Abstract

Aims/hypothesisFew studies have investigated the relationship between predefined dietary patterns and type 2 diabetes incidence; little is known about the generalisability of these associations. We aimed to assess the association between predefined dietary patterns and type 2 diabetes risk in European populations.MethodsFrom among a case-cohort of 12,403 incident diabetes cases and 16,154 subcohort members nested within the prospective European Prospective Investigation into Cancer and Nutrition study, we used data on 9,682 cases and 12,595 subcohort participants from seven countries. Habitual dietary intake was assessed at baseline with country-specific dietary questionnaires. Two diet-quality scores (alternative Healthy Eating Index [aHEI], Dietary Approaches to Stop Hypertension [DASH] score) and three reduced rank regression (RRR)-derived dietary-pattern scores were constructed. Country-specific HRs were calculated and combined using a random-effects meta-analysis.ResultsAfter multivariable adjustment, including body size, the aHEI and DASH scores were not significantly associated with diabetes, although for the aHEI there was a tendency towards an inverse association in countries with higher mean age. We observed inverse associations of the three RRR-derived dietary-pattern scores with diabetes: HRs (95% CIs) for a 1-SD difference were 0.91 (0.86, 0.96), 0.92 (0.84, 1.01) and 0.87 (0.82, 0.92). Random-effects meta-analyses revealed heterogeneity between countries that was explainable by differences in the age of participants or the distribution of dietary intake.Conclusions/interpretationAdherence to specific RRR-derived dietary patterns, commonly characterised by high intake of fruits or vegetables and low intake of processed meat, sugar-sweetened beverages and refined grains, may lower type 2 diabetes risk.Electronic supplementary materialThe online version of this article (doi:10.1007/s00125-013-3092-9) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

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