Derivation of Dietary Patterns dependent on Diabetes status using ordinal Supervised Robust Profile Clustering: Results from Hispanic Community Health Study/Study of Latinos

TL;DR

This study extends supervised robust profile clustering to ordinal outcomes to identify dietary patterns associated with varying levels of diabetes severity among Hispanic/Latino adults, revealing specific food consumption linked to increased diabetes risk.

Contribution

The paper introduces an ordinal extension of supervised robust profile clustering, enabling the analysis of dietary patterns across multiple levels of diabetes status.

Findings

Higher intake of fruits, snack foods, and refined grains linked to increased diabetes severity.

Ordinal sRPC outperforms standard models in identifying diet-diabetes associations.

Dietary patterns vary by ethnicity and geography within the Hispanic/Latino population.

Abstract

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.