Dataset Information

Assessing susceptibility to age-related macular degeneration with proteomic and genomic biomarkers.

ABSTRACT: Age-related macular degeneration (AMD) is a progressive disease and major cause of severe visual loss. Toward the discovery of tools for early identification of AMD susceptibility, we evaluated the combined predictive capability of proteomic and genomic AMD biomarkers. We quantified plasma carboxyethylpyrrole (CEP) oxidative protein modifications and CEP autoantibodies by ELISA in 916 AMD and 488 control donors. CEP adducts are uniquely generated from oxidation of docosahexaenoate-containing lipids that are abundant in the retina. Mean CEP adduct and autoantibody levels were found to be elevated in AMD plasma by approximately 60 and approximately 30%, respectively. The odds ratio for both CEP markers elevated was 3-fold greater or more in AMD than in control patients. Genotyping was performed for AMD risk polymorphisms associated with age-related maculopathy susceptibility 2 (ARMS2), high temperature requirement factor A1 (HTRA1), complement factor H, and complement C3, and the risk of AMD was predicted based on genotype alone or in combination with the CEP markers. The AMD risk predicted for those exhibiting elevated CEP markers and risk genotypes was 2-3-fold greater than the risk based on genotype alone. AMD donors carrying the ARMS2 and HTRA1 risk alleles were the most likely to exhibit elevated CEP markers. The results compellingly demonstrate higher mean CEP marker levels in AMD plasma over a broad age range. Receiver operating characteristic curves suggest that CEP markers alone can discriminate between AMD and control plasma donors with approximately 76% accuracy and in combination with genomic markers provide up to approximately 80% discrimination accuracy. Plasma CEP marker levels were altered slightly by several demographic and health factors that warrant further study. We conclude that CEP plasma biomarkers, particularly in combination with genomic markers, offer a potential early warning system for assessing susceptibility to this blinding, multifactorial disease.

SUBMITTER: Gu J

PROVIDER: S-EPMC2690477 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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Assessing susceptibility to age-related macular degeneration with proteomic and genomic biomarkers.

Gu Jiayin J Pauer Gayle J T GJ Yue Xiuzhen X Narendra Umadevi U Sturgill Gwen M GM Bena James J Gu Xiaorong X Peachey Neal S NS Salomon Robert G RG Hagstrom Stephanie A SA Crabb John W JW

Molecular & cellular proteomics : MCP 20090206 6

Age-related macular degeneration (AMD) is a progressive disease and major cause of severe visual loss. Toward the discovery of tools for early identification of AMD susceptibility, we evaluated the combined predictive capability of proteomic and genomic AMD biomarkers. We quantified plasma carboxyethylpyrrole (CEP) oxidative protein modifications and CEP autoantibodies by ELISA in 916 AMD and 488 control donors. CEP adducts are uniquely generated from oxidation of docosahexaenoate-containing lip ...[more]

PMID: 19202148

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Project description:ObjectiveAge-related macular degeneration (AMD) is a significant cause of blindness, initially characterized by the accumulation of sub-Retinal pigment epithelium (RPE) deposits, leading to progressive retinal degeneration and, eventually, irreversible vision loss. This study aimed to elucidate the differential expression of transcriptomic information in AMD and normal human RPE choroidal donor eyes and to investigate whether it could be used as a biomarker for AMD.MethodsRPE choroidal tissue samples (46 Normal samples, 38 AMD samples) were obtained from the GEO (GSE29801) database and screened for differentially expressed genes in normal and AMD patients using GEO2R and R to compare the degree of enrichment of differentially expressed genes in the GO, KEGG pathway. Firstly, we used machine learning models (LASSO, SVM algorithm) to screen disease signature genes and compare the differences between these signature genes in GSVA and immune cell infiltration. Secondly, we also performed a cluster analysis to classify AMD patients. We selected the best classification by weighted gene co-expression network analysis (WGCNA) to screen the key modules and modular genes with the strongest association with AMD. Based on the module genes, four machine models, RF, SVM, XGB, and GLM, were constructed to screen the predictive genes and further construct the AMD clinical prediction model. The accuracy of the column line graphs was evaluated using decision and calibration curves.ResultsFirstly, we identified 15 disease signature genes by lasso and SVM algorithms, which were associated with abnormal glucose metabolism and immune cell infiltration. Secondly, we identified 52 modular signature genes by WGCNA analysis. We found that SVM was the optimal machine learning model for AMD and constructed a clinical prediction model for AMD consisting of 5 predictive genes.ConclusionWe constructed a disease signature genome model and an AMD clinical prediction model by LASSO, WGCNA, and four machine models. The disease signature genes are of great reference significance for AMD etiology research. At the same time, the AMD clinical prediction model provides a reference for early clinical detection of AMD and even becomes a future census tool. In conclusion, our discovery of disease signature genes and AMD clinical prediction models may become promising new targets for the targeted treatment of AMD.

Project description:PurposeAge-related macular degeneration (AMD) can result in severe vision loss and blurriness in the older population. The early and intermediate stages of AMD typically start without noticeable symptoms and can only be detected with a comprehensive eye exam. Because of the quiet onset of the disease, it is necessary to identify potential biomarkers to aid in the diagnosis, staging, and association with disease onset. Inflammasome signaling proteins are prominent biomarkers in the central nervous system, and the inflammasome has been shown to play a role in the innate inflammatory response in aging and AMD.MethodsSerum from healthy controls and AMD patients were analyzed for the protein levels of Apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC), interleukin (IL)-18 and C-reactive protein (CRP) to determine cutoff points, positive and negative predictive values, and receiver operator characteristic curves, as well as univariate and multivariate linear and logistic regression models.ResultsASC, IL-18, and CRP were elevated in the serum of AMD patients when compared to healthy controls. The area under the curve (AUC) for ASC was 0.98 with a cutoff point of 365.6 pg/mL, whereas IL-18 had an AUC of 0.73 and a cutoff point of 242.4 pg/mL, and the AUC for CRP was 0.67 with a cutoff point of 8,684,152 pg/mL. Levels of IL-18 had a statistically significant linear correlation with that of ASC with an adjusted R 2 of 0.1906, indicating that 19% of IL-18 could be explained by ASC protein levels in serum. Moreover, a logistic regression model for the diagnosis of AMD consists of ASC and having a diagnosis of hypertension, indicating that these two factors (elevated levels of ASC and a diagnosis of hypertension [HTN]) are associated with the diagnosis of AMD.ConclusionsASC, IL-18, and CRP are elevated in patients with AMD, and the protein levels of IL-18 are partially the result of ASC protein expression. Moreover, elevated protein levels of ASC in serum and a diagnosis of HTN increase the odds of patients having a diagnosis of AMD.Translational relevanceBiomarkers of AMD may be used to monitor disease risk, response to treatment and disease progression.

Project description:Age-related macular degeneration (AMD) is a leading cause of severe vision loss worldwide, with a global prevalence that is predicted to substantially increase. Identifying early biomarkers indicative of progression risk will improve our ability to assess which patients are at greatest risk of progressing from intermediate AMD (iAMD) to vision-threatening late-stage AMD. This is key to ensuring individualized management and timely intervention before substantial structural damage. Some structural biomarkers suggestive of AMD progression risk are well established, such as changes seen on color fundus photography and more recently optical coherence tomography (drusen volume, pigmentary abnormalities). Emerging biomarkers identified through multimodal imaging, including reticular pseudodrusen, hyperreflective foci, and drusen sub-phenotypes, are being intensively explored as risk factors for progression towards late-stage disease. Other structural biomarkers merit further research, such as ellipsoid zone reflectivity and choriocapillaris flow features. The measures of visual function that best detect change in iAMD and correlate with risk of progression remain under intense investigation, with tests such as dark adaptometry and cone-specific contrast tests being explored. Evidence on blood and plasma markers is preliminary, but there are indications that changes in levels of C-reactive protein and high-density lipoprotein cholesterol may be used to stratify patients and predict risk. With further research, some of these biomarkers may be used to monitor progression. Emerging artificial intelligence methods may help evaluate and validate these biomarkers; however, until we have large and well-curated longitudinal data sets, using artificial intelligence effectively to inform clinical trial design and detect outcomes will remain challenging. This is an exciting area of intense research, and further work is needed to establish the most promising biomarkers for disease progression and their use in clinical care and future trials. Ultimately, a multimodal approach may yield the most accurate means of monitoring and predicting future progression towards vision-threatening, late-stage AMD.

Dataset Information

Assessing susceptibility to age-related macular degeneration with proteomic and genomic biomarkers.

Publications

Assessing susceptibility to age-related macular degeneration with proteomic and genomic biomarkers.

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