使用有序监督稳健轮廓聚类推导依赖于糖尿病状态的饮食模式：来自Hispanic Community Health Study/Study of Latinos的结果
Derivation of Dietary Patterns dependent on Diabetes status using ordinal Supervised Robust Profile Clustering: Results from Hispanic Community Health Study/Study of Latinos
AI总结：
本研究扩展了监督稳健轮廓聚类（sRPC）以处理有序结果，提出了ordinal sRPC模型，利用Hispanic Community Health Study/Study of Latinos数据识别与三级糖尿病状态相关的饮食模式，发现特定食物摄入与糖尿病严重程度增加相关。
摘要：
糖尿病的负担对美国Hispanic/Latino居民造成了不成比例的影响，饮食被认为是主要的可改变风险因素。依赖于结果的饮食模式有助于深入了解哪些食物可能与糖尿病严重程度的增加和进展相关。然而，美国Hispanic/Latino成年人在民族和地理上的异质性，使得随着风险增加，难以识别和区分他们饮食内部的差异。监督稳健轮廓聚类（sRPC）是一种灵活的联合模型，能够识别与糖尿病相关的饮食模式，同时剔除由民族和地理定义的模式。然而，sRPC此前仅应用于二元结果。我们扩展现有模型开发了有序sRPC。使用Hispanic Community Health Study/Study of Latinos的基线饮食数据（2008-2011年），我们展示了该模型在识别与三级糖尿病状态（即正常、糖尿病前期、糖尿病）相关的饮食模式方面的效用。模拟研究证实，与标准监督潜类别模型相比，有序sRPC改进了对这些模式的识别和表征。结果表明，摄入更多水果、零食和精制谷物面包的参与者可能更有可能与糖尿病状态严重程度的增加相关。
英文摘要：
The burden of diabetes has disproportionately impacted Hispanic/Latino residents in the United States, with diet recognized as a major modifiable risk factor. Outcome-dependent dietary patterns provide insight into what foods may be associated with the increased severity and progression of diabetes. However, the ethnic and geographical heterogeneity of US Hispanic/Latino adults makes it difficult to identify and distinguish differences within their diet as risk increases. Supervised robust profile clustering (sRPC) is a flexible joint model that can identify dietary patterns associated with diabetes, while partitioning out those defined by their ethnicity and geography. However, sRPC has only been applied to binary outcomes. We extend the existing model to develop the ordinal sRPC. Using baseline dietary data (2008-2011) from the Hispanic Community Health Study/Study of Latinos, we illustrate the utility of our model to identify dietary patterns associated with the three-levels of diabetes status (i.e. normal, pre-diabetes, diabetes). Simulation studies confirmed that ordinal sRPC improved identification and characterization of these patterns compared to a standard supervised latent class model. Results indicated that participants who had greater consumption of fruits, snack foods, and refined grain breads may be more likely to be associated with an increasing severity of diabetes status.
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