Health effects associated with foods characteristic of the Nordic diet: a systematic literature review

Peer reviewe

Diet and body constitution in relation to subgroups of breast cancer defined by tumour grade, proliferation and key cell cycle regulators

BACKGROUND: The general lack of clear associations between diet and breast cancer in epidemiological studies may partly be explained by the fact that breast cancer is a heterogeneous disease that may have disparate genetic associations and different aetiological bases. METHOD: A total of 346 incident breast cancers in a prospective cohort of 17,035 women enrolled in the Malmö Diet and Cancer study (Sweden) were subcategorized according to conventional pathology parameters, proliferation and expression of key cell cycle regulators. Subcategories were compared with prediagnostic diet and body measurements using analysis of variance. RESULTS: A large hip circumference and high body mass index were associated with high grade tumours (P = 0.03 and 0.009, respectively), whereas low energy and unadjusted fat intakes were associated with high proliferation (P = 0.03 and 0.004, respectively). Low intakes of saturated, monounsaturated and polyunsaturated fatty acids were also associated with high proliferation (P = 0.02, 0.004 and 0.003, respectively). Low energy and unadjusted fat intakes were associated with cyclin D(1 )overexpression (P = 0.02 and 0.007, respectively), whereas cyclin E overexpression was positively correlated with fat intake. Oestrogen receptor status and expression of the tumour suppressor gene p27 were not associated with either diet or body constitution. CONCLUSION: Low energy and low total fat (polyunsaturated fatty acids in particular) intakes, and high body mass index were associated with relatively more malignant breast tumours. Dietary behaviours and body constitution may be associated with specific types of breast cancer defined by conventional pathology parameters and cyclin D(1 )and cyclin E expression. Further studies including healthy control individuals are needed to confirm our results

Identifying dietary patterns using a normal mixture model: application to the EPIC study

Background: Finite mixture models posit the existence of a latent categorical variable and can be used for probabilistic classification. The authors illustrate the use of mixture models for dietary pattern analysis. An advantage of this approach is taking classification uncertainty into account. Methods: Participants were a random sample of women from the European Prospective Investigation into Cancer. Food consumption was measured using dietary questionnaires. Mixture models identified latent classes in food consumption data, which were interpreted as dietary patterns. Results: Among various assumptions examined, models allowing the variance of foods to vary within and between classes fit better than alternatives assuming constant variance (the K-means method of cluster analysis also makes the latter assumption). An eight-class model was best fitting and five patterns validated well in a second random sample. Patterns with lower classification uncertainty tended to be better validated. One pattern showed low consumption of foods despite being associated with moderate body mass index. Conclusion: Mixture modelling for dietary pattern analysis has advantages over both factor and cluster analysis. In contrast to these other methods, it is easy to estimate pattern prevalence, to describe patterns and to use patterns to predict disease taking classification uncertainty into account. Owing to substantial error in food consumptions, any analysis will usually find some patterns that cannot be well validated. While knowledge of classification uncertainty may aid pattern evaluation, any method will better identify patterns from food consumptions measured with less error. Mixture models may be useful to identify individuals who under-report food consumption

Specific food group combinations explaining the variation in intakes of nutrients and other important food components in the European Prospective Investigation into Cancer and Nutrition: An application of the reduced rank regression method

Objective: To identify combinations of food groups that explain as much variation in absolute intakes of 23 key nutrients and food components as possible within the country-specific populations of the European Prospective Investigation into Cancer and Nutrition (EPIC). Subjects/Methods: The analysis covered single 24-h dietary recalls (24-HDR) from 36 034 subjects (13 025 men and 23 009 women), aged 35–74 years, from all 10 countries participating in the EPIC study. In a set of 39 food groups, reduced rank regression (RRR) was used to identify those combinations (RRR factors) that explain the largest proportion of variation in intake of 23 key nutrients and food components, namely, proteins, saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, cholesterol, sugars (sum of mono- and disaccharides), starch, fibre, alcohol, calcium, iron, potassium, phosphorus, magnesium, vitamin D, β-carotene, retinol and vitamins E, B1, B2, B6, B12 and C (RRR responses). Analyses were performed at the country level and for all countries combined. Results: In the country-specific analyses, the first RRR factor explained a considerable proportion of the total nutrient intake variation in all 10 countries (27.4–37.1%). The subsequent RRR factors were much less important in explaining the variation (less than or equal to6%). Strong similarities were observed for the first country-specific RRR factor between the individual countries, largely characterized by consumption of bread, vegetable oils, red meat, milk, cheese, potatoes, margarine and processed meat. The highest explained variation was seen for protein, potassium, phosphorus and magnesium (50–70%), whereas sugars, β-carotene, retinol and alcohol were only marginally explained (less than or equal to5%). The explained proportion of the other nutrients ranged between these extremes. Conclusions: A combination of food groups was identified that explained a considerable proportion of the nutrient intake variation in 24-HDRs in every country-specific EPIC population in a similar manner. This indicates that, despite the large variability in food and nutrient intakes reported in the EPIC, the variance of intake of important nutrients is explained, to a large extent, by similar food group combinations across countries

Perspective: An Extension of the STROBE Statement for Observational Studies in Nutritional Epidemiology (STROBE-nut): Explanation and Elaboration

