Abstract
Background:
Dietary patterns analysis may provide insights into the influence of overall diet on overweight/obesity. In the past two decades, the relation between dietary patterns and overweight/obesity has been a research focus and a number of results were reported in the research field.
Methods:
An electronic literature search was conducted in PubMed and Web of Science, to identify human studies published by Mar 2015 and written in English. The following keywords or phrases were involved: dietary patterns, dietary pattern, factor analysis, principal component analysis, diet, obesity, adiposity, overweight and BMI. All the studies were retrieved and prudent/healthy (n=17) and western/unhealthy (n=18) dietary patterns were identified.
Results:
When compared with the lowest categories of a prudent/healthy dietary pattern, a reduced overweight/obesity risk was shown in the highest (OR=0.64; 95% CI: 0.52, 0.78; P<0.0001). While there was an increased overweight/obesity risk in the highest when compared with the lowest categories of a western/unhealthy dietary pattern (OR=1.65; 95% CI: 1.45, 1.87; P<0.0001).
Conclusion:
A prudent/healthy dietary pattern and limit intake of western/unhealthy dietary pattern should be followed, which helps to keep a healthy body mass.
Keywords: Dietary patterns, Overweight, Obesity, BMI, Meta-analysis
Introduction
In recent years, the prevalence of overweight/obesity has been increasing around the world (1). Resulting from an interaction of genotype and environment, overweight/obesity is a complicated multifactorial chronic disease (2). Therefore, the etiology of overweight/obesity needs to be understood and this is a condition associated with an increased risk of coronary heart disease, hypertension, diabetes mellitus, gallbladder disease, osteoarthritis and some types of cancers (3).
Currently, a mass of studies concerning diet and overweight/obesity have been published. However, the role of diet in the etiology of overweight/obesity remains controversial. One reason for inconsistent findings may be the traditional single nutrient-based approach in nutritional epidemiology, commonly applied in most nutritional epidemiological researches (4–5). Therefore, a measurement of the overall dietary intake-dietary pattern has been recommended, which reflects the dietary intake complexity as an approach to investigate the links between diet and disease (6).
A large number of studies employed factor analysis or principal component analysis to derive dietary pattern (7–23). In these statistical techniques, variables are aggregated into factors that represent the eating patterns of the population being studied. Among multiethnic women including Japanese and Chinese women (mean age of 53.9 yr for all subjects), the ‘meat’ pattern was positively associated with body mass index (BMI), whereas ‘vegetable’, ‘bean’ and ‘cold foods’ patterns were negatively correlated (24). Food factors could not consistently predict changes in BMI or obesity development (25), whereas dietary patterns were significantly related to BMI changes over time (7–23).
As various studies concerning dietary patterns and overweight/obesity have been published, the objective of this systematic review was to appraise critically the literature published to date and conduct meta-analysis to pool studies results, to clarify the association between dietary patterns and overweight/obesity.
Methods
Search strategies
An electronic literature search was conducted in PubMed and Web of Science, to identify human studies published by 2016 and written in English, included following keywords or phrases: dietary patterns, dietary pattern, factor analysis, principal component analysis, diet, obesity, adiposity, overweight and BMI. In order to identify studies that examined diet and overweight/obesity risk, four independent reviewers read the abstracts of articles acquired in the initial search. All reviewers agreed on relevant articles and full-text versions of articles were reviewed to identify studies that examined food and/or dietary patterns through the application of factor analysis, RRR and principal component analysis.
Studies included criteria
To be eligible, a study had to fulfill the following criteria: first, it had to be an original report regarding the relationship between dietary patterns and overweight/obesity; and then, odds ratios (ORs) for overweight/obesity had to be presented (or the data to calculate them).
In order to reduce error, only the most common patterns of dietary consumption were identified from the remaining articles. Besides, selected dietary patterns were similar in terms of factor loadings of foods most commonly consumed within those dietary patterns. For example, the identified prudent/healthy dietary pattern tended to have high loadings of following foods, such as fruit, vegetables, poultry, fish, low-fat dairy and whole grains, while the western/unhealthy dietary pattern tended to have high loadings of following foods, such as red and/ or processed meats, refined grains, potatoes, sweets and high-fat dairy. Studies that identified dietary patterns having similar foods loadings with prudent/healthy and western/unhealthy patterns but were named differently were also included.
