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

Genetic risk factors for ME/CFS identified using combinatorial analysis

Background

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating chronic disease that lacks known pathogenesis, distinctive diagnostic criteria, and effective treatment options. Understanding the genetic (and other) risk factors associated with the disease would begin to help to alleviate some of these issues for patients.

Methods

We applied both GWAS and the PrecisionLife combinatorial analytics platform to analyze ME/CFS cohorts from UK Biobank, including the Pain Questionnaire cohort, in a case–control design with 1000 cycles of fully random permutation. Results from this study were supported by a series of replication and cohort comparison experiments, including use of disjoint Verbal Interview CFS, post-viral fatigue syndrome and fibromyalgia cohorts also derived from UK Biobank, and compared results for overlap and reproducibility.

Results

Combinatorial analysis revealed 199 SNPs mapping to 14 genes that were significantly associated with 91% of the cases in the ME/CFS population. These SNPs were found to stratify by shared cases into 15 clusters (communities) made up of 84 high-order combinations of between 3 and 5 SNPs. p-values for these communities range from 2.3 × 10–10 to 1.6 × 10–72. Many of the genes identified are linked to the key cellular mechanisms hypothesized to underpin ME/CFS, including vulnerabilities to stress and/or infection, mitochondrial dysfunction, sleep disturbance and autoimmune development. We identified 3 of the critical SNPs replicated in the post-viral fatigue syndrome cohort and 2 SNPs replicated in the fibromyalgia cohort. We also noted similarities with genes associated with multiple sclerosis and long COVID, which share some symptoms and potentially a viral infection trigger with ME/CFS.

Conclusions

This study provides the first detailed genetic insights into the pathophysiological mechanisms underpinning ME/CFS and offers new approaches for better diagnosis and treatment of patients.

Hypocalcemia: a key biomarker in hospitalized COVID-19 patients

Background

At the end of 2019 a new respiratory syndrome emerged in China named Coronavirus disease 2019 (COVID-19) due to SARS-CoV-2 infection. Considering the severity of the disease in adult subjects with one or more chronic pathologies, it was mandatory to find simple and effective biomarkers for negative prognosis of the disease easily available at the admission to the hospital.

Methods

To identify possible parameters showing association with the outcome in COVID-19 patients with pre-existing chronic diseases, blood biochemical profiles of 511 patients, enrolled from March to June 2020, were retrospectively evaluated. The pathological conditions taken into consideration were diabetes, arterial hypertension, chronic kidney disease, cardiovascular diseases, chronic obstructive pulmonary disease, obesity, and cancer. All the data were collected upon admission to the emergency room (ER) during the indicated period.

Results

We observed that serum and ionized calcium were prevalently altered in our cohort. We determined that hypocalcemia was a major parameter associated with mechanical ventilation and poor prognosis, correlating also with the presence of comorbidities such as cardiovascular diseases, chronic kidney disease, and cancer. In addition, we found a positive correlation between hypocalcemia and clinical complications during hospitalizations.

Conclusions

Our results strengthen the relevance of serum calcium concentration as a useful prognostic biomarker in hospitalized COVID-19 patients.

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

Indication extension or repositioning of drugs can, if done well, provide a faster, cheaper, and derisked route to the approval of new therapies, creating new options to address pockets of unmet medical need for patients and offering the potential for significant commercial and clinical benefits. We look at the promises and challenges of different repositioning strategies and the disease insights and scalability that new high-resolution patient stratification methodologies can bring. This is exemplified by a systematic analysis of all development candidates and on-market drugs, which identified 477 indication extension opportunities across 30 chronic disease areas, each supported by patient stratification biomarkers. This illustrates the potential that new artificial intelligence (AI) and combinatorial analytics methods have to enhance the rate and cost of innovation across the drug discovery industry.