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Abstract

Importance

Peripheral blood inflammatory indices provide a noninvasive and cost-effective means to assess systemic inflammation. This study highlights the potential of pan-immune-inflammation value (PIV) as a reliable biomarker for obstructive sleep apnea (OSA) severity, with implications for clinical risk stratification.

Objective

To evaluate the association between peripheral blood inflammatory markers and OSA severity and identify predictive biomarkers for disease classification.

Design

Prospective observational study.

Setting

Single tertiary academic medical center (Peking University Third Hospital), outpatient sleep clinic.

Participants

266 adults with snoring (18–70 years) undergoing sleep monitoring, laryngoscopy, and blood tests. Exclusion criteria included recent infection, systemic illness, surgery, or pregnancy.

Intervention or Exposures

No therapeutic intervention was performed. Exposures included varying severities of OSA and soft palate obstruction assessed by the Müller maneuver. Inflammatory indices (PIV, SIRI, SIL, neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio [MLR], and platelet-to-lymphocyte ratio) were calculated from routine blood parameters.

Main Outcome Measures

The primary outcome was the association between inflammatory indices and OSA severity as measured by apnea-hypopnea index (AHI). Secondary outcome was the correlation between inflammatory markers and soft palate-level obstruction.

Results

PIV and SIRI were significantly elevated in severe OSA cases (P = .001 and P = .012, respectively). PIV was independently associated with AHI severity (OR = 1.010, 95% CI [1.003, 1.017]). PIV and SIRI positively correlated with AHI (r = .236 and r = .218, both P < .001). MLR, PIV, and SIRI levels increased with greater soft palate collapse, though not independently predictive in multivariate analysis.

Conclusions

PIV is an independent, accessible biomarker for assessing OSA severity. Composite indices (PIV, SIRI) may better reflect systemic inflammation than traditional single markers and could complement clinical phenotyping of OSA.

Relevance

These findings support the use of composite inflammatory indices in OSA phenotyping and prognosis. Future studies should investigate inflammation-targeted strategies and validate these biomarkers in larger, multicenter cohorts.

Level of Evidence:

Graphical Abstract

Keywords

obstructive sleep apnea peripheral blood biomarkers inflammation

Key Message

Composite indices such as PIV and SIRI demonstrate superior predictive performance compared with single traditional markers.

PIV serves as an independent inflammatory marker in peripheral blood for predicting the severity of OSA.

Systemic inflammation may interact with local upper airway collapse, offering new insights into OSA pathophysiology.

Introduction

Obstructive sleep apnea (OSA) is a systemic, multisystem disorder affecting nearly one billion individuals worldwide and is associated with a significantly-increased risk of cardiovascular, metabolic, and other systemic diseases.¹ Although the precise pathophysiological mechanisms of OSA remain incompletely understood, intermittent upper airway collapse during sleep—resulting in chronic intermittent hypoxia (CIH)—is widely recognized as a central pathological feature. CIH triggers repetitive cycles of hypoxia and reoxygenation, promoting oxidative stress and systemic inflammation, which ultimately contribute to multiorgan damage.

In recent years, inflammation has garnered increasing attention in the context of OSA. Intermittent nocturnal hypoxemia and reperfusion injury stimulate the generation of reactive oxygen species, activating both local and systemic inflammatory pathways. Elevated levels of inflammatory markers such as C-reactive protein and interleukin-6 have been consistently observed in patients with OSA, suggesting that systemic inflammation may play a pivotal role in the development of OSA-related comorbidities.^2,3 Additionally, repetitive airway obstruction induces localized hypoxia and mechanical injury in upper airway tissues, particularly in the soft palate—a key anatomical site of collapse in OSA. Histological studies have demonstrated increased infiltration of immune cells in the soft palate, with elevated expression of CD3, CD20, and CD68, indicative of T cells, B cells, and macrophages, respectively, compared with that in healthy controls and primary snorers.^4,5 These findings support the hypothesis that a reciprocal interaction between local and systemic inflammation may form a self-perpetuating cycle that exacerbates upper airway narrowing and disease progression.^6,7

