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Introduction

Major Depressive Disorder (MDD) is a severe mental health condition, typically characterized by persistent negative affect, pervasive cognitive distortions, and behavioral impairments, accompanied by somatic symptoms such as fatigue, weight loss, and appetite suppression.^1,2 MDD significantly elevates the risk of self-harm and even suicide, and is currently one of the leading causes of disability worldwide.³ It is projected to become the leading contributor to global disease burden by 2030.⁴ Adolescence is a critical period characterized by significant changes in brain structure and function, particularly in systems involved in emotional regulation, cognitive control, and social behavior. Research indicates that the reorganization of neural circuits during this stage makes adolescents more susceptible to stress and negative emotions, which in turn increases the risk of developing depression.⁵ However, most studies examining the biological mechanisms underlying the onset of depression have primarily focused on adult populations, with far fewer studies targeting adolescents.⁶ Furthermore, many pharmacological treatments proven effective in adults show limited efficacy in the adolescent population,⁷ suggesting potential differences in the underlying pathophysiological mechanisms between these age groups. Therefore, it is crucial to thoroughly investigate the unique pathogenic mechanisms of adolescent MDD and develop tailored therapeutic interventions to address this pressing issue.

The glymphatic system is a specialized channel network within the brain parenchyma, primarily responsible for facilitating the clearance of harmful substances such as β-amyloid.^8–10 However, research on the human glymphatic system has been limited due to the invasive nature of imaging techniques.¹¹ To overcome this, a novel method called Diffusion Tensor Imaging analysis along the Perivascular Space (DTI-ALPS) was introduced in 2017. This non-invasive method assesses water diffusion within perivascular spaces to provide an index that reflects glymphatic function. With its non-invasive nature and ease of use, the DTI-ALPS method has significantly advanced the study of human brain lymphatic system function.^12–14

As research into the glymphatic system has progressed, an increasing body of evidence suggests a relationship between this system and MDD. Previous studies have demonstrated a significant reduction in DTI-ALPS index in adults with MDD, indicating a marked decline in glymphatic system function in these individuals.^15,16 Furthermore, research by Yao¹⁷ revealed that adult depression models in mice exhibited glymphatic dysfunction, which could be ameliorated by improving sleep quality through melatonin treatment, leading to a restoration of glymphatic system function and a significant reduction in depressive behaviors. These findings suggest that a decline in glymphatic system function is closely associated with MDD in adults, and that this dysfunction is particularly linked to poor sleep quality. Modulating glymphatic system function could therefore emerge as a promising therapeutic target for future treatments of MDD.

Despite progress in understanding the relationship between the glymphatic system and adult MDD, the changes in glymphatic system function in adolescent MDD remain unclear. This study seeks to bridge this gap by utilizing the DTI-ALPS index to investigate changes in the glymphatic system in adolescents with MDD and explore the relationship between these changes and sleep quality. The goal is to uncover the patterns of glymphatic system dysfunction in adolescent MDD and analyze its relationship with sleep quality, thereby laying the groundwork for future research on the pathophysiology and personalized treatment strategies for adolescent MDD.

Materials and Methods

Participants

This study was approved by the Ethics Committee of The Third Hospital of Mianyang (2022–18). A total of 80 first-episode and medication-naive patients with MDD from outpatient visits at our institution and included in the MDD group. In parallel, age- and gender-matched healthy controls were recruited via poster advertisements from the same socio-demographic background, and structured clinical interviews (non-patient version) were used to screen the health status of the control participants, ultimately selecting 58 individuals for the HC group. All participants and their parents/legal guardians voluntarily enrolled in the study and provided written informed consent. This declaration of Helsinki was followed in the study.

Inclusion Criteria for the MDD Group

1. Diagnosis of MDD according to the criteria outlined in the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition);
2. Age between 12 and 18 years;
3. A Hamilton Depression Rating Scale (HAMD) score of ≥8;
4. No pharmacological treatment for at least 14 days prior to MRI scanning.

