Association of visceral adiposity and systemic inflammation with sleep disordered breathing in normal weight, never obese adolescents.

内脏脂肪堆积和全身炎症与正常体重、从未肥胖的青少年睡眠呼吸障碍的关联

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作者:Danisi Jacqueline M, Fernandez-Mendoza Julio, Vgontzas Alexandros N, Calhoun Susan L, He Fan, Liao Duanping, Bixler Edward O
OBJECTIVE/BACKGROUND: While obesity is a known risk factor for sleep disordered breathing (SDB), a large proportion of children with SDB are not overweight as per body mass index percentile (BMI%) criteria. This study aimed to examine whether premorbid or concurrent adiposity phenotypes and inflammation are associated with SDB in normal weight youth. PATIENTS/METHODS: A total of 242 persistently non-overweight (BMI%<85) subjects from the Penn State Child Cohort (PSCC, N = 421, 5-12 y at baseline and 12-23 y at follow-up), were studied. The apnea/hypopnea index (AHI) was ascertained via polysomnography (PSG) at both time points. At follow-up, a dual-energy X-ray absorptiometry (DXA) scan assessed android and gynoid distribution and subcutaneous (SAT) and visceral (VAT) adiposity composition, while a fasting blood draw was assayed for C-reactive protein (CRP) and interleukin-6 (IL-6) levels. Multivariable linear regression models with AHI at follow-up as primary outcome were adjusted for sex, race, adenotonsillectomy, age and AHI at baseline. RESULTS AND CONCLUSIONS: Increased waist circumference (β = 0.227, p = 0.001) at baseline, but not BMI%, neck or hip circumference, was significantly associated with a higher AHI at follow-up. VAT (β = 0.309, p < 0.001), IL-6 (β = 0.243, p < 0.001), SAT (β = 0.235, p = 0.013), CRP (β = 0.221, p = 0.001), and an android distribution (β = 0.196, p = 0.003) at follow-up were significantly associated with a higher AHI at follow-up. Childhood central adiposity predicts SDB in adolescence, even in individuals who have never been overweight since childhood as per BMI criteria. Visceral adiposity and inflammation are concurrent to adolescent SDB, which supports the clinical utility of these biomarkers in predicting its associated cardiometabolic risk.

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