Associations Between Cardiovascular Risk Factors and Audiometric Hearing: Findings From the Canadian Longitudinal Study on Aging
Notice bibliographique
Résumé
OBJECTIVES: The objectives of the study were to determine, among a population-based sample of Canadian adults, if risk factors for cardiovascular disease (alone and in combination) were associated with hearing loss. Cross-sectional and longitudinal associations (the latter with about 3 years of follow-up) were examined. Risk factors considered included diabetes, dyslipidemia, hypertension, obesity, and smoking. We also aimed to determine if associations were modified by sex and age group (45 to 54, 55 to 64, 65 to 74, and 75 to 86 years old at baseline). DESIGN: A secondary analysis of data collected for the Canadian Longitudinal Study on Aging was performed. Data were collected in two waves, the first between 2012 and 2015, and the second between 2015 and 2018. Hearing was measured using screening air-conduction pure-tone audiometry. The outcome of interest was defined as the mid-frequency (1000, 2000, 3000, and 4000 Hz) pure-tone average for both ears. Diabetes was defined based on self-reported physician diagnosis, use of diabetes medications, or a hemoglobin A1c level ≥6.5%. Dyslipidemia was determined by blood lipid profile as defined using the Canadian guidelines for the diagnosis and treatment of dyslipidemia (low-density lipoprotein cholesterol ≥3.5 mmol/L or non-high-density lipoprotein cholesterol ≥4.3 mmol/L). Hypertension was determined by self-reported physician diagnosis or an average systolic blood pressure ≥140 mm Hg or an average diastolic blood pressure ≥90 mm Hg. Obesity was defined as a waist-to-height ratio ≥0.6. Smoking history was determined by self-report (current/former/never-smoker). Two composite measures of cardiovascular risk were also constructed: a count of the number of risk factors and a general cardiovascular risk profile (Framingham) score. Independent associations between risk factors for cardiovascular disease and hearing were determined using multivariable regression models. Survey weights were incorporated into the analyses. All results were disaggregated by sex. Effect modification according to age was determined using multiplicative interaction terms between the age group and each of the risk factor variables. A complete case (listwise deletion) approach was performed for the primary analysis. We then repeated the multivariable regression analyses using multiple imputation using chained equations to determine if the different approaches to dealing with missing data qualitatively changed the outcomes. RESULTS: In longitudinal analyses, hypertension and the general cardiovascular risk profile score were associated with greater loss of hearing over the 3-year follow-up period for both sexes. In addition, smoking in males and obesity in females were associated with faster rates of hearing decline. In cross-sectional analyses, smoking, obesity, diabetes, and composite measures were each independently associated with worse hearing for both sexes (although for females, obesity was only associated with hearing loss in the 55 to 64-year-old age group). The results were similar for the complete case and multiple imputation approaches, but more cross-sectional associations were observed using multiple imputation. CONCLUSIONS: Diabetes, obesity, hypertension, and smoking were associated with hearing loss. Higher combinations of risk factors increased the risk of hearing loss. Further studies are needed to confirm age and sex differences and whether interventions to address these risk factors could slow the progression of hearing loss in older adults.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».