Literature DB >> 27830350

Longitudinal Changes in Audiometric Phenotypes of Age-Related Hearing Loss.

Kenneth I Vaden1, Lois J Matthews2, Mark A Eckert2, Judy R Dubno3.   

Abstract

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.

Entities:  

Keywords:  animal models; audiogram classification; longitudinal; metabolic presbyacusis; sensory presbyacusis; supervised machine learning classifiers

Mesh:

Year:  2016        PMID: 27830350      PMCID: PMC5352606          DOI: 10.1007/s10162-016-0596-2

Source DB:  PubMed          Journal:  J Assoc Res Otolaryngol        ISSN: 1438-7573


  19 in total

1.  Longitudinal threshold changes in older men with audiometric notches.

Authors:  G A Gates; P Schmid; S G Kujawa; B Nam; R D'Agostino
Journal:  Hear Res       Date:  2000-03       Impact factor: 3.208

2.  Effects of furosemide applied chronically to the round window: a model of metabolic presbyacusis.

Authors:  Richard A Schmiedt; Hainan Lang; Hiro-oki Okamura; Bradley A Schulte
Journal:  J Neurosci       Date:  2002-11-01       Impact factor: 6.167

3.  Extended high-frequency thresholds in older adults.

Authors:  L J Matthews; F S Lee; J H Mills; J R Dubno
Journal:  J Speech Lang Hear Res       Date:  1997-02       Impact factor: 2.297

4.  Presbycusis phenotypes form a heterogeneous continuum when ordered by degree and configuration of hearing loss.

Authors:  Paul D Allen; David A Eddins
Journal:  Hear Res       Date:  2010-02-06       Impact factor: 3.208

5.  Longitudinal changes in hearing sensitivity among men: the Veterans Affairs Normative Aging Study.

Authors:  Katharina V Echt; Sherri L Smith; Andrea Backscheider Burridge; Avron Spiro
Journal:  J Acoust Soc Am       Date:  2010-10       Impact factor: 1.840

6.  Longitudinal study of pure-tone thresholds in older persons.

Authors:  Fu-Shing Lee; Lois J Matthews; Judy R Dubno; John H Mills
Journal:  Ear Hear       Date:  2005-02       Impact factor: 3.570

7.  The 5-year incidence and progression of hearing loss: the epidemiology of hearing loss study.

Authors:  Karen J Cruickshanks; Ted S Tweed; Terry L Wiley; Barbara E K Klein; Ronald Klein; Rick Chappell; David M Nondahl; Dayna S Dalton
Journal:  Arch Otolaryngol Head Neck Surg       Date:  2003-10

8.  Age-related auditory pathology in the CBA/J mouse.

Authors:  Su-Hua Sha; Ariane Kanicki; Gary Dootz; Andra E Talaska; Karin Halsey; David Dolan; Richard Altschuler; Jochen Schacht
Journal:  Hear Res       Date:  2008-06-07       Impact factor: 3.208

9.  Effects of age and mild hearing loss on speech recognition in noise.

Authors:  J R Dubno; D D Dirks; D E Morgan
Journal:  J Acoust Soc Am       Date:  1984-07       Impact factor: 1.840

10.  Predicting sample size required for classification performance.

Authors:  Rosa L Figueroa; Qing Zeng-Treitler; Sasikiran Kandula; Long H Ngo
Journal:  BMC Med Inform Decis Mak       Date:  2012-02-15       Impact factor: 2.796

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  12 in total

1.  A Novel Method for Classifying Hearing Impairment in Epidemiological Studies of Aging: The Wisconsin Age-Related Hearing Impairment Classification Scale.

Authors:  Karen J Cruickshanks; David M Nondahl; Mary E Fischer; Carla R Schubert; Ted S Tweed
Journal:  Am J Audiol       Date:  2020-02-03       Impact factor: 1.493

2.  Identification of the Spectrotemporal Modulations That Support Speech Intelligibility in Hearing-Impaired and Normal-Hearing Listeners.

Authors:  Jonathan H Venezia; Allison-Graham Martin; Gregory Hickok; Virginia M Richards
Journal:  J Speech Lang Hear Res       Date:  2019-04-15       Impact factor: 2.297

3.  Neural signatures of auditory hypersensitivity following acoustic trauma.

Authors:  Matthew McGill; Ariel E Hight; Yurika L Watanabe; Aravindakshan Parthasarathy; Dongqin Cai; Kameron Clayton; Kenneth E Hancock; Anne Takesian; Sharon G Kujawa; Daniel B Polley
Journal:  Elife       Date:  2022-09-16       Impact factor: 8.713

4.  Investigating the characteristics of genes and variants associated with self-reported hearing difficulty in older adults in the UK Biobank.

Authors:  Morag A Lewis; Bradley A Schulte; Judy R Dubno; Karen P Steel
Journal:  BMC Biol       Date:  2022-06-27       Impact factor: 7.364

5.  Metabolic and Sensory Components of Age-Related Hearing Loss.

Authors:  Kenneth I Vaden; Mark A Eckert; Lois J Matthews; Richard A Schmiedt; Judy R Dubno
Journal:  J Assoc Res Otolaryngol       Date:  2022-01-21

6.  Transient-Evoked Otoacoustic Emissions Reflect Audiometric Patterns of Age-Related Hearing Loss.

Authors:  Kenneth I Vaden; Lois J Matthews; Judy R Dubno
Journal:  Trends Hear       Date:  2018 Jan-Dec       Impact factor: 3.293

7.  Age-Related Hearing Loss Associations With Changes in Brain Morphology.

Authors:  Mark A Eckert; Kenneth I Vaden; Judy R Dubno
Journal:  Trends Hear       Date:  2019 Jan-Dec       Impact factor: 3.293

8.  Gender-specific hearing loss in German adults aged 18 to 84 years compared to US-American and current European studies.

Authors:  Petra von Gablenz; Eckhard Hoffmann; Inga Holube
Journal:  PLoS One       Date:  2020-04-23       Impact factor: 3.240

Review 9.  Translational and interdisciplinary insights into presbyacusis: A multidimensional disease.

Authors:  Mark A Eckert; Kelly C Harris; Hainan Lang; Morag A Lewis; Richard A Schmiedt; Bradley A Schulte; Karen P Steel; Kenneth I Vaden; Judy R Dubno
Journal:  Hear Res       Date:  2020-10-31       Impact factor: 3.208

10.  Forgotten Fibrocytes: A Neglected, Supporting Cell Type of the Cochlea With the Potential to be an Alternative Therapeutic Target in Hearing Loss.

Authors:  David N Furness
Journal:  Front Cell Neurosci       Date:  2019-12-06       Impact factor: 5.505

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