10.14639/0392-100X-N2532
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.
text/xml
1827-675X
2532
en
Luciana Bastos-Rodrigues
http://creativecommons.org/licenses/by-nc-nd/4.0
ACTA Otorhinolaryngologica Italica
case-control studies
Evaluation of clinical and genetic factors in obstructive sleep apnoea
Text.Serial.Journal
https://www.actaitalica.it/
43
OSAHS
2023-10-10
2023-02-23
2023-12-19
2023-12-19
409-416
1
Luciana Bastos-Rodrigues
2023/10/10
10.14639/0392-100X-N2532
409
Centro de Tecnologia em Medicina Molecular, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil; Department of Nutrition, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
1827-675X
case-control studies
en
416
Evaluation of clinical and genetic factors in obstructive sleep apnoea
https://www.actaitalica.it/article/download/2532/1043
43
ACTA OTORHINOLARYNGOLOGICA ITALICA 2023;43:409-416; doi: 10.14639/0392-100X-N2532




OSAHS


Evaluation of clinical and genetic factors
in obstructive sleep apnoea
Valutazione dei fattori clinici e genetici nella sindrome da apnee ostruttive del sonno
Maria de Lourdes Rabelo Guimarães1, Pedro Guimarães de Azevedo1, Renan Pedra Souza2,
Bianca Gomes-Fernandes1, Eitan Friedman3, Luiz De Marco1,4, Luciana Bastos-Rodrigues1,5
1
 Centro de Tecnologia em Medicina Molecular, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte,
Brazil; 2 Laboratório de Biologia Integrativa, Grupo de Pesquisa em Bioestatística e Epidemiologia Molecular, Instituto de
Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil; 3 The Genetic Center for Early Detection,
Assuta Medical Center, Tel-Aviv, the Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel; 4 Department of Surgery,
Universidade Federal de Minas Gerais, Belo Horizonte, Brazil; 5 Department of Nutrition, Universidade Federal de Minas Gerais,
Belo Horizonte, Brazil


SUMMARY
Purpose. To evaluate the correlation between several presumed candidate genes for ob-
structive 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-            Received: February 23, 2023
senting 76.5% specificity and 71.6% sensitivity.                                                  Accepted: May 2, 2023
Conclusions. No genetic variant tested showed a statistically significant association with        Published online: October 10, 2023
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   Correspondence
stratification for OSA.                                                                           Luciana Bastos-Rodrigues
                                                                                                  Department of Nutrition, Universidade Federal de
KEY WORDS: obstructive sleep apnea, genetic polymorphisms, phenotype, algorithms,                 Minas Gerais, Belo Horizonte, Brazil. Av. Alfredo
case-control studies                                                                              Balena, 190, room 114, Belo Horizonte 30130-100,
                                                                                                  Brazil. Tel. +55 31 3409-9134
                                                                                                  E-mail: lucianabr@ufmg.br
RIASSUNTO
Scopo. Valutare la correlazione tra alcuni geni potenzialmente coinvolti nella sindrome
delle apnee notturne (OSA) ed i fenotipi clinici di OSA e proporre un modello predittivo            How to cite this article: Guimarães MLR, Aze-
                                                                                                    vedo PG, Souza RP, et al. Evaluation of clinical
generale per la diagnosi di OSA.
                                                                                                    and genetic factors in obstructive sleep apnoea.
Metodi. Uno studio caso-controllo ha confrontato modelli di polisonnografia, dati clinici,          Acta Otorhinolaryngol Ital 2023;43:409-416. htt-
comorbidità, parametri dentali, e dati genetici riguardanti i polimorfismi di PER3, BDNF,           ps://doi.org/10.14639/0392-100X-N2532
NRXN3, APOE, HCRTR2, MC4R tra soggetti con diagnosi di OSA e controlli non affetti
etnicamente appaiati. Un modello logistico di regressione è stato sviluppato per predire          © Società Italiana di Otorinolaringoiatria
l’OSA utilizzando i dati combinati.                                                               e Chirurgia Cervico-Facciale
Risultati. Lo studio caso-controllo ha incluso 161 pazienti OSA e 81 controlli. L’età media
era di 53,5 ± 14,0 anni, per la maggior parte maschi (57%) e con indice di massa corporea                        OPEN ACCESS
(BMI) medio di 27,5 ± 4,3. Nessuno dei marcatori genetici studiati ha mostrato un’associa-        This is an open access article distributed in accordance with
zione statisticamente significativa con OSA dopo aver aggiustato il modello per età e BMI.        the CC-BY-NC-ND (Creative Commons Attribution-Non-
L’algoritmo predittivo ha incluso le variabili genere, età, russamento, ipertensione, respira-    Commercial-NoDerivatives 4.0 International) license. The
                                                                                                  article can be used by giving appropriate credit and mentio-
zione orale e il numero di alleli T di PER3 (rs228729), mostrando una specificità del 76,5%
                                                                                                  ning the license, but only for non-commercial purposes and
e una sensibilità del 71,6%.                                                                      only in the original version. For further information: https://
Conclusioni. Nessuna delle varianti genetiche analizzate ha mostrato un’associazione sta-         creativecommons.org/licenses/by-nc-nd/4.0/deed.en