Nutritional epidemiology is an inherently complex and multifaceted research area. Dietary intake is a complex exposure and is challenging to describe and assess, and links between diet, health, and disease are difficult to ascertain. Consequently, adequate reporting is necessary to facilitate comprehension, interpretation, and generalizability of results and conclusions. The STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement is an international and collaborative initiative aiming to enhance the quality of reporting of observational studies. We previously presented a checklist of 24 reporting recommendations for the field of nutritional epidemiology, called "the STROBE-nut." The STROBE-nut is an extension of the general STROBE statement, intended to complement the STROBE recommendations to improve and standardize the reporting in nutritional epidemiology. The aim of the present article is to explain the rationale for, and elaborate on, the STROBE-nut recommendations to enhance the clarity and to facilitate the understanding of the guidelines. Examples from the published literature are used as illustrations, and references are provided for further reading.Funding Agencies|Forte grantt hrough the Swedish Network in Epidemiology and Nutrition (NEON) [2013-0022]; Research Foundation-Flanders (FWO) [G0D4815N]; Schlumberger Foundation; Medical Research Council [MR/L02019X/1]</p

Identifying dietary patterns using a normal mixture model: application to the EPIC study

Background Finite mixture models posit the existence of a latent categorical variable and can be used for probabilistic classification. The authors illustrate the use of mixture models for dietary pattern analysis. An advantage of this approach is taking classification uncertainty into account. Methods Participants were a random sample of women from the European Prospective Investigation into Cancer. Food consumption was measured using dietary questionnaires. Mixture models identified latent classes in food consumption data, which were interpreted as dietary patterns. Results Among various assumptions examined, models allowing the variance of foods to vary within and between classes fit better than alternatives assuming constant variance (the K-means method of cluster analysis also makes the latter assumption). An eight-class model was best fitting and five patterns validated well in a second random sample. Patterns with lower classification uncertainty tended to be better validated. One pattern showed low consumption of foods despite being associated with moderate body mass index. Conclusion Mixture modelling for dietary pattern analysis has advantages over both factor and cluster analysis. In contrast to these other methods, it is easy to estimate pattern prevalence, to describe patterns and to use patterns to predict disease taking classification uncertainty into account. Owing to substantial error in food consumptions, any analysis will usually find some patterns that cannot be well validated. While knowledge of classification uncertainty may aid pattern evaluation, any method will better identify patterns from food consumptions measured with less error. Mixture models may be useful to identify individuals who under-report food consumption

Lifetime and baseline alcohol intake and risk of cancer of the upper aero-digestive tract in the European Prospective Investigation into Cancer and Nutrition (EPIC) study

Recent alcohol consumption is all established risk factor for squamous cell carcinoma (SCC) or the upper aero-digestive tract. In contrast, the role or lifetime exposure to alcohol with regard to risk of SCC is not well established. Historical data oil alcohol use are available in 271,253 participants of the European Prospective Investigation into Cancer and Nutrition (EPIC). During 2,330,381 person years, 392 incident SCC cases (279 men and 113 women) were identified. Cox regression vas applied to model sex-specific associations between lifetime alcohol intake and SCC risk adjusting for potential confounders including smoking. Compared to men who drank 0.1-6.0 g/day alcohol at lifetime, the relative risks (RR) for developing SCC were significantly increased for men who drank 30.1-60.0 g/day (RR 1.65, 95% confidence interval: 1.00-2.71), 60.1-96.0 g/day (RR 2.20, 95%CI 1.23-3.95), and >96.0 g/day, (RR 4.63, 95% CI 2.52-8.48), and for former drinkers (RR 4.14, 95% CI 2.38-7.19). These risk estimates did not considerably change when baseline alcohol intake was analyzed. Compared to women who drank 0.1-6.0 g/day alcohol intake at lifetime, the RR were significantly increased for women who drank >30 g/d (RR 6.05, 95% CI 2.98-12.3). Applying similar categories, the relative risk for baseline alcohol intake was 3.26 (95%CI 1.82-5.87). We observed a stronger association between alcohol intake at lifetime and risk of SCC in women compared to men (p for interaction = 0.045). The strong dose-response relation for lifetime alcohol use underscores that alcohol is an important risk factor of SCC of the upper aero-digestive tract throughout life. (C) 2009 UIC

Dietary carbohydrates, glycemic index, glycemic load, and endometrial cancer risk within the European Prospective Investigation into Cancer and Nutrition Cohort

The associations of dietary total carbohydrates, overall glycemic index, total dietary glycemic load, total sugars, total starch, and total fiber with endometrial cancer risk were analyzed among 288,428 women in the European Prospective Investigation into Cancer and Nutrition cohort (1992-2004), including 710 incident cases diagnosed during a mean 6.4 years of follow-up. Cox proportional hazards models were used to estimate relative risks and 95% confidence intervals. There were no statistically significant associations with endometrial cancer risk for increasing quartile intakes of any of the exposure variables. However, in continuous models calibrated by using 24-hour recall values, the multivariable relative risks were 1.61 (95% confidence interval: 1.06, 2.45) per 100 g/day of total carbohydrates, 1.40 (95% confidence interval: 0.99, 1.99) per 50 units/day of total dietary glycemic load, and 1.36 (95% confidence interval: 1.05, 1.76) per 50 g/day of total sugars. These associations were stronger among women who had never used postmenopausal hormone therapy compared with ever users (total carbohydrates P-heterogeneity = 0.04). Data suggest no association of overall glycemic index, total starch, and total fiber with risk, and a possible modest positive association of total carbohydrates, total dietary glycemic load, and total sugars with risk, particularly among never users of hormone replacement therapy