Uncovering the genetic relationship between dementia and age‐related hearing loss
Bibliographic record
Abstract
BACKGROUND: Hearing loss is an important modifiable midlife risk factor for dementia. Recent studies have explored a potential genetic relationship between dementia and age-related hearing loss (ARHL). However, due to variability in phenotype definitions and analysis methods, these studies have not reached a clear consensus on the specific genetic factors underlying both conditions. We have previously investigated the genetics of different ARHL subtypes and have shown that genes linked to frontotemporal dementia also play a role in the metabolic subtype of ARHL. By employing a comprehensive phenotyping strategy that involves highly specific, objective measures of cognition and audiological measures of hearing, we aim to examine the genetic relationship between dementia and ARHL with increased granularity. METHOD: Through the Canadian Longitudinal Study on Aging (CLSA), we have access to high-quality genotype, cognitive, and audiological data for 17,221 older adults. Estimates of hearing loss subtypes were calculated from CLSA participant audiograms. Cognitive impairment, an early dementia trait, was defined using a CLSA-specific indicator and the association between this indicator and hearing loss subtypes was investigated. In the coming months, we aim to assess potential causality between cognitive impairment and the metabolic subtype of ARHL using Mendelian randomization. We will also apply regression and fine-mapping analyses to identify genetic variants, genes and pathways that may affect both dementia and ARHL. RESULT: ; OR[95%CI]=1.016[1.011,1.022]), whereas no significant association was observed with sensory hearing loss (p = 0.35; OR[95%CI]=0.99[0.988,1.004]). We are currently preparing genetic data for further analysis and expect to uncover genetic variants that correlate with both metabolic hearing loss and cognitive impairment and/or establish a causative pathway between these phenotypes. CONCLUSION: This study has shown cognitive impairment to be significantly associated with the metabolic subtype of ARHL. Identifying correlated genetic variants and/or a causal link between these conditions will enhance our understanding of the biological mechanisms underlying the genetics of dementia and ARHL. Additionally, our findings may guide the development of improved dementia prediction tools through the incorporation of measures of hearing and genetic data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".