Introduction

Obstructive sleep apnea syndrome (OSAS) is a popular disease with moderate to severe sleep apnea affecting 6 to 13% of adult persons [1]. It is recognized by recurrent partial or complete upper airway closure and subsequent paroxysmal nocturnal hypoxia, resulting in episodic sleep arousals and precipitous daytime sleepiness [2].

OSA could result in complications such as cardiovascular disorders (CVD) and diabetes [3]. And CVD includes a large proportion of complications [4,5,6]. It is recognized that CVD complications of OSAS patients may be accompanied by endothelial dysfunction, exaggerated oxidative stress, systemic inflammation, and stimulation of the sympathetic system.

The chronic systematic inflammation of OSAS represents a crucial part of the evolution of CVD [12]. Recent research implies that both WBC and NLR are good indices of inflammation [13,14,15,16,17]. Neutrophils chiefly mediate innate immune response by secreting mediators while lymphocytes mediate adaptive immune response by regulating inflammation [18]. Also, some research showed that platelet was activated and aggregated in cases with OSAS, which was also important in inflammation [19, 20].

Recently, studies introduced PLR as a novel inflammatory marker to predict the adverse outcomes of CVD [14,15,16,17, 21]. It is found that HCT was increased in OSAS patients which might be called secondary erythrocytosis and this was suggested in view of the hypoxemic state [22]. Red cell distribution width (RDW), which assessed the variability of erythrocytes, was also reported to be increased in relation to inflammation in OSAS [23].

Patients and methods

This retrospective cross-sectional analysis included 100 patients with OSA from those attending Chest departments in Benha University Hospital from 1 January 2021 to 1 January 2023 period. Inclusion criteria included patients with diagnosed OSA by polysomnography and age equal to or more than 18 years. Exclusion criteria: patients known to have cardiovascular, renal, or hepatic diseases were excluded by history and laboratory (LFTs, KFTs). Patients with hematological disorders and malignancies were excluded by history. Patients diagnosed with obesity hypoventilation were excluded by ABG, complex sleep apnoea, central sleep apnea Cheyne-Stokes sleeping disorder, or REM-induced OSAS were excluded from the PSG results. Written consent was taken from all patients, and all of them were subjected to full history taking and clinical examination, Electrocardiogram. Chest X-ray posteroanterior view, CBC with differential leucocytic count, and a full night of polysomnography with a diagnosis of OSA severity by AHI using Somnoscreen plus PSG, Somnomdics, Germany. All those data were taken from the recordings in the sleep lab. All data were collected, tabulated, and statistically analyzed.

Statistical analysis

The statistical analysis was conducted using the Software, Statistical Package for Social Science, (SPSS Inc. Released 2009-PASW Statistics for Windows Version 26.0. Chicago: SPSS Inc.). All variables were tested for normality of distribution using the Shapiro–Wilk test. All P values were two-sided. Statistical significance was accepted at p value < 0.05. A p value > 0.05 was considered non-significant.

Results

The study population consisted of 100 patients divided into three groups according to disease severity (OSA classification) (mild: 5 ≤ apnea–hypopnea index (AHI) < 15; moderate: 15 ≤ AHI < 30; and severe: 30 ≤ AHI). This article divided patients into three groups according to body mass index (BMI (normal weight: BMI < 25; overweight: 25 ≤ BMI ≤ 30; and obesity: 30 < BMI) (Table 1).

Table 1 Baseline characteristics of cases (N = 100)
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The median age (IQR) was 43 years [39, 51]. About 85% of the study cases were males and 15% were females. Most patients (97%) were under 65 years of age. Median BMI (IQR) was 35 (31.3, 39)}. Seventy-nine patients (79%) were obese (BMI > 30) [35] compared with 16% overweight and 5% normal weight. Median AHI (IQR) was 56.45 (35.35, 77.98). Eighty patients (80%) reported a severe grade of AHI compared with 15% moderate and 5% mild. Median DI (IQR) was 56.9 (29.85, 79.05) (Table 1). We found that the median neutrophile count was 3.94 with IQR of (2.73, 5.33). The median lymphocyte count was 2.7 with IQR of (2.19, 3.25). Median platelet count was 247 with an IQR of (205, 291.25). Median HCT% was 46.85 with IQR of (44.2, 49.6). Median RDW-CV% was 12.03 with IQR of (11.3, 13.08). The median N/L ratio was 1.6 with an IQR of (1.1, 2.2). Median P/L was 94 with IQR of (80, 116.5) (Table 2).

Table 2 Hematological indices of cases (N = 100)
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Comparison between mild, moderate, and severe groups of OSA revealed that there were significant differences between the three groups regarding BMI, AHI, DI, and P/L ratio (p = 0.018, 0.001, 0.001, 0.024) respectively, where the severe OSA group recorded the greatest BMI (35.6 compared with 29 and 34), AHI (62.1 compared with 7.5 and 23.5), DI (65.7 compared with 13.8 and 22) and P/L ratio (95 compared with 63 and 95). There was a statistically significant difference between the mild and severe groups regarding the median N/L ratio (p = 0.041) (Table 3).

Table 3 Comparison between the three groups according to OSA severity regarding age, BMI, DI, and hematological indices
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Spearman’s correlation analysis showed that there was a statistically significant positive correlation between OSA classification and BMI (rho = 0.248, p = 0.013), platelet level (rho = 0.233, p = 0.02), N/L ratio (rho = 0.224, p = 0.025) and P/L ratio (rho = 0.218, p = 0.03). No statistically significant correlation was detected between AHI and other parameters (p > 0.05) (Table 4).

