Approved Research

The brain, genetic, and environmental correlates of hearing impairment and its effects on cognition and life quality

Lay summary

Background: Hearing loss (HL) is the most common sensory deficit with an estimate of 2.5 billion of people affected with HL by 2050. Increased cognitive decline and reduced quality of life in HL patients were reported. Multiple factors such as aging, noise, drugs, allergies, or genes, lead to hearing loss. Dementia shares some etiologies of HL and has common and similar findings in brain imaging and molecular markers, such as brain atrophy and glial-like change. Both HL and dementia contribute to deterioration of quality of life and social burden, thus, clarify the relationship between HL and dementia is an important issue. Aims of our study: (1) To classify the risk of HL and the HL related cognitive decline (2) To investigate the differences of cognitive function, structural and functional image features of magnetic resonance imaging (MRI) between HL individuals and the healthy controls. (3) To build prediction models of the trajectory of brain age, hearing loss level, risk of dementia and success rate of hearing intervention in HL individuals. Experimental design/Method: We plan to use the data of UK biobank. We define pure tone average more than 25 decibel hearing level as subjects and compare data with healthy control. Collection of the characteristics such as age, smoking behavior, socioeconomic status, comorbidity, activity level, environmental exposures and neuropsychiatric symptoms; blood samples with genetic information, inflammatory factors and basic biochemistry; cognition evaluation; brain image data of both structure and function MRI will be obtained. We will compare the longitudinal changes of brain images and other biomarkers between HL and healthy control. Significance: This study might document that HL will lead to longitudinal changes of both structures and functional connectivity of brain. We might clarify the relationship and the effect of HL on cognitive decline and life of quality. By using the prediction model based on machine learning algorithms, we could predict pathological brain age, risk of dementia and success rate of hearing intervention in the HL individual.