Centro de Tecnologia em Medicina Molecular, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
Laboratório de Biologia Integrativa, Grupo de Pesquisa em Bioestatística e Epidemiologia Molecular, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
Acta Otorhinolaryngol Ital. 2023 Dec;43(6):409-416. doi: 10.14639/0392-100X-N2532. Epub 2023 Oct 10.
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.
This case-control study compared polysomnographic patterns, clinical data, morbidities, dental factors and genetic data for polymorphisms in between confirmed OSA cases and ethnically matched clinically unaffected controls. A logistic regression model was developed to predict OSA using the combined data.
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/m. 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 presenting 76.5% specificity and 71.6% sensitivity.
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.
评估几个阻塞性睡眠呼吸暂停（OSA）的假定候选基因与临床 OSA 表型之间的相关性，并提出一种用于 OSA 诊断的预测综合模型。
本病例对照研究比较了经多导睡眠图（PSG）证实的 OSA 病例和种族匹配的临床无影响对照者的 PSG 模式、临床数据、合并症、牙科因素和多态性的遗传数据。使用联合数据开发了一种逻辑回归模型来预测 OSA。
队列包括 161 例 OSA 病例和 81 例对照者。病例的平均年龄为 53.5 ± 14.0 岁，大多数为男性（57%），平均体重指数（BMI）为 27.5 ± 4.3 kg/m。在调整年龄和 BMI 后，未发现任何基因标记与 OSA 具有统计学显著相关性。一个预测算法包括性别、年龄、打鼾、高血压、张口呼吸和 基因座 T 等位基因数变量，其特异性为 76.5%，敏感性为 71.6%。
未检测到与 OSA 表型具有统计学显著相关性的遗传变异。逻辑回归分析得出了一种用于诊断 OSA 的预测模型，如果通过更大的前瞻性研究得到验证，该模型可在临床上用于 OSA 的风险分层。