Table 4 Correlation between OSA classification and other parameters (n = 100)
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Univariate analysis by ordinal logistic regression revealed that BMI, platelets count, and P/L ratio were significant positive predictors for OSA classification (p = 0.029, 0.048, 0.017) respectively (Table 5).

Table 5 Ordinal logistic regression for predictors associated with OSA classification
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Discussion

In the current work, there was a statistically significant difference between the three groups of OSA severity regarding platelet to lymphocyte ratio, and there was a statistically significant difference between mild and severe OSA regarding median neutrophil to lymphocyte ratio. There was a statistically significant positive correlation between OSA severity and platelet level, neutrophil to lymphocyte, and platelet to lymphocyte ratios, while the relation between OSA and other parameters (HT, RDW, − CV%, and neutrophils) was statistically nonsignificant. In the current study, univariate analysis by ordinal logistic regression revealed that platelets count, and P/L ratio were the most significant positive predictors for OSA severity (0.048, 0.017) respectively.

The chronic systematic inflammation of OSAS may play an important role in the progression of CVD [12]. Recent studies suggest that both WBC and NLR are good indicators of inflammation [13,14,15,16,17]. Neutrophils mainly mediate innate immune response by secreting mediators while lymphocytes mediate adaptive immune response by regulating inflammation [18]. Besides, some studies reported platelet was activated and aggregated in patients with OSAS, which was also relevant in inflammation [19, 20].

Many, studies introduced PLR as a novel inflammatory marker to predict the adverse outcomes of CVD [14,15,16,17, 21]. In view of hypoxemic states, Choi et al. 2006 found that HCT was elevated in OSAS patients which might be called secondary erythrocytosis [22]. Ozsu, et al. 2012 reported that red cell distribution width (which assessed the variability of erythrocytes) was increased in relation to inflammation in OAS, this disagrees with our results, which show a nonsignificant correlation between OSA severity and both HCT and RWD [23].

Mindan et al. (2018) results showed that there was a positive correlation between the levels of hematological indices including (WBC, LYM, NLR, MPV, PDW, PLR, RDW, and HCT) and the severity of obstructive sleep apnea [23]. These results agreed with our work; which showed a positive correlation between OSA severity and platelets; platelet to lymphocyte and neutrophil to lymphocyte ratios. Mindan et al. (2018) proposed that these hematological indices could be alternatives to markers (like IL6 and CRP) to evaluate the inflammation in OSAS patients, which was useful for assessing the severity of OSAS [24].

The relationship between OSAS and accompanying changes in hematological parameters is complicated and can be explained by the following: acute and chronic hypoxia may be associated with MPV, PDW, and HCT changes. It was found that MPV and PDW were negatively related to average SpO2 and minimum SpO2 and implied that hypoxia could activate platelet function [19]. Rahangdale et al. (2011) demonstrated that a high level of oxygen desaturation was linked with higher platelet surface adhesion molecules, activated glycoprotein receptor expression, platelet-monocyte aggregation, and platelet-neutrophil aggregation. A hypoxemic state is interrelated with high hematocrit levels, as oxyhemoglobin desaturation can stimulate erythropoiesis, leading to increased hematocrit [25]. Svatikova et al. reported that ANP (atrial natriuretic peptide) was increased overnight in those untreated OSAS patients, and ANP levels decreased with CPAP treatment. It indicated that hemoconcentration might lead to increased hematocrit [26]. Another mechanism appears to be sympathetic overactivity. It results in many pathophysiological changes such as recurrent arousals and increased inspiratory effort. OSAS patients exhibited high levels of sympathetic nerve activity even when they were fully awake, which contributed to platelet activation and CVD [27].

Larsson et al. (1989) suggested that platelet aggregability was increased by high levels of circulating catecholamine in vivo. Therefore, hematological indices associated with platelet activation (e.g., PLR) might change in OSAS patients caused by catecholamine discharge [28].

Some researchers declared that nuclear factor kappa B (NF-κB), a master transcription factor that regulated the downstream inflammatory gene expression, was found to be selectively activated by hypoxia and reoxygenation [29]. NF-κB activity also resulted in an increased number of circulating neutrophils and monocytes. And the apoptosis of neutrophils was dysregulated in the process of OSAS [30]. Both lead to elevated levels of neutrophils in the peripheral blood of OSAS patients. As for lymphocytes, OSAS patients combined with CVDs were found to have a lower lymphocyte level compared to those without CVDs, which could be due to the uncontrolled inflammatory pathway [31].

Moreover, some researchers demonstrated that lower lymphocyte counts were related to activation of the hypothalamus-hypophysis-adrenal (THA) axis, increased production of systemic cortisol levels, and altered sleeping habits [32]. The NLR, a novel marker of systemic inflammation, was associated with many chronic diseases and could be an indicator used to predict CVDs in OSAS patients [33]. On the other hand, many pro-inflammatory cytokines, such as IL6, could significantly promote the production and activation of platelets, which contributed to the changes in those hematological parameters including PLR, MPV, and PDW [34].

Conclusion

The hematological indices including neutrophil to lymphocyte ratio and platelet to lymphocyte ratio could be alternatives to expensive time-consuming biochemical markers to evaluate the inflammation and severity in the OSAS population because they were comparatively cheap, readily measurable, easy, and practical laboratory markers.

Limitations

Small study size, not all hematological markers like MPV and PDW were included.