Quality assessment
The Newcastle-Ottawa quality assessment scale was used for quality assessment. Ten questions were assessed and each satisfactory answer received 1 point, resulting in a maximum score of 10. Only studies for which the majority of questions were deemed satisfactory (i.e. with a score of 6 or higher) were considered to be of high methodological quality.
Heterogeneity assessment
Chi-square test was adopted to test the heterogeneity across studies. Besides, a random-effects model was employed to account for the possible heterogeneity between studies, which defaulted to a fixed-effects model in the absence of heterogeneity (26). A P-value of less than 0.05 was considered significant.
Statistical analysis
The original studies reported dietary pattern results in terms of quintiles, quartiles or tertiles of dietary factor scores and overweight/obesity, BMI. Therefore, a meta-analysis was conducted to combine the results, in which the overweight/obesity risk was evaluated in the highest when compared with the lowest categories of prudent/healthy and western/unhealthy. In addition, Review Manager, version 5.0 (Nordic Cochrane Centre, Copenhagen, Denmark) was adopted to conduct statistical analysis. ORs were pooled for dichotomous outcomes from each study, means ± standard deviation were pooled for continuous variables from each study and 95% CI for each outcome was estimated to reflect point estimate uncertainty. In order to determine whether differences in age, sample size, study design and race affected conclusions, sensitivity analysis was performed. By inspecting the funnel plot and formal testing funnel plot asymmetry with Begg’s test (27), publication bias was assessed. These calculations were carried out by applying Stata/SE, ver. 10 (Stata Corp., College Station, TX, USA).
Results
Overview of studies included in the systematic review and meta-analysis
Fig. 1 showed the study selection process. Across the two databases, 540 papers fulfilled the search criteria. After reviewing the title and abstract, 463 articles were excluded, because the relationship between diet and overweight/obesity was not examined. Among the remaining 77 articles, 18 had dietary patterns but had no categorized participants by groups of dietary pattern scores, 13 were duplicates, 10 had no measurement of overweight/obesity, 15 were reviews or commentary articles, 4 only had means of BMI without measurement of overweight/obesity. Consequently, 17 articles including 18 studies (7–23) (one article (14) including 2 studies) met the inclusion criteria and they were included in the analysis. Study characteristics are displayed in Table 1.
Fig. 1:
Flow chart of article screening and selection process
Table 1:
Characteristics of 17 articles (18 studies) included in the meta-analysis (1998–2015)
| Author Publication Year | Location | Study design | Total number of subjects | Race | Age | Definition of dietary pattern | Diet-assessment method | Dietary patterns identified | Outcome |
|---|---|---|---|---|---|---|---|---|---|
| Pala V 2013 | European countries | Cohort | 14,989 | White | 2–10y | Principal component analysis | Children’s Eating Habits Questionnaire (CEHQ) (43; past one year)1 | Snacking; Sweet and Fa; Vegetables and Whole meal, Protein and Water | overweight/obese |
| Okubo H 2008 | Japan | Cross-Sectional | 4, 394 | Yellow | 18–20 | Factor analysis | Food-frequency questionnaire (30 groups; last month)1 | Healthy; Japanese traditional; Western; Coffee and dairy products | Overweight; BMI |
| Paradis AM 2009 | Canada | Cross-Sectional | 664 | White | 18–55 | Factor analysis | Food-frequency questionnaire (61; last month)1 | Western; Prudent | Obesity; BMI |
| Nkondjock A 2010 | Cameroon | Cross-Sectional | 571 | Black | 21–59 | Factor analysis | Food-frequency questionnaire (100; past one year)1 | Fruits and Vegetables; Meats | Overweight and Obesity; BMI |
| Zhang JG 2105 | China | Cohort | 2,363 | Yellow | 18–44y | Factor analysis | 24-h dietary recalls | Traditional south; Traditional north; Snack; High protein | Obesity |
| Hamer M 2009 | UK | Cohort | 2, 931 | White | >16 | Factor analysis | Interview(400; usual intake)1 | Fast food; Health aware; Traditional; Sweet | BMI |
