 2011 and May 2012, by the Health Research Unit of Memorial University. This study population was sampled by stratified random digit dialing with proportional allocation methodology. Participants were recruited in the same manner as was used by the CCS. All were non-institutionalized adult residents of NL, aged 35–70 years. Residence in NL was defined as having lived in the province for at least two years prior to the beginning of the study. Other inclusion criteria included a minimum of an 8th grade level of English speaking and reading skills and no cognitive impairment, psychological conditions, or pregnancy. From a list of phone numbers (landlines and cell phones) provided by Info Canada, an initial sample of 450 persons was recruited randomly by telephone. After exclusion, 306 participants were identified as potential respondents and mailed a survey package containing a written consent form and a FFQ. Finally, 205 individuals completed and returned the survey package, giving a response rate of 67 %.

Data from FFQs with 20 continuous blanks or reporting energy intakes outside the range of 500–5000 kcal were excluded [18]. After exclusion, a total of 554 participants of the former population and 192 participants of the current population remained and data provided by them were entered into further analysis (Fig. 1).
Figure 1
Fig. 1

Participant recruitment for FFQVP and CCS

Data collection

For both studies, a modified FFQ, based on the well-known Hawaii FFQ, was used to gather dietary intake data. The original Hawaii FFQ was used in Hawaii/Southern California to assess the general food intake of a multi-ethnic population [19]. It has been validated and widely used in the United States [2022]. In our new version of the FFQ, some food items unusual to the NL population (for example, tamales and ham hocks) were deleted or altered, and some items commonly consumed in NL (for example, cloudberries/bake-apples, game meats, and pickled/smoked fish) were added. The food items listed in the NL FFQ, which has been validated by our team [16], include nine major categories: beverages, dairy products, mixed dishes, vegetables, meat and fish, cereals and grains, fruits, desserts and sweets, and miscellaneous.

Dietary assessments of participants using data collected via this FFQ were carried out 12–24 months prior to conducting a telephone interview. During the interview each participant was required to recall food intake over the past 24 months - the frequency of each food item consumed, the number of servings, and the approximate size of portions habitually consumed at a single sitting. The units of frequency ranged from per day, per week, per month to rarely or never, and the portion sizes included standard and smaller or larger than standard (standard ± 25 %). A standard serving size for each food item or beverage was described on the FFQ in common household measures and grams. The FFQ also included food photographs to indicate standard portion sizes.

Except for several independent food items, the 169 food items were categorized into 39 predefined food groups, based on their nutritional characteristics and their role in the diet (Table 1). Independent food items comprised their own groups, given that they could not be appropriately combined with others, for example, eggs, beer, jam, and fruit pies. Total energy and nutrient intakes for individuals were calculated according to the composition values from the 2005 Canadian Nutrient File (CNF) and the Elizabeth Stewart Hands and Associations (ESHA) Food Processor database software [23].
Table 1

Food groups used in the dietary pattern analysis

Food group

Food items

Milk

Whole milk, 2 % milk, skim milk, milk shake

Yogurt

Yogurt drink, yogurt (regular/light, plain/fruit/frozen)

Coffee

Coffee (regular/decaffeinated)

Tea

Tea (regular/herbal)

Sugar

Sugar (in tea/coffee)

Soft drinks

Cola, Pepsi, diet/other soft drinks

Egg

Egg (boiled/fried)

Cheese

Cream cheese (regular fat), cheese (regular fat, light, ultra light), cottage, ricotta cheese

Mixed dishes

Soups (creamed), pasta (with meat sauce), mixed dishes (with cheese), pizza (with meat), meat stew with vegetables, chili with meat

Red meat

Ground beef (regular/medium/lean), roast beef, steak, pork chop, roast pork, baked ham, bacon, veal, lamb, hot dog, wiener, sausage, corned beef, cold cuts

Game

Sea-bird, seal, caribou, moose, partridge, other wild birds

Cured/processed red meat

Baked ham, bacon, hot dog, wiener, corned beef, cold cuts, salted/dried meat, pickled meat

Cured/processed meat

Baked ham, bacon, hot dog, wiener, corned beef, cold cuts, fried chicken, salted/dried meat, pickled meat, fried/canned/smoked/salted/dried/pickled fish

