In this study, the associations between PSG parameters and anthropometric features have been determined considering age and gender effect. The gender and age-independent models based on the anthropometric features were established successfully to assess the risk of OSAS. The applicable models were demonstrated to possess high prediction accuracy for classifying AHI higher or lower than 15, in particular for Han-Taiwanese subjects. The anthropometric feature importance for effecting OSAS severity was obtained for each subgroup. The large-scale statistics, including Han-Taiwanese anthropometric features, sleep stage details and PSG results were also provided.
For both genders, better accuracy can be observed in the younger groups, and the waist circumference showed the highest importance for its affects on the AHI except for the case of younger males. It is known that visceral fat, which is a type of body fat deposited around internal organs, is related to the AHI and waist circumference is a unique indicator for indicating visceral fat distribution. Similarly, a prior study, which used traditional statistical methods, reported the observation that waist circumference was a better predictor for the severity of OSAS compared with the BMI and neck circumference (36). Another study revealed that OSAS prevalence was exacerbated in menopausal females and waist circumference served as the main factor (37). Additionally, the BMI and waist circumference showed similar importance for effecting the AHI in the males and elderly female groups, but there is a difference in younger females. There were statistically significant correlations between the BMI, waist circumference and sleep stage percentage, for the males and elderly female groups, but not for younger females. These results maybe induced by the menopause effect. This effect may not lead to weight gain directly, but it may be correlated to the fat distribution changes. In perimenopause females, the increased abdominal adiposity deposition and decreased lean body mass were observed. This change is similar to the fat distribution of males which tends to develop a greater degree of upper body obesity.
With respect to neck circumference, which is an indicator of fat distribution in the upper airway, there are the significant correlations between the snoring index and arousal index. Increased neck circumference may narrow the upper airway and increase the turbulence of airflow, thereby causing snoring. Besides, the collapsible upper airway and the increased upper airway resistance may increase in the respiratory effort in order to keep ventilating. This is the mechanism by which increasing respiratory effort may stimulate transient arousal in an individual and lead to sleep fragmentation. Similar findings were demonstrated in previous studies. Large neck circumference has been reported as a factor in snoring (38).
Furthermore, for both genders, the younger groups reported better overall sleep quality and lower sleep disorder indices. These results may be due to the collagen loss of upper airway connective tissue with ageing. With the decrease in neuromuscular tension, the upper airway easily collapses during sleep. For elderly females, the decreased hormone levels during menopause also result increased sleep disorder indexes and affect sleep quality. Similarly, numerous studies have shown that obstruction events and the severity of OSAS are significantly more likely in elderly patients (39, 40). Collectively, the observations of this study are consistent with previous studies demonstrating that the risk of OSAS was affected by age, gender, and body profiles.
There are some limitations to this study, which should be addressed in the future. First of all, in this study, the dataset was limited to a South-East Asian population, with craniofacial factors, rather than from diverse body profiles with a wider geographical distribution. Hence the results of this paper should be viewed with this in mind, since craniofacial factors, which also affects sleep-disordered breathing should be considered as predictors (41). Next, the clinical standard for classifying OSAS severity requires a PSG to determine the AHI. This sleep examination is still conducted by manual interpretation, and since the PSG results were scored by different technologists, the scoring variability can affect the accuracy (42). Although the data was derived from one sleep center, which regularly performed inter-scoring training, scoring variability could still have affected the results. Furthermore, the first night effect, which is a phenomenon on the first night of testing characterized by an altered sleep cycle and impacted sleep physiology, can also cause inaccuracies of PSG results (43). To minimize this effect, some PSG parameters, such as sleep efficiency, should be used to rule out subjects and rearrange the PSG for avoiding the bias.
Another limitation concerns the lack of some interacting factors of OSAS, while OSAS has been recognized as multifactorial sleep-disordered breathing. Some behaviors, including smoking, alcohol use, environmental parameters, and menopausal status are highly associated with OSAS (44, 45). To understand influence of background details, the questionnaire can be used to obtain personal habits.
OSAS also influenced by different diseases (44). The situation of comorbidity also affects the results of PSG. The disease-related parameters that are already available from clinical information can be obtained and serve as significant variables for preforming pre-screen classification.
In future work, a dataset with comprehensive dimensions, which include personal habits, personal comorbidity, more anthropometric features, and body compositions, will be collected for training a novel model.