| Chan R 2014 | China | Cross-Sectional | 351 | Yellow | 10–12 | Factor analysis | Food-frequency questionnaire (32; past one year)1 | Fator 1; Fator 2; Fator 3; | Overweight and Obesity |
| Lioret S 2008 | France | Cross-Sectional | 748 | White | 3–11 | Factor analysis | Interview (32; 7-d record)1 | Pattern 1; Pattern 2 | Overweight |
| Silva Bdel P 2014 | Brazil | Cross-Sectional | 1,026 | White | 20–60y | RRR | Food-frequency questionnaire (70; past one year)1 | Fator 1; Fator 2; Fator 3; | Obesity |
| Suga wara N 2014 | Japan | Cross-Sectional | 338 | Yellow | 40.7 | Principal component analysis | brief self-administered diet history questionnaire (BDHQ) (56 groups; past one year)1 | Healthy; Processed Food ; Alcohol; Accompanying | Obesity |
| McDonald CM 2008 | Colombia | Cross-Sectional | 3, 075 | White | 5–12 | Principal component analysis | Food-frequency questionnaire (38 items)1 | Snacking; Cheaper protein; Traditional/starch; Animal protein | Overweight/Obesity |
| Shin KO 2007 | Korea | Cohort | 1, 441 | Yellow | 5.2 | Factor analysis | Food-frequency questionnaire (100)1 | Korean healthy; Animal foods; Sweets | Overweight |
| Denova-Gutierrez E 2011 | Mexico | Cross-Sectional | 6, 070 | White | 20–70 | Factor analysis | Food-frequency questionnaire (116)1 | Prudent; Westernized,; high animal protein/fat | Overweight/Obesity; BMI |
| Denova-Gutierrez E 2010 | Mexico | Cross-Sectional | 5, 240 | White | 20–70 | Factor analysis | Food-frequency questionnaire (116; past one year)1 | prudent, Western, and high protein/fat | Overweight/Obesity; BMI |
| Cho YA 2011 | Korea | Cross-Sectional | 1, 118 | White | 30–70 | Factor analysis | Food-frequency questionnaire (103; past one year)1 | Vegetable-Seafood; Meat-Fat; Snack | Overweight; Obesity |
Number of food items and reference period in parentheses
Meta-analysis
Prudent/healthy dietary pattern
When all studies were combined in the random-effects model, there was evidence of a decrease in overweight/obesity risk in the highest (Fig. 2) compared with the lowest categories of the prudent/healthy dietary pattern (OR=0.64; 95% CI: 0.52, 0.78; P<0.0001). In this study, the heterogeneity was very apparent (P<0.0001, I2 =71%).
Fig. 2:
Forest plot for ORs of the highest compared with the lowest categories of intake of the prudent/healthy dietary pattern and overweight/obesity
Western/unhealthy dietary pattern
Fig. 3 showed that when compared with the lowest categories (OR=1.65; 95% CI: 1.45, 1.87; P<0.0001), overweight/obesity risk would be increased in the highest categories in the random-effects model and there was less evidence of heterogeneity (P=0.13, I2=53%).
Fig. 3:
Forest plot for ORs of the highest compared with the lowest categories of intake of the western/unhealthy dietary pattern and overweight/obesity
Publication bias
Funnel plots revealed little evidence of asymmetry and thus little evidence of obvious publication bias. (The highest was compared with the lowest categories: prudent/healthy Begg’s test P=0.623; western/unhealthy Begg’s test P=0.070).
Sensitivity analysis
Sensitivity analysis revealed that differences in age, sample size, study design and race had an effect on the association between dietary patterns and overweight/obesity.
When the highest were compared with the lowest categories of prudent/healthy dietary pattern, a negative association between prudent/healthy dietary pattern and overweight/obesity was more obvious (Table 2) in the following studies, which were of cross-sectional design (7–9,14,17,23) and in small sample size (7,9,10,14,21). Besides, subjects were white (7,9,12,14,17,23) and more than 20 yr old (8,14,17,18). In contrast, when compared with the lowest categories of western/unhealthy pattern (Table 2), a positive association was more obvious for those in the highest in studies that were of cohort design (12,18,21) and in small sample size (7,9,10,14,21,23). Furthermore, subjects were yellow and others (8,10,18,21) and less than 20 yr old (8,14,17,18). As these variables had a significant impact on the association between dietary patterns and overweight/obesity, their differences may partially explain the heterogeneity observed between studies.
