Diet is a risk factor for many diseases. In nutritional epidemiology, studying reproducible dietary patterns is critical to reveal important associations with health. However, it is challenging: diverse cultural and ethnic backgrounds may critically impact eating patterns, showing heterogeneity, leading to incorrect dietary patterns and obscuring the components shared across different groups or populations. Moreover, covariate effects generated from observed variables, such as demographics and other confounders, can further bias these dietary patterns. Identifying the shared and group-specific dietary components and covariate effects is essential to drive accurate conclusions. To address these issues, we introduce a new modeling factor regression, the Multi-Study Factor Regression (MSFR) model. The MSFR model analyzes different populations simultaneously, achieving three goals: capturing shared component(s) across populations, identifying group-specific structures, and correcting for covariate effects. We use this novel method to derive common and ethnic-specific dietary patterns in a multi-center epidemiological study in Hispanic/Latinos community. Our model improves the accuracy of common and group dietary signals and yields better prediction than other techniques, revealing significant associations with health. In summary, we provide a tool to integrate different groups, giving accurate dietary signals crucial to inform public health policy.

翻译：饮食是多种疾病的危险因素。在营养流行病学中，研究可重复的膳食模式对于揭示与健康的重要关联至关重要。然而，这存在挑战：不同的文化和种族背景可能严重影响饮食模式，表现出异质性，导致错误的膳食模式并混淆不同群体或人群共有的成分。此外，来自观察变量（如人口统计学和其他混杂因素）的协变量效应可能进一步使这些膳食模式产生偏差。识别共享的和群体特异性的膳食成分及协变量效应对于得出准确结论至关重要。为解决这些问题，我们引入了一种新的建模因子回归方法——多研究因子回归（MSFR）模型。MSFR模型同时分析不同人群，实现三个目标：捕捉人群间的共享成分、识别群体特异性结构以及校正协变量效应。我们利用这一新方法在西班牙裔/拉丁裔社区的多中心流行病学研究中推导出共同的和种族特异性的膳食模式。我们的模型提高了共同及群体膳食信号的准确性，并比其他技术产生更好的预测结果，揭示了与健康的重要关联。总之，我们提供了一种整合不同群体的工具，为制定公共卫生政策提供关键且准确的膳食信号。