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

NCT07612618 | Not Yet Recruiting DMC

• 1 other identifier: CFH 2026-2-5072
• Study Type: observational
• Target: 1,000
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Age-related Hearing Loss

Keywords

Age-related hearing loss; prediction model; machine learning; proteomics; cognitive impairment

Outcome Measures

Primary Outcomes (1)

• AUC of ARHL machine learning model and cognitive-related protein biomarkers

  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

Study Arms (1)

Community-Dwelling Older Adults Group

Older adults with bilaterally symmetric hearing and no middle ear abnormalities

Other: Not applicable- observational study

Interventions

Not applicable- observational study OTHER

Not applicable-observational study

Community-Dwelling Older Adults Group

Eligibility Criteria

• Age: 60 Years - 100 Years
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• 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

You may qualify if:

• 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).

You may not qualify if:

• 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).

Sponsors & Collaborators

• Chinese PLA General Hospital: lead

Biospecimen

Retention: SAMPLES WITH DNA

Blood

MeSH Terms

Conditions

Presbycusis; Cognitive Dysfunction

Condition Hierarchy (Ancestors)

Hearing Loss, Sensorineural; Hearing Loss; Hearing Disorders; Ear Diseases; Otorhinolaryngologic Diseases; Sensation Disorders; Neurologic Manifestations; Nervous System Diseases; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Cognition Disorders; Neurocognitive Disorders; Mental Disorders

Central Study Contacts

Denghao Zheng

CONTACT

+8666876060; Zhengdh2648@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor, Chief Physician

Study Record Dates

• First Submitted: May 21, 2026
• First Posted: May 29, 2026
• Study Start: June 1, 2026
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2027
• Last Updated: May 29, 2026

Record last verified: 2026-05