Table 2:
Dietary patterns and overweight/obesity: sensitivity analysis
| Study characteristic | Category | Prudent/healthy dietary pattern (95% CI) | Western/unhealthy dietary pattern (95% CI) |
|---|---|---|---|
| Age (yr) | >20 | 0.58 (0.41, 0.81) | 1.55 (1.42, 1.70) |
| <20 | 0.80 (0.65, 0.98) | 1.91 (1.54, 2.36) | |
| Sample size | Large (>1000) | 0.76 (0.50, 1.17) | 1.51 (1.38, 1.65) |
| Small (<1000) | 0.59 (0.44, 0.80) | 2.12 (1.74, 2.58) | |
| Race | White | 0.63 (0.45, 0.87) | 1.59 (1.46, 1.74) |
| Yellow and Other | 0.71 (0.45, 1.13) | 1.66 (1.31, 2.10) | |
| Study design | Cross-Sectional | 0.62 (0.48, 0.81) | 1.59 (1.45, 1.73) |
| Cohort | 0.81 (0.36, 1.83) | 1.84 (1.34, 2.51) |
Discussion
The results indicate that a prudent/healthy dietary pattern may decrease overweight/obesity risk, while a western/unhealthy dietary pattern may increase overweight/obesity risk. In this meta-analysis, the results from 17 articles including 18 studies (7–25) which published from 1998 to 2016 were evaluated. Diet in terms of food groups (e.g., fruit and vegetables) or its content of single nutrient (e.g., dietary fat or fiber) or single food could have been related to overweight/obesity, the relationships could not reflect between human diets complexity, high correlations between intakes of various nutrients or food items and various nutrient-to-nutrient biochemical interactions and overweight/obesity. Our findings facilitate to elucidate the potential relation between dietary patterns reflecting dietary intake complexity and overweight/obesity.
Sensitivity analysis revealed that age, sample size, study design and race had an effect on the association between dietary patterns and overweight/obesity, which may partially explain the evident heterogeneity between studies. More importantly, the cohort study has complete information regarding the subject’s exposure. Therefore, results of cohort studies were more convincing than cross-sectional studies in this review. When compared with a larger sample study, a smaller sample study could control the data quality better. However, a larger sample study would have higher credibility and stronger representativeness than a smaller sample. The race difference may result in different cooking methods or food groups, culturally related and may differ by ethnicity. In our studies, the relationships between dietary patterns and overweight/obesity were different in different races.
Relationships between dietary patterns and overweight/obesity are not a new topic because these associations have already been reported in previous studies (13,24). In a review of 30 cross-sectional studies, summarizing the associations between BMI and dietary patterns defined by means of diet index, factor or cluster analysis. Although dietary patterns of each study were identified by different statistical methods, a diet rich in fruits and vegetables was inversely associated with BMI and a diet rich in meat and fat was positively associated with BMI (28). In our analyses, when the results of all studies were pooled, both ORs and mean differences of BMI were all statistically significant when the highest was compared with the lowest categories of prudent/healthy and western/unhealthy dietary pattern, which made our review more credible.
Our meta-analyses may not include all studies. As some large studies were not involved in our meta-analyses, in which factor scores were not categorized. It should be taken into account that FFQ has limitations in judging dietary patterns. Nevertheless, this bias was also observed in a large number of nutritional surveys independent of the method adopted (28). Moreover, FFQ was shown to be a reproducible and valid tool to identify dietary patterns through factor analysis (29,30). Limitations and subjective nature of factor analysis and principal component analysis technique are widely acknowledged (31, 32), however, cannot be avoided. Therefore, any association shown in this analysis is likely to be attenuated due to these limitations.
In our analysis, some potential limitations should be considered. Firstly, significant heterogeneity was present in our analysis, which introduced a warning concerning the generalization of present results. Secondly, confounding factors were poorly handled in numerous selected studies. Consequently, data included in this meta-analysis may suffer from different degrees of completeness and accuracy. In order to compensate for data heterogeneity, a sensitivity analysis has been performed. Nevertheless, for a number of potential confounding variables, such as sex, physical activity, and economic status, a sensitivity analysis was unable to be performed through these potential confounding variables, because this information was not provided in original articles. These limitations must be noted and the results should be considered with caution.
In addition, other dietary patterns may have relation with overweight/obesity as well. For example, a Japanese traditional pattern was associated with increased overweight/obesity risk, while a coffee and dairy products pattern were related to decreased overweight/obesity risk (8). For the purpose of our analyses, only the most commonly identified dietary patterns were identified, which might, therefore, cause bias in our results.
