Institute of Diabetes Research, Helmholtz Zentrum München, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, Neuherberg, Germany.
Forschergruppe Diabetes e.V., Neuherberg, Germany.
PLoS One. 2021 Nov 8;16(11):e0259629. doi: 10.1371/journal.pone.0259629. eCollection 2021.
Accumulating evidence links dietary intake to inflammatory processes involved in non-communicable disease (NCD) development. The dietary inflammatory index (DII) designed by Shivappa et al. has been shown to capture the inflammatory potential of dietary behavior in a large number of epidemiological studies. Thus, the DII may serve as future tool to assess someone's nutritional inflammatory capacities and hence, the individual risks for NCD development later in life. The calculation method of the DII, however, can benefit from alternative mathematical steps, particularly regarding the transformation from standardized daily food consumption to percentile scores. Here, we provide novel approaches, the scaling-formula (SF) and scaling-formula with outlier detection (SFOD) methods, with the aim to optimize the DII calculation method proposed by Shivappa and colleagues. We illustrate on simulated data specific limitations of the original DII calculation and show the benefits of the SF/SFOD by using simulated data and data from the prospective TEENDIAB study cohort, which supports the application of SF/SFOD in future epidemiological and clinical studies.
越来越多的证据表明，饮食摄入与非传染性疾病（NCD）发展过程中涉及的炎症过程有关。 Shivappa 等人设计的饮食炎症指数（DII）已在大量流行病学研究中表明，它可以捕捉饮食行为的炎症潜力。因此，DII 可以作为评估个体营养炎症能力的未来工具，从而评估个体日后患 NCD 的风险。然而，DII 的计算方法可以受益于替代的数学步骤，特别是关于从标准化每日食物摄入量到百分位数分数的转换。在这里，我们提供了新的方法，即比例公式（SF）和带异常值检测的比例公式（SFOD）方法，旨在优化 Shivappa 及其同事提出的 DII 计算方法。我们用模拟数据说明了原始 DII 计算的具体局限性，并通过使用模拟数据和前瞻性 TEENDIAB 研究队列的数据展示了 SF/SFOD 的优势，这支持了 SF/SFOD 在未来的流行病学和临床研究中的应用。
