High Monocyte-to-Lymphocyte Ratio is Associated with Obstructive Sleep Apnea Hypopnea Syndrome

Introduction

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common sleep disorder characterized by partial or complete obstruction of the upper airway during sleep, leading to pauses or reductions in breathing. These respiratory symptoms cause oxygen deprivation and impaired carbon dioxide elimination, typically accompanied by snoring, disrupted sleep patterns, and daytime symptoms such as excessive sleepiness and impaired concentration.¹

Epidemiological studies have shown that the global prevalence of sleep-disordered breathing (AHI ≥ 5 events/) in the population aged 30–69 years ranges from 711 million to 961 million,² Among them, an estimated 272 million to 458 million people have moderate to severe sleep-disordered breathing (AHI≥15 events/h).³ The prevalence of OSA increases with age, male gender, and higher body mass index.⁴

OSAHS can lead to fragmented sleep and decreased oxygen saturation.⁵ Studies have shown that fragmented sleep can result in excessive daytime sleepiness, reduced quality of life⁶, and an increased risk of accidents.⁷ Additionally, it increases the risk of developing other diseases,⁸ including cardiovascular diseases,⁹ neurological diseases,^10,11 metabolic diseases,¹² cancer and reproductive disorders.^13,14

OSAHS is currently recognized as an inflammatory disease. There is evidence to suggest that chronic intermittent hypoxia (IH) associated with OSAHS, sleep deprivation, and fragmentation can elevate levels of various markers such as inflammation, oxidative stress, procoagulant, and thrombotic activity.¹⁵ The gold standard for diagnosing OSAHS is a polysomnography (PSG) examination,¹⁶ which requires specialized facilities and overnight monitoring. Some researchers propose the use of biological and hematological parameters as biomarkers for early diagnosis and monitoring of OSAHS,¹⁷ however, these methods are often costly and require additional testing.

In recent years, the use of composite inflammatory parameters such as neutrophil-to-lymphocyte ratio (NLR) as specific biochemical markers for OSAHS has garnered increasing attention from researchers. NLR, MLR, systemic immune-inflammation index (SII), system inflammation response index (SIRI), and Pan-Immune-Inflammation Value (PIV) are biomarkers of endogenous stress and various disease risks,^18–22 and these composite inflammatory parameters are easily accessible and convenient to calculate. Studies have shown that NLR is associated with the severity of OSAHS.²³ Some research has investigated the relevance of SII and SIRI in the development of OSAHS^24,25. However, there is currently no reported research on the relationship between MLR, PIV, and the occurrence of OSAHS, nor comparative studies of the association between different inflammatory parameters and the development of OSAHS.

Materials and Methods

Study Population

A total of 414 patients who underwent polysomnography (PSG) and complete blood count at the Chao Lake Hospital, Anhui Medical University, from January 2021 to May 2023 were retrospectively analyzed. PSG and blood tests were performed within one week. Patients were divided into two groups based on their AHI scores: the control group (AHI < 5 events per hour) and the OSAHS group (AHI ≥ 5 events per hour). Inclusion criteria were: patients aged 18–80 years who were hospitalized for snoring and underwent PSG and complete blood count; patients who were conscious, able to communicate naturally, and had no severe neurological or psychiatric disorders. Exclusion criteria were: patients with hematological, gastrointestinal, or gynecological malignancies; chronic inflammatory diseases, infections, congestive heart failure, severe renal failure, liver failure, central sleep apnea, or cerebrovascular diseases; or those receiving treatment. A total of 104 patients with chronic inflammatory diseases (n=57), central sleep apnea (n=11), and those receiving CPAP therapy (n=36) were excluded, resulting in the inclusion of 310 patients, comprising 231 in the control group (105 males, 126 females) and 79 in the OSAHS group (42 males, 37 females). This study was approved by the Ethics Committee of our hospital.

Polysomnography

All patients underwent overnight examinations in the PSG examination room under the supervision of experienced technicians. Parameters including electroencephalography, electrooculography, chin electromyography, leg electromyography, electrocardiography, leg, chest and abdominal movements, nasal airflow (thermistor), pulse oximetry, and body position were recorded. According to the American Academy of Sleep Medicine (AASM) guidelines, the apnea-hypopnea index (AHI) was defined as the sum of the apnea index (AI) and hypopnea index (HI), with respiratory events defined as a decrease in airflow of more than 50% of baseline for at least 10 seconds, accompanied by an arousal or a decrease in oxygen saturation of at least 3%.²

General Information

The clinical data of all patients at admission were collected, including age, sex, height, weight, routine blood tests, and other parameters. BMI (weight/height ²), NLR (neutrophils/lymphocytes), PLR (platelets/lymphocytes), MLR (monocytes/lymphocytes), SII (neutrophils × platelets/lymphocytes), SIRI (neutrophils × monocytes/lymphocytes), PIV (neutrophils × platelets × monocytes/lymphocytes).

