Associations between dietary methods and biomarkers, and between fruits and vegetables and risk of ischaemic heart disease, in the EPIC Norfolk Cohort Study
Associations between dietary methods and biomarkers, and between fruits and vegetables and risk of ischaemic heart disease, in the EPIC Norfolk Cohort Study
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DOI：
10.1093/ije/dyn111
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发表时间：
2008-10-01
影响因子：
7.7
通讯作者：
Day, Nick
中科院分区：
文献类型：
--
作者：
Bingham, Sheila;Luben, Robert;Day, Nick
背景 评估饮食的方法很容易出现测量误差，这在大型队列研究中可能会很大。生物标志物可用作客观指标，用于比较使用不同方法对营养暴露的估计。方法对 12 474 名男性和女性进行横断面比较，将维生素 C、钠、钾、纤维、碳水化合物、脂肪和植物雌激素的生物标志物与来自食物日记和食物频率问卷 (FFQ) 的摄入量之间的回归进行横断面比较，以及膳食和血浆维生素 C 引起缺血性心脏病 (IHD) 风险的比值比。 0.001）通过食物日记评估生物标志物与摄入量之间的关联。从 FFQ 获得的数据的系数显着减弱，尤其是维生素 C、钾和植物雌激素 (Z P < 0.05)。 IHD 风险与血浆维生素 C (P < 0.001) 以及通过食物日记评估的维生素 C 和水果和蔬菜的摄入量相关 (P < 五分位数趋势 0.001, 0.001)，但与 FFQ 无关 (P 五分位数趋势 0.923, 0.186)。 结论 反映生物标记物结果的营养数据可减少测量误差，从而提高队列研究中基因营养相互作用研究的统计功效。
Background Methods for assessing diet are prone to measurement error, which may be substantial in large cohort investigations. Biomarkers can be used as objective measures with which to compare estimates of nutritional exposure using different methodsMethods Cross sectional comparisons in 12 474 men and women of regression between biomarkers for vitamin C, sodium, potassium, fibre, carbohydrate, fat and phytoestrogens with intakes derived from food diaries and food frequency questionnaires (FFQ), and odds ratios for risk of ischaemic heart disease (IHD) by dietary and plasma vitamin C.Results There were strong (P < 0.001) associations between biomarkers and intakes as assessed by food diary. Coefficients were markedly attenuated for data obtained from the FFQ, especially so for vitamin C, potassium and phytoestrogens (Z P < 0.05). Risk of IHD was associated with plasma vitamin C (P < 0.001) and intake of vitamin C and fruit and vegetables assessed by food diary (P < quintile trends 0.001, 0.001) but not by the FFQ (P quintile trends 0.923, 0.186).Conclusions Nutritional data that reflect the findings from biomarkers reduce measurement error and will thus improve statistical power in studies of gene nutrient interactions in cohort studies.