Sleep-disordered breathing and metabolic syndrome across gender, age, and sleep subtypes in East Asians.

Background: Previous research has classified sleep-disordered breathing (SDB) into various symptom phenotypes. The cardiovascular risks associated with SDB differ by gender, age, and subtype, with uncertainty about the mediating role of metabolic syndrome in these risks. This study has three primary objectives: (1) to analyze the correlation between SDB and MetS risk across gender-age groups and symptom subtypes, (2) to identify the nocturnal hypoxia parameter most indicative of this relationship, and (3) to evaluate the link between sleep parameters (insomnia, daytime sleepiness, snoring, and sleep duration) and MetS. Combining these parameters into a sleep quality score may improve predictions of health outcomes.

Methods: Participants underwent type IV sleep monitoring and completed structured questionnaires. MetS was defined according to the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2020 Edition). We conducted a latent class analysis to categorize patients based on SDB symptoms and characteristics.

Results: The severity of SDB was independently associated with an increased risk of metabolic syndrome, particularly in males under 60 and females aged 60 and above. A total of 1,483 SDB patients were categorized into four distinct clusters: Cluster 1 included the pure insomnia group with fewer daytime symptoms; Cluster 2 consisted of the minimally symptomatic group; Cluster 3 comprised the insomnia group with multiple daytime symptoms; and Cluster 4 encompassed the group with upper airway symptoms and sleepiness. Among the SDB subtypes, there was no significant difference in the prevalence of metabolic syndrome. However, the pure insomnia group had the highest prevalence of hypertension.

Conclusion: These findings highlight the importance of considering gender, age differences, and sleep symptom subtypes when evaluating and managing metabolic syndrome. Tailored strategies, early identification, and consideration of different subtypes are necessary to optimize treatment.