                                                                                                                                                          409
M.L.R. Guimarães et al.




tisticamente significativa con i fenotipi OSA. L’analisi di regressione logistica ha espresso un modello predittivo per la diagnosi di OSA che
potrebbe essere applicato in clinica per la stratificazione del rischio per OSA, una volta validato in studi prospettici più ampi.
PAROLE CHIAVE: apnea ostruttiva del sonno, polimorfismi genetici, fenotipo, algoritmo, studi caso-controllo



Introduction                                                             spectrum of manifestations, drug and alcohol abuse, psy-
                                                                         chiatric disorders, and dementia; age 18 to 85 years and
Obstructive sleep apnoea (OSA) is characterised by re-
                                                                         body mass index (BMI) ≤ 35 kg/m2. The control group con-
current events of upper airway obstruction during sleep
                                                                         sisted of individuals referred due to clinical suspicion of
associated with clinical signs and symptoms such as ex-
                                                                         OSA, but who did not meet diagnostic criteria (AHI < 5 per
cessive sleepiness, snoring, choking, and breathing inter-
                                                                         hour). For stratification of cases and controls the following
ruptions during sleep 1. OSA has a high prevalence 2 and
                                                                         variables were used: socioeconomic status, sex, age, height,
is related to increased morbidity and mortality from all
                                                                         weight, and BMI (≤ 35 kg/m2). BMI was limited to ≤ 35 kg/
causes 3.
                                                                         m2 and age to < 85 years to avoid increased AHI levels. In
Although considerable progress has been made in under-
                                                                         addition, dental data such as the presence of self-reported
standing the pathophysiology of OSA 4,5, the precise mech-
                                                                         bruxism, pain in masticatory muscles, noise upon move-
anisms that lead to upper airway obstruction are not fully
                                                                         ment of the temporomandibular joint, tongue size, floor of
understood 6. Despite the heterogeneity and complexity of
                                                                         mouth (i.e., sublingual space), presence of open or crossed
OSA, diagnosis, assessment of severity and management of
                                                                         bite were also analysed.
OSA remain linked to a single indicator, the apnoea hypo-
pnea index (AHI) 6.
                                                                         Genetic analysis
Several factors that increase the risk of OSA are well es-
                                                                         Genomic DNA was extracted from peripheral blood 13
tablished, such as obesity, body fat distribution, craniofa-
                                                                         or saliva 14. 20 ng of DNA of each genotyped individ-
cial morphology, and neural control of the upper airway
                                                                         ual was used for TaqMan SNP genotyping assays for
muscles 5,7. These risk factors are, in part, genetically deter-
                                                                         PER3 gene variants (rs228697, rs228727, rs228729 and
mined. Identification of genes associated with risk factors
                                                                         rs10462020), BDNF (rs6265), NRXN3 (rs10146997),
for OSA will undoubtedly help to understand the molecular
mechanisms underlying the disorder and aid in the manage-                APOE (rs7412 and rs429358), HCRTR2 (rs2653349),
ment of OSA 8-11.                                                        MC4R (rs17782313) according to the manufacturer’s in-
The discovery of genotypic and clinical markers can help in              structions (Applied Biosystems, Foster City, CA, USA).
the early detection of the disease, as well as in the preven-            Genotyping was performed by real-time polymerase
tion and personalised treatment of this pathology. Thus, the             chain reaction using allelic discrimination using a Strat-
aim of this study was to evaluate the correlation between                agene Mx3005 instrument (MxPro QPCR System, 2007
several presumed candidate genes, phenotypes and clini-                  Software, La Jolla, CA). At least 10% of the samples were
cal data that may contribute to the development of OSA                   genotyped twice for quality control.
and to propose a comprehensive predictive model for its
diagnosis.                                                               Data analysis
                                                                         Data were analysed using the statistical program R (version
                                                                         3.6.3). In univariate analysis, logistic regression models
Materials and methods                                                    with a logarithmic link function were adjusted using the
Participant identification and evaluation                                participant’s diagnosis as a dependent variable (case-con-
A case-control study was carried out in a medical-dental                 trol) and as an independent variable all clinical-epidemio-
clinic in Belo Horizonte, Brazil, and recruitment period                 logical variables using the generalised linear model func-
was August 2018 to January 2020. Study participants                      tion. For genetic variables, the analysis was carried out
were divided into two groups: patients diagnosed with                    based on five genetic models: co-dominance, dominance,
OSA (case group) and non-OSA participants (control                       recessive, over dominant and log-additive with the SNPas-
group). Each participant underwent complete OSA-fo-                      soc package.
cused clinical evaluation and polysomnography. The fol-
lowing inclusion criteria were used for defining OSA cas-                Predictive OSA model using logistic regression
es: diagnosis of OSA according to the American Academy                   A logistic regression model was developed to predict
of Sleep Medicine criteria 12 and absence of craniofacial                OSA. The variables that reached a level of significance
dysmorphism, genetic syndromes with OSA as part of the                   less than 0.20 in the univariate analysis were considered