Poultry

Fried chicken, chicken (roasted or stewed/skin removed)

Fish

Shellfish, fish (baked or broiled), fried/canned/smoked/salted/dried/pickled fish

Processed fish

Canned/smoked/salted/dried/pickled fish

Fruit juice

Orange/grapefruit/apple/grape/other fruit juice, fruit drinks/lemonade, iced tea

Other fruit

Apple, pear, grape, banana, beach, plum, nectarine, apricot, cantaloupe, watermelon, honeydew melon, mango, papaya, apple sauce, all other fruit with the exception of berries

Root vegetables

Potatoes (mashed, baked), fried potatoes/French fries, carrots (raw or cooked)

Cruciferous vegetables

Broccoli, cabbage, coleslaw, cauliflower, asparagus or brussel sprouts

Other green

Spinach/other green-leaf vegetables, green salad

Beans, peas

Peas, lima beans, green/yellow beans, beans/lentils, pea soup

Tomato sauce

Tomatoes (fresh/canned), ketchup

Other vegetables

Corn, cucumber, onions, beets, yellow squash, zucchini or eggplant, sweet pepper, bean sprout, avocado, other vegetables

Total cereals and grains

Bran or granola cereal, whole wheat cereals, cereals (not sugar coated), hot cereals, sugar coated cereals, other breakfast cereals, sugar on cereal, 100 % whole grain/dark bread, 60 % whole grain/light rye, white bread, white bread rolls, whole wheat rolls, crackers, bran/oat muffin, other muffins, pancake, waffles, macaroni, spaghetti, noodles, rice, crisp snacks

Whole grains

Whole wheat cereals, 100 % whole grain/dark bread, 60 % whole/light rye, whole wheat rolls

Dessert and sweet

Cakes, pies and tarts, donuts and sweet rolls, cookies, iced cream, light or diet ice cream, pudding, diet or light pudding, jell-o, popsicles, freezies, candy (with/without chocolate)

Vegetable juice

Vegetable juice

Beer

Beer, ale

Whiter wine

White wine

Red wine

Red wine, sherry

Liquor

Liquor

Citrus

Citrus fruits

Berries

Berries

Dried fruits

Dried fruits

Canned fruits

Canned fruits

Pies, tarts

Pies, tarts

Jam, jelly

Jam, jelly, honey syrup

Pickled vegetables

Pickles, relish

The study sample from the CCS was administered a PHQ to collect socio-demographic and medical information including age, sex, date of birth, marital status, educational attainment, medical history (for example, history of diabetes or high cholesterol), bowel screening history, medication use (for example, multivitamins and nonsteroidal anti-inflammatory drugs), alcohol and tobacco use, reproductive factors, self-reported physical activity and other information.

Less extensive socio-demographic information was gathered as part of the telephone interview with the study population in FFQVP. This included age, sex, size of community, marital status, employment status, level of education, and smoking habits.

Statistical analyses

The appropriateness of factor analysis for each study sample was verified by Bartlett’s Test of Sphericity (BTS) and the Kaiser-Meyer-Olkin (KMO) measurement. BTS was used to test the homogeneity of variances and KMO measurement was conducted for testing sampling adequacy. KMO values could not be less than 0.5 to ensure the suitability of factor analysis use in this study [24]. Exploratory common factor analysis was used for factor extraction, and orthogonal rotation (varimax option) was used for simpler structures with greater interpretability. A factor was retained when it met the following criteria: factor eigen value > 1.50, identification of a break point in the scree plot (the difference between each two points becomes small suddenly), the proportion of variance explained (at least 50 % of variance in this study), and factor interpretability (the fewer the factors, the greater the interpretability). A rotated loading matrix described the strength and direction of the associations between the retained factors and food groups. If a food group had a factor loading ≥0.5 (for the FFQVP population) or ≥0.35 (for the CCS population), it was loaded on a factor. We also retained food groups that had negative correlations (≤ − 0.2) to incorporate the valuable information concerning infrequently consumed foods within each factor [25]. Dietary patterns were named according to the characteristics of food groups loaded on a retained factor.

Differences in demographic information between the two study populations were detected by t-test and chi-square test. Statistical analyses were performed using the Statistical Analysis System (SAS, version 9.2) software. Differences with p-value <0.05 were considered to be statistically significant.