Conclusion
The results provide evidence of an inverse association between a prudent/healthy dietary pattern and overweight/obesity risk and a positive association between a western/unhealthy dietary pattern and overweight/obesity risk. Confirming this association may be significant for clinical implications, primary prevention strategies of overweight/obesity and overweight/obesity-related diseases. Our meta-analysis results probably highlight the association between dietary patterns and obesity/overweight risk.
Ethical considerations
Ethical issues (Including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, etc.) have been completely observed by the authors.
Acknowledgements
The funds of National Natural Science Foundation of China (No.81571528, No. 81202294) was responsible for Co-author Dong Hu. The funds of National Natural Science Foundation of China (No. 81302524) was responsible for Co-author Jing Wu.
Footnotes
Conflict of Interests
The authors declare that there is no conflict of interest.
References
- 1.World Health Organization (2000). Obesity: Preventing and Managing the Global Epidemic. World Health Organ Tech Rep Ser, 894:i–xii, 1–253. [PubMed] [Google Scholar]
- 2.Cornier MA, Marshall JA, Hill JO, et al. (2011). Prevention of as overweight/obesity a strategy to optimize cardiovascular health. Circulation, 124: 840–50. [DOI] [PubMed] [Google Scholar]
- 3.Hamer M, Brunner EJ, Bell J, et al. (2013). Physical activity patterns over 10 waist circumference: the Whitehall II and body mass index years in relation to cohort study. Obesity (Silver Spring), 21: E755–61. [DOI] [PubMed] [Google Scholar]
- 4.Nasreddine L, Naja F, Akl C, et al. (2014). Dietary, lifestyle and socio-economic in Lebanese children adiposity central correlates of overweight, obesity and adolescents. Nutrients, 6:1038–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Osler M, Helms Andreasen A, Heitmann B, et al. (2002). Food intake patterns and risk of coronary heart disease: a prospective cohort study examining the use of traditional scoring techniques. Eur J Clin Nutr, 56:568–74. [DOI] [PubMed] [Google Scholar]
- 6.Lucock MD, Martin CE, Yates ZR, et al. (2014). Diet and our genetic legacy in the recent anthropocene: a Darwinian perspective to nutritional health. J Evid Based Complementary Altern Med, 19:68–83. [DOI] [PubMed] [Google Scholar]
- 7.Rezazadeh A, Rashidkhani B. (2010). The association of general and central obesity with major dietary patterns of adult women living in Tehran, Iran. J Nutr Sci Vitaminol (Tokyo), 56: 132–8. [DOI] [PubMed] [Google Scholar]
- 8.Okubo H, Sasaki S, Murakami K, et al. (2008). Three major dietary patterns are all independently related to the risk of obesity among 3760 Japanese women aged 18–20 years. Int J Obes (Lond), 32:541–9. [DOI] [PubMed] [Google Scholar]
- 9.Paradis AM, Godin G, Pérusse L, et al. (2009). Associations between dietary patterns and obesity phenotypes. Int J Obes (Lond), 33:1419–26. [DOI] [PubMed] [Google Scholar]
- 10.Nkondjock A, Bizome E. (2010). Dietary patterns associated with hypertension prevalence in the cameroon defence forces. Eur J Clin Nutr, 64:1014–21. [DOI] [PubMed] [Google Scholar]
- 11.Silva Bdel P, Neutzling MB, Camey S, et al. (2014). Dietary patterns and hypertension: a population-based study with women from Southern Brazil. Cad Saude Publica, 30(5):961–71. [DOI] [PubMed] [Google Scholar]
- 12.Hamer M, Mishra GD. (2010). Dietary patterns and cardiovascular risk markers in the UK low income diet and nutrition survey. Nutr Metab Cardiovasc Dis, 20:491–7. [DOI] [PubMed] [Google Scholar]
- 13.Sugawara N, Yasui-Furukori N, Sato Y, et al. (2014). Dietary patterns are associated with obesity in Japanese patients with schizophrenia. BMC Psychiatry, 14:184. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Lioret S, Touvier M, Lafay L, et al. (2008). Dietary and physical activity patterns in french children are related to overweight and socioeconomic status. J Nutr, 138:101–7. [DOI] [PubMed] [Google Scholar]
