De Vito Roberta, Stephenson Briana, Sotres-Alvarez Daniela, Siega-Riz Anna-Maria, Mattei Josiemer, Parpinel Maria, Peters Brandilyn A, Bainter Sierra A, Daviglus Martha L, Van Horn Linda, Edefonti Valeria
Department of Biostatistics and Data Science Institute, Brown University, Providence, RI, USA.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Nutr J. 2025 May 4;24(1):71. doi: 10.1186/s12937-025-01138-0.
A posteriori dietary patterns (DPs) are critical for capturing actual dietary behaviour. However, assessing their reproducibility across (sub)populations requires novel modelling approaches beyond descriptive statistics. Multi-study factor analysis derives DPs that are shared among all studies/subpopulations and those specific to a study or subpopulation of interest. Bayesian implementation of the multi-study factor analysis (BMSFA) is more flexible than frequentist as it imposes fewer assumptions and improves factor selection.
We applied BMSFA to 24-h dietary recalls from the baseline visit (2008-2011) of the US Hispanic Community Health Study/Study of Latinos (n = 16,415). The analysis was conducted on 42 common nutrients to identify shared and subpopulation-specific DPs. Subpopulations were defined based on the cross-classification of ethnic background (Cuban, Dominican Republic, Mexican, Puerto Rican, Central and South American) and study site (Bronx, Chicago, Miami, San Diego) resulting in 12 Ethnic Background Site (EBS) categories. Regression analysis characterized DPs in terms of food groups, overall diet quality, socio-demographic/lifestyle factors, adjusting for survey design.
We identified four shared DPs across all EBS categories: Plant-based foods, Processed foods, Dairy products, and Seafood. Additionally, twelve EBS-specific DPs were identified-one for each EBS category. Most EBS-specific DPs were further grouped into overarching profiles: Animal vs. vegetable source, Animal source only, and Poultry vs. dairy products, to capture nuances within animal-based DPs. Puerto Rican background participants from Chicago expressed a strikingly different DP from all others (i.e., high on beta-carotene and low on starch/iron/thiamin). Higher overall diet quality was observed with increasing categories of Plant-based foods, Seafood, and the "Puerto Rican background - Chicago" EBS-specific DP, whereas increasing categories of Dairy products, Processed foods, and the remaining EBS-specific DPs were related to lower diet quality. Compared to non-US-born participants, US-born individuals had significantly higher adjusted mean scores in absolute value for most DPs. Specifically, they exhibited lower adherence to the Plant-based foods and Dairy products DPs but higher adherence to Processed foods, Seafood, and six EBS-specific DPs.
The BMSFA successfully captured sources of dietary homogeneity and heterogeneity among US Hispanic/Latino adults across ethnic backgrounds and study sites. The study highlighted the crucial role of nativity on DPs.
事后饮食模式(DPs)对于捕捉实际饮食行为至关重要。然而,评估其在不同(亚)人群中的可重复性需要超越描述性统计的新型建模方法。多研究因素分析得出所有研究/亚人群共有的饮食模式以及特定于感兴趣的研究或亚人群的饮食模式。多研究因素分析的贝叶斯实现(BMSFA)比频率学派方法更灵活,因为它假设较少且改进了因素选择。
我们将BMSFA应用于美国西班牙裔社区健康研究/拉丁裔研究(2008 - 2011年)基线访视时的24小时饮食回忆数据(n = 16,415)。对42种常见营养素进行分析以识别共有的和特定于亚人群的饮食模式。亚人群根据种族背景(古巴、多米尼加共和国、墨西哥、波多黎各、中美洲和南美洲)和研究地点(布朗克斯、芝加哥、迈阿密、圣地亚哥)的交叉分类来定义,从而产生12个种族背景 - 地点(EBS)类别。回归分析根据食物组、总体饮食质量、社会人口统计学/生活方式因素对饮食模式进行特征描述,并对调查设计进行了调整。
我们在所有EBS类别中识别出四种共有的饮食模式:植物性食物、加工食品、乳制品和海鲜。此外,还识别出了12种特定于EBS的饮食模式,每个EBS类别一种。大多数特定于EBS的饮食模式进一步归为总体概况:动物源与植物源、仅动物源以及家禽与乳制品,以捕捉动物性饮食模式中的细微差别。来自芝加哥的波多黎各背景参与者表现出与所有其他参与者截然不同的饮食模式(即β - 胡萝卜素含量高,淀粉/铁/硫胺素含量低)。随着植物性食物、海鲜以及特定于“波多黎各背景 - 芝加哥”EBS的饮食模式类别的增加,总体饮食质量更高,而乳制品、加工食品以及其余特定于EBS的饮食模式类别的增加与较低的饮食质量相关。与非美国出生的参与者相比,美国出生的个体在大多数饮食模式的调整后平均得分绝对值上显著更高。具体而言,他们对植物性食物和乳制品饮食模式的依从性较低,但对加工食品、海鲜以及六种特定于EBS的饮食模式的依从性较高。
BMSFA成功捕捉了美国西班牙裔/拉丁裔成年人在种族背景和研究地点之间饮食同质性和异质性的来源。该研究突出了出生地对饮食模式的关键作用。