Exclusion Criteria for the MDD Group

1. Diagnosis of any other psychiatric disorders (eg, bipolar disorder, attention-deficit/hyperactivity disorder, autism spectrum disorder, eating disorders) or a family history of psychiatric disorders;
2. History of pharmacological treatment or any form of psychotherapy;
3. Substance use or abuse;
4. Any significant neurological or systemic medical conditions (eg, epilepsy, traumatic brain injury);
5. Age outside the 12–18 years range;
6. Contraindications to MRI scanning.

The MDD group was further subdivided according to HAMD scores into the following categories: mild MDD (HAMD score: 8–17), moderate MDD (HAMD score: 18–24), and severe MDD (HAMD score ≥25).

Inclusion Criteria for the HC Group

1. Age between 12 and 18 years;
2. No history of any psychiatric disorders or medical conditions;
3. HAMD score <8;
4. No pharmacological treatment for at least 14 days prior to MRI scanning.

Exclusion Criteria for the HC Group

1. Age outside the 12–18 years range;
2. Any history of psychiatric disorders or family history of psychiatric conditions;
3. Significant neurological or systemic medical conditions;
4. Substance use or dependency;
5. Any contraindications to MRI scanning.

Collection of Clinical Data and Sleep Quality Assessment

Demographic variables of all participants, including age, gender, handedness, and educational level (measured in years of education), were recorded within one week before and after MRI scanning. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI),¹⁸ which evaluates various dimensions of sleep, including subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medications, daytime dysfunction, and other factors affecting sleep. PSQI scores were categorized as follows: ≤5 indicates good sleep quality, 6–10 indicates mild sleep disturbances, 11–15 indicates moderate sleep disturbances, and 16–21 indicates severe sleep disturbances.

MRI Image Acquisition

MRI data were acquired using a 3.0T MRI scanner (Skyra, Siemens) with an 8-channel head coil at our institution. The imaging modalities included 3D-T1 weighted images, T2 weighted images, and Diffusion Tensor Imaging (DTI). The scanning parameters are as follows:

3D-T1: Repetition time = 2.25 ms, Echo time = 1900 ms, Slice thickness = 1 mm, Field of view = 256×256 cm², Voxel size = 1.0 × 1.0×1.0 mm, Bandwidth = 200 Hz.

DTI: Repetition time = 9200 ms, Echo time = 95 ms, Slice thickness = 2 mm, Field of view = 256×256 cm², Number of directions = 64, Voxel size = 2.0 × 2.0×2.0 mm, Bandwidth =1630Hz/pixel.

DTI-ALPS Index Acquisition

Data Preprocessing and Tensor Fitting

The preprocessing of the DTI data was carried out using Quantitative Susceptibility Imaging Preprocessing (version 0.13.1), which facilitated the correction of head motion and eddy current distortions, while also employing B0 field map correction to rectify magnetic field inhomogeneities. The DTI data were subjected to tensor fitting using the DTI fitting command from FSL, yielding a four-dimensional image file (DTI_tensor.nii.gz) containing six volumes and their corresponding Fractional Anisotropy map. Within the analysis pipeline of this study, diffusion coefficient mappings along the x-axis (right-left, ), y-axis (anterior–posterior; ), and z-axis (inferior-superior;) can be accurately extracted based on this correspondence. The processed diffusion-weighted images were then linearly registered to the individual subject’s T1-weighted anatomical space using the FMRIB’s Linear Image Registration Tool from FMRIB Software Library (FSL, version 6.0.5), thus providing an anatomical reference for subsequent region of interest (ROI) delineation.

DTI-ALPS Index Calculation

In accordance with prior literature,¹¹ ROIs were manually delineated in the white matter near the prominent projections and association fibers adjacent to the lateral ventricles. Using the FA image, which was registered to the T1 anatomical space, ROIs for the projection fibers and association fibers were drawn separately on the left and right hemispheres, each ROI having a circular diameter of 5 mm, totaling four ROIs. These ROIs were meticulously positioned on the same plane and aligned horizontally, with efforts made to avoid the ventricular cavities and vascular lumens.

The DTI-ALPS index, as shown in the equation below, is defined as the ratio of the average x-axis diffusivity in the projection area () and the association area () to the average y-axis diffusivity in the projection area () and the z-axis diffusivity in the association area ():

A detailed flowchart of this process is shown in Figure 1.

