Interplay between Lipids and Complement Proteins—How Multiomics Data Integration Can Help Unravel Age-related Macular Degeneration Pathophysiology: A Proof-of-concept Study
Simon Nusinovici PhD , Lei Zhou PhD , Lavanya Raghavan MD , Yih Chung Tham PhD , Hengtong Li MS , Danny Cheung MD , Xiaomeng Wang PhD , Chui Ming Gemmy Cheung MD, PhD , Tien Yin Wong MD, PhD , Usha Chakravarthy MD, PhD , Ching-Yu Cheng MD, PhD
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Abstract
Objective
Our objectives were to identify correlation patterns between complement and lipid pathways using a multiomics data integration approach and to determine how these interconnections affect age-related macular degeneration (AMD).
Design
Nested case-control study.
Subjects and Controls
The analyses were performed in a subset of the Singapore Indian Eye Study. We randomly selected 155 AMD cases and age- and sex-matched them with 155 controls.
Methods
Firstly, a multiomics 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.
Main Outcome Measures
Age-related macular degeneration was determined according to the Beckman classification system. We measured in serum samples 35 complement proteins and 66 lipids, and used 9 genetic variants.
Results
Among the 155 AMD cases, 93 (60.0%) had early and 62 (40.0%) intermediate AMD. Firstly, we identified 2 clusters between complement proteins and lipids involving (1) mannan-binding lectin serine protease 1 and several different high-density lipoprotein particles, and (2) complement factor H-related protein 1, carboxypeptidase N subunit 2 and complement component C8 gamma chain, and sphingomyelin and different cholesterol. Secondly, we identified 1 interaction between complement protein 1R and sphingomyelin with an odds of AMD 2 times higher for individuals with low levels of sphingomyelin and complement protein 1R (odds ratio = 2.13 [1.09, 4.17]).
Conclusions
We report here, using a cutting-edge multiomics integration approach, the complex interconnections between genetic, metabolomics, and proteomic data. This method permitted us to obtain a holistic picture and identify multiomics 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.
Financial Disclosure(s)
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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