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 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 signalling in severe COVID-19.

Hypocalcemia: A key biomarker in hospitalized COVID-19 patients

“…In our monocentric study, first we observed that 44.8% of our hospitalized COVID-19 patients had low serum and ionized calcium level, corrected to the concentration of albumin. This percentage is not as high as those reported in other studies [ [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] ], but the results are nonetheless relevant considering that the recorded calcium value interquartile range is from 8.3 to 9 mg/dL for serum calcium (reference range 8.5 to 10.5 mg/dL) and from 1.05 to 1.16 mmol/L for ionized calcium (reference range 1.1 to 1.3 mmol/L), shifting the population curve towards the lower limit and without any case of hypercalcemia. Indeed, the average value of both parameters highlights the prevalent condition of hypocalcemia (calcium level especially ≤8.5 mg/dL).…”

“…Several biochemical parameters have been evaluated and particular attention has been focused on calcemia, also considering the primary role of calcium for viral infection and replication [ 9 ]. Particularly, it has been reported that a high percentage of COVID-19 hospitalized patients showed hypocalcemia [ [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] ], a condition that can be critical, especially for patients with pre-existing chronic pathologies [ [20] , [21] , [22] , [23] ]. Our retrospective analysis also confirms that serum calcium concentration is the most altered parameters among hospitalized COVID-19 patients, especially in those with the most critical conditions.…”

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 16,17,18 .…”