Determining hormone metabolite concentrations when enzyme immunoassay accuracy varies over time
Determining hormone metabolite concentrations when enzyme immunoassay accuracy varies over time
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DOI：
10.1111/2041-210x.12338
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发表时间：
2015-05-01
影响因子：
6.6
通讯作者：
Dehnhard, Martin
中科院分区：
文献类型：
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作者：
Davidian, Eve;Benhaiem, Sarah;Dehnhard, Martin
酶免疫测定法（EIAs）被广泛用于定量激素代谢物的浓度。实验室条件的变化可能会影响代谢物浓度测量的准确性，并在不同精度的结果合并进行统计分析时导致误解。这个问题与行为和进化生态学的研究非常相关，因为这些研究通常旨在了解激素浓度在个体、环境或实验条件之间的变化。当EIA准确性发生变化时，我们提出了一种基于重新分析样本子集来标准化激素代谢物浓度的方法。我们使用2011年至2013年期间在斑点鬣狗（Crocuta Crocuta）粪便中测量的糖皮质激素代谢物浓度（fmcs），并使用先前验证的EIA。准确度的变化是通过监测粪便控制池的代谢物浓度来评估的，粪便控制池是用粪便样本进行系统分析的。对这些池进行聚类分析，发现两个不同的样本集具有不同的EIA精度；集群1‘和集群2’。然后，我们重新分析了第1类（n=138）的所有样本，其EIA精度与第2类相似，并将重新测量的fgmc与初始fgmc进行线性回归，以预测第2类中的fgmc。为了确定允许可靠预测的重新分析的最小样本数，我们通过拟合重新分析样本数减少的线性回归来评估模型预测质量的变化。这表明，考虑到我们的数据集，重新分析27个样本将足以产生可靠的预测。为了检验我们方法的稳健性，我们对27个随机选择的样本进行了新的线性回归，并使用其方程对集群1的所有fgmc进行了标准化。标准化的fgmc与重新测量的fgmc相似，对27个样本的回归与对完整数据集的回归一样有效。我们的标准化方法允许不同精度的结果组合在一起。它是一种简单可靠的替代昂贵，耗时且通常不切实际的完整样品组的重新分析，可以应用于各种各样的物种和样品类型。
Enzyme immunoassays (EIAs) are widely used to quantify concentrations of hormone metabolites. Modifications in laboratory conditions may affect the accuracy of metabolite concentration measurements and lead to misinterpretations when results of different accuracy are combined for a statistical analysis. This issue is of great relevance to studies in behavioural and evolutionary ecology because these usually aim at understanding how hormone concentrations vary between individuals, environments or experimental conditions. We present a method based on re-assaying a subset of samples to standardize hormone metabolite concentrations when changes in EIA accuracy occur. We used glucocorticoid metabolite concentrations (fGMCs) measured in faeces of spotted hyaenas (Crocuta crocuta) between 2011 and 2013 with a previously validated EIA. Changes in accuracy were assessed by monitoring the metabolite concentration of faecal control pools' that were systematically assayed with faecal samples. A cluster analysis on these pools identified two distinct sample sets with different EIA accuracy; Cluster 1' and Cluster 2'. We then re-assayed all samples of Cluster 1 (n=138) with an EIA accuracy similar to that of Cluster 2 and fitted a linear regression to the remeasured fGMCs against the initial fGMCs to predict fGMCs in Cluster 2. To determine the minimum number of samples to re-assay that allows reliable predictions, we assessed the variation in the quality of model predictions by fitting linear regressions on decreasing numbers of re-assayed samples. This revealed that re-assaying 27 samples would be sufficient to generate reliable predictions considering our data set. To test the robustness of our method, we fitted a new linear regression to 27 randomly chosen samples and used its equation to standardize all fGMCs of Cluster 1. The standardized fGMCs were similar to the remeasured fGMCs, and the regression on 27 samples was as effective at standardizing fGMCs as the regression fitted on the complete data set. Our standardization method permits the combination of results of different accuracy. It is a simple and reliable alternative to the costly, time-consuming and often impractical re-assaying of complete sample sets that can be applied to a wide variety of species and sample types.