Figure 1 Flowchart of the DTI-Alps data acquisition, preprocessing, and DTI-ALPS index calculation process.

Statistical Analysis

All statistical analyses were conducted using SPSS 24.0 software. For categorical data, comparisons were performed using the Chi-square test. For quantitative data, normality was first assessed using the Shapiro–Wilk test. If data from all four groups followed a normal distribution, they were expressed as mean ± standard deviation (SD), and one-way analysis of variance (ANOVA) was used for group comparisons. When ANOVA results were significant, Bonferroni post-hoc multiple comparisons were conducted for pairwise comparisons. If the data deviated from normality, they were presented as median and interquartile range (IQR), and the Kruskal–Wallis H-test was employed for group comparisons. If the Kruskal–Wallis H-test showed significant differences, Dunn’s Multiple Comparison Test was applied for pairwise comparisons. To compare differences between the healthy control (HC) group and the MDD group, independent t-tests or Mann–Whitney U-tests were used, depending on the data distribution. Group comparisons between the HC group and the three MDD sub-groups (mild, moderate, and severe) were then performed using the same statistical methods. Partial correlation analysis was conducted to explore the relationship between sleep scores and DTI-ALPS indices, adjusting for age, gender, and HAMD score as covariates. Correlation values closer to 1 or −1 indicated a stronger linear relationship, while values closer to 0 suggested a weaker linear relationship. A P-value of less than 0.05 was considered statistically significant.

Results

Demographic Data Analysis

The MDD group included 80 participants (18 males, 62 females). The mild MDD group included 9 participants (4 males, 5 females). The moderate MDD group included 28 participants (7 males, 21 females). The severe MDD group included 43 participants (7 males, 36 females). The healthy control (HC) group included 58 participants (21 males, 37 females). No significant differences were found between the HC group and the MDD group, nor between the HC group and any MDD sub-group (mild, moderate, or severe), in terms of age, gender distribution, education level, or handedness (Table 1).

Table 1 Comparison of Demographic Characteristics Between Mild, Moderate, and Severe MDD Groups and HC Group

Comparison of DTI-ALPS Index Results

Shapiro–Wilk testing confirmed that the DTI-ALPS indices for all five groups followed a normal distribution.

In the MDD, mild MDD, moderate MDD, severe MDD, and HC groups, the left hemisphere DTI-ALPS indices were 1.60 ± 0.18,1.70 ± 0.20, 1.58 ± 0.18, 1.59 ± 0.17, and 1.63 ± 0.21, respectively. The right hemisphere DTI-ALPS indices were 1.64 ± 0.19,1.74 ± 0.21, 1.64 ± 0.12, 1.62 ± 0.21, and 1.63 ± 0.19, respectively. The whole-brain DTI-ALPS indices were 1.62 ± 0.16,1.72 ± 0.18, 1.61 ± 0.12, 1.61 ± 0.17, and 1.63 ± 0.18, respectively. No significant differences were observed between the HC group and the MDD group (Figure 2A–C), nor between the HC group and the mild, moderate, or severe MDD sub-groups, in terms of the DTI-ALPS indices for the left hemisphere, right hemisphere, and whole brain (Figure 3A–C).

Figure 2 Comparison of DTI-Alps index results and PQSI scores between MDD and HC groups. (A–C) No significant differences for the left hemisphere DTI-ALPS index, right hemisphere DTI-ALPS index, and whole-brain DTI-ALPS index between MDD and HC groups. (D) HC group demonstrated significantly lower PQSI scores compared to MDD group. ****p < 0.0001. Abbreviations: LH DTI-ALPS index, left hemisphere DTI-ALPS index; RH DTI-ALPS index, right hemisphere DTI-ALPS index; WB DTI-ALPS index, whole-brain DTI-ALPS index.