Inflammatory status in OSA is also closely linked to disease progression and multisystem complications, including cardiovascular and endocrine disorders.^8–10 Therefore, there is an urgent need for accessible and reliable biomarkers to evaluate inflammation in patients with OSA. Composite inflammatory indices derived from routine blood tests have gained popularity due to their convenience and low cost. The systemic immune-inflammation index (SII), which integrates platelet, neutrophil, and lymphocyte counts, reflects the balance between immune and inflammatory responses. The pan-immune-inflammation value (PIV) incorporates neutrophils, platelets, monocytes, and lymphocytes, offering a broader evaluation of systemic inflammation. Similarly, the systemic inflammatory response index (SIRI), based on neutrophils, monocytes, and lymphocytes, quantifies systemic inflammatory activity.¹¹ These easily-obtainable peripheral blood markers have shown prognostic value in malignancies, autoimmune diseases, and cardiopulmonary conditions.^12–14 However, their roles in OSA remain underexplored.

Given the central involvement of inflammation in OSA pathophysiology and its complications, the absence of simple and practical biomarkers to assess inflammatory burden represents a critical gap in clinical management. Therefore, this study aimed to evaluate the association between peripheral blood inflammatory indices and OSA severity—as assessed by the apnea-hypopnea index (AHI) and upper airway obstruction—to identify hematological markers with potential predictive value for disease classification and risk stratification.

Materials and Methods

Study Design and Patients

This prospective study enrolled patients presenting with snoring at the Department of Otolaryngology, Peking University Third Hospital, between March 2024 and January 2025. The inclusion criteria were as follows: (1) age between 18 and 70 years; (2) completion of portable sleep monitoring (PM), fiberoptic laryngoscopic examination, and routine blood testing as part of clinical evaluation; and (3) provision of written informed consent. Exclusion criteria included the following: (1) acute illnesses within the past month that could influence inflammatory markers, such as upper respiratory tract infections or gastroenteritis; (2) prior diagnosis of OSA with any history of treatment; (3) presence of severe systemic diseases, including malignancies, hematologic disorders, or immunodeficiency; (4) major surgery within the previous 6 months; and (5) pregnancy or lactation. The study was approved by the Institutional Review Board of Peking University Third Hospital (Approval No. IRB00006761-M2023504). Written informed consent was obtained from all participants prior to enrollment. Demographic and clinical data were collected from electronic medical records, including age, sex, body mass index (BMI), sleep study results (AHI, mean and minimum oxygen saturation), complete blood count parameters, and fiberoptic nasopharyngoscopy findings.

Sleep Monitoring

PM was performed in all participants, as recommended by the Chinese Guideline for Primary Care of Adult Obstructive Sleep Apnea (2018)¹⁵ and the American Academy of Sleep Medicine (AASM) Clinical Practice Guideline.¹⁶ The apnea-hypopnea index (AHI) was calculated as the total number of apnea and hypopnea events per hour of sleep. Apnea was defined as a ≥90% reduction in airflow lasting at least 10 s. Hypopnea was defined as a ≥30% reduction in airflow lasting at least 10 s, accompanied by either a ≥3% oxygen desaturation and/or an arousal. OSA severity was classified based on the AHI as follows: mild OSA (5 ≤ AHI < 15 events/hour), moderate OSA (15 ≤ AHI < 30 events/hour), and severe OSA (AHI ≥ 30 events/hour).

