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Asthma severity amongst cases was measured according to the frequency of waking at night with asthma symptoms (3+ times per week vs Cilengitide nasal allergies including hayfever. Cases and controls were mailed a food frequency questionnaire (FFQ), based on one used previously (18), which asked about usual dietary intake in the past 12?months, and included over 200 items of food and drink. The modified FFQ was calibrated against a 7-day weighed record and repeatability of the FFQ was tested in a subsample (14). Replies were received from 720 cases and 980 controls. buy Palbociclib After allowing for likely wrong addresses, the overall response rate was 55% (19). Prior to coding, we excluded 76 individuals for whom information on 46 or more food items (equivalent to two pages) was missing. In the remainder, items were coded as ��never eaten�� if a frequency of consumption was not specified. We estimated weekly intake (g) of foods and food groups by multiplying frequency of consumption by the weight of standard portion sizes. Total energy intake was estimated from food intakes using British food composition tables (20). We used Schofield equations to estimate basal metabolic rate (BMR) (21) and excluded subjects from the analysis if their ratio of energy intake to BMR was below the 0.5th sample centile or above the 99.5th sample centile (22). Principal component analysis was applied to the FFQ data to identify dietary patterns. This was performed in controls only to avoid potential bias. A varimax selleck kinase inhibitor rotation was applied to improve the interpretability of the patterns obtained. The number of patterns was determined by examination of the screen plot of the eigenvalues. This is a plot of the eigenvalues vs the component number. The aim was to identify an ��elbow�� which corresponds to the point after which the addition of more components explains relatively little more of the variance. We extracted the first five components (dietary patterns), which explained 17% of the variance in the original 216 items. A component score was created for each individual for each of the principal components identified. It is possible for individuals who score highly for one dietary pattern to also score highly for another dietary pattern. Thus the dietary patterns do not identify mutually exclusive clusters of individuals. Individual foods that correlated >0.3 or