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Classifying Human Audiometric Phenotypes of Age-Related Hearing Loss from Animal Models

  • Judy R. DubnoEmail author
  • Mark A. Eckert
  • Fu-Shing Lee
  • Lois J. Matthews
  • Richard A. Schmiedt
Research Article

Abstract

Age-related hearing loss (presbyacusis) has a complex etiology. Results from animal models detailing the effects of specific cochlear injuries on audiometric profiles may be used to understand the mechanisms underlying hearing loss in older humans and predict cochlear pathologies associated with certain audiometric configurations (“audiometric phenotypes”). Patterns of hearing loss associated with cochlear pathology in animal models were used to define schematic boundaries of human audiograms. Pathologies included evidence for metabolic, sensory, and a mixed metabolic + sensory phenotype; an older normal phenotype without threshold elevation was also defined. Audiograms from a large sample of older adults were then searched by a human expert for “exemplars” (best examples) of these phenotypes, without knowledge of the human subject demographic information. Mean thresholds and slopes of higher frequency thresholds of the audiograms assigned to the four phenotypes were consistent with the predefined schematic boundaries and differed significantly from each other. Significant differences in age, gender, and noise exposure history provided external validity for the four phenotypes. Three supervised machine learning classifiers were then used to assess reliability of the exemplar training set to estimate the probability that newly obtained audiograms exhibited one of the four phenotypes. These procedures classified the exemplars with a high degree of accuracy; classifications of the remaining cases were consistent with the exemplars with respect to average thresholds and demographic information. These results suggest that animal models of age-related hearing loss can be used to predict human cochlear pathology by classifying audiograms into phenotypic classifications that reflect probable etiologies for hearing loss in older humans.

Keywords

metabolic presbyacusis sensory presbyacusis endocochlear potential animal models audiogram classification supervised machine learning classifiers 

Notes

Acknowledgments

This work was supported (in part) by grant P50 DC00422 from NIH/NIDCD, with a supplement from the American Recovery and Reinvestment Act. The project also received support from the South Carolina Clinical & Translational Research (SCTR) Institute, with an academic home at the Medical University of South Carolina, NIH/NCRR Grant number UL1 RR029882. This investigation was conducted in a facility constructed with support from Research Facilities Improvement Program Grant Number C06 RR14516 from the National Center for Research Resources, National Institutes of Health. Assistance by Jayne B. Ahlstrom, John H. Mills, and Bradley A. Schulte is gratefully acknowledged.

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Copyright information

© Association for Research in Otolaryngology 2013

Authors and Affiliations

  • Judy R. Dubno
    • 1
    Email author
  • Mark A. Eckert
    • 1
  • Fu-Shing Lee
    • 1
  • Lois J. Matthews
    • 1
  • Richard A. Schmiedt
    • 1
  1. 1.Department of Otolaryngology-Head and Neck SurgeryMedical University of South CarolinaCharlestonUSA

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