Presbyacusis, or age-related hearing loss, can be characterized in humans as metabolic and sensory phenotypes, based on patterns of audiometric thresholds that were established in animal models. The metabolic phenotype is thought to result from deterioration of the cochlear lateral wall and reduced endocochlear potential that decreases cochlear amplification and produces a mild, flat hearing loss at lower frequencies coupled with a gradually sloping hearing loss at higher frequencies. The sensory phenotype, resulting from environmental exposures such as excessive noise or ototoxic drugs, involves damage to sensory and non-sensory cells and loss of the cochlear amplifier, which produces a 50-70 dB threshold shift at higher frequencies. The mixed metabolic + sensory phenotype exhibits a mix of lower frequency, sloping hearing loss similar to the metabolic phenotype, and steep, higher frequency hearing loss similar to the sensory phenotype. The current study examined audiograms collected longitudinally from 343 adults 50-93 years old (n = 686 ears) to test the hypothesis that metabolic phenotypes increase with increasing age, in contrast with the sensory phenotype. A Quadratic Discriminant Analysis (QDA) was used to classify audiograms from each of these ears as (1) Older-Normal, (2) Metabolic, (3) Sensory, or (4) Metabolic + Sensory phenotypes. Although hearing loss increased systematically with increasing age, audiometric phenotypes remained stable for the majority of ears (61.5 %) over an average of 5.5 years. Most of the participants with stable phenotypes demonstrated matching phenotypes for the left and right ears. Audiograms were collected over an average period of 8.2 years for ears with changing audiometric phenotypes, and the majority of those ears transitioned to a Metabolic or Metabolic + Sensory phenotype. These results are consistent with the conclusion that the likelihood of metabolic presbyacusis increases with increasing age in middle to older adulthood.
Keywords: animal models; audiogram classification; longitudinal; metabolic presbyacusis; sensory presbyacusis; supervised machine learning classifiers.