Network analysis to identify proteomic markers for brain aging and dementia in healthy older adults: Basic science and pathogenesis: Genetics and omics of AD. (7th December 2020)

• Record Type: Journal Article
• Authors: Short, Meghan I.; Zare, Habil; Beiser, Alexa S.; Larson, Martin G.; Vasan, Ramachandran S.; Seshadri, Sudha
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 3
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.037711
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Biological processes involved in preclinical stages of Alzheimer's Disease and Related Dementias (ADRD) are not well understood. The plasma proteome is a promising tool to identify pathways involved in ADRD development, linking genetic and environmental risk factors with disease outcomes. The goal of this study is to leverage proteome data in a community‐based cohort to identify proteins and pathways involved in early stages of dementia. This is the first study to employ a network analysis approach, which can detect subtle but consistent variations in protein expression, to identify biological pathways associated with ADRD and endophenotypes from the plasma proteome. Method: A panel of 1305 proteins was measured in plasma from 1861 participants from the Framingham Heart Study Offspring cohort (mean age 55, 54% women). Weighted Co‐expression Network Analysis (WGCNA) was used to identify groups ("modules") of co‐expressed proteins. Summary measures of proteins in the modules ("eigenproteins") were related to total brain volume (n = 1038), hippocampal volume (n = 1038), and white matter hyperintensity (n = 1022) using linear regression, and incident dementia (128 events, 1740 at risk) and Alzheimer's Disease (AD; 94 events, 1740 at risk) over 20 years of follow‐up using Cox proportional hazards regression. Result: Network analysis resulted in 4 modules containing 42, 67, 165, and 272 proteins, respectively. Two modules were associated with total brain volume, one directly (0.17% per standard deviation [SD] increase in eigenprotein; p = 0.005) and one inversely (‐0.26% per SD increase in eigenprotein; p = 1.3 × 10 −5 ). Of these, the first was associated with incident dementia (HR [95% CI]: 0.82 [0.68‐0.99]; p = 0.04), and the second was associated with incident dementia (HR [95% CI]: 1.22 [1.04‐1.44]; p = 0.01), and AD (HR [95% CI]: 1.26 [1.04‐1.52]; p = 0.02). Associations with brain volume remained significant after multiple testing corrections; associations with incident AD and dementia did not. KEGG pathways for axon guidance and cytokine‐cytokine receptor interaction were the most enriched from the two modules, respectively. Conclusion: Network analysis identified two groups of proteins associated with a brain MRI endophenotype and suggestive of association with incident dementia. These results identify possible biological pathways implicated in early stages of dementia, which we will validate in other cohort studies.