Age-related Hearing LossNCT07612618
Prediction of Age-Related Hearing Loss Based on Comprehensive Risk Factors
An observational study of Age-related Hearing Loss, run by Chinese PLA General Hospital.
Trial 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. Read more about this study
- Who is it for?
- Ages 60 to 100 yearsHealthy volunteers can take part
- Conditions the study is for
- Age-related Hearing Loss
- What is being tested?
- Not applicable- observational study
- How many people
- 1,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Jun 2026to 31 Dec 2027
- How long will I take part?
- 1 Year
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 60 to 100 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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
Who may be able to join
- 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).
Who cannot join
- 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 locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
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
- Being tested
- Not applicable- observational study
- Keywords
- Age-related hearing loss, prediction model, machine learning, proteomics, cognitive impairment
- Medical subject headings
- Presbycusis, Cognitive Dysfunction
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Not applicable- observational studyOther
Not applicable-observational study
| Study group | Role | What they receive |
|---|---|---|
| Community-Dwelling Older Adults Group | Older adults with bilaterally symmetric hearing and no middle ear abnormalities Receives: Other: Not applicable- observational study |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: Baseline and 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 May 2026
- Study start1 Jun 2026 (estimated)
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Age-related Hearing LossStudies of the same condition, open and under way.
- More trials run by Chinese PLA General HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
- Target duration
- 1 Year
Sponsor and investigators
- Lead sponsor
- Chinese PLA General Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Shiming Yang, PhD, Chinese PLA General Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07612618
- Sponsor's protocol ID
- CFH 2026-2-5072
- Other IDs
- CFH 2026-2-5072
- Status last verified
- May 2026
- First submitted
- 21 May 2026
- Last updated
- 29 May 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 29 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Age-Related Hearing Loss Based on Comprehensive Risk Factors
- ClinicalTrials.gov ID
- NCT07612618
- Acronym / sponsor ID
- CFH 2026-2-5072
- Conditions
- Age-related Hearing Loss
- Keywords
- Age-related hearing loss, prediction model, machine learning, proteomics, cognitive impairment
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Chinese PLA General HospitalUniversity, hospital or other
- Study start
- 1 Jun 2026 (estimated)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 29 May 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Year
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Community-Dwelling Older Adults Group | – | Other: Not applicable- observational studyOlder adults with bilaterally symmetric hearing and no middle ear abnormalities |
Who may qualify
- Ages
- 60 to 100 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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
Key inclusion criteria (4 listed)
- 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).
Key exclusion criteria (3 listed)
- 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).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Not applicable- observational studyNot applicable-observational study | Other | Community-Dwelling Older Adults Group |
Primary outcome
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 May 2026.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
An observational study of Age-related Hearing Loss, run by Chinese PLA General Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 29 May 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Denghao Zheng (+8666876060, Zhengdh2648@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AUC of ARHL machine learning model and cognitive-related protein biomarkers (time frame: Baseline and 12 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 May 2026.