Ethics statement

This research was approved by the HREB at Memorial University of Newfoundland. (Reference number 14.098).

Results

Demographic information

In total, the study sample was made up of 554 participants from the CCS population and 192 participants from the FFQVP population. All of the study participants were aged 35–70 years. Individuals from the CCS (58.7 ± 7.7) were significantly older than those from the FFQVP (56.2 ± 8.7). The gender distributions between the two populations were significantly different (p < 0.0001). The percentage of males in the CCS (58.1 %) was much higher than in the FFQVP study (22.4 %). Also, distributions of education attainment and marital status between these two study groups were significantly different (Table 2).
Table 2

Demographic information of study participants from both CCS and FFQVP

Demographic information

CSS

FFQVP

Pa

Age (mean ± SD)

58.7 ± 7.7

56.2 ± 8.7

<0.0001

Sex

  

<0.0001

 Male

322 (58.1 %)

43 (22.4 %)

 Female

232 (41.9 %)

149 (77.6 %)

Marital Status

  

<0.0001

 Single

17 (2.9 %)

15 (7.8 %)

 Separated/divorced/widowed

74 (13.4 %)

26 (13.5 %)

 Married/living together

463 (83.7 %)

151 (78.7 %)

Level of education

  

<0.0001

 Some school without high school certificate

156 (28.4 %)

27 (14.0 %)

 High school certificate

300 (54.6 %)

51 (26.6 %)

 Post-secondary education

98 (17 %)

114 (59.4 %)

aP value from t test within CCS and FFQVP groups

Factor analysis

The observed KMOs for the two populations were 0.68 for the CCS and 0.60 for the FFQVP suggesting that the two samples from different populations were adequate for factor analysis. P values from the BTS were <0.0001, suggesting homogeneity of variance across the samples. Figure 2 shows the scree plots for both study populations. For the CCS sample, the first three eigenvalues, 3.73, 3.24, and 1.56, drop substantially. After the fourth eigenvalue (1.43), the values remain more consistent (1.39 for the fifth, and 0.89 for the sixth). As a result, the third point is considered a break point. As for the FFQVP sample, differences between each two eigenvalues change to gentle from sharp after the fourth value. Accordingly, the fourth point is regarded as a break point on this plot. All eigenvalues before each break point are greater than 1.50. Combined with total variance explained and factor interpretability, a 3-factor solution was selected for the study population from CCS. This explained 54 % of variance. The first four factors were retained for the study population from FFQVP, and this explained 63 % of variance (Table 3).
Figure 2
Fig. 2

Scree plots for eigenvalues from factor extraction in two studies

Table 3

Factor Loadings and Explained Variances (VAR) of the Major Dietary Patterns identified in two studies, using an exploratory common factor analysis

Food groups

Factor loadingsa

Current population

Former population

Meat

Vegetables/Fruits

Fish

Grain

Meat

Plant-based diet

Fish

Milk

       

Yogurt

       

Coffee

   

−0.31

   

Tea

       

Sugar

       

Soft drinks

   

−0.20

   

Egg

       

Cheese

      

−0.24

Mixed dishes

    

0.43

  

Red meat

0.83

   

0.88

  

Game

       

Cured/processed red meat

0.90

   

0.91

  

Cured/processed meat

0.93

   

0.92

  

Poultry

       

Fish

  

0.78

   

0.73

Processed fish

  

0.70

   

0.68

Fruit juice

 

−0.25

     

Other fruits

     

0.42

0.48

Root vegetables

       

Cruciferous vegetables

     

0.58

 

Other greens

 

0.68

   

0.50

 

Beans, peas

     

0.52

 

Tomato sauce

 

0.60

   

0.41

 

Other vegetables

 

0.75

   

0.57

 

Total cereals and grains

   

0.55

 

0.38

 

Whole grains

   

0.52

 

0.36

 

Desserts and sweets

       

Vegetable juice

       

Beer

   

−0.24

   

White wine

   

−0.26

   

Red wine

       

Liquor

       

Citrus

       

Berries

 

0.50

    

0.49

Dried fruit

       

Canned fruit

       

Pies, tarts

       

Jam, jelly

       