Hematological Analysis

After overnight fasting, 2 to 3 mL of venous blood was collected from each participant. Complete blood counts were performed using an automated hematology analyzer to measure white blood cells, absolute neutrophil and lymphocyte counts, platelet count, hemoglobin level, platelet distribution width, mean platelet volume, and red cell distribution width. The following inflammatory indices were calculated: Neutrophil-to-Lymphocyte Ratio (NLR) = Neutrophil count/Lymphocyte count, Monocyte-to-Lymphocyte Ratio (MLR) = Monocyte count/Lymphocyte count, Platelet-to-Lymphocyte Ratio (PLR) = Platelet count/Lymphocyte count, SII = Neutrophil count × Platelet count/Lymphocyte count, PIV = Neutrophil count × Platelet count × Monocyte count/Lymphocyte count, SIRI = Neutrophil count × Monocyte count/Lymphocyte count

Laryngoscopic Müller Maneuver

Topical anesthesia was applied using 1% tetracaine to both nasal cavities and the oropharyngeal mucosa. With the patient seated, a flexible fiberoptic nasopharyngoscope was inserted through one nasal passage to systematically examine the nasopharynx, oropharynx, hypopharynx, soft palate, tongue base, and epiglottis during quiet respiration. Cross-sectional areas at key upper airway levels—including the soft palate, retroglossal region, and epiglottis—were recorded during expiration. Patients were then instructed to perform the Müller maneuver, involving forced inspiration against a closed nose and mouth, to simulate negative intraluminal pressure. Airway collapse at each level was graded on a four-point scale:¹⁷ grade 0: <25% reduction in cross-sectional area, grade 1: 25% to 49% reduction, grade 2: 50% to 74% reduction, grade 3: ≥75% reduction.

Statistical Analysis

Statistical analyses were conducted using SPSS version 26.0 (IBM Corp, Armonk, NY, USA). All tests were two-tailed, with a significance threshold of P < .05. Continuous variables conforming to a normal distribution are presented as mean ± standard deviation, whereas non-normally distributed variables are reported as median with interquartile range (IQR). Categorical variables are expressed as counts and percentages [n (%)]. Normality of continuous data was assessed prior to analysis. Group comparisons were performed using one-way analysis of variance for normally-distributed variables, and the Kruskal-Wallis test for non-normal variables. Categorical data were compared using the chi-squared test. Variables with P < .2 in univariate analyses were included in multivariate ordinal logistic regression. Spearman correlation analysis was employed to evaluate associations between peripheral blood inflammatory markers and OSA severity.

Results

Patient Characteristics by OSA Severity

A total of 266 patients were enrolled, including 54 non-OSA, 71 mild OSA, 60 moderate OSA, and 81 severe OSA cases. The mean ages were 37.15 ± 15.10, 39.10 ± 13.25, 45.02 ± 12.51, and 40.96 ± 12.59 years across the groups, respectively (P = .002). The proportion of males increased significantly with OSA severity: 51.9%, 62.0%, 87.1%, and 93.8% (P < .001). Mean BMI values also rose progressively from non-OSA to severe OSA groups: 24.02 ± 4.60, 26.02 ± 4.39, 27.10 ± 3.99, and 29.32 ± 3.85 kg/m² (P < .001). Median nocturnal mean oxygen saturation levels decreased with severity: 95.0% (IQR 95.0–96.0), 95.0% (93.0–95.0), 94.0% (93.0–94.3), and 92.0% (90.0–93.0) (P < .001). Similarly, minimum oxygen saturation values declined significantly: 89.5% (88.8–92.0), 86.0% (83.5–89.0), 80.5% (77.0–84.0), and 72.0% (65.8–78.3) (P < .001). Detailed comparisons are summarized in Table 1.

Table 1.

Demographic and Baseline Clinical Characteristics of the Participants.

