Introduction
Type 2 diabetes (T2DM) is a chronic metabolic disorder characterized primarily by insulin resistance and relative insulin deficiency.1,2 The global prevalence continues to rise, representing a growing public health concern. Populations most commonly affected include individuals with obesity, the elderly, and other high-risk groups. The main symptoms include polyuria, polydipsia, and fatigue.3,4 Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder characterized by recurrent episodes of partial or complete upper airway obstruction during sleep, leading to intermittent pauses in respiration.5 The primary symptoms include snoring, sudden awakenings during sleep, daytime sleepiness, and difficulty concentrating. OSA is closely linked to T2DM. Studies have shown that diabetes can exacerbate the occurrence of sleep apnea, while sleep apnea further worsens glucose metabolism abnormalities, creating a vicious cycle through various metabolic pathways such as hypoxemia, sympathetic nervous system activation, and elevated inflammatory factors.6,7
Continuous Positive Airway Pressure (CPAP) therapy is widely regarded as the standard and most effective treatment for obstructive sleep apnea (OSA).8 By continuously providing positive pressure airflow to patients, positive pressure ventilation can maintain the opening of the upper airway, prevent respiratory collapse, and significantly improve nighttime breathing disorders.9 Positive pressure ventilation not only improves sleep quality and alleviates daytime sleepiness but has also been reported to potentially enhance metabolic function in patients with metabolic syndrome or diabetes.10 Preliminary studies have suggested that positive pressure ventilation therapy may indirectly affect glucose metabolism, improve insulin sensitivity, and help control blood sugar by reducing sympathetic nervous system excitation, improving oxygenation status, and alleviating chronic hypoxemia.11,12 In addition to improving respiratory function and sleep quality, CPAP treatment has been shown to confer beneficial effects on various metabolic parameters. Studies have indicated that CPAP can reduce systemic inflammation, alleviate oxidative stress, and regulate abnormal lipid metabolism, thereby lowering cardiovascular risk.13 Furthermore, CPAP improves sleep architecture and supports hormonal regulation, thereby facilitating weight management and enhancing energy metabolism.14 In patients with diabetes, the metabolic benefits of CPAP include improved insulin resistance and potentially better glycemic control, highlighting its important role in glucose metabolism regulation.15
In recent years, many studies have shown that Neutrophil-to-Lymphocyte Ratio (NLR) and Platelet-to-Lymphocyte Ratio (PLR) are associated with the development of T2DM and OSA.16,17 However, there is still a lack of systematic research on whether NLR and PLR are related to the long-term efficacy of CPAP treatment. Therefore, we hope to predict the long-term glycaemic control effect of CPAP through early changes in NLR and PLR after treatment, in order to identify high-risk patients for early warning and intervention.
Materials and Methods
Research Object
This study retrospectively recruited the type 2 diabetes patients with obstructive sleep apnea who received positive pressure ventilation from January 2020 to January 2023. Participants were eligible if they: (1) were ≥ 18 years old; (2) met American Academy of Sleep Medicine (AASM) diagnostic criteria for obstructive sleep apnea (OSA)—either moderate-to-severe disease (apnea–hypopnea index [AHI] ≥ 15 events·h−¹) confirmed by polysomnography (PSG) or mild disease (5 ≤ AHI < 15 events·h−¹) accompanied by clinically significant daytime sleepiness,18 nocturnal choking, nadir oxygen saturation < 85%, or comorbid hypertension, coronary artery disease, or poorly controlled diabetes warranting CPAP therapy;19 and (3) possessed complete clinical records. Patients were excluded if they: (1) received concurrent alternative therapeutic interventions; (2) had malignant neoplasms or other severe systemic disorders; (3) exhibited marked hepatic or renal impairment; (4) suffered from major psychiatric illness or demonstrated poor treatment compliance; or (5) had incomplete laboratory data. CPAP adherence was assessed using usage data recorded by the device memory card. CPAP Adherence was quantified via device memory–card downloads. Consistent with established clinical standards, adequate adherence was defined as CPAP use ≥ 4 h·night−¹ for ≥ 70% of nights per week.9 Only participants meeting this threshold were retained for analysis. Assuming a medium effect size (d = 0.5), 80% statistical power, and α = 0.05, power analysis indicated minimum enrolment of 64 participants per group for continuous outcomes, 272 per group for categorical outcomes, and a total of ≥ 118 participants for multivariable logistic-regression modelling.
Treatment Methods
Patients received standardized antihyperglycemic therapy followed by nocturnal CPAP ventilation via nasal mask. During the initial treatment night, optimal positive pressure was titrated with the mask securely fitted. Subsequently, nightly CPAP therapy was administered for 4–8 hours at a pressure range of 4.0–20 cm H2O (1 cm H2O = 0.098 kPa), maintained consistently for 6 months.
