Development of a New Branded UK Food Composition Database for an Online Dietary Assessment Tool.

The current UK food composition tables are limited, containing ~3300 mostly generic food and drink items. To reflect the wide range of food products available to British consumers and to potentially improve accuracy of dietary assessment, a large UK specific electronic food composition database (FCDB) has been developed. A mapping exercise has been conducted that matched micronutrient data from generic food codes to "Back of Pack" data from branded food products using a semi-automated process. After cleaning and processing, version 1.0 of the new FCDB contains 40,274 generic and branded items with associated 120 macronutrient and micronutrient data and 5669 items with portion images. Over 50% of food and drink items were individually mapped to within 10% agreement with the generic food item for energy. Several quality checking procedures were applied after mapping including; identifying foods above and below the expected range for a particular nutrient within that food group and cross-checking the mapping of items such as concentrated and raw/dried products. The new electronic FCDB has substantially increased the size of the current, publically available, UK food tables. The FCDB has been incorporated into myfood24, a new fully automated online dietary assessment tool and, a smartphone application for weight loss.

Dietary intakes and food sources of phenolic acids in the European Prospective Investigation into Cancer and Nutrition (EPIC) study.

Phenolic acids are secondary plant metabolites that may have protective effects against oxidative stress, inflammation and cancer in experimental studies. To date, limited data exist on the quantitative intake of phenolic acids. We estimated the intake of phenolic acids and their food sources and associated lifestyle factors in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Phenolic acid intakes were estimated for 36,037 subjects aged 35-74 years and recruited between 1992 and 2000 in ten European countries using a standardised 24 h recall software (EPIC-Soft), and their food sources were identified. Dietary data were linked to the Phenol-Explorer database, which contains data on forty-five aglycones of phenolic acids in 452 foods. The total phenolic acid intake was highest in Aarhus, Denmark (1265·5 and 980·7 mg/d in men and women, respectively), while the intake was lowest in Greece (213·2 and 158·6 mg/d in men and women, respectively). The hydroxycinnamic acid subclass was the main contributor to the total phenolic acid intake, accounting for 84·6-95·3% of intake depending on the region. Hydroxybenzoic acids accounted for 4·6-14·4%, hydroxyphenylacetic acids 0·1-0·8% and hydroxyphenylpropanoic acids ≤ 0·1% for all regions. An increasing south-north gradient of consumption was also found. Coffee was the main food source of phenolic acids and accounted for 55·3-80·7% of the total phenolic acid intake, followed by fruits, vegetables and nuts. A high heterogeneity in phenolic acid intake was observed across the European countries in the EPIC cohort, which will allow further exploration of the associations with the risk of diseases.

Fatty acid patterns and risk of prostate cancer in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition.

BACKGROUND: Fatty acids in blood may be related to the risk of prostate cancer, but epidemiologic evidence is inconsistent. Blood fatty acids are correlated through shared food sources and common endogenous desaturation and elongation pathways. Studies of individual fatty acids cannot take this into account, but pattern analysis can. Treelet transform (TT) is a novel method that uses data correlation structures to derive sparse factors that explain variation. OBJECTIVE: The objective was to gain further insight in the association between plasma fatty acids and risk of prostate cancer by applying TT to take data correlations into account. DESIGN: We reanalyzed previously published data from a case-control study of prostate cancer nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. TT was used to derive factors explaining the variation in 26 plasma phospholipid fatty acids of 962 incident prostate cancer cases matched to 1061 controls. Multiple imputation was used to deal with missing data in covariates. ORs of prostate cancer according to factor scores were determined by using multivariable conditional logistic regression. RESULTS: Four simple factors explained 38% of the variation in plasma fatty acids. A high score on a factor reflecting a long-chain n-3 PUFA pattern was associated with greater risk of prostate cancer (OR for highest compared with lowest quintile: 1.36; 95% CI: 0.99, 1.86; P-trend = 0.041). CONCLUSION: Pattern analyses using TT groupings of correlated fatty acids indicate that intake or metabolism of long-chain n-3 PUFAs may be relevant to prostate cancer etiology.