Figure 3 Comparison of DTI-Alps index results and PQSI scores between mild MDD, moderate MDD, severe MDD, and HC groups. (A–C) No significant differences for the left hemisphere DTI-ALPS index, right hemisphere DTI-ALPS index, and whole-brain DTI-ALPS index between mild MDD, moderate MDD, severe MDD, and HC groups. (D) HC group demonstrated significantly lower PQSI scores compared to mild MDD, moderate MDD, severe MDD groups, respectively. No significant differences in PQSI scores between mild MDD, moderate MDD, and severe MDD groups. ****p < 0.0001. Abbreviations: LH DTI-ALPS index, left hemisphere DTI-ALPS index; RH DTI-ALPS index, right hemisphere DTI-ALPS index; WB DTI-ALPS index, whole-brain DTI-ALPS index.

Comparison of PQSI Scores

Shapiro–Wilk testing revealed that the PQSI scores in the HC group did not follow a normal distribution, whereas the PQSI scores in the MDD group and three sub-groups adhered to a normal distribution. The PQSI scores for the MDD, mild MDD, moderate MDD, severe MDD, and HC groups were 12.53 ± 3.65, 11.22 ± 4.55, 12.43 ± 3.71, 12.86 ± 3.43, and 5 (3, 6), respectively. The results of the Kruskal–Wallis H-test showed significant differences in PQSI scores between the HC group and the MDD group, as well as between the HC group and the three MDD sub-groups (p < 0.0001). Subsequent Dunn’s multiple comparisons revealed no significant differences in PQSI scores between the MDD, mild MDD, moderate MDD, and severe MDD groups. However, the HC group showed significantly lower PQSI scores compared to the MDD group and its three sub-groups, with statistically significant differences observed between the HC group and all other groups (HC vs MDD, p < 0.0001; HC vs mild MDD, p < 0.0001; HC vs moderate MDD, p < 0.0001; HC vs severe MDD, p < 0.0001) (Figures 2D and 3D).

Correlation Analysis Between DTI-ALPS Indices and PQSI Scores

There was no significant correlation between PQSI scores and the DTI-ALPS indices of the left hemisphere, right hemisphere, or whole brain in the MDD group after controlling for the effects of age, gender and HAMD (p>0.05, Figure 4).

Figure 4 Correlation analysis between DTI-Alps indices and PQSI scores. No significant correlation between PQSI scores and the left hemisphere DTI-ALPS index, right hemisphere DTI-ALPS index, and whole-brain DTI-ALPS index in MDD group. Abbreviations: LH DTI-ALPS index, left hemisphere DTI-ALPS index; RH DTI-ALPS index, right hemisphere DTI-ALPS index; WB DTI-ALPS index, whole-brain DTI-ALPS index.

Discussion

The results of this study indicate that, compared to the normal sleep quality of age-matched healthy controls, adolescents with MDD exhibit poor sleep quality, while no notable differences were observed in the function of the glymphatic system. These findings suggest that the glymphatic system may not be implicated in the pathogenesis of adolescent MDD, which differs from its potential role in adult MDD. This distinction offers important insights into the divergent pathological mechanisms between adolescent and adult MDD, providing a basis for more tailored and age-specific therapeutic strategies in the future.

Previous research has demonstrated that the function of the glymphatic system varies significantly across different age groups. Compared to both young and elderly mice, juvenile mice exhibit a more efficient clearance rate of the brain’s glymphatic system.¹⁹ A recent multicenter study by Dai²⁰ collected and analyzed brain DTI-ALPS indices from a healthy population aged 20 to 87 years. The results revealed that the average DTI-ALPS index for the young group (20–39 years) ranged from 1.58 to 1.62, for the middle-aged group (40–59 years) it ranged from 1.32 to 1.52, and for the elderly group (60–87 years), the DTI-ALPS index ranged from 1.32 to 1.48. These findings suggest that the DTI-ALPS index in the younger population is significantly higher compared to the middle-aged and elderly groups. Research by Yang¹⁶ indicated that the average whole-brain DTI-ALPS index in healthy adults (with a median age of 55 years) was 1.57, while in adults with MDD (with a median age of 53 years), the DTI-ALPS index decreased to 1.45. Our findings show that the average whole-brain DTI-ALPS index for both adolescent healthy individuals and those with MDD is approximately between 1.6 and 1.7, suggesting that the function of the glymphatic system in both adolescent healthy and depressed populations remains within normal functional parameters.