Data Collection
We collected baseline information and disease-related features of patients, including Age, Gender, Body Mass Index (BMI), Smoking, Drinking, Cardiovascular Disease, Diabetic Complications, Physical Activity, Duration of diabetes, Apnea-Hypopnea Index (AHI), Lowest Peripheral Oxygen Saturation (SpO2), Sleep duration, Percentage of deep sleep. CPAP adherence was assessed using usage data recorded by the device’s memory card. The evaluation indicators included Nightly Usage Time and Frequency of Use. Hemoglobin A1c (HbA1c), Fasting Blood Glucose (FBG), Homeostasis Model Assessment of Insulin Resistance (HOMA-IR).
We collected baseline and 7-day post-treatment NLR and PLR values, and calculated the absolute reductions by subtracting post-treatment levels from baseline measurements to quantify the magnitude of NLR and PLR decline. We also collected HbA1c and FBG levels 6 months after treatment. According to established criteria reported in the literature,20 effective glycemic control was defined as achieving a post-treatment hemoglobin A1c (HbA1c) level of <7% or a reduction of >0.5%, and/or a fasting blood glucose (FBG) level of <7.2 mmol/L or a decrease of >10% from baseline.
Statistical Analysis
All analyses were performed in R (v 4.4.0). Continuous variables are presented as median (minimum–maximum) and compared with either the independent-samples t-test or the Mann–Whitney U-test, as appropriate. Categorical variables are expressed as frequency (percentage) and analysed with the χ²-test or Fisher’s exact test. Reductions in NLR and PLR were stratified into quartiles (Q1–Q4), glycaemic control status (effective vs ineffective) served as the dependent variable. Three multivariable logistic-regression models were fitted. Model 1 included only the NLR and PLR reductions. Model 2 additionally adjusted for age, sex, and BMI. Model 3 further adjusted for AHI, nadir oxygen saturation, nightly CPAP usage duration, weekly CPAP usage frequency, baseline HbA1c, FBG, and HOMA-IR. Interaction analyses were conducted with Model 3. Restricted cubic-spline functions were employed to explore potential linear or non-linear associations between NLR/PLR reductions and long-term glycaemic control. Predictive performance was evaluated with receiver-operating-characteristic (ROC) analysis; areas under the curve (AUCs) were calculated and compared using DeLong’s test to determine whether NLR or PLR offered superior prognostic accuracy.
Results
Differences in Baseline and Disease Characteristics Between Patients with Effective and Ineffective Glycaemic Control
The results showed that the proportion of males in the ineffective group was significantly higher than that in the effective group (71.7% vs 62.94%, P=0.02198). The apnea hypopnea index (AHI) was significantly higher in the ineffective group compared to the effective group (23 vs 20, P=0.00634). The lowest blood oxygen saturation was significantly lower in the ineffective group than in the effective group (87.3% vs 89.5%, P=0.00516).
Additionally, the duration of CPAP device use per night was shorter in the ineffective group (5.1 vs 5.4 hours, P=0.0399). The percentage of patients using the device for ≥ 3 days/week was also lower in the ineffective group compared to the effective group (46.7% vs 58.04%, P=0.01038).
Glycated hemoglobin (HbA1c), fasting blood glucose (FBG), and HOMA-IR were all significantly higher in the ineffective group than in the effective group (FBG: 10.8 vs 10.3, P=0.043; HOMA-IR: 3.0 vs 2.7, P=0.017). Other indicators such as age and BMI showed no significant differences between the two groups (Table 1).
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Table 1 The Baseline Information and Disease Characteristics Differences Between Effective and Ineffective Blood Sugar Control
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Relationship Between the Decrease in NLR and PLR and Improvement in Blood Glucose Levels
In Model 1, a significant positive correlation was observed between the decrease in NLR and improvement in blood glucose levels (Q3 vs Q1, OR=1.085, P=0.001) (Q4 vs Q1, OR=1.560, P<0.0001). In Model 2, it was also found that there was a significant positive correlation between the decrease in NLR and the improvement in blood glucose (Q4 vs Q1, OR=1.555, P<0.0001). After adjusting multiple confounding factors in model 3, the decline of NLR was still positively correlated with the improvement of blood glucose (Q4 vs Q1, OR=1.530, P<0.0001) (Table 2).
