Multimodal Identification of Molecular Factors Linked to Severe Diabetic Foot Ulcers Using Artificial Intelligence.

Diabetic foot ulcers (DFUs) are a severe complication of diabetes mellitus (DM), which often lead to hospitalization and non-traumatic amputations in the United States. Diabetes prevalence estimates in South Texas exceed the national estimate and the number of diagnosed cases is higher among Hispanic adults compared to their non-Hispanic white counterparts. San Antonio, a predominantly Hispanic city, reports significantly higher annual rates of diabetic amputations compared to Texas. The late identification of severe foot ulcers minimizes the likelihood of reducing amputation risk. The aim of this study was to identify molecular factors related to the severity of DFUs by leveraging a multimodal approach. We first utilized electronic health records (EHRs) from two large demographic groups, encompassing thousands of patients, to identify blood tests such as cholesterol, blood sugar, and specific protein tests that are significantly associated with severe DFUs. Next, we translated the protein components from these blood tests into their ribonucleic acid (RNA) counterparts and analyzed them using public bulk and single-cell RNA sequencing datasets. Using these data, we applied a machine learning pipeline to uncover cell-type-specific and molecular factors associated with varying degrees of DFU severity. Our results showed that several blood test results, such as the Albumin/Creatinine Ratio (ACR) and cholesterol and coagulation tissue factor levels, correlated with DFU severity across key demographic groups. These tests exhibited varying degrees of significance based on demographic differences. Using bulk RNA-Sequenced (RNA-Seq) data, we found that apolipoprotein E () protein, a component of lipoproteins that are responsible for cholesterol transport and metabolism, is linked to DFU severity. Furthermore, the single-cell RNA-Seq (scRNA-seq) analysis revealed a cluster of cells identified as keratinocytes that showed overexpression of in severe DFU cases. Overall, this study demonstrates how integrating extensive EHRs data with single-cell transcriptomics can refine the search for molecular markers and identify cell-type-specific and molecular factors associated with DFU severity while considering key demographic differences.