Age-related Hearing Loss
Conditions
Keywords
Age-related hearing loss, prediction model, machine learning, proteomics, cognitive impairment
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.
Interventions
OTHERNot applicable- observational study
Not applicable-observational study
Sponsors
Chinese PLA General Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
60 Years to 100 Years
Healthy volunteers
Yes
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).
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| AUC of ARHL machine learning model and cognitive-related protein biomarkers | 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 |
Contacts
CONTACTDenghao Zheng
Outcome results
None listed