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Table 2 Multivariate Logistic Regression Analysis of the Association Between the Reduction in NLR and Improvement in Blood Glucose Control
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In Model 1, Q2, Q3, and Q4 showed a significant positive correlation with glycaemic improvement compared to Q1 (Q2 vs Q1, OR=1.166, P=0.004) (Q3 vs Q1, OR=1.209, P<0.0001) (Q4 vs Q1, OR=1.438, P<0.0001). In Model 2, there was a significant positive correlation between Q2, Q3, and Q4 and glycaemic improvement (Q2 vs Q1, OR=1.165, P=0.004) (Q3 vs Q1, OR=1.206, P<0.0001) (Q4 vs Q1, OR=1.442, P<0.0001). In Model 3, the effects of Q2, Q3, and Q4 on glycaemic improvement were still significant compared to the reference group (Q2 vs Q1, OR=1.143, P=0.010) (Q3 vs Q1, OR=1.200, P<0.0001) (Q4 vs Q1, OR=1.449, P<0.0001), but the OR values were lower than those of Model 1 and Model 2, indicating a weaker effect (Table 3).
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Table 3 Multivariate Logistic Regression Analysis of the Association Between the Reduction in PLR and Improvement in Blood Glucose Control
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Restricted cubic spline analysis showed that there was a significant correlation between the decrease in NLR and PLR and glycaemic improvement (P for TOTAL was less than 0.05). Nonlinear analysis shows that there is no significant nonlinear relationship between the decrease in NLR and PLR and glycaemic improvement (P for Nonlinear is greater than 0.05), indicating that there may be a linear or near linear relationship between them, and the greater the decrease in NLR and PLR, the higher the possibility of glycaemic improvement (Figure 1A and B).
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Figure 1 RCS Analysis (A) The association between the decrease in Neutrophil-to-Lymphocyte Ratio (NLR) and glycaemic improvement (P for Nonlinear = 0.3057, P for TOTAL = 0.0054) (B) The association between the decrease in Platelet-to-Lymphocyte Ratio (PLR) and glycaemic improvement (P for Nonlinear = 0.3910, P for TOTAL = 0.0167).
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Interaction Analysis Between NLR and PLR Decrease and Other Covariates
The interaction analysis demonstrated that the reduction in NLR and the duration of CPAP use per night were significantly positively correlated with glycaemic improvement (interaction term β=0.057, p=0.032, OR=1.059, 95% CI: 1.005–1.115). This finding suggested that the beneficial effect of NLR reduction on glycaemic control was progressively enhanced with longer nightly CPAP usage. No significant interactions were found between the decrease in NLR and other factors such as gender (p=0.833), AHI (p=0.819), or minimum blood oxygen saturation (p=0.742). Similarly, there were no significant interactions between the decline in PLR and gender, AHI, or the lowest blood oxygen levels (Table 4).
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Table 4 Interaction Analysis Between the Reductions in NLR and PLR and Other Covariates
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ROC Curve Evaluation of the Predictive Effect of the Decrease in NLR and PLR on Glycaemic Improvement
The results demonstrated that the AUCs values for NLR and PLR were 0.602 and 0.601, respectively, indicating comparable predictive performance. Glycaemic improvement was observed when reductions in NLR and PLR surpassed thresholds of 1.538 and 38.232, respectively. Further analysis using DeLong’s test yielded a P-value of 0.7023, confirming that both the decrease in NLR and PLR effectively predict glycaemic improvement, with no significant difference in their predictive abilities (Figure 2A and B).
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Figure 2 (A) ROC curve of the decrease in NLR (AUC= 0.602, AUC 95% CI: 0.558–0.646, Threshold = 1.538, Youden index = 0.173, Sensitivity= 0.587, Specificity = 0.585) (B) ROC curve of the decrease in PLR (AUC= 0.601, AUC 95% CI: 0.557–0.645, Threshold = 38.232, Youden index = 0.172, Sensitivity= 0.458, Specificity = 0.714).
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Discussion
Glycaemic control is essential for patients with type 2 diabetes complicated by obstructive sleep apnea. Evidence suggests that intermittent hypoxia characteristic of OSA induces oxidative stress and elevates systemic inflammation, thereby exacerbating insulin resistance. Consequently, blood glucose levels rise, and chronic hyperglycemia can impair autonomic nervous system function, further exacerbating obstructive sleep apnea. Moreover, sustained poor glycaemic control contributes to endothelial dysfunction and increases the risk of atherosclerosis, hypertension, and other cardiovascular diseases.
