TY - JOUR
T1 - Evaluation of clinical and genetic factors in obstructive sleep apnoea
AU - Guimarães, Maria de Lourdes Rabelo
AU - de Azevedo, Pedro Guimarães
AU - Souza, Renan Pedra
AU - Gomes-Fernandes, Bianca
AU - Friedman, Eitan
AU - De Marco, Luiz
AU - Bastos-Rodrigues, Luciana
N1 - Publisher Copyright:
© Società Italiana di Otorinolaringoiatria e Chirurgia Cervico-Facciale.
PY - 2023
Y1 - 2023
N2 - Purpose. To evaluate the correlation between several presumed candidate genes for obstructive sleep apnoea (OSA) and clinical OSA phenotypes and propose a predictive com-prehensive 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) pre-senting 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.
AB - Purpose. To evaluate the correlation between several presumed candidate genes for obstructive sleep apnoea (OSA) and clinical OSA phenotypes and propose a predictive com-prehensive 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) pre-senting 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.
KW - algorithms
KW - case-control studies
KW - genetic polymorphisms
KW - obstructive sleep apnea
KW - phenotype
UR - http://www.scopus.com/inward/record.url?scp=85180532299&partnerID=8YFLogxK
U2 - 10.14639/0392-100X-N2532
DO - 10.14639/0392-100X-N2532
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C2 - 37814975
AN - SCOPUS:85180532299
SN - 0392-100X
VL - 43
SP - 409
EP - 416
JO - Acta Otorhinolaryngologica Italica
JF - Acta Otorhinolaryngologica Italica
IS - 6
ER -