Latest on Can We Predict Hearing Loss Long Before Symptoms Ever Start?

By combining detailed audiometric testing data with genetic biobanks rather than relying on generic medical billing codes, researchers have significantly improved their ability to predict sensorineural hearing loss. This precision phenotyping approach paves the way for early genetic risk screening and personalized therapeutic interventions before clinical hearing loss manifests. Precision Phenotypes Outperform Billing Codes: Polygenic risk scores derived from quantitative audiometric data predicted sensorineural hearing loss far more accurately than models built using standard diagnostic billing codes.

Large-Scale Biobank Integration: The team integrated de-identified audiometric hearing thresholds from 16,000 individuals with Vanderbilts BioVU genomic repository to map specific genetic variants linked to auditory impairment. Foundation for Proactive Interventions: Earlier, high-resolution genetic profiling could soon allow clinicians to identify at-risk patients prior to symptom onset and tailor emerging gene therapies to specific frequency configurations. Sensorineural hearing loss is one of the most common sensory deficits globally, yet detecting it early enough to alter its clinical trajectory has remained a significant hurdle in modern medicine.

What we know

Traditionally, uncovering the genetic underpinnings of complex sensory conditions has relied heavily on massive electronic health record (EHR) databases. However, these repositories predominantly track patient conditions through diagnostic billing codes—coarse labels that often fail to capture the subtle, subjective, and physiological variations inherent in auditory impairment. Now, a multidisciplinary team of clinical audiologists, otolaryngologists, and bioinformaticians at Vanderbilt University Medical Center (VUMC) has demonstrated that replacing generic diagnostic codes with granular clinical test results, a concept known as “precision phenotyping”, substantially enhances the discovery of genetic variants associated with hearing loss.

The findings, published in JAMA Otolaryngology–Head & Neck Surgery, point toward a future where patients predisposed to hearing loss can be reliably identified before noticeable auditory decline occurs. In typical genetic biobank studies, researchers rely on International Classification of Diseases (ICD) diagnostic codes to divide individuals into “cases” or “controls.” While expedient for processing millions of patient charts, these billing codes act as an imprecise proxy for sensory function. “Diagnostic codes are very common because thats the data thats available in large genetic biobanks that facilitate this research,” explained co-first author Andie DeFreese, AuD, a clinical audiologist and doctoral candidate in the Department of Hearing and Speech Sciences at Vanderbilt Health.

“Using them leads to a kind of gray area in which were not able to accurately define who has hearing loss and who doesnt.” Because two patients sharing an identical diagnostic code might possess drastically different degrees, patterns, or frequency thresholds of hearing loss, genetic associations derived from these codes tend to be blunted by noise in the data. To overcome this limitation, the Vanderbilt team leveraged BioVU, Vanderbilt Healths extensive biobank containing de-identified genetic information linked to electronic health records. The investigators de-identified and cross-referenced complete clinical audiometric records, including comprehensive hearing threshold measurements across various audio frequencies, with the genomic data of 16,000 consenting individuals.

Using this rich dataset, the researchers constructed polygenic risk scores (PRS) based on precise quantitative audiometric measurements and tested them in an independent validation cohort. The polygenic risk scores established via precision phenotyping demonstrated significantly superior predictive power for hearing loss compared to risk scores modeled strictly on standard diagnostic codes. “In pairing these resources, we can use a more precise phenotype for hearing loss instead of diagnostic codes that can be inaccurate,” DeFreese noted.

Key details

“This better prediction power tells us the importance of precision phenotyping not just for hearing loss but for all disciplines in medicine that take this risk prediction approach.” The ability to forecast hearing loss at the genetic level carries vital clinical implications. Proactive identification can inform early auditory rehabilitation, noise-protection strategies, and monitoring protocols well before hair cell degradation translates into functional communication deficits. Furthermore, precision genomics is increasingly critical as biological and molecular therapeutics enter the auditory health landscape.