Statistical Analysis

Using SPSS 26.0 statistical software for analysis. For normally distributed quantitative data, the mean and standard deviation (Mean±SD) were used, while for non-normally distributed data, the median and quartiles were represented [M(Q25, Q75)]. Non-parametric Mann–Whitney U-test was used for between-group comparisons of non-normally distributed data, and independent sample t-test or one-way analysis of variance (ANOVA) was used for normally distributed data. Qualitative data were represented using frequency and percentage, and between-group comparisons were made using the chi-squared test. In general, inflammatory parameters were included in the single-factor logistic regression analysis, and those with P<0.2 were selected for inclusion in the multiple-factor logistic regression analysis to determine the independent factors related to OSAHS. The discriminative ability of the multiple-factor model was evaluated using the receiver operating characteristic (ROC) curve, and the calibration of the model was assessed using the Hosmer-Lemeshow test. Pearson correlation analysis was used to evaluate the correlation between each inflammatory parameter and AHI score. Using the SIRI, SII, PIV, MLR, NE, and MONO data of the patients, ROC curves were generated using SPSS 26.0 statistical software to analyze the predictive value of SIRI, SII, PIV, MLR, NE, and MONO for OSAHS. The optimal cut-off value for the ROC curve was chosen as the MLR value corresponding to the point where the sum of specificity and sensitivity was maximal. Differences with a P<0.05 were considered statistically significant.

Results and Analysis

General Data Comparison

Finally, 310 valid data points were included, including 147 males and 163 females, with an average age of 45 years. The OSAHS group was older, lower in height, heavier, and had a significantly increased BMI. MONO, LY, SIRI, PIV, NLR, and MLR were all higher than those in the control group (P<0.05) (Table 1).

Table 1 Comparison of Clinical and Blood Routine Data Between OSAHS Group and Control Group

Influencing Factors of OSAHS

Univariate Logistic Regression Analysis Was Used to Determine the Inflammatory Factors Associated with OSAHS

Taking P<0.2 as the standard, the inflammatory parameters with differences between the control group and the OSAHS group were included in univariate logistic regression analysis, and the related inflammatory factors affecting the occurrence of OSAHS were screened. The results showed that NE, MONO, SIRI, SII, PIV, NLR and MLR were risk factors for OSAHS, P<0.05 (Table 2).

Table 2 Univariate and Multivariate Logistic Regression Analysis of OSAHS Influencing Factors

The factor with statistical significance (P<0.2) in the single-factor logistic regression analysis was included in the multi-factor logistic regression analysis using the Forward: LR method to avoid the issue of multicollinearity. Neutrophil count, monocyte count, eosinophil count, SIRI, SII, PIV, NLR, and MLR were included in the multi-factor logistic regression analysis model to screen for independent inflammatory factors related to OSAHS. The results showed that MLR [OR=1.103, 95% CI (1.059–1.149), P<0.01] was an independent risk factor for OSAHS (Table 2).

Model Discrimination Test: The ROC curve for the prediction model [AUC=0.673, 95% CI (0.601–0.744), P<0.01] indicates a certain discriminative ability of the predictive model when AUC>0.65 (Figure 1A). Model calibration test: The calibration ability of the predictive model was evaluated using the Hosmer-Lemeshow test. The results showed that the Hosmer-Lemeshow Χ²=8.752, P=0.364 > 0.05, indicating that the difference between the model predicted values and the actual observed values was not statistically significant, suggesting that the predictive model has good calibration ability (Figure 1B).

Figure 1 Discrimination ROC graph and calibration graph of multivariate logistic regression analysis of OSAHS. (A) Model discrimination ROC graph; (B) model calibration graph: sort the predicted probabilities of each research object from small to large and divide them into ten groups according to decile. The actual observation value and model predicted value of each group are expressed in the form of coordinate points, and the model calibration ability is judged as a whole. The closer the predicted curve is to the actual observation curve, the better the calibration capability of the model.

MLR, SIRI and AHI Score Correlation

The Spearman correlation analysis results indicated that MLR and SIRI were positively correlated with AHI score (r=0.273; r=0.265, P<0.05), as shown in Table 3. As the MLR increased, the AHI score also increased, indicating a more severe condition of OSAHS in patients. MLR is positively correlated with the lowest oxygen saturation and mean oxygen saturation (r=−0.185; r=−0.155, P<0.05). A higher MLR is associated with more severe nocturnal hypoxia in patients (Table 4). The scatter plot (Figure 2) constructed based on the MLR and AHI of the patients showed a certain linear correlation between MLR and AHI.

Table 3 Correlation Analysis Between MLR and Other Inflammatory Parameters and AHI Score

Table 4 MLR Correlated Analysis with the Lowest Blood Oxygen Saturation, Average Blood Oxygen Saturation, and AHI Score

Figure 2 Correlation Analysis of MLR and AHI.

ROC Curve Analyzed the Predictive Value of MLR, SIRI and Other Parameters on OSAHS

The predictive value of inflammatory parameters such as MLR and SIRI (inflammatory parameters that are significant in single-factor logistic regression analysis) was evaluated using ROC curves. The results showed that when the MLR threshold was set at 0.248, it had the highest predictive efficacy for OSAHS, with a sensitivity of 34.2% and a specificity of 93.5%. The area under the ROC curve (AUC) was 0.673 [95% CI (0.601–0.744), P<0.01], indicating a certain predictive value for OSAHS (Figure 3), which was superior to other inflammatory parameters (Table 5).