Pickled vegetables

       

Proportion of VAR explained (%)

22 %

20 %

12 %

9 %

24 %

20 %

10 %

Cumulative VAR explained (%)

22 %

42 %

54 %

63 %

24 %

44 %

54 %

aFactor loadings ≥ 0.5 will be loaded on a factor in FFQVP population while factor loadings ≥ 0.35 will be loaded on a factor in CCS population; negative loading ≤ −0.20 will be retained; other loadings are not shown in the table

According to the results obtained from the factor loading matrix shown in Table 3, the retained factors were labelled, depending on the given food groups loaded on them. A factor loading ≥ 0.35 of a certain food group indicated a greater contribution of that food group to the specific pattern for the CCS population. The three retained factors were identified as three dietary patterns and were labelled Meat, Plant-based diet, and Fish. The first pattern was defined as the Meat pattern, and characterized by high loadings for red meat, cured/processed red meat, cured/processed meat, and mixed dishes. The second pattern, which loaded heavily on fruits, cruciferous vegetables, other green vegetables, beans, peas, other vegetables, tomato sauce, total cereals and grains, and whole grains, was labelled the Plant-based diet pattern. The final pattern was named Fish because it had high loadings of fish, processed fish, berries and other local fruits and negative loadings in the food groups of cheese.

The four retained factors were identified as four dietary patterns for the FFQVP population and were labelled Meat, Vegetables/fruits, Fish, and Grains. The four-factor dietary pattern was identified based on the results retained from the factor loading matrix (Table 3), where a higher factor loading of a given food group indicated a greater contribution of that food group to the specific pattern. The first pattern was labelled because of a high intake of red meat, cured/processed meat, and cured/processed red meat. The Vegetables/fruits pattern indicates a preference for several vegetable/fruit groups, including greens, tomato sauce, berries, and other vegetables. The Fish pattern had an emphasis on fish and processed fish. We named the final pattern Grains, since it was characterized by a high consumption of whole grains, cereals, and grains, and a low consumption of beer, white wine, and coffee.

Discussion

Even though dietary pattern analysis has emerged as a possible approach examining possible diet-health relationship, little research has been conducted to assess the stability of dietary patterns derived for an identical population over time. In this study, we compared the major dietary patterns derived from two time-separated studies of the NL adult population assessed by a self-administered comprehensive FFQ.

The present study derived a three-factor dietary pattern for the CCS and a four-factor dietary pattern for the FFQVP. We observed both similarities and differences in dietary patterns between the two studies. The total variances explained for the CCS and FFQVP studies were similar, 54 % and 63 %, respectively. Both identified meat and plant-based food as the top two major factors, which in combination explained almost equal amounts of variation (42 % and 44 %). According to the factor loading matrix, the patterns labelled Meat pattern and Fish pattern derived for the CCS were largely the same as those two derived from the FFQVP. The meat pattern was similar to the Western pattern of many previous studies [26, 27] in the food items contained (for example, red meat, processed meat, other high-fat food). This pattern has been positively associated with cancer [28], cardiovascular diseases [29, 30], and obesity [31]. The Fish pattern, which is characterized by high intakes of fish and processed fish, seems to be different from any pattern described in other research. Geographic isolation and the historical importance of the cod fishery in NL may be the leading cause of this unique phenomenon [32]. The Plant-based diet pattern derived for CCS was a combination of the Vegetable/fruit and Grains pattern in the FFQVP. This pattern is comparable to the Prudent and/or Vegetable/fruit patterns described in other studies, with a high consumption of vegetables, fruits, and other plant-based foods [26, 33, 34], and has been reported to have a protective effect against coronary heart disease [35], type 2 diabetes [26] and CRC [36]. Also, the main food items of whole grain, cereals and grains from the Grains pattern can contain substantial sources of dietary fibre, consumption of which has been shown to be beneficial to health, especially by decreasing the risk of chronic diseases such as CRC [25, 37, 38].