“Genetics is becoming increasingly relevant for precision therapy, especially now that the Food and Drug Administration has approved its first gene therapy for genetic hearing loss,” said corresponding author Taha Jan, MD, Assistant Professor of Otolaryngology–Head and Neck Surgery at VUMC. “This work is an example of how our world-class clinicians and scientists at Vanderbilt Health are pushing the boundaries of precision medicine.” Looking ahead, the research group plans to refine these genomic models to predict not just the presence or absence of hearing loss, but also the specific auditory configurations and frequencies likely to deteriorate, providing an unprecedented roadmap for lifelong hearing health. Funding: The research was funded by a Vanderbilt Lacy-Fischer Interdisciplinary Grant, an American Otological Society Fellowship Grant and by the National Institute on Deafness and Other Communication Disorders, part of the National Institutes of Health (grants R03DC021550, R21DC021276, R21DC023019, K08DC019683 and R21DC022058).

This article was edited by a Neuroscience News editor. Original Research is Open Access: JAMA Otolaryngology–Head & Neck Surgery (Oct 1, 2026). “Precision Phenotyping With Audiometric Data and Gene Discovery for Sensorineural Hearing Loss.” Authors: Andrea J.

DeFreese, AuD; Tanguy Rubat du Mérac, MSc; Quanhu Sheng, PhD; Srishti Nayak, PhD; and Taha A. Precision Phenotyping With Audiometric Data and Gene Discovery for Sensorineural Hearing Loss The genetic architecture underlying hearing sensitivity as a quantitative trait is critical for advancing precision medicine in hearing health. Despite extensive genome-wide association study (GWAS) efforts, few strong genetic drivers have been identified.

Why it matters

To compare the sensitivity for detecting genetic variants associated with sensorineural hearing loss (SNHL) using precision phenotyping with pure-tone averages (PTAs) with common phenotyping using diagnostic coding approaches. In this genetic association study, 2 separate GWASs were conducted using different phenotyping approaches for SNHL: (1) diagnostic code control and (2) precision phenotyping with PTA data. Whole-genome sequencing data from a population with European genetic ancestry collected at a single tertiary center biobank was used.

For the control GWAS based on diagnostic codes, case and control participants were defined using primarily International Classification of Diseases, Ninth Revision (ICD-9) and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnosis and procedure codes related to SNHL hearing loss. For the precision phenotyping GWAS, hearing status was defined as a continuous variable, using the standard 3-frequency PTA of the better hearing ear from the audiogram. Data analysis was conducted from August 2025 to April 2026.

GWAS summary statistics were used to calculate estimates of single-nucleotide variant (SNV)–based heritability for PTA and ICD phenotypes; to map the genetic risk loci to candidate genes using functional annotation; to evaluate genetic correlations with existing GWASs; and to calculate polygenic risk scores for testing in an independent cohort from the National Institutes of Healths All of Us Research Program. Overall, 10 164 case participants (mean [SD] age, 63.9 [21.3] years; 5118 [50.4%] female) and 51 305 control participants (mean [SD] age, 51.1 [21.0] years; 28 784 [56.1%] female) were included in the first GWAS; 16 057 participants with PTA (mean [SD] age, 55.5 [21.8] years; 9147 [57.0%] female) were included in the second GWAS. PTA-based GWAS identified 3 genome-wide significant loci mapped to 4 genes: EML6, SPTBN1, ARHGEF28, and EYA4 (SNV-based heritability estimate [h2] = 11.78%; SE = 2.84%).

ICD-based GWAS identified no significant loci (h2 = 2.90%; SE = 0.77%). Genome-wide correlations between the PTA-based GWAS and 3 hearing-related ability traits were found (eg, PTA-based GWAS and hearing aid use: r = 0.985; SE = 0.163; P = 1.56 × 10−9). In the All of Us cohort, PTA-derived polygenic risk scores were significantly associated with self-reported deafness (odds ratio [OR], 1.09; 95% CI, 1.07-1.12), whereas ICD-derived scores were not (OR, 1.00; 95% CI, 0.97-1.02).

In this genetic association study, precision phenotyping using audiometric PTAs improved genetic discovery for SNHL compared with diagnostic code–based approaches. Higher heritability estimates, identification of genome-wide significant loci, and stronger polygenic risk estimation indicate that quantitative hearing measures better capture genetic architecture, supporting precision medicine in hearing health.