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

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.

Not Yet Recruiting

Trial opening soon.

Get Notified

Key information

Conditions

Age-related Hearing Loss Cognition Disorders Cognitive Dysfunction Ear Diseases Hearing Disorders Hearing Loss Hearing Loss, Sensorineural Mental Disorders Nervous System Diseases Neurocognitive Disorders Neurologic Manifestations Otorhinolaryngologic Diseases Pathological Conditions, Signs and Symptoms Presbycusis Sensation Disorders Signs and Symptoms

Age range

60 year–100 year

Sex eligibility

All sexes

Study type

Observational

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

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

Treatment and study plan

Not applicable- observational study

Other

Not applicable-observational study

Primary outcomes

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

Study contacts

Contact information is provided by the study sponsor or research team.

Denghao Zheng

CONTACT

[email protected]

+8666876060

Sponsors and collaborators

Lead sponsor

Chinese PLA General Hospital

Other

Registry information

Important dates

Study start: 2026
Primary completion: 2027
Study completion: 2027
First posted: May 29, 2026
Registry last updated: May 29, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed