Abstract
ABSTRACT
Severe cognitive decline due to dementia is an important clinical indicator signaling neurodegeneration. Predicting cognitive decline rates can be useful in establishing prognosis and modifying treatment regimens. The rate of decline is highly variable across patients with dementia and may be determined by an interplay of many factors. This variability poses challenges to existing tools for predicting disease progression. Changes in heart and brain tissue have been associated with cognitive decline, yet tissue-specific molecular data are difficult to obtain. In this study, we sought to estimate cognitive decline-related transcripts in the brain and heart of people actively living with and without dementia. We reconstructed transcriptomic profiles in brain and heart with the transcriptome-imputation algorithms
BrainGENIE
and
HeartGENIE,
and identified several hundreds of transcripts associated with cognitive decline over a two-year assessment window, with the cerebellar hemisphere, left ventricle of the heart, and atrial appendage showing the largest number of cognitive-related transcripts. Pathway analysis revealed an enrichment among cognition-related transcripts of gene sets involved in synaptic, proteostasis/autophagy, immunologic, and mitochondrial respiratory chain functioning. Hundreds of perturbagens show significant concordance with cognitive decline-related transcriptomic signatures in brain and heart, pointing to potentially druggable targets. Top brain- and heart-specific cognition-associated transcripts were incorporated into LASSO regression models predicting future cognitive decline and were compared to two models, one utilizing age, sex and educational attainment, and a hybrid model incorporating both data types. Our models were able to achieve statistically significant correlations in the test set, yet had weak overall. Together, these findings demonstrate that brain and heart transcriptomes capture distinct aspects of biology related to cognitive decline, providing more nuanced insights into normal and aberrant brain aging that may inform drug-repurposing and risk-prediction efforts.