Epidemiological topology data analysis links severe COVID-19 to RAAS and hyperlipidemia associated metabolic syndrome conditions

Abstract

The emergence of COVID-19 created incredible worldwide challenges but offers unique opportunities to understand the physiology of its risk factors and their interactions with complex disease conditions, such as metabolic syndrome. To address the challenges of discovering clinically relevant interactions, we employed a unique approach for epidemiological analysis powered by Redescription-based TDA (RTDA). Here RTDA was applied to Explorys data to discover associations among severe COVID19 and metabolic syndrome. This approach was able to further explore the probative value of drug prescriptions to capture the involvement of RAAS and hypertension with COVID-19, as well as modification of risk factor impact by hyperlipidemia on severe COVID-19. RTDA found higher-order relationships between RAAS pathway and severe COVID-19 along with demographic variables of age, gender, and comorbidities such as obesity, statin prescriptions, hyperlipidemia, chronic kidney failure and disproportionately affecting African Americans. RTDA combined with CuNA (Cumulant-based Network Analysis) yielded an higher-order interaction network derived from cumulants that furthered supported the central role that RAAS plays. TDA techniques can provide a novel outlook beyond typical logistic regressions in epidemiology. From an observational cohort of electronic medical records, it can find out how RAAS drugs interact with comorbidities, such as hypertension and hyperlipidemia, of patients with severe bouts of COVID-19. Where single variable association tests with outcome can struggle, TDA's higher-order interaction network between different variables enables the discovery of the comorbidities of a disease such as COVID-19 work in concert. Code for performing TDA/RTDA is available in https://github.com/IBM/Matilda and code for CuNA can be found in https://github.com/BiomedSciAI/Geno4SD/.