Interplay between Lipids and Complement Proteins—How Multiomics Data Integration Can Help Unravel Age-related Macular Degeneration Pathophysiology: A Proof-of-concept Study

Abstract

RationaleWe hypothesized that the integration of multi-omics data sets can help unravel some complex aspects of AMD pathophysiology. Our objectives were to identify correlation patterns between genetic variants, complement proteins and lipids using a multi-omics data integration approach and to determine how these interconnections affect AMD. Materials and methodsThe analyses were performed in a subset of the Singapore Indian Eye Study (SINDI) study. We randomly selected 155 AMD cases and age- and sex-matched them with 155 controls. We measured in serum samples 35 complement proteins and 66 lipids, and used nine genetic variants. Firstly, a multi-omics data integration method was used to identify correlation patterns between the omics data. Then, we tested possible interactions between the lipids and complement proteins using logistic regression models. ResultsAmong the 155 AMD cases, 93 (60.0%) had early and 62 (40.0%) intermediate AMD. Firstly, we identified two clusters between complement proteins and lipids involving (1) MASP1 and several different HDL particles, and (2) FHR1, CPN2 and CO8G, and sphingomyelin and different cholesterol. Secondly, we identified one interaction between C1R and sphingomyelin with an odds of AMD 2 times higher for individuals with low levels of sphingomyelin and C1R (OR=2.13 [1.09, 4.17]). ConclusionWe report here, using a cutting-edge multi-omics integration approach, the complex interconnections between genetic, metabolomics and proteomic data. This method permitted us to obtain a holistic picture and identify multi-omics signature of AMD pathophysiology. These results advocate for a personalized therapeutic approach that accounts for multiple pathways. However, these results need to be validated in larger studies with different ethnic groups.