Advancing personalized healthcare in infectious disease with multi-omics profiling

Personalized health profiling using multi-omics data has the potential to reveal immunometabolic disruptions among individuals living with HIV (PWH) who are effectively undergoing antiretroviral therapy (ART). We recently integrated transcriptomics, metabolomic and Olink's Proximity Extension Assay proteomics platform to identify the immunometabolic impairments in a cohort of treated PWH. The patient stratification identified that proteomics had the highest impact on the clustering. Considering pertinent clinical and lifestyle factors, we identified that 44% of PWH were at risk of encountering immunometabolic complications.

 

Leveraging sophisticated machine learning methodologies and a Bayesian classifier, we pinpointed six plasma biomarkers that effectively characterized the at-risk phenotype and were instrumental in revealing a network of proteins associated with monocyte immunosenescence.

 

Our research also sheds light on a host-induced tryptophan pathway played a crucial role in fostering chronic inflammation and its effects on neuropsychiatric clinical manifestations among PWH. By combining other cohorts' data from India and Sweden, we have now curated a panel of biomarkers that can identify HIV-induced aging in PWH who are on therapy.

 

Learning Objectives