- 15.Chan R, Chan D, Lau W, et al. (2014). A cross-sectional study to examine the association between dietary patterns and risk of overweight and obesity in Hong Kong Chinese adolescents aged 10–12 years. J Am Coll Nutr, 33:450–8. [DOI] [PubMed] [Google Scholar]
- 16.Zhang JG, Wang ZH, Wang HJ, et al. (2015). Dietary patterns and their associations with general obesity and abdominal obesity among young Chinese women. Eur J Clin Nutr, 69:1009–14. [DOI] [PubMed] [Google Scholar]
- 17.McDonald CM, Baylin A, Arsenault JE, et al. (2009). Overweight is more prevalent than stunting and is associated with socioeconomic status, maternal obesity, and a snacking dietary pattern in school children from bogota, colombia. J Nutr, 139:370–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Shin KO, Oh SY, Park HS. (2007). Empirically derived major dietary patterns and their associations with overweight in korean preschool children. Br J Nutr, 98:416–21. [DOI] [PubMed] [Google Scholar]
- 19.Pala V, Lissner L, Hebestreit, et al. (2013). Dietary patterns and longitudinal change in body mass in European children: a follow-up study on the IDEFICS multicenter cohort. Eur J Clin Nutr, 67:1042–9. [DOI] [PubMed] [Google Scholar]
- 20.Denova-Gutiérrez E, Castañón S, Talavera JO, et al. (2011). Dietary patterns are associated with different indexes of adiposity and obesity in an urban mexican population. J Nutr, 141:921–7. [DOI] [PubMed] [Google Scholar]
- 21.Okubo H, Sasaki S, Horiguchi H, et al. (2006). Dietary patterns associated with bone mineral density in premenopausal Japanese farmwomen. Am J Clin Nutr, 83:1185–92. [DOI] [PubMed] [Google Scholar]
- 22.Denova-Gutiérrez E, Castañón S, Talavera JO, et al. (2010). Dietary patterns are associated with metabolic syndrome in an urban mexican population. J Nutr, 140:1855–63. [DOI] [PubMed] [Google Scholar]
- 23.Cho YA, Shin A, Kim J. (2011). Dietary patterns are associated with body mass index in a korean population. J Am Diet Assoc, 111:1182–6. [DOI] [PubMed] [Google Scholar]
- 24.Maskarinec G, Novotny R, Tasaki K. (2000). Dietary patterns are associated with body massindex in multi ethnic women. J Nutr, 130: 3068–72. [DOI] [PubMed] [Google Scholar]
- 25.Togo P, Osler M, Sørensen TI, et al. (2004). A longitudinal study of food intake patterns and obesity in adult Danish men and women. Int J Obes Relat Metab Disord, 28: 583–93. [DOI] [PubMed] [Google Scholar]
- 26.Higgins JP, Thompson SG, Deeks JJ, et al. (2003). Measuring inconsistency in meta-analyses. BMJ, 327(7414):557–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Begg CB, Mazumdar M. (1994). Operating characteristics of a rank correlation test for publication bias. Biometrics, 50(4):1088–101. [PubMed] [Google Scholar]
- 28.Bouchard-Mercier A, Paradis AM, Godin G, et al. (2010). Associations between dietary patterns and ldl peak particle diameter: A cross-sectional study. J Am Coll Nutr, 29:630–7. [DOI] [PubMed] [Google Scholar]
- 29.Mills VC, Skidmore PM, Watson EO, et al. (2015). Relative validity and reproducibility of a food frequency questionnaire for identifying the dietary patterns of toddlers in new zealand. J Acad Nutr Diet, 115:551–8. [DOI] [PubMed] [Google Scholar]
- 30.Beck KL, Kruger R, Conlon CA, et al. (2012). The relative validity and reproducibility of an iron food frequency questionnaire for identifying iron-related dietary patterns in young women. J Acad Nutr Diet, 112:1177–87. [DOI] [PubMed] [Google Scholar]
- 31.Martinez-Gonzalez MA, Bes-Rastrollo M. (2014). Dietary patterns, Mediterranean Dietary and cardiovascular disease. Curr Opin Lipidol, 25:20–6. [DOI] [PubMed] [Google Scholar]
- 32.Liu D, Jin J, Tian J, et al. (2015). Quality assessment and of systematic reviews and meta-analyses of endoscopic ultrasound analysis diagnosis. PLoS One, 10:e0120911. [DOI] [PMC free article] [PubMed] [Google Scholar]