Prediction of Age-Related Hearing Loss Based on Comprehensive Risk Factors
ConditionAge-related Hearing Loss
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age60-100
SponsorChinese PLA General Hospital
About this trial
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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
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).
Disqualifiers
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).
Trial 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
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Not applicable- observational study
Other interventionNot applicable-observational study
Treatment groups
1,000 Participants
are divided into 1 treatment groupGroup A: Community-Dwelling Older Adults Group1 intervention
Trial outcomes
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
Time frame
Baseline and 12 monthsLocations
Sponsors and contacts
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Source ClinicalTrials.gov
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