Variables	Normal (n = 54)	Mild OSA (n = 71)	Moderate OSA (n = 60)	Severe OSA (n = 81)	Statistics	p value
Age (years, $\bar{x}$ ± s)	37.15 ± 15.10	39.10 ± 13.25	45.02 ± 12.51	40.96 ± 12.59	15.242^a	.002*
Sex [n (%)]					37.546^c	<.001*
Male	28 (51.9)	44 (62.0)	49 (87.1)	76 (93.8)
Female	26 (48.1)	27 (38.0)	11 (18.3)	5 (6.2)
Height [cm, median (IQR)]	168.0 (161.8, 176.5)	168.0 (162.0, 175.5)	174.5 (166.0, 178.0)	174.5 (170.0, 178.0)	13.753^b	.003*
Weight (kg, $\bar{x}$ ± s)	69.13 ± 15.68	75.15 ± 14.87	80.74 ± 13.24	88.68 ± 12.50	20.664^a	<.001*
BMI (kg/m², $\bar{x}$ ± s)	24.02 ± 4.60	26.02 ± 4.39	27.10 ± 3.99	29.32 ± 3.85	16.325^a	<.001*
AHI (events/h, $\bar{x}$ ± s)	2.53 ± 1.33	9.19 ± 2.63	23.29 ± 3.77	50.12 ± 16.63	354.934^a	<.001*
Mean O₂ saturation [%, median (IQR)]	95.0 (95.0, 96.0)	95.0 (93.0, 95.0)	94.0 (93.0, 94.3)	92.0 (90.0, 93.0)	90.417^b	<.001*
Lowest O₂ saturation [%, median (IQR)]	89.5 (88.8, 92.0)	86.0 (83.5, 89.0)	80.5 (77.0, 84.0)	72.0 (65.8, 78.3)	177.069^b	<.001*

Data are presented as mean ± standard deviation, median (IQR), or number (%), as appropriate.

Abbreviations: AHI, apnea-hypopnea index; BMI, body mass index; IQR, interquartile range; OSA, obstructive sleep apnea.

Analysis of variance (ANOVA).

Kruskal-Wallis test.

Chi-squared test.

p < .05.

Differences in Inflammatory Markers According to OSA Severity

Univariate analysis showed that median PIV values increased with OSA severity: 147.90 (94.14–211.66) in controls, 169.38 (110.82–236.29) in mild, 157.03 (106.47–223.58) in moderate, and 209.52 (137.17–311.74) in severe OSA groups (P = .005). Pairwise comparisons revealed significantly-higher PIV in the severe group versus controls (P = .001) and moderate OSA (P = .011). Median SIRI values were 63.15 (37.86–81.08), 71.25 (48.46–103.06), 63.06 (51.42–99.11), and 79.79 (56.62–110.72) across the groups (P = .012), with the severe group showing significantly-higher SIRI than controls (P = .001), though differences with mild (P = .133) and moderate (P = .061) groups were not significant. Detailed data are presented in Table 2.

Table 2.

Univariate Analysis of AHI Grading and Peripheral Blood Inflammatory Markers.

Variables	Normal (n = 54)	Mild OSA (n = 71)	Moderate OSA (n = 60)	Severe OSA (n = 81)	Statistics	p value
NLR ( $\bar{x}$ ± s)	1.86 ± 0.71	2.21 ± 1.18	2.03 ± 1.03	2.09 ± 0.91	1.347^a	.260
PLR ( $\bar{x}$ ± s)	125.30 ± 38.88	133.26 ± 52.81	115.66 ± 38.11	125.63 ± 41.53	1.765^a	.154
MLR ( $\bar{x}$ ± s)	0.184 ± 0.064	0.201 ± 0.076	0.198 ± 0.070	0.227 ± 0.143	2.337^a	.074
SII ( $\bar{x}$ ± s)	447.09 ± 193.12	549.06 ± 335.94	473.79 ± 275.33	548.41 ± 281.57	2.194^a	.089
PIV (median [IQR])	147.90 (94.14, 211.66)	169.38 (110.82, 236.29)	157.03 (106.47, 223.58)	209.52 (137.17, 311.74)	12.784^b	.005*
SIRI [median (IQR)]	63.15 (37.86, 81.08)	71.25 (48.46, 103.06)	63.06 (51.42, 99.11)	79.79 (56.62, 110.72)	10.947^b	.012*

Data are presented as mean ± standard deviation, median (IQR), or number (%), as appropriate.