According to findings obtained from the FFQVP and CCS, we conclude that dietary patterns derived by exploratory common factor analysis for those two studies are almost the same, except for the number of factors retained and total variance explained by the retained patterns. These minor differences may be attributed to several reasons. First of all, the sample size may be too small to be representative of the whole population as there were only 554 study participants from the CCS and 192 from the FFQVP. Secondly, distributions of sex and age between the two study populations were significantly different. There were more males in the CCS than in the FFQVP. According to previous studies, dietary patterns are likely to vary between genders as well as age groups. For example, an association between women and higher loadings on healthy dietary patterns has been reported by previous studies [30, 33, 39]. Also, according to one of our earlier studies, older people are more likely to follow a healthier dietary pattern according to results from multivariable linear regression [40]. However, small sample size (stratified by sex or age groups) limited us to conduct factor analysis in this study. Additionally, study participants from the CCS were controls to the CRC cases, and therefore likely to be family members of the cases or diagnosed persons and thus interested in cancer and/or its possible association with nutritional factors. Such individuals may not be able to truly represent the general population. However, study participants in the FFQVP were randomly recruited from the general population. Further, information bias may exist because study participants were required to recall their dietary intakes one or two years prior to the interview or survey.

This study is the first nutritional epidemiological research conducted in the NL population to compare major dietary patterns derived from two independent studies using an identical method but conducted nearly a decade apart. The results of this study provide an overall picture of the dietary exposure of the NL adult population and updated information on the current dietary habits of residents of the province. In addition, this study will provide guidance and reference for future researchers to conduct related studies on this topic through an improved method and study design.

Conclusion

After a comparison was made of the dietary patterns followed by participants of two separate studies conducted at two different times (FFQVP and the CCS projects), no considerable differences were found. Therefore we conclude that the major dietary patterns followed by the NL adult population have been reasonably stable for almost a decade. However, because of issues on methodology and study design, further investigations to determine the reproducibility and validity of the dietary pattern analysis assessed by the FFQs should be conducted in the future.

Abbreviations

BTS: 

Bartlett’s Test of Sphericity

CNF: 

Canadian Nutrient File

CRC: 

Colorectal cancer

CSS: 

Case–control study

ESHA: 

Elizabeth Stewart Hands and Associations

FFQ: 

Food-frequency Questionnaire

FFQVP: 

Food-frequency questionnaire validation project

KMO: 

Kaiser-Meyer-Olkin

NFCCR: 

Newfoundland Familial Colorectal Cancer Registry

NL: 

Newfoundland and Labrador

ON: 

Ontario

PHQ: 

Personal history questionnaire

SAS: 

Statistical Analysis System

Declarations

Acknowledgement

This work is part of Z.C.’s thesis project, which was supported through the Faculty of Medicine’s Dean’s Fellowship, Shree Mulay Community Health Graduate Student Award, and a NLCAHR’s Master Research grant provided to Z.C. We wish to extend our appreciation to all study participants for their time, efforts, and cooperation.

Authors’ Affiliations

(1)
Division of Community Health and Humanities, Faculty of Medicine, Memorial University of Newfoundland, St. John’s, Canada
(2)
Clinical Epidemiology Unit, Faculty of Medicine, Memorial University of Newfoundland, St John’s, Canada

