Department of Food Science and Human Nutrition, University of Kansas Medical Center, Kansas City, KS, USA.
J Nutr. 2013 Jul;143(7):1136-40. doi: 10.3945/jn.112.169904. Epub 2013 May 22.
Many algorithms have been developed in the past few decades to estimate nonheme iron absorption from the diet based on single meal absorption studies. Yet single meal studies exaggerate the effect of diet and other factors on absorption. Here, we propose a new algorithm based on complete diets for estimating nonheme iron absorption. We used data from 4 complete diet studies each with 12-14 participants for a total of 53 individuals (19 men and 34 women) aged 19-38 y. In each study, each participant was observed during three 1-wk periods during which they consumed different diets. The diets were typical, high, or low in meat, tea, calcium, or vitamin C. The total sample size was 159 (53 × 3) observations. We used multiple linear regression to quantify the effect of different factors on iron absorption. Serum ferritin was the most important factor in explaining differences in nonheme iron absorption, whereas the effect of dietary factors was small. When our algorithm was validated with single meal and complete diet data, the respective R(2) values were 0.57 (P < 0.001) and 0.84 (P < 0.0001). The results also suggest that between-person variations explain a large proportion of the differences in nonheme iron absorption. The algorithm based on complete diets we propose is useful for predicting nonheme iron absorption from the diets of different populations.
在过去的几十年中，已经开发出许多算法来根据单餐吸收研究估算非血红素铁的饮食吸收。然而，单餐研究夸大了饮食和其他因素对吸收的影响。在这里，我们提出了一种基于完整饮食的新算法来估计非血红素铁的吸收。我们使用了来自 4 项完整饮食研究的数据，每项研究有 12-14 名参与者，共有 53 人（19 名男性和 34 名女性），年龄在 19-38 岁。在每项研究中，每个参与者在三个为期 1 周的期间内被观察，在此期间他们食用不同的饮食。饮食在肉类、茶、钙或维生素 C 方面是典型的、高的或低的。总样本量为 159（53×3）个观察值。我们使用多元线性回归来量化不同因素对铁吸收的影响。血清铁蛋白是解释非血红素铁吸收差异的最重要因素，而饮食因素的影响较小。当我们的算法用单餐和完整饮食数据进行验证时，各自的 R^2 值分别为 0.57（P<0.001）和 0.84（P<0.0001）。结果还表明，个体间的差异解释了非血红素铁吸收差异的很大一部分。我们提出的基于完整饮食的算法可用于预测不同人群饮食中非血红素铁的吸收。