Abbreviations: ANOVA, analysis of variance; IQR, interquartile range; MLR, monocyte-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; OSA, obstructive sleep apnea; PIV, pan-immune-inflammation value; PLR, platelet-to-lymphocyte ratio; SII, systemic immune-inflammation index; SIRI, systemic inflammatory response index.

ANOVA.

Kruskal-Wallis test.

Chi-squared test.

p < .05.

Variables with P < .2 in univariate analysis were included in multivariate ordinal logistic regression to identify independent predictors of AHI severity. OSA severity was the dependent variable (non-OSA = 0, mild = 1, moderate = 2, severe = 3). Independent variables included sex (male = 1, female = 0), age, BMI, PIV, SIRI, PLR, MLR, and SII as continuous covariates. After adjustment, male sex was strongly associated with higher AHI severity (OR = 4.768, 95% CI [2.508, 9.603]). Age (OR = 1.040, [1.018, 1.062]) and BMI (OR = 1.209, [1.131, 1.293]) also showed positive associations. In multivariable models adjusting for BMI, age, and sex, PIV remained an independent predictor of AHI severity (OR 1.010, [1.003, 1.017]). See Table 3 for details.

Table 3.

Multivariate Analysis of AHI Grading and Peripheral Blood Inflammatory Markers.

Variables	$\bar{x}$	SE	Wald $χ$ ²	p value	OR (95% CI)
Sex	1.562	0.327	22.716	<.001*	0.210 [0.110, 0.399]
Age	0.039	0.011	12.960	<.001*	1.040 [1.018, 1.062]
BMI	0.190	0.034	31.126	<.001*	1.209 [1.131, 1.293]
PIV	0.010	0.004	7.289	.007*	1.010 [1.003, 1.017]
SIRI	−1.394	1.661	0.705	.401	0.248 [0.010, 6.432]
PLR	−0.001	0.011	0.009	.922	0.999 [0.977, 1.021]
MLR	−0.807	6.837	0.014	.906	0.446 [6.755E-7, 29480.1]
SII	−0.002	0.003	0.519	.998	0.998 [0.993, 1.003]

Abbreviations: BMI, body mass index; CI, confidence interval; MLR, monocyte-to-lymphocyte ratio; OR, odds ratio; PIV, pan-immune-inflammation value; PLR, platelet-to-lymphocyte ratio; SE, standard error; SIRI, systemic inflammatory response index; SII, systemic immune-inflammation index.

p < .05.

Spearman correlation analysis revealed significant positive correlations between AHI and MLR (r = .160, P = .009), SII (r = .133, P = .031), PIV (r = .236, P < .001), and SIRI (r = .218, P < .001). Detailed correlations are provided in Table 4. In addition, Spearman correlation analyses showed that mean SpO₂ was negatively correlated with PIV (r = −.149, P = .024) and SIRI (r = −0.148, P = .024), while lowest SpO₂ was similarly correlated with PIV (r = −0.137, P = .026) and SIRI (r = −.125, P = .043) (Supplemental Material Tables S1 and S2).

Table 4.

Spearman Correlation Analysis Between AHI and Peripheral Blood Inflammatory Markers.

Variables	r	p value
NLR	.08	.191
PLR	−.10	.866
MLR	.16	.009 *
SII	.133	.031 *
PIV	.236	<.001 *
SIRI	.218	<.001 *

Abbreviations: MLR, monocyte-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; PIV, pan-immune-inflammation value; PLR, platelet-to-lymphocyte ratio; SII, systemic immune-inflammation index; SIRI, systemic inflammatory response index.

p < .05.