References

  1. National Research Council, Committee on Diet and Health. Diet and health: implication for reducing chronic disease risk. Washington, DC: National Academy Press; 1989.Google Scholar
  2. Lee CN, Reed DM, MacLean CJ, Yano K, Chiu D. Dietary potassium and stroke. N Engl J Med. 1988;318:995–6.View ArticlePubMedGoogle Scholar
  3. Kant AK, Schatzkin A, Block G, Ziegler RG, Nestle M. Food group intake patterns and associated nutrient profiles of the US population. J Am Diet Assoc. 1991;91:1532–7.PubMedGoogle Scholar
  4. Randall E, Marshall JR, Graham S, Brasure J. Patterns in food use and their associations with nutrient intakes. Am J Clin Nutr. 1990;52:739–45.PubMedGoogle Scholar
  5. Huijbregts PP, Feskens EJ, Kromhout D. Dietary patterns and cardiovascular risk factors in elderly men: the Zutphen Elderly Study. Int J Epidemiol. 1995;24:313–20.View ArticlePubMedGoogle Scholar
  6. Huijbregts P, Feskens E, Rasanen L, Fidanza F, Nissinen A, Menotti A, et al. Dietary pattern and 20 year mortality in elderly men in Finland, Italy, and The Netherlands: longitudinal cohort study. BMJ. 1997;315:13–7.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Franchi M. Food choice: beyond the chemical content. Int J Food Sci Nutr. 2012;63 Suppl 1:17–28.View ArticlePubMedGoogle Scholar
  8. Diet, nutrition and the prevention of chronic diseases. World Health Organ Tech Rep Ser. 2003; 916:i–viii. 1–149, backcover.Google Scholar
  9. Zhu Y, Wu H, Wang PP, Savas S, Woodrow J, Wish T, et al. Dietary patterns and colorectal cancer recurrence and survival: a cohort study. BMJ Open. 2013;3.Google Scholar
  10. Zhu Y, Wang PP, Zhao J, Green R, Sun Z, Roebothan B, et al. Dietary N-nitroso compounds and risk of colorectal cancer: a case–control study in Newfoundland and Labrador and Ontario, Canada. Br J Nutr. 2014;111:1109–17.View ArticlePubMedGoogle Scholar
  11. Yan J, Liu L, Roebothan B, Ryan A, Chen Z, Yi Y, et al. A preliminary investigation into diet adequacy in senior residents of Newfoundland and Labrador, Canada: a cross-sectional study. BMC Public Health. 2014;14:302.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Sun Z, Zhu Y, Wang PP, Roebothan B, Zhao J, Dicks E, et al. Reported intake of selected micronutrients and risk of colorectal cancer: results from a large population-based case–control study in Newfoundland, Labrador and Ontario, Canada. Anticancer Res. 2012;32:687–96.PubMedGoogle Scholar
  13. Sun Z, Liu L, Wang PP, Roebothan B, Zhao J, Dicks E, et al. Association of total energy intake and macronutrient consumption with colorectal cancer risk: results from a large population-based case–control study in Newfoundland and Labrador and Ontario, Canada. Nutr J. 2012;11:18.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Chen Z, Wang PP, Woodrow J, Zhu Y, Roebothan B, McLaughlin JR, et al. Dietary patterns and colorectal cancer: results from a Canadian population-based study. Nutr J. 2015;14:8.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Zhao J, Halfyard B, Roebothan B, West R, Buehler S, Sun Z, et al. Tobacco smoking and colorectal cancer: a population-based case–control study in Newfoundland and Labrador. Can J Public Health. 2010;101:281–9.PubMedGoogle Scholar
  16. Liu L, Wang PP, Roebothan B, Ryan A, Tucker CS, Colbourne J, et al. Assessing the validity of a self-administered food-frequency questionnaire (FFQ) in the adult population of Newfoundland and Labrador, Canada. Nutr J. 2013;12:49.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Wang PP, Dicks E, Gong X, Buehler S, Zhao J, Squires J, et al. Validity of random-digit-dialing in recruiting controls in a case–control study. Am J Health Behav. 2009;33:513–20.View ArticlePubMedGoogle Scholar
  18. Willett W. Nutritional epidemiology. 2nd ed. New York: Oxford University Press; 1998.View ArticleGoogle Scholar
  19. Stram DO, Hankin JH, Wilkens LR, Pike MC, Monroe KR, Park S, et al. Calibration of the dietary questionnaire for a multiethnic cohort in Hawaii and Los Angeles. Am J Epidemiol. 2000;151:358–70.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Fluge O, Gravdal K, Carlsen E, Vonen B, Kjellevold K, Refsum S, et al. Expression of EZH2 and Ki-67 in colorectal cancer and associations with treatment response and prognosis. Br J Cancer. 2009;101:1282–9.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Takata Y, Maskarinec G, Franke A, Nagata C, Shimizu H. A comparison of dietary habits among women in Japan and Hawaii. Public Health Nutr. 2004;7:319–26.View ArticlePubMedGoogle Scholar