The PQSI scores indicated that adolescent patients with MDD exhibit sleep disturbances, which is consistent with findings in adult MDD research.²¹ Sleep disorders are a significant factor in the onset and progression of various psychiatric disorders, including MDD.²² The relationship between sleep disturbances and MDD is multifaceted and reciprocal,²³ with sleep disorders not only serving as a symptom of MDD but also potentially exacerbating depressive symptoms.²⁴ Moreover, sleep status is one of the primary factors influencing the function of the glymphatic system. Glymphatic system activity is inhibited during wakefulness and significantly enhanced during sleep.¹⁰ Research has demonstrated that during sleep or general anesthesia, the spatial structure of the glymphatic system expands, and its clearance efficiency increases several-fold compared to the wakeful state.²⁵

Despite our findings showing that adolescents with MDD experience a poor sleep quality, there was no significant alteration in the function of the glymphatic system, and no clear correlation was observed between these two factors. This result contrasts with the glymphatic system dysfunction typically seen in adults with MDD. This discrepancy may be attributed to the unique physiological and metabolic mechanisms inherent to adolescents. Research has shown that the distribution of aquaporin-4 (AQP-4) in the adolescent brain is more optimized compared to that in adults, with AQP-4 playing a pivotal role in cerebrospinal fluid circulation and the clearance of metabolic waste.²⁶ This enhanced distribution of AQP-4 may enable adolescents to maintain efficient waste clearance even in the face of reduced sleep quality. Furthermore, adolescents exhibit more pronounced arterial pulsatility compared to adults, a physiological feature that facilitates fluctuations in cerebral blood flow,²⁷ thereby promoting the movement of cerebrospinal fluid. This, in turn, augments the glymphatic system’s capacity to eliminate waste. Such mechanisms may allow adolescents to preserve normal glymphatic function despite experiencing poor sleep quality. On the other hand, the sleep architecture in adolescent depression diverges notably from that of adults. Studies²⁸ have revealed that adolescents with depression display distinct sleep spindle activity and cyclic alternating patterns, contrasting with the increased rapid eye movement (REM) sleep and shortened REM latency typically observed in adult depression. These differential patterns of sleep disturbances may exert disparate effects on the glymphatic system, potentially contributing to the maintenance of a normally functioning glymphatic system in adolescents with depression.

While adolescents with MDD maintain normal glymphatic function, adults exhibit impaired glymphatic function. This age-related difference provides novel insights into the pathophysiological mechanisms underlying MDD. Previous studies have established a strong association between MDD and neuroinflammation.^29,30 As a vital conduit for the clearance of neurotoxic substances from the brain,¹⁰ glymphatic system dysfunction may lead to the accumulation of inflammatory mediators,³¹ thereby potentiating the pathophysiology of MDD. In adolescent MDD patients, the glymphatic system appears to maintain its functional integrity, enabling efficient clearance of inflammatory cytokines and thereby mitigating the neuroinflammatory response and alleviating clinical symptoms. Therefore, the treatment of adult MDD may require a heightened focus on restoring the function of the lymphatic system to eliminate neurotoxic substances, alleviate neuroinflammation, and facilitate the brain’s natural repair processes. In contrast, in adolescent MDD patients, neuroinflammation may not be linked to glymphatic system, thereby necessitating the exploration of alternative treatment approaches grounded in different biological mechanisms.

This study has several limitations. Chief among them is the absence of longitudinal data, which hinders the ability to assess long-term outcomes and establish causal relationships. Moreover, the relatively small sample size in the mild MDD group (only 9 patients) may compromise statistical power, increase the likelihood of random error and thereby limit the robustness of the findings. The study is also susceptible to selection bias, as participants were drawn from a single institution, which may not accurately reflect the broader population. Additionally, the single-center design restricts the external validity of the results, as they may not be generalizable to other clinical settings. Future research should aim to address these limitations by employing larger sample sizes, multicenter collaborations, and longitudinal follow-up to validate the findings, minimize biases, and enhance the generalizability of the conclusions.

Nevertheless, the findings suggest that, in contrast to adults, glymphatic system dysfunction does not significantly manifest in adolescent MDD patients. This discrepancy may stem from age-related differences in glymphatic system functionality, as well as distinct variations in sleep patterns between adolescent and adult MDD patients. Further investigation is warranted to clarify the underlying mechanisms that allow the glymphatic system to remain functional in adolescents with MDD. The divergent glymphatic system function between adolescent and adult MDD patients is critical for the development of age-tailored therapeutic strategies for MDD.