Inflammatory Markers and Soft Palate Collapse Assessed by Müller Maneuver

Among the 266 patients, 134 completed full electronic nasopharyngoscopy with Müller maneuver, revealing varying degrees of soft palate obstruction: 14 patients with grade 0, 44 with grade 1, 46 with grade 2, and 30 with grade 3 obstruction. Univariate analysis demonstrated significant differences in MLR (P = .032), PIV (P = .032), and SIRI (P = .030) across obstruction grades. Pairwise comparisons indicated that patients with grade 3 obstruction had significantly–higher MLR values than those with grade 0 (P = .005), grade 1 (P = .030), and grade 2 (P = .006) (Table 5).

Table 5.

Univariate Analysis of Soft Palate-Level Obstruction Severity and Peripheral Blood Inflammatory Markers.

Variables	Grade 0 (n = 14)	Grade 1 (n = 44)	Grade 2 (n = 46)	Grade 3 (n = 30)	Statistics	p value
NLR ( $\bar{x}$ ± s)	1.64 ± 0.70	1.97 ± 0.91	1.93 ± 0.76	2.24 ± 0.88	1.828	.145
PLR ( $\bar{x}$ ± s)	111.38 ± 36.55	123.99 ± 61.86	123.62 ± 30.54	130.29 ± 38.97	0.553	.647
MLR ( $\bar{x}$ ± s)	0.159 ± 0.043	0.201 ± 0.099	0.176 ± 0.049	0.217 ± 0.068	3.023	.032*
SII ( $\bar{x}$ ± s)	415.32 ± 209.44	466.87 ± 286.86	500.25 ± 228.33	584.17 ± 265.71	1.841	.143
PIV ( $\bar{x}$ ± s)	152.58 ± 91.04	184.42 ± 146.25	193.32 ± 122.08	267.54 ± 174.85	3.018	.032*
SIRI ( $\bar{x}$ ± s)	0.592 ± 0.289	0.775 ± 0.536	0.731 ± 0.412	1.012 ± 0.588	3.063	.030*

p < .05.

Variables with P < .2 in univariate analysis were included in a multivariate ordinal logistic regression to identify independent predictors of soft palate obstruction severity (grade 0 = 0, grade 1 = 1, grade 2 = 2, grade 3 = 3). Independent variables included sex (male = 1, female = 0), age, BMI, PIV, SIRI, NLR, MLR, and SII as continuous covariates. The analysis showed male sex was significantly associated with greater obstruction severity (OR = 0.310, 95% CI [0.124, 0.776]), and BMI positively correlated with obstruction grade (OR = 1.166, [1.072, 1.268]). However, none of the peripheral inflammatory markers, including PIV, SIRI, NLR, MLR, and SII, were independently associated with the severity of soft palate collapse (Table 6).

Table 6.

Multivariate Analysis of Soft Palate-Level Obstruction Severity and Peripheral Blood Inflammatory Markers.

Variables	$β$	SE	Wald $χ$ ²	p value	OR (95% CI)
Sex	−1.170	0.468	6.265	.012*	0.310 [0.124, 0.776]
Age	0.015	0.016	0.961	.327	1.015 [0.985, 1.047]
BMI	0.154	0.043	12.943	<.001*	1.166 [1.072, 1.268]
PIV	0.010	0.015	0.462	.497	1.010 [0.981, 1.040]
SIRI	−2.045	3.785	0.292	.589	0.129 [7.768E-5, 215.66]
NLR	0.850	1.613	0.278	.598	2.340 [0.099, 55.18]
MLR	−2.350	4.595	0.262	.609	0.095 [1.171E-5,776.812]
SII	−0.003	0.007	0.155	.694	0.997 [0.985, 1.010]

Abbreviations: BMI, body mass index; CI, confidence interval; MLR, monocyte-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; OR, odds ratio; PIV, pan-immune-inflammation value; SE, standard error; SII, systemic immune-inflammation index; SIRI, systemic inflammatory response index.

p < .05.

