The size of the VIP dietary database, with over 60,000 records between 1992 and 2005, is a significant strength of this study and provided the opportunity to evaluate relationships between food patterns and health among different subgroups with reasonable power. The level of low energy reporting as well as previously known health problems in the participants and their close relatives were taken into account, all of which are factors that likely confound the relationship between food intake and health. Due to the large number of participants, fairly small differences were usually highly significant at P < 0.01. As the aim of the current study was to identify factors that might contribute to bias in studies of disease etiology, using a cross-sectional design when looking at risk-markers for CVD (rather than prospectively studying CVD as an end-point) it can only generate hypotheses, and does not allow any conclusions on causality.
With data driven methods, such as cluster and factor analysis of food patterns, researchers are compelled to make numerous subjective decisions, and no gold standard exist for these procedures. Therefore, the usefulness of food patterns has been questioned [14, 15], and the need for further development and evaluation of the methods is reported [1, 16].
A problematic aspect of studies continuing over extended time periods is that it is plausible that data collection, end-point ascertainment as well as lifestyle habits vary during the data collection period. This is an important aspect, and an example of a two edged sword in nutritional epidemiology. On the one hand FFQ questions should be left unchanged for comparison, but on the other hand the panorama of food items change and with that selection preferences. We have not adjusted for screening year in the presented analyses. However, it is unlikely that end-point ascertainments have changed over time due to the general nature of these questions and standardization/calibration procedures for lab analyses, but lifestyle habits certainly have changed both in Sweden as a whole and in the study area. We are presently describing 25-year time trends in food selection in VIP, and in several parts the results accord with longitudinal production and consumption information (Johansson et al., manuscript). We mean that adoption of the questions to changes in the market is important when specific food components are to be evaluated, such as specific fatty acids, but that the generalized nature of the FFQ questions is sufficient for more general aspects, such as time trends and, as here, food patterns.
Another major concern is that many studies on dietary patterns do not validate their food intake data. The golden standard, doubly labeled water technique, is expensive and therefore not feasible in most trials, particularly in larger epidemiological studies. However, a comparison between reported energy intake and calculated energy expenditure should be a minimal requirement for all dietary intake studies, including food pattern studies . This would enable exclusion of participants clearly misreporting their intake, as well as allowing for reporting the proportion of low energy reporters in each FPG. Mattisson et al  compared three common methods to classify misreporting in large-scale epidemiological studies and concluded that using individual PAL-values is preferable to using a fixed cut-off point. In the present study an individual PAL-value was calculated by comparing the reported physical activity at work and at leisure with the categories used in a two-question questionnaire on physical activity developed by Johansson and Westerterp . This questionnaire has been validated with doubly labeled water in a small scale study with promising results, and seems to be suitable for use in large-scale epidemiological studies. Participants with implausible food intake data were excluded from the cluster analysis. After clusters had been developed, Low Energy Reporters were identified. The first un-adjusted analyses showed that the Fruit & vegetables group among women and the High Fat group among men reported the lowest energy intake. However, when the analyses were repeated with Adequate Reporters and Low Energy Reporters in separate groups, we found that the differences in energy intake in the crude analyses were explained by differences in the proportion of Low Energy Reporters in the FPGs, indicating that this is an important factor to recognize and discuss in relation to FPG results. Some studies report one, often very large, FPG with low energy intake [18, 19]; however, authors often do not discuss whether this might be due to a higher level of low energy reporting in this group and what effect this might have on their conclusions. This failure to recognize and control for low energy reporting is one possible explanation for conflicting and/or inconsistent results when studying associations between food patterns and health. In the present study, the association with health was studied both for the total sample and separately for Adequate and Low Energy Reporters.
There are well known associations between food habits and characteristics such as gender, age, education, and socio-economic status, and with health behaviors such as physical activity, smoking, and drinking . In the present study, we also found clear differences among FPG with regard to previous health of the participants and their close relatives. Some, but not all, studies on the relationship between food patterns and health have taken the participants previous medical history into account through exclusion, stratification, or adjustment , but we could not find any study that included the health of close relatives in the analysis. This may lead to remaining bias in estimates of the association between food patterns and later health.