This study demonstrated that the reductions in NLR and PLR within seven days after CPAP treatment are closely associated with long-term glycaemic control outcomes. NLR, as a common inflammatory marker, is usually associated with the systemic inflammatory state and immune balance of the body.21 Patients with T2DM and OSA typically exhibit elevated NLR levels, potentially due to hyperglycemia–induced upregulation of pro-inflammatory cytokines.22,23 The intermittent hypoxia (IH) symptoms observed by patients at night can increase the activity of the NF-κB pathway, further elevating the levels of inflammatory factors.24 CPAP treatment addresses the fundamental pathological issue of OSA—intermittent hypoxia and reoxygenation caused by repeated nighttime breathing pauses—by maintaining the openness of the upper airway. This, in turn, reduces the levels of oxidative stress markers.25,26 As a result, the patient’s sleep architecture was significantly improved, accompanied by reduced sympathetic nervous system activity, lowered cortisol levels, and decreased neutrophil release, leading to a reduction in NLR levels. Additionally, PLR serves as a biomarker for inflammation and thrombosis. In T2DM, elevated blood glucose levels promote platelet activation and worsen endothelial damage.27,28 Long term chronic low-grade inflammation can lead to a decrease in lymphocytes, resulting in further elevation of PLR.29 Intermittent hypoxia and sleep fragmentation in OSA contribute to platelet activation and heightened inflammatory responses, resulting in increased PLR levels.30 CPAP treatment reduces HIF-1 α levels by improving intermittent hypoxia, thereby lowering platelet levels.31 Optimizing the patient’s sleep structure also reduces cortisol levels, inhibits lymphocyte apoptosis, and increases lymphocyte counts, resulting in a decrease in PLR levels. Patients who show a significant reduction in NLR and PLR levels early in treatment demonstrate marked improvement in inflammatory status and exhibit greater sensitivity to therapy. Consequently, these patients are more likely to benefit from long-term treatment.
Interaction analysis indicates that among patients with longer nightly CPAP usage, reductions in NLR exert a more pronounced effect on long-term treatment outcomes, underscoring the critical role of adherence. This also suggests that clinicians should emphasize consistent CPAP use when advising patients to achieve better glycaemic control.
Our ROC analysis indicates comparable predictive performance between reductions in NLR and PLR. Although both NLR and PLR are inflammatory indicators, they have different mechanisms. NLR is often associated with acute inflammation and activation of immune responses, while PLR is often associated with chronic inflammation or blood coagulation. These two factors predict the long-term efficacy of CPAP from different perspectives and can both serve as potential predictors of glycaemic improvement, providing more choices for clinical doctors’ judgment. The development of these biomarkers also provides a new method for predicting the long-term efficacy of CPAP treatment in T2DM patients with OSA, which helps to develop personalized treatment plans.
NLR and PLR, as simple, economical, and easily obtainable blood inflammatory markers, have certain clinical monitoring value. In patients with T2DM combined with OSA, evaluating changes in NLR and PLR within 7 days after CPAP treatment not only helps assess the trend of inflammatory responses but may also provide information for predicting glycaemic control after CPAP therapy.
This study has several limitations. First, its retrospective design introduces potential selection bias. Second, the relatively small sample size limits the ability to fully exclude the influence of other factors on long-term glycaemic control. Therefore, in the future, more comprehensive influencing factors can be included, and dynamic analysis of factor changes can be incorporated to conduct prospective, larger scale randomized controlled trials. The AUC values for the reductions in NLR and PLR were 0.602 and 0.601, respectively, demonstrating a moderate predictive capability. However, the relatively low AUC values suggest that these markers alone may have limited effectiveness in predicting glycaemic control following CPAP treatment. Future studies should consider integrating NLR and PLR with additional metabolic and inflammatory markers to develop a multifactorial prediction models, thereby improving overall predictive accuracy.
Conclusion
This study indicates that reductions in NLR and PLR within seven days following CPAP treatment independently predict long-term glycaemic control, with comparable predictive efficacy. The reduction in NLR exhibits a stronger association with glycaemic control among patients who maintain longer nightly CPAP usage. This study provides a reference for predicting CPAP efficacy in patients with T2DM and OSA.
Data Sharing Statement
Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available. Mengmeng Zhang is the lead author; Fuzai Yin is the correspondence author.
Ethics Clearance
This paper has been reviewed by relevant departments of our hospital, such as the Science and Education Department, Medical Department and Ethics Committee of Qinhuangdao First Hospital. The research content involved in this research meets the requirements of medical ethics and academic morality of our hospital, and the research content is reasonable, the risks are controllable, and there are no violations. The relevant research carried out is in line with the safe, standardized and true scientific research guiding principles, and in line with the requirements of the clinical research ethics code.
Informed Consent
All the selected patients were informed and agreed to the study and provided an informed consent to participate.
Funding
There is no funding to report.
Disclosure
The authors declare that there is no competing interest associated with the manuscript.
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