Recent studies consider lifestyle risk score (LRS), an aggregation of multiple lifestyle exposures, in identifying association of gene-lifestyle interaction with disease traits. However, not all cohorts have data on all lifestyle factors, leading to increased heterogeneity in the environmental exposure in collaborative meta-analyses. We compared and evaluated four approaches (Naïve, Safe, Complete and Moderator Approaches) to handle the missingness in LRS-stratified meta-analyses under various scenarios. Compared to "benchmark" results with all lifestyle factors available for all cohorts, the Complete Approach, which included only cohorts with all lifestyle components, was underpowered, and the Naïve Approach, which utilized all available data and ignored the missingness, was slightly liberal. The Safe Approach, which used all data in LRS-exposed group and only included cohorts with all lifestyle factors available in the LRS-unexposed group, and the Moderator Approach, which handled missingness via moderator meta-regression, were both slightly conservative and yielded almost identical p-values. We also evaluated the performance of the Safe Approach under different scenarios. We observed that the larger the proportion of cohorts without missingness included, the more accurate the results compared to "benchmark" results. In conclusion, we generally recommend the Safe Approach to handle heterogeneity in the LRS based genome-wide interaction meta-analyses.

中文翻译：

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用于汇总多种生活方式因素的生活方式风险评分：在按基因进行生活方式交互的荟萃分析中处理各个生活方式成分的缺失

最近的研究考虑了生活方式风险评分（LRS），这是多种生活方式暴露的总和，用于确定基因与生活方式相互作用与疾病特征之间的关联。但是，并非所有队列研究人员都获得了所有生活方式因素的数据，从而导致在协作荟萃分析中环境暴露的异质性增加。我们比较并评估了四种方法（朴素，安全，完整和主持人方法）来处理在各种情况下进行LRS分层的荟萃分析中的缺失。与所有队列中所有生活方式因素均可得到的“基准”结果相比，仅包含所有生活方式组成部分的队列的“完整方法”的动力不足，而利用所有可用数据并忽略缺失的朴素方法则较为宽松。安全方法 该研究使用了LRS暴露组中的所有数据，并且仅包含了LRS暴露组中所有可用生活方式因素的队列，而通过主持人元回归处理缺失的“主持人方法”则略为保守，并且产生了几乎相同的p值。我们还评估了在不同情况下安全方法的效果。我们观察到，包括“缺失”的同类人群比例越大，与“基准”结果相比，结果越准确。总之，我们通常建议在基于LRS的全基因组相互作用荟萃分析中处理异质性的安全方法。都略显保守，并且产生几乎相同的p值。我们还评估了在不同情况下安全方法的效果。我们观察到，不包括缺失的同类人群比例越大，与“基准”结果相比，结果越准确。总之，我们通常建议在基于LRS的全基因组相互作用荟萃分析中处理异质性的安全方法。都略显保守，并且产生几乎相同的p值。我们还评估了安全方案在不同情况下的性能。我们观察到，包括“缺失”的同类人群比例越大，与“基准”结果相比，结果越准确。总之，我们通常建议在基于LRS的全基因组相互作用荟萃分析中处理异质性的安全方法。