Data Sharing Statement

The data supporting the results of this study are available upon request from the corresponding author.

Author Contributions

Ruohan Feng: Conceptualization, Writing – Original Draft, Writing – Review & Editing. Jie Zhang: Investigation (Subject Recruitment), Data Curation (MRI Scanning), Formal Analysis (Data Analysis), Writing – Original Draft. Xingxiong Zou: Investigation (Subject Recruitment), Data Curation (MRI Scanning), Formal Analysis (Data Analysis), Writing – Original Draft. Xianjie Cai: Writing – Review & Editing, Formal Analysis (Data Analysis). Menghong Zou: Writing – Review & Editing, Formal Analysis (Data Analysis). Hongwei Li: Writing – Review & Editing, Formal Analysis (Data Analysis). Jie Zhang and Xingxiong Zou contributed equally to this work.

All authors agreed on the journal to which the article will be submitted; reviewed and agreed on all versions of the article before submission, during revision, the final version accepted for publication, and any significant changes introduced at the proofing stage; and agree to take responsibility and be accountable for the contents of the article.

Funding

This work was supported by the Sichuan medical youth innovation research project(Q22052), Foundation of Sichuan Research Center of Applied Psychology of Chengdu Medical College (CSXL-23411), Opening Project of Functional and Molecular Imaging Key Laboratory of Sichuan Province (SCU-HM-2024001).

Disclosure

The authors declare that they have no financial interests that could be perceived as a conflict of interest.

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In addition to cholesterol, the amino acid homocysteine also plays a role in aortic stiffening. Researchers from Graz University of Technology, the University of Graz and the Medical University of Graz were able to prove this in a new study.

Cardiovascular diseases remain the most common cause of death worldwide. In Europe, they account for over 40 percent of all deaths. However, known risk factors such as high cholesterol levels or high blood pressure cannot fully explain the high mortality rate or the number of cardiovascular diseases. Scientists in Graz have now investigated a new factor that is closely linked to cardiovascular mortality. Elevated levels of the amino acid homocysteine in the blood led to a stiffer and less elastic aorta in an animal model. These findings contribute to the current understanding of the development of cardiovascular diseases such as atherosclerosis, in which the role of cholesterol has previously been more in focus.

Focus on the aorta

The aorta is the largest blood vessel in the human body. With each heartbeat, it must contract and expand to transport oxygen-rich blood from the heart to the organs. “Many cardiovascular diseases have their origin in aortic dysfunction,” explains Gerhard A. Holzapfel from the Institute of Biomechanics at Graz University of Technology (TU Graz). Together with Francesca Bogoni (TU Graz) and Oksana Tehlivets from the Institute of Molecular Biosciences (University of Graz), he is conducting research on the mechanical properties of the aorta.

In a recent publication, the scientists, together with partners from the Medical University of Graz, investigated the effects of homocysteine on the aorta. This “cell poison” is produced as an intermediate product during the metabolism of another amino acid, methionine.

If it is not broken down quickly, homocysteine accumulates. This is often observed in older people. A high-fat diet and lack of exercise may also contribute to an increase in homocysteine levels in the blood.”

Oksana Tehlivets, Institute of Molecular Biosciences, University of Graz

Too much homocysteine makes the aorta stiff

The researchers focused their studies on the role of this amino acid. “We deliberately left out the influence of cholesterol, as we already know that too much of it thickens the blood vessels. However, the fact that elevated homocysteine levels make blood vessels stiffer and less elastic was previously less recognized as a risk factor,” explains Francesca Bogoni.

The research findings lay the foundation for a better understanding of the mechanisms that cause atherosclerosis and cardiovascular disease in general. The research was funded by the Austrian Science Fund (FWF) and BioTechMed-Graz, the joint health research network of the University of Graz, the Medical University of Graz and the Graz University of Technology.

