Prediction of Age-Related Hearing Loss Based on Comprehensive Risk Factors

May 21, 2026 updated by: Shiming Yang, PhD, Chinese PLA General Hospital
This study aims to develop a predictive model for age-related hearing loss (ARHL) based on multi-source risk factors and artificial intelligence techniques. A retrospective analysis will be conducted on 1,000 cases with 15-year longitudinal clinical data, including audiological assessments and noise exposure history. Machine learning algorithms will be employed to construct a predictive model for hearing loss progression. Additionally, a prospective cohort of 100 community-dwelling elderly individuals will be enrolled. Blood samples will be collected for low-abundance targeted proteomics analysis to screen for biomarkers associated with cognitive impairment. This study will establish an early risk identification tool for ARHL and propose strategies for the screening and prevention of dementia in individuals with hearing impairment, thereby providing evidence-based support for early intervention in auditory and cognitive health in the elderly.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

1000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

This study enrolls community-dwelling adults aged ≥60 years from multiple Chinese centers. Inclusion: occupational noise exposure, longitudinal pure-tone audiometry, complete clinical data. Exclusion: non-age/noise hearing loss (e.g., otitis media, otosclerosis, Meniere's disease), missing data >20%, severe mental/cognitive impairment.

The prospective cohort (n=100) recruited from community health centers in North and East China. Inclusion: permanent local residents (≥9 months/year), able to complete assessments, WHO ARHL criteria (PTA≥25 dB HL), written consent. Exclusion: severe psychiatric disorders, major organ failure (NYHA III-IV, eGFR<30), life expectancy <3 years, non-ARHL loss, diagnosed dementia, Parkinson's, stroke with severe sequelae, or other unsuitable conditions.

Prospective participants followed at baseline, 12 months. Among them, 50 ARHL with cognitive impairment (MoCA<26) and 50 with ARHL+normal cognition (MoCA≥26) receive proteomics analysis for biomarker discovery

Description

Inclusion Criteria:

  1. Age ≥ 60 years;
  2. Availability of longitudinal pure-tone audiometry data;
  3. Documented history of occupational noise exposure;
  4. Complete clinical data (including past medical history and medication history).

Exclusion Criteria:

  1. Hearing loss caused by non-age or non-noise factors (e.g., otitis media, otosclerosis, Meniere's disease);
  2. Missing clinical data >20%;
  3. Concurrent severe mental illness or cognitive impairment (unable to complete audiological assessment).

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Community-Dwelling Older Adults Group
Older adults with bilaterally symmetric hearing and no middle ear abnormalities
Other: Not applicable- observational study
Not applicable-observational study

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
AUC of ARHL machine learning model and cognitive-related protein biomarkers
Time Frame: Baseline and 12 months
To evaluate the discriminative performance (area under the receiver operating characteristic curve, AUC) of a machine learning-based predictive model for age-related hearing loss (ARHL) integrating multidimensional risk factors, and to identify serum protein biomarkers associated with cognitive impairment in ARHL patients. Based on a retrospective training cohort of 1,000 participants with 15-year longitudinal data and a prospective external validation cohort of 100 community-dwelling older adults aged 60 years and above, this primary outcome will assess the predictive accuracy (target AUC ≥0.8) of the optimal model (e.g., random forest, XGBoost, or neural network) using standardized pure-tone audiometry, and will determine the diagnostic performance (target AUC ≥0.75) of candidate protein biomarkers for cognitive decline (MoCA <26) through low-abundance targeted proteomics (pSILAC-HPLC-MS/MS). Repeated cognitive assessments (MoCA, MMSE, CDR) at baseline, 12 months will
Baseline and 12 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Chinese PLA General Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

June 1, 2026

Primary Completion (Estimated)

December 31, 2027

Study Completion (Estimated)

December 31, 2027

Study Registration Dates

First Submitted

May 21, 2026

First Submitted That Met QC Criteria

May 21, 2026

First Posted (Actual)

May 29, 2026

Study Record Updates

Last Update Posted (Actual)

May 29, 2026

Last Update Submitted That Met QC Criteria

May 21, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • CFH 2026-2-5072 (Other Grant/Funding Number: Capital's Funds for Health Improvement and Research)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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