  22. Stram DO, Longnecker MP, Shames L, Kolonel LN, Wilkens LR, Pike MC, et al. Cost-efficient design of a diet validation study. Am J Epidemiol. 1995;142:353–62.PubMedGoogle Scholar
  23. ESHA Food Processor. [http://www.esha.com/foodprosql].
  24. Cerny BA, HFK. A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivar Behav Res. 1977;12:43–7.View ArticleGoogle Scholar
  25. Yang EJ, Kerver JM, Song WO. Dietary patterns of Korean Americans described by factor analysis. J Am Coll Nutr. 2005;24:115–21.View ArticlePubMedGoogle Scholar
  26. van Dam RM, Rimm EB, Willett WC, Stampfer MJ, Hu FB. Dietary patterns and risk for type 2 diabetes mellitus in U.S. men. Ann Intern Med. 2002;136:201–9.View ArticlePubMedGoogle Scholar
  27. Walker M, Aronson KJ, King W, Wilson JW, Fan W, Heaton JP, et al. Dietary patterns and risk of prostate cancer in Ontario, Canada. Int J Cancer. 2005;116:592–8.View ArticlePubMedGoogle Scholar
  28. Magalhaes B, Peleteiro B, Lunet N. Dietary patterns and colorectal cancer: systematic review and meta-analysis. Eur J Cancer Prev. 2012;21:15–23.View ArticlePubMedGoogle Scholar
  29. Eilat-Adar S, Mete M, Fretts A, Fabsitz RR, Handeland V, Lee ET, et al. Dietary patterns and their associationwith cardiovascular risk factors in a population undergoing lifestyle changes: The Strong Heart Study. Nutr Metab Cardiovasc Dis. 2013;23(6):528-35.Google Scholar
  30. Kerver JM, Yang EJ, Bianchi L, Song WO. Dietary patterns associated with risk factors for cardiovascular disease in healthy US adults. Am J Clin Nutr. 2003;78:1103–10.PubMedGoogle Scholar
  31. Naja F, Nasreddine L, Itani L, Chamieh MC, Adra N, Sibai AM, et al. Dietary patterns and their association with obesity and sociodemographic factors in a national sample of Lebanese adults. Public Health Nutr. 2011;14:1570–8.View ArticlePubMedGoogle Scholar
  32. History of Newfoundland Cod Fishery. [https://www.cdli.ca/cod/history5.htm].
  33. Park SY, Murphy SP, Wilkens LR, Yamamoto JF, Sharma S, Hankin JH, et al. Dietary patterns using the Food Guide Pyramid groups are associated with sociodemographic and lifestyle factors: the multiethnic cohort study. J Nutr. 2005;135:843–9.PubMedGoogle Scholar
  34. Bamia C, Orfanos P, Ferrari P, Overvad K, Hundborg HH, Tjonneland A, et al. Dietary patterns among older Europeans: the EPIC-Elderly study. Br J Nutr. 2005;94:100–13.View ArticlePubMedGoogle Scholar
  35. Stricker MD, Onland-Moret NC, Boer JM, van der Schouw YT, Verschuren WM, May AM, et al. Dietary patterns derived from principal component- and k-means cluster analysis: long-term association with coronary heart disease and stroke. Nutr Metab Cardiovasc Dis. 2013;23(3):250-6.Google Scholar
  36. Kurotani K, Budhathoki S, Joshi AM, Yin G, Toyomura K, Kono S, et al. Dietary patterns and colorectal cancer in a Japanese population: the Fukuoka Colorectal Cancer Study. Br J Nutr. 2010;104:1703–11.View ArticlePubMedGoogle Scholar
  37. Mizoue T, Yamaji T, Tabata S, Yamaguchi K, Shimizu E, Mineshita M, et al. Dietary patterns and colorectal adenomas in Japanese men: the Self-Defense Forces Health Study. Am J Epidemiol. 2005;161:338–45.View ArticlePubMedGoogle Scholar
  38. Satia JA, Tseng M, Galanko JA, Martin C, Sandler RS. Dietary patterns and colon cancer risk in Whites and African Americans in the North Carolina Colon Cancer Study. Nutr Cancer. 2009;61:179–93.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Schulze MB, Hoffmann K, Kroke A, Boeing H. Dietary patterns and their association with food and nutrient intake in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study. Br J Nutr. 2001;85:363–73.View ArticlePubMedGoogle Scholar
  40. Chen Z, Liu L, Roebothan B, Ryan A, Colbourne J, Baker N, et al. Four major dietary patterns identified for a target-population of adults residing in Newfoundland and Labrador, Canada. BMC Public Health. 2015;15:69.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© Chen et al. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Advertisement