Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2013 Oct;14(5):687-701.
doi: 10.1007/s10162-013-0396-x. Epub 2013 Jun 6.

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

Affiliations
Free PMC article

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

Judy R Dubno et al. J Assoc Res Otolaryngol. .
Free PMC 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.

Figures

FIG. 1
FIG. 1
Schematic boundaries of audiograms corresponding to five phenotypes of age-related hearing loss, based on five hypothesized conditions of cochlear pathology (adapted from Schmiedt 2010).
FIG. 2
FIG. 2
Mean thresholds (±1 standard error) of 338 exemplars in four audiometric phenotypes.
FIG. 3
FIG. 3
Classification rates for exemplars for four audiometric phenotypes and the overall classification rate.
FIG. 4
FIG. 4
Mean (±1 standard error) ages of exemplars (left, darker bars) and non-exemplars (right, lighter bars) for four audiometric phenotypes.
FIG. 5
FIG. 5
Percentage of male subjects for exemplars (left, darker bars) and non-exemplars (right, lighter bars) for four audiometric phenotypes.
FIG. 6
FIG. 6
Percentage of subjects reporting positive noise histories for exemplars (left, darker bars) and non-exemplars (right, lighter bars) for four audiometric phenotypes. Each panel reports noise histories for different types of noise exposures.
FIG. 7
FIG. 7
Mean audiograms and standard errors of exemplars (filled symbols) and non-exemplars (open symbols) in four audiometric phenotypes.
FIG. 8
FIG. 8
Distributions of the maximum probabilities from quadratic discriminant analysis (QDA) for exemplar and non-exemplar audiograms for four audiometric phenotypes.

Similar articles

See all similar articles

Cited by 41 articles

See all "Cited by" articles

Publication types

MeSH terms

LinkOut - more resources

Feedback