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                                                                                         Evaluation of clinical and genetic factors in obstructive sleep apnoea




suitable for the final model. However, only variables with                         tween cases and controls for AHI, AHI REM, AHI NREM,
p < 0.05 remained in the multivariate logistic regression                          AHI dorsal, AHI non-dorsal, mean time of apnoea/hypo-
model.                                                                             pnoea, score 3 of arousal threshold, minimum saturation,
                                                                                   and desaturation index (Tab. II).
Results                                                                            Patients with OSA reported significantly more persistent
                                                                                   nocturnal snoring than controls, with rates of 34.3% and
Overall, 161 OSA cases and 81 controls were included.                              17.4% (p < 0.001), respectively. In addition, there was a
The mean age of the participants was 51.5 ± 14.3 years                             significant difference between cases and controls for the
with a male predominance (57.0%) and a mean BMI of                                 presence of hypertension (23.1% vs 6.2%, p = 0.015), lung
26.6 ± 4.3 kg/m2. The mean age of cases was 53.5 ± 14.1                            disease self-reported (asthma, bronchitis, chronic obstruc-
years and mean BMI was 27.5 ± 4.4 kg/m2; mean age of                               tive pulmonary disease) (3.3% vs 0.4%, p = 0.038) and
the controls was 47.4 ± 13.9 years and the mean BMI                                presence of mouth breathing (28.5% vs 4.9%, p < 0.001)
was 24.8 ± 3.8 kg/m2. The polysomnographic variables                               in patients with OSA compared with controls. Regarding
of the sample participants showed a mean of AHI 17.05                              dental data, there was a significant difference between
ev/h, AHI in REM 21.05 ev/h, minimum saturation                                    cases and controls for enlarged tongue (54.5% vs 16.5%,
82.01%, desaturation index 16.13/h. Sociodemographic,                              p = 0.000), high palate (54.5% vs 18.2%, p = 0.003) and
clinical, and dental variables are presented in Table I.                           Angle class II malocclusion (27.3% vs 6.2%, p = 0.007) in
Dental data were not available in the medical records of                           patients with OSA (Tab. III).
16% patients.                                                                      Univariate analysis results revealed a significant difference
Polysomnographic data showed a significant difference be-                          for gender, age, weight and BMI between cases and con-
                                                                                   trols (Tab. III).

