揭示饮食复杂性:在饮食模式研究中进行网络分析的范围审查和报告指南
Rebecca M J Taylor1, Jack A Moore1, Amy R Griffiths1
1School of Psychology, Faculty of Medicine, Health, and Life Science, Swansea University, Wales SA2 8PP, UK.
Nutrients
|October 29, 2025
概括
网络分析揭示了复杂的饮食模式,但方法上的不一致性阻碍了解释. 本次审查提出了指导原则和检查清单 (MRS-DN),以提高饮食网络研究的可靠性.
科学领域:
- 营养科学 营养科学
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 饮食研究经常单独检查食物,错过了复杂的相互作用及其对健康的影响.
- 网络分析提供了一种全面的方法来研究食品的共同消费和饮食关系.
- 将网络分析应用于饮食数据的方法不一致性挑战了解释.
研究的目的:
- 用饮食数据的网络分析系统地绘制和综合研究.
- 识别当前研究中的方法挑战和不一致性.
- 建立可靠的饮食网络分析的指导原则.
主要方法:
- 根据PRISMA-ScR标准进行范围审查.
- 从171个已识别的文章中对18项符合条件的研究进行了系统的搜索和综合.
- 分析网络分析方法,包括高斯图形模型和统计技术.
主要成果:
- 高斯图形模型 (GGMs) 是最常见的方法 (61%),经常有规范化 (93%).
- 显著的挑战包括未被承认的中心性指标的局限性 (72%),过度依赖横截面数据和处理不正常数据的不良处理 (36%没有解决).
- 虽然一些研究通过SGCGM或日志转换处理了非正常数据,但许多没有.
结论:
- 提出了五个指导原则:模型的合理性,设计问题对齐,透明的估计,谨慎的度量解释和对非正常数据的稳健处理.
- 一个CONSORT风格的检查清单,饮食网络的最小报告标准 (MRS-DN) 被引入,以指导未来的研究.
- 采用这些原则和MRS-DN检查清单可以提高饮食网络研究的可靠性和可解释性.
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