Table 5 Area Under the ROC Curve for Predicting Snoring Using Various Inflammatory Parameters

Figure 3 Analysis of ROC curve of MLR in OSAHS.

Discussion

Our study found a significant correlation between high MLR and the prevalence of OSAHS. Moreover, we have demonstrated that MLR is an independent risk factor for OSAHS. MLR is likely to be an economical, objective, and simple blood biomarker that holds certain value for the early identification of high-risk OSAHS patients. Conducting PSG tests for these patients for further diagnosis can alleviate the economic burden on patients. Therefore, MLR may be an effective biomarker for predicting and evaluating OSAHS in clinical practice.

This study revealed for the first time the relationship between MLR, PIV, and the occurrence of OSAHS, comparing the correlation of different inflammatory composite parameters with the occurrence of OSAHS. The correlation between MLR and OSAHS was shown to be superior to inflammatory indicators such as NLR and SII. Comparative data indicated that the MLR level in the OSAHS group was significantly higher than in the control group. Both single-factor and multiple logistic regression analyses suggested that MLR is independently associated with the occurrence of OSAHS. Additionally, MLR was positively correlated with AHI scores (r=0.273, P<0.05), with an area under the ROC curve of 0.673, 95% CI (0.601–0.744), indicating a certain predictive value for OSAHS. Our research suggests that elderly patients with higher BMI, NLR, and SII levels are more likely to suffer from OSAHS, which is consistent with the findings of Senaratna et al^4,23,24

Zahide’s study found that there was no statistically significant difference in the MLR between OSAHS patients and the normal population.²⁶ However, in our study, we observed a significant correlation between MLR and the occurrence of OSAHS. This difference in findings may be attributed to variations in sample size and differences in the study populations. In comparison to Zahide’s study, our study had a larger sample size and used more sound statistical methods, which may render our results more reliable. Nonetheless, further validation through large-sample, multi-center studies is still necessary.

MLR has been demonstrated to be a novel hematological parameter in several medical fields, with elevated levels likely resulting from an increase in monocyte count and/or a decrease in lymphocyte count. MLR has been established as an independent predictor of severe Klebsiella pneumoniae infection risk,²⁷ and has independent prognostic value in patients with laryngeal squamous cell carcinoma (LSCC)²⁸, while also serving as a prognostic biomarker for risk stratification in lung cancer patients²⁹. These studies collectively indicate that an elevated MLR level may indicate the initiation of an inflammatory process. Patients with OSAHS often experience IH, and monocytes play a crucial role in inflammatory recruitment and immune suppression following IH. During inflammation, monocytes increase and are recruited to inflamed sites, where they differentiate into end-stage cells and promote the renewal of dendritic cells and tissue macrophages.¹⁹ Research has demonstrated that monocytes in severe OSAHS patients exhibit higher NLRP3 activity than the control group, which is directly related to AHI and the index of hypoxemia. Monocytes in the context of IH increase NLRP1 signaling in a hypoxia-inducible factor-3α-dependent manner and/or in combination with plasma from OSA patients.³⁰ The increased plasma norepinephrine levels among OSAHS patients lead to activation of the sympathetic nervous system and an increase in serum cortisol. Elevated cortisol levels subsequently result in a decrease in lymphocyte concentration.³¹ As a composite indicator of monocytes and lymphocytes, MLR can better reflect these changes in the disease process.

Our research has found that MLR is significantly associated with the occurrence of OSAHS, and it outperforms inflammation markers such as NLR, SII, and SIRI. Monocytes and differentiated macrophages are important components of the innate immune system, capable of regulating the secretion of inflammatory cytokines and tissue remodeling.³² Under the influence of IH caused by OSAHS, total monocytes in the bone marrow and peripheral blood significantly increase.³³ Monocytes in OSAHS patients suppress NK cell activity by releasing TGF-β, demonstrating their ability to modulate NK cell activity through immunomodulation.³⁴ MLR can reflect the inflammatory state within OSAHS patients through both monocytes and lymphocytes, providing valuable predictive insights for OSAHS. Additionally, MLR is easily obtainable through routine blood tests, with low testing costs and widespread practical value. The use of MLR for OSAHS prediction is scientifically sound, reasonable, and innovative.

Shortcomings of this study: 1) This was a single-center and retrospective study; therefore, we cannot establish a causal relationship between MLR and OSAHS. The sample size of cases is still limited, and further exploration of the relationship between various hematological indicators and inflammatory parameters with the occurrence and development of OSAHS requires larger sample sizes and multi-center studies. 2) All patients in this study were newly diagnosed with OSAHS. It is worth further investigating whether MLR and SIRI remain significantly associated with the condition for patients with a history of OSAHS who have received CPAP or surgical treatment. 3) Inflammatory markers were limited, only including relevant inflammatory parameters in routine blood tests, without incorporating CRP, interleukins, tumor necrosis factor, and other inflammatory parameters. Further research could involve capturing more inflammatory factors to clearly elucidate the relationship between inflammation and OSAHS.