Table I. Sociodemographic, clinical, dental variables of patients in the sample.   Analysis of genetic factors
 Variable                                   Category                  %            BDNF (rs6265) and PER3 (rs228729) were the only
 Sociodemographic                                                                  variants that showed a significant association with OSA
  Gender male/female                             57/43                             (p = 0.013; p < 0.001, respectively). However, after
  Age (mean/SD)                               51.5 ± 14.3                          adjusting for age and BMI, BDNF (rs6265) and PER3
  BMI                                          26.6 ± 4.3                          (rs228729) no longer exhibited a significant association
 Clinical                                                                          with OSA phenotype (p = 0.062; p = 0.066, respectively)
  Snoring every night                                                   66         (Tab. IV). None of the other genotypes had an associa-
  Daytime sleepiness                                                    59
                                                                                   tion between the tested genotype and OSA phenotype
                                                                                   (Tab. IV).
  Wake up feeling refreshed                                             50
  Alcohol consumption                                                   47
                                                                                   Prediction of the presence of OSA using logistic
  Mouth breathing                                                       34
                                                                                   regression model
  Hypertension                                                          29
                                                                                   The values obtained in the multivariate logistic regression
  Decreased libido                                                      28         model (Tab. V) indicated that the probability of a patient
 Dental                                                                            having OSA can be best predicted in the equation derived
  Ogival palate                                                         73         from the final model:
  Increased tongue                                                      71
  Pain on palpation TMJ/muscles                                         45
  Bruxism                                                               43
  TMJ noise                                                             33
  Floor of mouth                                                        12         z = -3.705-0.855 × (X1) + 0.031 × (X2) + 1.643 × (X3) +
  Crossbite                                                             13         1.105 × (X4) + 1.093 × (X5) + 1.209 × (X6) + 0.528 × (X7)
  Mallampati                                    Grade 1                2.43        Where: Dx = diagnosis; exp = exposure; X1 = 1, if the
                                                Grade 2                8.53        gender is female, otherwise 0; X2 = age of the patient in
                                                Grade 3               25.60        years; X3 = 1, if subjective snoring is present, otherwise 0;
                                                Grade 4               53.65        X4 = 1, if hypertension is present, otherwise 0; X5 = 1, if not
  Malocclusion (%)                              Class II               27.3        breathing well through the nose, otherwise 0; X6 = number
SD: standard deviation; BMI: body mass index; TMJ: temporomandibular joint.        of T alleles of PER3 (rs228729).



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M.L.R. Guimarães et al.




Table II. Comparison between cases and controls for polysomnographic variables.
 Variable                                       Category       Cases          Controls                Coefficient    Standard   Z value   P value
                                                                                                                       error
 Polysonographic
 AHI (mean/SD)                                                      24.3 ± 16.8        2.6 ±1.5              1.069    0.209      5.113     0.000
 Apnoea ratio (mean/SD)                                               1.8 ± 7.9        0.3 ± 0.4             0.217    0.344      0.631     0.528
 Hypopnoea ratio (mean/SD)                                           3.1 ± 12.2       4.4 ± 19.3            -0.006    0.010     -0.550     0.576
 REM AHI (mean/SD)                                                  25.8 ± 21.0        3.5 ± 4.7             0.188    0.030      4.745     0.000
 NREM AHI (mean/SD)                                                 22.6 ± 18.1        1.8 ± 1.7             1.041    0.232      4.492     0.000
 AHI dorsal (mean/SD)                                               20.1 ± 23.7        3.0 ± 4.7             0.134    0.039      3.435     0.001
 Non-dorsal AHI (mean/SD)                                           16.8 ± 20.9       3.6 ± 10.3             0.108    0.033      3.243     0.001
 Average time of apnoea/hypopnoea (mean/SD)                         29.2 ± 15.8      20.3 ± 15.2             0.059    0.024     2.483      0.013
 Arousal threshold (n/%)                                  2            64/26.4          23/9.5              -0.965    0.535     -1.805     0.071
                                                          3            37/15.3          23/9.5              -1.444    0.546     -2.643     0.008
 Minimum saturation (mean/SD)                                       79.2 ± 11.5       87.9 ± 8.1            -0.181    0.031     -5.899     0.000
 Time below 90% (mean/SD)                                           14.2 ± 27.9       6.1 ± 21.3             0.020    0.012      1.733     0.083
 Oxygen desaturation index (mean/SD)                                20.4 ± 20.2       2.8 ± 6.8              0.206    0.044     4.734      0.000
SD: standard deviation, AHI: apnea-hypopnea index, REM: rapid eye movement, NREM: non rapid eye movement.



