Exploratory factor analysis might work well in elucidating the major dietary patterns prevailing in specific study populations. However, patterns extracted in one study population and their associations with disease risk cannot be reproduced with this data-specific method in other study populations. To construct less population-dependent pattern variables of similar content as original exploratory patterns, we proposed to derive so-called simplified pattern variables. They represent the sum of the unweighted standardised food variables which loaded high at the pattern of interest. Data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study suggest that these simplified pattern variables might adequately approximate factor analysis-based dietary patterns. A simplified pattern variable based on the six highest loading food variables showed a correlation >0.95 with the originally derived factor score, which consisted of forty-seven food variables. Moreover, simplified pattern variables might adequately approximate patterns across different study populations. A simplified pattern variable showed similar factor loadings, ranging from 0.34 to 0.52, as well as similar associations with nutrient intake as a 'western' pattern originally reported from an US study population. These simplified pattern variables can subsequently be used to study pattern associations with disease risk, especially in multi-centre studies. It is therefore an approach that might overcome one of the most frequently claimed limitations of factor analyses applied in epidemiology: their non-comparable risk estimates.