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Background <p>Obstructive sleep apnea (OSA) is a major risk factor for hypertension, yet the role of lipid metabolism in this association remains unclear. The atherogenic index of plasma (AIP) reflects lipid dysregulation and atherosclerotic risk, but its value in predicting incident hypertension among OSA patients has not been well established.</p> Methods <p>This retrospective study included 175 OSA patients diagnosed by polysomnography between January 2021 and January 2024. Patients were divided into a hypertension group (<i>n</i> = 84) and a non-hypertension group (<i>n</i> = 91) based on the occurrence of hypertension. Clinical, lipid, and sleep parameters were collected. Logistic regression was used to identify independent risk factors, and a nomogram model was constructed. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were also calculated.</p> Results <p>Patients with incident hypertension had higher age, BMI, and AIP levels, and lower minimum oxygen saturation (LsPO₂) (all <i>P</i> &lt; 0.05). Multivariate analysis revealed that age, BMI, and AIP were independent risk factors, whereas LsPO₂ was a protective factor. Each 0.1-unit increase in AIP was associated with an 8% higher risk (OR = 1.08, 95% CI: 1.05–1.12, <i>P</i> &lt; 0.001), and individuals in the highest tertile had a 2.73-fold greater risk. Incorporating AIP into the combined model increased the AUC from 0.729 to 0.810 (<i>P</i> &lt; 0.001), with NRI = 0.416 and IDI = 0.054 (both <i>P</i> &lt; 0.001). The nomogram showed good calibration, and DCA indicated high clinical net benefit across a wide threshold range.</p> Conclusions <p>AIP is an independent predictor of incident hypertension in OSA patients. The nomogram integrating AIP enables individualized risk assessment and demonstrates good predictive performance and clinical utility.</p>

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Predictive value of the atherogenic index of plasma for incident hypertension in patients with obstructive sleep apnea

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月6日

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  • Shurun Zuo,
  • Yonghong Xu,
  • Lei Ma,
  • Yongliang Du,
  • Haiquan Li

摘要

Background

Obstructive sleep apnea (OSA) is a major risk factor for hypertension, yet the role of lipid metabolism in this association remains unclear. The atherogenic index of plasma (AIP) reflects lipid dysregulation and atherosclerotic risk, but its value in predicting incident hypertension among OSA patients has not been well established.

Methods

This retrospective study included 175 OSA patients diagnosed by polysomnography between January 2021 and January 2024. Patients were divided into a hypertension group (n = 84) and a non-hypertension group (n = 91) based on the occurrence of hypertension. Clinical, lipid, and sleep parameters were collected. Logistic regression was used to identify independent risk factors, and a nomogram model was constructed. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were also calculated.

Results

Patients with incident hypertension had higher age, BMI, and AIP levels, and lower minimum oxygen saturation (LsPO₂) (all P < 0.05). Multivariate analysis revealed that age, BMI, and AIP were independent risk factors, whereas LsPO₂ was a protective factor. Each 0.1-unit increase in AIP was associated with an 8% higher risk (OR = 1.08, 95% CI: 1.05–1.12, P < 0.001), and individuals in the highest tertile had a 2.73-fold greater risk. Incorporating AIP into the combined model increased the AUC from 0.729 to 0.810 (P < 0.001), with NRI = 0.416 and IDI = 0.054 (both P < 0.001). The nomogram showed good calibration, and DCA indicated high clinical net benefit across a wide threshold range.

Conclusions

AIP is an independent predictor of incident hypertension in OSA patients. The nomogram integrating AIP enables individualized risk assessment and demonstrates good predictive performance and clinical utility.