Version 1
: Received: 15 August 2024 / Approved: 16 August 2024 / Online: 16 August 2024 (08:41:40 CEST)
How to cite:
Flores, M.; Omo-Okhuasuyi, A.; Jin, Y.; Chen, Y.; ElHefnawi, M. Identification of Risk Factors Linked to Diabetic Foot Ulcers from Multimodal Electronic Health Records and Transcriptomics Using Machine Learning. Preprints2024, 2024081212. https://doi.org/10.20944/preprints202408.1212.v1
Flores, M.; Omo-Okhuasuyi, A.; Jin, Y.; Chen, Y.; ElHefnawi, M. Identification of Risk Factors Linked to Diabetic Foot Ulcers from Multimodal Electronic Health Records and Transcriptomics Using Machine Learning. Preprints 2024, 2024081212. https://doi.org/10.20944/preprints202408.1212.v1
Flores, M.; Omo-Okhuasuyi, A.; Jin, Y.; Chen, Y.; ElHefnawi, M. Identification of Risk Factors Linked to Diabetic Foot Ulcers from Multimodal Electronic Health Records and Transcriptomics Using Machine Learning. Preprints2024, 2024081212. https://doi.org/10.20944/preprints202408.1212.v1
APA Style
Flores, M., Omo-Okhuasuyi, A., Jin, Y., Chen, Y., & ElHefnawi, M. (2024). Identification of Risk Factors Linked to Diabetic Foot Ulcers from Multimodal Electronic Health Records and Transcriptomics Using Machine Learning. Preprints. https://doi.org/10.20944/preprints202408.1212.v1
Chicago/Turabian Style
Flores, M., Yidong Chen and Mahmoud ElHefnawi. 2024 "Identification of Risk Factors Linked to Diabetic Foot Ulcers from Multimodal Electronic Health Records and Transcriptomics Using Machine Learning" Preprints. https://doi.org/10.20944/preprints202408.1212.v1
Abstract
Diabetic Foot Ulcer (DFU) is a severe complication of diabetes mellitus (DM), often leading to hospitalization and non-traumatic amputations in the United States. It is particularly prevalent among individuals of Hispanic descent, with a prevalence rate exceeding 10% [1, 2]. The target of this study is to identify markers of severity of DFU. This work follows a strategy that uses first Electronic Health Records and machine learning to identify risk factors reported as measurements of blood tests, such as cholesterol, blood sugar, and specific proteins in a cohort of thousands of patients. After this RNA risk factors are predicted using samples of bulk and single-cell gene expression across patients with different levels of DFU severity. We found the Albu-min/Creatinine Ratio (ACR) test as a key factor in severe DFUs. We also found a cluster of cells that harbor high expression of Apolipoprotein E protein (APOE) and it is significant in non-healing DFU cases. Overall, the study shows how the use of hundreds of thousands of EHR can reduce and inform the search space of molecules in a few samples of bulk-single cell transcriptomics and identify molecular markers of severity of DFU.
Biology and Life Sciences, Immunology and Microbiology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