The area under the ROC curve was 79.4% (CI 95% 72.2%-                                  Discussion
86.6%) suggesting that the model achieves 76.5% specific-
                                                                                       In this report, the contribution and association of sequence
ity and 71.6% sensitivity assuming a 0.763 cutoff bridge
                                                                                       variants of several OSA candidate genes 15-20 was tested in
(Fig. 1).
                                                                                       Brazilian OSA cases. None of these variants was signifi-
                                                                                       cantly associated with OSA phenotype.
                                                                                       The choice of variants for the present study was based on
                                                                                       previous studies. For example, Canales et al. 23 found an as-
                                                                                       sociation between OSA and decreased morning expression
                                                                                       of clock genes, such as PER3. Yuksekkaya et al. 21 reported
                                                                                       that the BDNF rs6265 (196G/G) genotype may be useful
                                                                                       to assess OSA in non-obese patients. Furthermore, find-
                                                                                       ings showed that BDNF, but not NGF, was significantly in-
                                                                                       creased in a subpopulation of muscle fibres in patients with
                                                                                       snoring and OSA 20. The MC4R gene is largely expressed
                                                                                       in the hypothalamus and is intimately involved in appetite
                                                                                       regulation, autonomic and endocrine functions, and insulin
                                                                                       resistance. OSA is one of the deleterious consequences of
                                                                                       obesity. A case report of a child with obesity at 23 months
                                                                                       of age showed homozygous mutations of the MC4R gene
                                                                                       (Pro299his) through DNA sequencing of the genes in-
                                                                                       volved in early-onset obesity. Overnight polysomnogra-
                                                                                       phy was significant for severe OSA (AHI 36 events/h and
                                                                                       oxygen saturation 50%). The authors suggested that future
                                                                                       research may provide more information on possible asso-
                                                                                       ciations between MC4R and OSA 17. Central abdominal fat
                                                                                       is a strong risk factor for diabetes, cardiovascular disease
                                                                                       and OSA. Common variants in NRXN3 are associated with
                                                                                       waist circumference, BMI and obesity 24. A case-control
Figure 1. The ROC curve for the logistic regression model was based on
the definition of the chance of having OSA. The area under the ROC curve               study investigated whether variations in the APOE-ε gene
was 79.40%.                                                                            were associated with craniofacial changes, AHI and BMI



412
                                                                                            Evaluation of clinical and genetic factors in obstructive sleep apnoea




Table III. Comparison between cases and controls for all clinical-epidemiological and dental variables.
 Variable                             Category            Cases           Controls         Coefficient          Standard           Z value           P value
                                                                                                                  error
 Sociodemographic
    Gender male/female                                         104/57            33/47         -0.964             0.279            -3.458            0.001
    Age (mean/SD)                                            53.6 ± 14.0      47.3 ± 14.0       0.030             0.010            3.031             0.002
    Weight                                                   79.1 ± 15.0      69.4 ± 14.3      0.050              0.012            4.393             0.000
    Height                                                    1.7 ± 0.1        1.7 ± 0.1       0.016              0.025            0.647             0.518
    BMI                                                      27.5 ± 4.4       24.9 ± 3.8       0.179              0.042            4.233             0.000
 Clinical
    Daytime sleepiness (n/%)                                    97/40.1        46/19.0          0.094            0.305              0.308            0.758
    Snoring every night (n/%)                                   83/34.3        42/17.4          1.151            0.324              3.548            0.000
    Wake up feeling refreshed (n/%)                             57/23.6        37/15.3          0.320            0.291              1.090            0.272
    Hypertension (n/%)                                          56/23.1         15/6.2          0.825            0.337              2.445            0.015
    Cancer (n/%)                                                  5/2.1          3/1.2         -0.211            0.745             -0.284            0.777
    Diabetes (n/%)                                               16/6.6          4/1.7          0.731            0.579              1.263            0.207
    Fibromyalgia (n/%)                                            3/1.2          4/1.7         -1.039            0.778             -1.335            0.182
    Decreased libido (n/%)                                      43/17.8        24/9.91         -0.192            0.300             -0.619            0.536
    Heart disorders (n/%)                                        10/4.1          3/1.2          0.103            0.619              0.166            0.868
    Emotional problems (n/%)                                    38/15.7         16/6.6          0.190            0.341              0.585            0.559
    Gastroesophageal reflux (n/%)                               35/14.5         12/5.0          0.448            0.371             1.205             0.228
    Thyroid disorders (n/%)                                      19/7.9         10/4.1         -0.084            0.421             -0.199            0.843
    Pulmonary disorders (n/%)                                     8/3.3          1/0.4          2.161            1.041             2.075             0.038
    Mouth breathing (n/%)                                       69/28.5         12/5.0         1.371             0.349             3.928             0.000
    Smoking (n/%)                                                8/3.3          3/1.2          0.280             0.693             0.405             0.686
    Alcohol consumption (n/%)                                   78/32.2        35/14.5         0.211             0.291             0.724             0.469
 Dental
    Bruxism (n/%)                                               67/27.7        36/14.9         -0.252            0.291             -0.866            0.387
    Pain on palpation TMJ / muscles                             77/31.8        32/13.2         -0.038            0.313             -0.122            0.903
    (n/%)
    TMJ noise (n/%)                                             59/24.4         20/8.3          0.225            0.322             0.699             0.484
    Increased tongue (n/%)                                     132/54.5        40/16.5          1.690            0.437              3.892            0.000
    Floor of mouth (n/%)                                         19/7.9          9/3.7         -0.299            0.426             -0.702            0.483
    Ogival palate (n/%)                                        132/54.5        44/18.2          1.339            0.455              2.944            0.003
    Mallampati (n/%)                          Grade 2             1/0.4          7/2.9         13.620          1029.122             0.013            0.989
                                              Grade 3           27/11.2         20/8.3         15.817          1029.122             0.015            0.988
                                              Grade 4          114/47.1        27/11.2         17.006          1029.121             0.017            0.987
    Malocclusion (n/%)                        Class II         66/27.3         15/6.2           0.954           0.355              2.688             0.007
                                              Class III         15/6.2          6/2.5          0.235            0.504              0.466             0.641
    Open bite (n/%)                                              2/0.8          1/0.4          -0.205           1.235              -0.166            0.868
    Crossbite (n/%)                                            25/10.3          6/2.5           0.616           0.484               1.273            0.203
SD: standard deviation; BMI: body mass index; TMJ: temporomandibular joint.



