Associations Between Body Composition and Sensorineural Hearing Loss Among Adults Based on the UK Biobank
Bibliographic record
Abstract
OBJECTIVE: To explore the association between body composition and sensorineural hearing loss (SNHL). STUDY DESIGN: Cross-sectional study, prospective study and Mendelian randomization (MR) analyses. SETTING: UK Biobank. METHODS: This cross-sectional study included 147,296 adult participants with complete data on body composition and the speech-reception-threshold (SRT) test. We further conducted a prospective study with 129,905 participants without SNHL at baseline and followed up to 15 years to explore the association between body composition and new-onset SNHL. Multivariable logistic regression and Cox regression models were used. Subgroup analyses stratified by age and sex were performed. We further assessed the causal association between body composition and SNHL using two-sample MR analyses. RESULTS: Our cross-sectional study revealed that fat percentage, especially leg (odds ratio [OR] 1.46, p = .029) and arm (OR 1.43, p = .004), were significant risk factors for SNHL. However, fat-free mass, especially in the arm (OR 0.27, p < .001) and leg (OR 0.58, p < .001) showed significant protective effects against SNHL, which was substantially consistent with the results of the prospective study. In addition, we found that young women with SNHL were more susceptible to body composition indicators. However, MR analyses revealed no evidence of significant causal association. CONCLUSION: Fat percentage, especially in the leg and arm, was a significant risk factor for SNHL, whereas fat-free mass, especially in the leg and arm, had significant protective effects against SNHL, however, these associations may not be causal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".