Exposomic Signatures of Tinnitus and/or Hearing Loss

Abstract

ABSTRACT Introduction We evaluated the risk factors associated with tinnitus and/or hearing loss (THL) among active duty (AD) members of the U.S. Army and Marine Aviation Community (AMAC) using an exposomic approach. Specifically, we aimed to determine the factors associated with the reported THL in the Military Health System. Methods Longitudinal data were obtained from the Medical Assessment and Readiness System housed at Womack Army Medical Center, Fort Bragg, NC, for a retrospective cohort study that included 78,546 AD AMAC members from October 2015 to December 2019. Multivariable mixed-effects logistic regression was used to assess the relationship between THL and numerous variables to include rank, service time, deployment, tobacco use, alcohol use, age, gender, race, ethnicity, and body mass index. Results Our analysis included a total of 220,044 person-years of observations. The THL incidence rate was 6.7 per 100 person-years, with an 8.1% period prevalence. THL was associated with age, gender, body mass index, race, deployment, service time, marital status, and tobacco use (all P < .05). Service time greater than 16 years had the greatest odds ratio of THL (4.46, 95% CI: 3.58-5.55, P < .001). Conclusions Our assessment shows the utility of using an exposomic approach to create member-specific personalized clinical algorithms for health outcomes. We examined individuals with THL diagnoses and identified a combination of risk factors from biomedical, lifestyle, environmental, and stochastic sources. Taken together, the risk factors identified across the four exposomic domains could help understand the etiology of THL. Our exposomic methodology could be the foundation for generating predictive models. Finally, a specific evaluation of occupational risk factors may provide insight into aspects not readily available from civilian literature. In upcoming years, as the Medical Assessment and Readiness System matures, we will expand our analyses to include prospective, untargeted metabolites and biomarker data.