in patients with OSA. The polymorphisms that define the                              study was underpowered due to the small sample size 18.
APOE-ε1-4 allele rs429358 and rs7412 were genotyped.                                 The orexin receptor type 2 (Ox2R or OX2), also known
There was no association of the APOE-ε4 allele with fa-                              as hypocretin receptor type 2 (HcrtR2), is a protein that in
cial profile among these patients with OSA. However, the                             humans is encoded by the HCRTR2 gene. Peever et al. 15
authors cautioned that in relation to genetic research, this                         demonstrated that genioglossus activity was increased by



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M.L.R. Guimarães et al.




Table IV. Association between polymorphisms and the presence of OSA.
 Polymorphism            Genotyped       %     Genotyped        %                   Non-adjusted                         Adjusted (age and BMI)
                          controls                cases
                                                                          OR       Lower       Upper   P value    OR        Lower      Upper     P value
 BDNF rs6265                 82          33.90       160        66.10    1.66       1.10       2.51    0.013      1.52       0.97      2.38       0.062
 HCRTR2 rs2653349            78          33.10       158        66.90    1.08       0.64       1.82     0.768     1.02       0.58      1.79       0.936
 MC4R rs17782313             82          34.30       157        65.70    1.18       0.78        1.8     0.428     0.75       0.45      1.24       0.258
 NRXN3 rs10146997            81          34.00       157        66.00    0.96       0.61       1.53     0.874     0.91       0.55      1.51       0.718
 APOE rs7412                 81          34.00       157        66.00    0.82       0.40       1.69     0.622     0.46        0.2      1.08       0.079
 PER3 rs228697               81          34.30       155        65.70    1.05       0.56       1.97     0.763     1.12       0.57       2.2       0.747
 PER3 rs228727               82          34.30       157        65.70    1.29       0.87       1.93     0.204     1.35       0.87      2.11       0.184
 PER3 rs228729               81          33.60       160        66.40    2.16       1.44       3.23    < 0.001    1.59       0.97      2.63       0.066
 APOE rs429358               81          33.90       158        66.10    1.46       0.85       2.53     0.159     1.41       0.79      2.51       0.239
 PER3 rs10462020             82          34.30       157        65.70    0.92       0.56       1.52     0.738     0.89       0.51      1.53       0.660