Discussion

OSA is increasingly recognized as an inflammatory disorder. Studies have shown that CIH, sleep deprivation, and fragmentation in OSA elevate markers of inflammation, oxidative stress, procoagulant activity, and thrombosis.¹⁸ Peripheral blood inflammatory biomarkers have gained attention for their ease of detection and cost-effectiveness, making them promising tools to assess OSA severity and related complications. This study analyzed correlations between peripheral inflammatory indices and both OSA severity and upper airway obstruction, highlighting systemic inflammation’s critical role in OSA pathogenesis and progression.

Compared to serum cytokines such as IL-6 and TNF-α, blood cell count analyses are more rapid, simpler, and economical. Topuz et al., in a retrospective study, found the SII was significantly-positively correlated with OSA severity (r = .30, p < .001), outperforming the NLR and PLR.¹⁹ Similarly, Kim et al. evaluated 1,102 patients with OSA and reported a significant correlation between SII and AHI in the severe OSA subgroup (p = .004), whereas NLR and PLR showed weaker associations. Case-control studies further demonstrated that NLR and SII are independent risk factors for stroke in patients with OSA (OR = 66.48 and 1.029, respectively; p < .001), with their combined use predicting stroke with an area under the curve (AUC) of 0.869, significantly higher than either marker alone, highlighting the synergistic value of composite inflammatory biomarkers for risk stratification.²⁰ PIV, a novel comprehensive biomarker associated with chronic inflammation, correlates positively with disease severity and serves as a reliable prognostic indicator in hypertension, coronary artery disease, and cancer.^21–23 Fuca et al. identified PIV as a new immune-inflammatory biomarker in metastatic colorectal cancer, demonstrating superior prognostic value compared with SII and PLR among patients receiving first-line therapy.²⁴ However, the relationship between PIV and OSA severity remains unexplored.

Our results show that both PIV and SIRI positively correlate with OSA severity as measured by AHI (PIV: r = .236, P < .001; SIRI: r = .218, P < .001). Multivariate regression analysis confirmed PIV as an independent predictor of OSA severity (OR = 1.010; 95% CI [1.003, 1.017]), while SIRI, though significant in univariate analysis, did not retain independent predictive value in multivariate modeling. PIV incorporates more parameters than traditional indices such as NLR, PLR, SII, and SIRI—specifically neutrophils, platelets, monocytes, and lymphocytes—potentially providing a more comprehensive reflection of the complex immune-inflammatory imbalance in OSA. By contrast, conventional markers like NLR or PLR failed to show significant associations, underscoring the superiority of composite inflammatory indices in OSA assessment. Prior research confirms that CIH in OSA activates the nuclear factor kappa B (NF-κB) pathway, promoting the release of proinflammatory cytokines such as IL-6 and TNF-α, thereby triggering systemic inflammation.⁹ This stimulates neutrophil and monocyte release while suppressing lymphocyte function. Additionally, hypoxia-induced sympathetic activation and the renin-angiotensin-aldosterone system exacerbate inflammation and reduce lymphocyte counts. Hypoxia also enhances platelet production via thrombopoietin and activates platelets to release inflammatory mediators, increasing platelet counts. Collectively, these mechanisms elevate the numerator components (neutrophils, monocytes, platelets) and reduce the denominator (lymphocytes) in the PIV calculation, reflecting an amplified systemic inflammatory state accompanied by immune suppression. While obesity and BMI are established contributors to systemic inflammation,²⁵ our analysis showed that PIV remained independently associated with AHI severity after adjusting for BMI, age, and sex. This suggests that the observed inflammatory response is not merely obesity-related but also reflects OSA-specific mechanisms, likely driven by intermittent hypoxia and arousals. Furthermore, correlations between inflammatory indices and mean/lowest SpO₂ support hypoxemia as a key driver of systemic inflammation in OSA.