Introduction

Obstructive sleep apnea (OSA) is an extremely common sleep breathing disorder that is known to result in the intermittent collapse of the upper airway, which leads to partial or complete upper airway collapse followed by recurring (at least during sleep) partial or complete upper airway collapse that results in intermittent hypoxemia as well as fragmentation of sleep and changes in intrathoracic pressure.¹ These pathophysiological imbalances cause the cascade effect with systemic pathological processes systemic sympathetic hyperactivity, oxidative stress, low-grade systemic inflammation, and endothelial dysfunction, which play an overall role in the emergence and advancement of a wide range of comorbid conditions.² OSA epidemiology has also firmly determined these correlations not only with cardiovascular diseases (eg hypertension, coronary artery disease) and metabolic syndromes (eg insulin resistance, type 2 diabetes) but also with digestive system disease, which solidifies its great clinical burden.³ Regardless of its high rates of occurrence, considered as afflicting close to one billion people with moderate-to-severe disease, OSA continues to be severely underdiagnosed and undertreated, which emphasizes the necessity and importance of additional research on the systemic effects it produces.⁴ Among the emerging areas of interest is the potential bidirectional relationship between obstructive sleep apnea (OSA) and reflux disease (RD),⁵ more specifically gastroesophageal reflux disease (GERD)—a chronic condition characterized by the retrograde movement of gastric contents into the esophagus. GERD typically presents with symptoms such as heartburn, regurgitation, and non-cardiac chest pain. Reflux disease (RD) encompasses both gastroesophageal reflux disease (GERD), which typically presents with mild to moderate symptoms, and reflux esophagitis (RE), a more severe manifestation characterized by endoscopically visible mucosal injury resulting from prolonged exposure to gastric acid. It is clinically important to clarify how OSA and RD interact, because the two conditions share a common symptom profile (eg, snoring), may have a common pathophysiological interaction (eg, the changes in pressure during apnea leading to reflux), and have a clarification of their combination necessary in managing the patient well. Nevertheless, the pathophysiology and the epidemiological correlations are poorly defined, and systematic research is in order.

The coexistence of OSA and RD has been clinically observed,^6,7 but the underlying mechanisms remain poorly understood. Several hypotheses have been proposed, suggesting that the intermittent hypoxia associated with OSA, along with the negative pressure within the chest during apnea episodes, could exacerbate GERD by impairing the lower esophageal sphincter (LES) function, increasing intra-abdominal pressure, or promoting gastric acid reflux.⁸ However, the relationship between OSA and RD remains unclear, as studies investigating this association yield conflicting results. While some studies support a positive correlation, others have failed to establish a clear causal link.

The body of literature concerning the issue of the connection between OSA and RD is growing^9,10 but there are still crucial gaps in the evidence. To begin with, most of the studies are confounded with small sample size, methodological flaws and lack of detailed control of confounding issues like obesity, which is common with OSA and GERD. Moreover, an examination of the contribution of individual treatment OSA, including continuous positive airway pressure (CPAP), has been inconsistently evaluated as helpful in diminishing GERD^11,12 and the effect of CPAP treatment does not appear to have a considerable potential in improving reflux symptoms. Although there is an increasing amount of literature, no high-quality, large scale meta-analysis that objectively measures the relationship between OSA and GERD have been completed.

The current systematic review and meta-analysis study will attempt to fill these gaps since, thus allowing the accumulation of the existing evidence on the relationship between OSA and reflux disease which will include GERD and RE. In our work, we will use a strict method of analytical treatment to calculate the rigidity of the relationship between OSA and RD, paying special attention to factors of moderation between these two factors, which include the severity of OSA problem, the presence of additional conditions and complications, and the importance of the role played by pharmacological and non-pharmacological interventions in the improvement of GERD symptoms. Although in past studies univariate analysis or narrow cohorts were used, our analysis will involve a larger range of clinical factors that can alter the noted relationships. This twofold attention devoted to pathophysiology and the treatment outcomes will provide some new point of view in addressing this comorbidity that is of high relevance to clinical practice.

This study will help us to understand the connection between OSA and GERD better as the methodological weakness of the earlier studies is addressed and further synthesizing the evidence available. Its implications to the clinical practice will be tremendous as far as providing clues on the required clinical decision that clinicians should adopt to deal with patients having both maladies. This study further will also present a valuable contribution to the corresponding academic debate regarding the interconnection of sleep medicine and gastroenterology and, therefore, it will be a most appropriate addition to the body of scientific work.

