[Predictive and diagnostic performance of composite anthropometric indicators for obstructive sleep apnea].

Authors

Xiaoxin Niu, Jiayi DU, Jiayi Yang, Simin Zhu, Yanuo Zhou, Zitong Wang, Chendi Lu, Yonglong Su, Yushan Xie, Haiqin Liu, Xiaoyong Ren, Yewen Shi

Published in

Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery. Volume 40. Issue 8. Pages 697-703;711.

Abstract

Objective:To investigate the associations between seven composite anthropometric indices-body mass index（BMI）, waist-to-height ratio（WHtR）, neck-to-height ratio（NHR）, body roundness index（BRI）, a body shape index（ABSI）, weight-adjusted-waist index（WWI）, and relative fat mass index（RFM） -and the severity of obstructive sleep apnea（OSA）, and to evaluate their predictive value and diagnostic efficacy for OSA severity. Methods:This retrospective study enrolled 4 364 patients with suspected OSA from the Department of Otolaryngology-Head and Neck Surgery at the Second Affiliated Hospital of Xi'an Jiaotong University between September 2021 and April 2025. All participants underwent overnight polysomnography（PSG） and were categorized into four groups based on the apnea-hypopnea index（AHI）: non-OSA, mild, moderate, and severe. Correlation analysis was used to examine the relationships between the indices and AHI. Logistic regression was employed to assess their associations with OSA severity. Principal component analysis（PCA） was applied for dimensionality reduction to extract comprehensive components. The predictive performance of individual indices and principal components was evaluated using receiver operating characteristic（ROC） curve analysis, with the area under the curve（AUC） calculated. The optimal cutoff point, sensitivity, and specificity of each index for detecting OSA and severe OSA were determined. Results:All composite anthropometric indices showed significant positive correlations with AHI（P<0.01）, with BMI demonstrating the strongest correlation（r=0.484）. PCA extracted three principal components（cumulative variance explained: 95.34%）: PC1（loading highly on BRI, WWI, NHR） was positively associated with OSA severity（OR=1.54, 95%CI 1.480-1.610）, whereas PC2（loading highly on ABSI, BMI, WWI） was negatively associated（OR=0.75, 95%CI 0.700-0.800）. ROC analysis revealed that BMI, BRI, WHtR, and NHR had the best overall predictive performance（AUC range: 0.706-0.708）. Diagnostic efficacy analysis indicated that NHR demonstrated superior balanced performance for OSA screening, whereas BRI and WHtR offered the highest sensitivity（>80.00%）. For identifying severe OSA, BRI and WHtR showed the best diagnostic efficacy. PC1 maintained robust performance across both screening and severe OSA identification tasks. Conclusion:In addition to BMI, composite anthropometric indices such as NHR, BRI, and WHtR are also effective tools for screening and assessing OSA severity, offering specific insights into regional fat distribution（NHR） and central obesity（BRI/WHtR）, respectively. Furthermore, the composite feature dominated by cervical and central obesity（PC1） was identified as an important risk factor for OSA.

PMID:
42576632
Bibliographic data and abstract were imported from PubMed on 11 Aug 2026.

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