As an exploratory analysis, we also examined the relationship between soft palate-level obstruction and systemic inflammatory markers. The soft palate is the most frequent site of collapse in OSA, and both drug-induced sleep endoscopy and finite element analyses have demonstrated that velopharyngeal collapse plays a pivotal role in airflow obstruction.^26,27 Histological studies have shown immune cell infiltration (T cells, B cells, macrophages) in the soft palate tissue of patients with OSA, suggesting the presence of local inflammation.^7,28,29 More severe palatal collapse may exacerbate upper airway obstruction, thereby worsening intermittent hypoxia and potentially amplifying systemic inflammation.³⁰ In turn, systemic inflammation together with repetitive vibration-induced mechanical trauma might further aggravate local inflammatory changes. These observations raise the possibility that local and systemic inflammation could interact in a bidirectional manner, potentially reinforcing each other. In our cohort, patients with grade 3 soft palate obstruction exhibited higher MLR, PIV, and SIRI levels in univariate analyses than those with lower grade obstruction, although these associations were not confirmed in multivariate models.

This study has several limitations. First, its cross-sectional design cannot establish causality or dynamic changes. The Müller maneuver depends on inspiratory effort and may be influenced by lung function; scoring also relies on subjective assessment and lacks quantitative standards. In addition, allergic rhinitis and nasal resistance may influence both local and systemic inflammation.^31,32 Although most participants did not show allergic symptoms, a potential influence of allergic conditions cannot be completely ruled out. Future studies should further control for or evaluate this factor to clarify its impact. Other sleep disorders such as restless legs syndrome, periodic limb movement disorder, and insomnia were not systematically excluded.^33,34 As these disorders may also elevate systemic inflammatory markers, residual confounding cannot be ruled out. Future research should involve multicenter cohorts and longitudinal designs for generalizability and causal inference, with dynamic imaging or objective airway assessment. Multi-omics approaches may clarify how intermittent hypoxia activates immune pathways and drives soft palate remodeling, while mechanism-based therapies could provide novel anti-inflammatory strategies for OSA.

Conclusion

The peripheral blood inflammatory marker PIV is significantly and independently correlated with AHI, serving as a reliable biomarker for OSA severity. The interplay between systemic inflammation and local airway obstruction may exacerbate OSA progression via a positive feedback loop. Composite indices such as PIV and SIRI show greater clinical predictive value than traditional single markers, highlighting their potential in OSA phenotyping and prognosis.

Supplemental Material

sj-docx-1-ohn-10.1177_19160216251407939 – Supplemental material for Correlation Between Peripheral Blood Inflammatory Markers and Obstructive Sleep Apnea Severity

Supplemental material, sj-docx-1-ohn-10.1177_19160216251407939 for Correlation Between Peripheral Blood Inflammatory Markers and Obstructive Sleep Apnea Severity by Yingting Qi, Tao Li, Yi Zhao, Yali Du, Jiayue Wang, Yan Yan and Furong Ma in Journal of Otolaryngology - Head & Neck Surgery

Footnotes

Author’s Note

During the preparation of this work,the author(s) used ChatGPT in order to polish the English text. After using this tool,the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article. All authors have approved the final article.

CRediT Authorship Contribution Statement

Yingting Qi and Tao Li contributed equally to this manuscript. Yingting Qi and Tao Li were responsible for the original draft preparation. Yingting Qi,Yi Zhao,Yali Du,and Jiayue Wang contributed to data collection,data analysis,and manuscript revision. Ma Furong and Yan Yan were responsible for conceptualization,methodology,investigation,supervision,writing—review and editing,and funding acquisition. All authors have read,edited,and approved the final version of the manuscript.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: The work is financially funded by National Natural Science Foundation of China (82271168) and Haidian Innovation and Transformation Special Science and Technology Innovation Research and Development Project of Peking University Third Hospital (HDCXZHKC2023202).

Ethical Approval

This study was approved by the Institutional Review Board of Peking University Third Hospital (Approval No. IRB00006761-M2023504).

Informed Consent Statement

Written informed consent was obtained from all participants before inclusion in the study.

ORCID iD

Yingting Qi

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Supplemental Material

Additional supporting information is available in the online version of the article.

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