Combinatorial Analysis of Phenotypic and Clinical Risk Factors Associated With Hospitalized COVID-19 Patients

Abstract

Characterization of the risk factors associated with variability in the clinical outcomes of COVID-19 is important. Our previous study using genomic data identified a potential role of calcium and lipid homeostasis in severe COVID-19. This study aimed to identify similar combinations of features (disease signatures) associated with severe disease in a separate patient population with purely clinical and phenotypic data. The PrecisionLife combinatorial analytics platform was used to analyze features derived from de-identified health records in the UnitedHealth Group COVID-19 Data Suite. The platform identified and analyzed 836 disease signatures in two cohorts associated with an increased risk of COVID-19 hospitalization. Cohort 1 was formed of cases hospitalized with COVID-19 and a set of controls who developed mild symptoms. Cohort 2 included Cohort 1 individuals for whom additional laboratory test data was available. We found several disease signatures where lower levels of lipids were found co-occurring with lower levels of serum calcium and leukocytes. Many of the low lipid signatures were independent of statin use and 50% of cases with hypocalcemia signatures were reported with vitamin D deficiency. These signatures may be attributed to similar mechanisms linking calcium and lipid signaling where changes in cellular lipid levels during inflammation and infection affect calcium signaling in host cells. This study and our previous genomics analysis demonstrate that combinatorial analysis can identify disease signatures associated with the risk of developing severe COVID-19 separately from genomic or clinical data in different populations. Both studies suggest associations between calcium and lipid signaling in severe COVID-19.

Genetic risk factors for ME/CFS identified using combinatorial analysis

“…The combinatorial approach is more sensitive than GWAS, enabling identification of novel genetic associations and mechanisms that may only be relevant to a subgroup of patients, leading to more validated associations than GWAS when analyzing the same datasets. This approach has been validated in multiple disease studies both by the authors and collaborators, in some cases using in vitro and in vivo disease assays to demonstrate novel target genes' disease modification potential, and in others by the presence in pharmaceutical companies' R&D pipelines of drug programs targeting mechanisms that were identified by combinatorial analysis, but which could not be found using GWAS on available patient datasets [24][25][26].…”

Systematic indication extension for drugs using patient stratification insights generated by combinatorial analytics

“… 33 It is a hypothesis-free method for the detection of high-order, disease-associated combinations of features (disease signatures—typically comprising three to ten features) that together are strongly associated with variations in disease risk, symptoms, progression rates, and therapy response commonly seen in subgroups of patients using a case-control cohort design. 34 , 35 …”

“…The combination of SNPs linked to a target of interest in a patient subgroup can thus serve as a biomarker to identify individuals comprising a subgroup within a heterogeneous patient population who would be most responsive to pharmacological modulation of the target. This approach has been validated in multiple disease populations using both phenotypic 35 and genetic data 36 , 37 and is essential for systematic indication extension.…”