Table V. Logistic regression model for obstructive sleep apnoea.
 Variable                             Estimate       Standard error     Z value            P value          OR             Inf. Lim.       Sup. Lim.
 (Intercept)                           -3.71            1.052           -3.521             0.000           0.02             0.003              0.177
 Gender female                         -0.85            0.417           -2.048             0.041           0.43             0.184               0.954
 Age                                    0.03            0.015            2.013             0.044           1.03             1.001               1.063
 Subjective snoring                     1.64            0.561            2.929             0.003           5.17             1.761              16.267
 Hypertension                           1.10            0.489            2.261             0.024           3.02             1.208               8.360
 Mouth breathing                        1.09            0.420            2.599             0.009           2.98             1.340               7.048
 Ogival palate                          1.21            0.574            2.107             0.035           3.35             1.082              10.532
 T alelle PER3 rs228729                 0.53            0.263            2.005             0.045           1.60             1.023              2.886



intracerebroventricular injection of orexin, therefore reduc-                   wake cycle can modulate circadian clock genes and, in turn,
tions in orexin may contribute to the suppression of upper                      the sleep cycle circadian rhythm can affect the occurrence
airway dilator activity, which may facilitate OSA.                              and duration of apnoea 22. A study conducted in a cohort
A previous study evaluated the association of the BDNF                          of elderly and obese veterans compared the relative gene
variant rs6265 (G > A) with OSA 21. To avoid the confound-                      expression of clock genes between those with and without
ing effect of obesity, the authors divided participants into                    OSA or related nocturnal hypoxaemia. The results showed
four groups based on the presence/absence of OSA and/or                         that PER3 expression was significantly decreased by 35%
obesity. No significant differences were observed regarding                     among those with OSA compared to those without. In ad-
the BDNF (rs6265) polymorphism between patients with                            dition, a trend of downregulation of PER3 was observed
and without OSA, and there was also no significant asso-                        with increasing severity of OSA 23. The present study is the
ciation with the BDNF gene and OSA polymorphism in a                            first to examine the association of PER3 (rs228729) with
regression analysis. In our study, unlike the study by Yük-                     OSA. We showed that this variant was significantly associ-
sekkaya et al. 21, age and BMI did affect the results, since                    ated with OSA in a codominant, dominant, recessive, and
BDNF 270C/T was significantly associated with OSA in a                          log-additive model, although after adjusting for age and
codominant, dominant, recessive, and log-additive model,                        BMI there was a significant association for dominant and
although after adjusting for age and BMI there was no lon-                      heterozygosity, but there was no further significant associa-
ger a significant association with OSA.                                         tion for OSA when analysing the log-additive.
Several studies in humans have suggested that PER3 plays                        Although the evidence points to the possibility of changes
a key role in maintaining circadian rhythm. Although there                      in the BDNF and PER3 genes being relevant for the de-
are still few studies on the relationships between circadian                    velopment of OSA 21-23, the type of analysis carried out in
clock genes and OSA, it is important to note that the sleep-                    the present study did not support the notion of such an as-



414
                                                                            Evaluation of clinical and genetic factors in obstructive sleep apnoea




sociation. These results may be due to the fact that there         PGA, BG-F: acquisition, analysis and interpretation of da-
is indeed no relationship between the specific BDNF and            ta; RPS: statistical analysis; MLRG: drafting of the manu-
PER3 gene polymorphisms and OSA. Alternatively, the                script; LB-R, EF, LDM: critical revision of the manuscript;
small sample size of the present cohort prevented any sub-         LB-R: study supervision.
tle association from being detected.                               All authors read and approved the final manuscript.
Using clinical and phenotypic data collected from patients
with OSA, an algorithm was constructed that allows the pre-        Ethical consideration
diction of OSA cases with reasonable specificity and sensi-        All procedures performed in studies involving human par-
tivity, which, if validated, may at least be applicable in South   ticipants were in accordance with the ethical standards of
American populations. Although the PER3 (rs228729) vari-           the institutional research committee (Ethics Committee of
ant, after adjusting for age and BMI, resulted in a significant    the Universidade Federal de Minas Gerais #2.980.453) and
association for dominant and heterozygosity, but no addi-          with the 1964 Helsinki declaration and its later amend-
tional significant association for OSA when analysing the          ments or comparable ethical standards. Informed consent
log-additive, it showed a p < 0.05 and remained in the model       was obtained from all individual participants included in
of multivariate logistic regression. Therefore, the results and    the study.
the investigation provide a reference for investigations fo-
cusing on the identification of OSA in Brazilian and similar
populations using the equation:                                    References
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