Dietary patterns synthesize multiple related diet components, which can be used by nutrition researchers to examine diet-disease relationships. Latent class models (LCMs) have been used to derive dietary patterns from dietary intake assessment, where each class profile represents the probabilities of exposure to a set of diet components. However, LCM-derived dietary patterns can exhibit strong similarities, or weak separation, resulting in numerical and inferential instabilities that challenge scientific interpretation. This issue is exacerbated in small-sized subpopulations. To address these issues, we provide a simple solution that empowers LCMs to improve dietary pattern estimation. We develop a tree-regularized Bayesian LCM that shares statistical strength between dietary patterns to make better estimates using limited data. This is achieved via a Dirichlet diffusion tree process that specifies a prior distribution for the unknown tree over classes. Dietary patterns that share proximity to one another in the tree are shrunk towards ancestral dietary patterns a priori, with the degree of shrinkage varying across pre-specified food groups. Using dietary intake data from the Hispanic Community Health Study/Study of Latinos, we apply the proposed approach to a sample of 496 US adults of South American ethnic background to identify and compare dietary patterns.

翻译：膳食模式综合反映了多种相关饮食成分的整合特征，营养研究者可据此探究饮食与疾病之间的关联。潜在类别模型常被用于从膳食摄入评估中推导膳食模式，其中每个类别轮廓代表对一组饮食成分的暴露概率。然而，基于潜在类别模型导出的膳食模式可能呈现高度相似性（即弱分离现象），导致数值不稳定性和推断困难，挑战科学解释的可行性。该问题在小样本亚群中更为突出。为解决这些问题，我们提出一种简易方法以提升潜在类别模型在膳食模式估计中的效能。我们开发了树正则化贝叶斯潜在类别模型，通过在不同膳食模式间共享统计效力，从而在有限数据条件下实现更优估计。该模型通过狄利克雷扩散树过程实现，该过程为类别间未知树结构设定先验分布。在树中相邻的膳食模式会被先验地压缩至其祖先模式的趋势方向，压缩程度因预设食物组别而异。基于西班牙裔社区健康研究/拉丁裔研究的膳食摄入数据，我们将所提方法应用于496名南美裔美国成年人样本，以识别并比较其膳食模式。