We identified a dietary pattern, characterized by high consumption of low-calorie/diet soft drinks, onions, sugar-sweetened beverages, burgers and sausages, crisps and other snacks, and white bread and low consumption of wholemeal bread, French dressing/vinaigrette, jam, and medium-/high-fiber breakfast cereals, that was positively correlated with insulin resistance and significantly associated with the risk of type 2 diabetes. Previous studies of type 2 diabetes using RRR analysis have used a variety of intermediate markers including nutrient intakes (5
), inflammation markers (7
), and biomedical risk factors including A1C, HDL cholesterol, and C-reactive protein (8
). Despite these differing intermediate markers, there are some similarities between the dietary patterns, with sugar-sweetened beverages, processed meat, and wholegrains/refined grains being identified as important predictors in each case and associated with risk of type 2 diabetes. Although somewhat difficult to compare, our results are also consistent with those from other dietary pattern research using factor and cluster analysis methods. Dietary patterns that have been shown to be protective against insulin resistance, metabolic syndrome, and type 2 diabetes were high in wholegrains (22
) and low in soft drinks (23
), white bread and refined grains (23
), crisps and other snacks (22
), and processed meat (20
). However, not all dietary patterns identified using these methods were found to be associated with type 2 diabetes or abnormal glucose tolerance (24
Soft drinks have previously been associated with increased risk of type 2 diabetes (25
). We also observed that diet soft drinks loaded highly on a dietary pattern associated with increased risk of diabetes, as did another recent study of dietary patterns and risk of metabolic syndrome (26
). This is probably due to reverse causality with those who are overweight or obese switching to diet soft drinks. Duffey and Popkin (27
) found that diet beverage consumers were more likely to be overweight, and in the current study, diet soft drink consumption was directly correlated with BMI (data not shown).
It is important to note that some foods in the dietary pattern may be indicators of other foods with which they are consumed. For example, jam and salad dressings are not consumed alone and may not be causally related to the outcome. Jam consumption was correlated with wholemeal/wholegrain bread but not with white bread, and salad dressing was correlated with salad vegetables (data not shown). Correlations between onions and other foods consumed did not appear to explain the presence of onions in the dietary pattern that was directly associated with type 2 diabetes and when investigated separately, onion intake was not associated with risk of type 2 diabetes. We know of no other evidence suggesting a link between onion intake and type 2 diabetes, and these vegetables would usually be considered healthy components of the diet (28
). Other studies using RRR have identified unexpected associations with legumes and some vegetables (7
), and therefore careful interpretation of dietary patterns is warranted. However, we were able to confirm the robustness of the dietary pattern identified after we performed sensitivity analysis in randomly split halves of the cohort and adjusted for energy intake, sex, and employment grade.
RRR is a new approach that uses previous knowledge of diet-disease relationships to inform the analysis process and focuses on the pathways through which diet may influence disease. Previous approaches to dietary pattern analysis such as cluster and factor analysis described the variations in food intake in the population, resulting in behavioral description of food intakes. These methods provide useful insight into the eating patterns actually evident within the population and identify at-risk groups. However, these may not represent optimal eating patterns, and associations with disease are not always detected. It should be acknowledged that not all studies will be able to use RRR, as it requires intermediate markers of exposure or disease (8
). Some studies have used nutrient intake as the intermediate or response variable, although a priori evidence may be lacking for strong relationships between nutrients and disease in some cases, one of the reasons for applying food-based dietary pattern approaches.
The dietary pattern in this study explained 5.7% of the variation in HOMA-IR. This result is comparable to that for other RRR studies that have used biomedical risk factors as the response variable (7
). Studies using nutrient intakes have tended to explain higher variation in those responses (5
). This finding is unsurprising, as HOMA-IR is a more remote response variable than nutrient intakes. Of note, BMI is a major determinant of HOMA-IR, explaining ~14% of the variation, and, therefore, in comparison, diet is an important contributory factor to insulin resistance.
A weakness of the RRR approach is the cross-sectional nature of underlying dietary pattern analysis. In our analysis, to reduce the impact of possible changes in dietary behavior due to existing disease, those with type 2 diabetes at baseline were excluded from the analysis. In addition, the dietary patterns were derived using HOMA-IR scores, and investigation of their association with diabetes was performed within the same cohort. Future work will test the predictive ability of this dietary pattern in other populations. It should also be noted that in the special case of only one response variable, RRR is identical to multiple linear regression (8
Strengths of this study include the large sample size, the prospective nature of the study, and the rigorous methods of outcome ascertainment. In the current study, type 2 diabetes was diagnosed using a 2-h OGTT in addition to self-report. Other studies investigating dietary patterns and type 2 diabetes, including those using RRR methods, have relied exclusively on self-report (5
). A limitation of this study is that whereas the FFQ is known to be a valid measure of nutrient intakes, there are currently no data available on the validity of the food intake data. Sociodemographic factors (age, sex, ethnicity, and employment grade), health behaviors (smoking, physical activity, and alcohol use), and other risk factors (blood pressure and BMI) were shown to attenuate the relationship between the dietary pattern and type 2 diabetes, although the relationship remained significant. Of the confounding factors included in the final model, physical activity is the most prone to measurement error, leading to the possibility of residual confounding. Adjustment for BMI and blood pressure (models 6, 7, and 8) attenuated the relationship between the dietary pattern and risk of diabetes; however, diet is likely to act through these factors and adjustment may lead to an underestimate of the diet-related risk (30
In this analysis, we identified a dietary pattern that was positively correlated with insulin resistance and significantly associated with the risk of type 2 diabetes. The dietary pattern was characterized by high consumption of low-calorie/diet soft drinks, onions, sugar-sweetened beverages, burgers and sausages, crisps and other snacks, and white bread and low consumption of wholemeal bread, French dressing/vinaigrette, jam, and medium-/high-fiber breakfast cereals. This research adds to the existing evidence that dietary patterns are an important risk factor for type 2 diabetes; however, further work is required to determine alternative pathways through which diet may influence risk of diabetes.