The Fruit & vegetables groups of both sexes, the female Tea & ice cream, and the male Tea, soda, & cookies group reported the healthiest food choices . Past food habit changes has been found to be more common in clusters with healthier food choices , and illness in family and friends has been shown to influence food choices . Unfortunately, the participants were not asked about changes in food pattern, but in light of the background characteristics of these four groups, it might be possible that the latter two groups had a healthier lifestyle by choice whereas the lifestyle and food pattern of the former two might be more influenced by known health issues in themselves or close relatives. Increased knowledge about the reasons behind people's lifestyle choices and factors that can help or hinder healthy choices is important when planning health interventions and warrant further studies.
Interestingly, the latter two groups also had the highest proportion of Adequate Reporters whereas the female Fruit & vegetables group had the highest and the male Fruit & vegetables group the second highest proportion of Low Energy Reporters. This is in accordance with other studies showing that low energy reporting is associated with reporting a healthy food pattern [21, 23]. Surprisingly, among males the High fat group had the highest level of low energy reporting.
Participants that did not take blood pressure medication but previously had been told by a doctor or nurse that they had high blood pressure were not classified as Previously Ill. This situation was more common among women, possibly reflecting the fact that many women have transient elevated blood pressure during pregnancy. However, it has been shown that untreated hypertension is associated with a four-fold increased risk for later stroke . Thus, we adjusted for this in the final regression analysis as it may have influenced choices of lifestyle and food patterns.
Perceived health has been associated with the risk of CVD [12, 25]. In the present study we found a significant association between FPG and perceived health for men only in the crude analysis (with highest proportion of participants with good health among the Fruit & Vegetables FPG), but this disappeared after adjustment.
We previously reported that most of the differences between women and men in food intake among VIP participants are consistent with Swedish national data [6, 26]. Larger differences in nutrient intake, especially fat (E%), carbohydrate (E%), fiber (g), carotenoids, vitamin C, and folate, were seen among female FPG than among male FPG. In both sexes, High fat and Fruit & vegetables FPG represented the opposite ends of the intake continuum for most nutrients. In a review of cluster and factor analysis , Newby and Tucker point out that gender is an important factor to include in the analysis; either by deriving the food patterns separately for women and men or by including sex in further analysis after deriving food patterns in a mixed population. In the present study we found large differences between women and men, both with regard to food intake and relevant background factors, and therefore we believe that, if cluster analysis is performed on a mixed population, it would be useful if the gender distribution is given for each FPG. Newby and Tucker also point out that in many studies that separated women and men, the derived patterns were similar between sexes . In the present study, the naming of the FPGs was similar between the sexes, even if actual food intake differed . For instance, women belonging to the female Fruit & vegetables FPG ate fruit and vegetables on average three times more frequently than the men belonging to the male Fruit & vegetables FPG, who even ate less fruit and vegetables than the female FPG with the lowest frequency. These kinds of differences between FPGs with the same or similar names are important to consider when drawing conclusions about associations between food patterns and health.
Low energy reporting has been associated with BMI and desire of weight change [27, 28], as well as with changed food pattern . In the very first regression analysis, before discrimination between reporting accuracy and previous health, the associations found between FPG and health outcomes were contradictory to present recommendations in that the Fruit & vegetables FPG seemed to have the highest risk for many clinical diagnoses. However, the regression analyses were thereafter repeated on the Previously Healthy Adequate Reporters alone, to avoid reverse causality as individuals with previous health problems may choose to eat healthier. In these analyses, most of the risks for clinical outcomes associated with belonging to the Fruit & vegetables group were attenuated and this FPG instead became protective. In the Malmö Diet and Cancer Cohort it was found that past food habit change was related to obesity, lifestyle and socio-economic factors which can seriously distort observed relationships between diet and health . They have also showed that exclusion of low energy reporters affects the results when studying associations between food patterns and cancer . This highlights that it is of crucial importance to recognize that mis-reporting, as well as changed food habits due to previous ill health or other causes, are major threats to the validity of all nutrition epidemiology studies as it causes misclassification of dietary exposures, contributing to attenuated associations between food intake and health outcomes.
In the present study, being in the High fat groups was associated with an increased risk for adverse health effects, mainly for IFG/IGT/diabetes in females and elevated S-lipid levels in males. The High fat groups had the highest or second highest intake of most macro- and micronutrients that are viewed as unhealthy and the lowest intake of most nutrients viewed as healthy. The associations between food habits and health are complex and are further complicated by associations with weight and physical activity. It has been shown that being normal weight but physically inactive is more detrimental to long term health than being moderately overweight and physically active [30, 31]. Whether a high intake of unhealthy foods or a low intake of healthy foods is most detrimental to long-term health is unclear. From a public health point of view a broad approach is necessary when planning health interventions.