Evaluation of clinical and genetic factors in obstructive sleep apnoea
Purpose. To evaluate the correlation between several presumed candidate genes for obstructive sleep apnoea (OSA) and clinical OSA phenotypes and propose a predictive comprehensive model for diagnosis of OSA.
Methods. This case-control study compared polysomnographic patterns, clinical data, morbidities, dental factors and genetic data for polymorphisms in PER3, BDNF, NRXN3, APOE, HCRTR2, MC4R between confirmed OSA cases and ethnically matched clinically unaffected controls. A logistic regression model was developed to predict OSA using the combined data.
Results. The cohort consisted of 161 OSA cases and 81 controls. Mean age of cases was 53.5 ± 14.0 years, mostly males (57%) and mean body mass index (BMI) of 27.5 ± 4.3 kg/ m2. None of the genotyped markers showed a statistically significant association with OSA after adjusting for age and BMI. A predictive algorithm included the variables gender, age, snoring, hypertension, mouth breathing and number of T alleles of PER3 (rs228729) presenting 76.5% specificity and 71.6% sensitivity.
Conclusions. No genetic variant tested showed a statistically significant association with OSA phenotype. Logistic regression analysis resulted in a predictive model for diagnosing OSA that, if validated by larger prospective studies, could be applied clinically to allow risk stratification for OSA.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
© Società Italiana di Otorinolaringoiatria e chirurgia cervico facciale , 2023
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