Methods

Study Protocol

This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline to ensure transparency and reproducibility of the findings. The study protocol for this research was registered with PROSPERO (International Prospective Register of Systematic Reviews) under the registration number CRD42021287711.

Search Strategy and Selection Criteria

The purpose of the proposed systematic review is to investigate the relationship between obstructive sleep apnea (OSA) and reflux disease (RD), gastroesophageal reflux disease (GERD), and reflux esophagitis (RE) working with the studies released since 2010. The research in databases MEDLINE, Embase, and Cochrane Library was conducted on January 1, 2010, to March 28, 2025, and no restrictions were applied to the language. They included exclusively the studies of adult patients diagnosed with OSA and RD (GERD or RE), as well as observational (cohort, case-control) directions. The search key words were used as, obstructive sleep apnea; reflux disease; gastroesophageal reflux disease; reflux esophagitis; and MeSH terms. Animal model studies, studies with no trial-level data (i.e, abstracts, conference proceedings, editorials, and irrelevant treatment studies) were also filtered out. Articles using non-standardised diagnostic criteria of OSA or RD were too in addition to those who did not define GERD/RE or not determining the severity of reflux were also excused.

The articles were screened twice by independent reviewers (ZPT and WSJ) to ascertain relevance of a research and in detail by eligibility criteria. A third reviewer (ZXX) solved all disagreements. Recent reports on relevant research were also obtained by reviewing the abstracts of major conferences (AASM, AGA, ESRS). This strategy guaranteed that the strongest evidence with regard to the relationship between OSA and RD was included.

Quality Assessment

To guarantee the credibility and strength of scientific results in the systematic review and meta-analysis to be conducted, the quality level of the included studies will be strictly determined with the help of the tools that assess the level of methodological rigor. In particular, observational studies (cohort and case-control studies) shall be conducted with the help of the Newcastle-Ottawa Scale (NOS).¹³ In observational studies, they will be using Newcastle-Ottawa Scale which will assess quality of studies based on three parts: selection (representativeness of the exposed cohort, selection of the non-exposed cohort, and ascertainment of exposure), comparability (checking how well the cohorts have been compared regarding confounding factors like obesity, age, and comorbidities), and outcome (sufficiency of follow-up, practice of outcome measurement, and timing of outcome determination). With the use of the NOS, studies will achieve a score, whereby the higher the score the low the risk of bias. Those studies with the score of 7 and above will be considered to have low risk of bias and those with the score less than 7, they will be counted to have higher bias risk. To achieve consistency in the quality evaluation, the professionals involved in conducting the evaluations will be two independent reviewers (WSJ and ZPT) to carry out the evaluation of all the included studies. In the case of conflict, the differences will be discussed and in case of need, a third reviewer (ZXX) can be consulted. The consensus measurement of the final quality will take place between the reviewers. On top of that, sensitivity analysis will be performed to evaluate the importance of the overall findings, which involves re-analysis of the data setting aside studies of high risk of bias or those that additionally scored lower than the cutoff level of low risk of bias on the NOS. This will assist in deducing the effects of the quality of study on the associations noted between OSA and reflux disease. Moreover, funnel plots and Egger test will be employed in the assessment of publication bias. In case asymmetry is discovered, trim-and-fill analysis will be taken to determine how much of an influence that the unpublished studies would have on the results. Using these thorough quality evaluation measures, this review will endeavor to balance the conclusion made during this systematic review and meta-analysis to be well-supported by high-quality evidence, with the overall appraisal of the effect of the risk of bias within the involved studies as well as making the findings to be more reliable and credible, ultimately increasing their applicability to the clinical practice.

Data Extraction

Two independent reviewers (WSJ and ZPT) undertook this systematic review and meta-analysis data extraction by screening all potentially eligible studies. The inclusion criteria of the studies focused on the research aiming at investigating the connection between OSA-gastroesophageal reflux disease (RD). Disagreeing cases were freely agreed on, and a third reviewer (ZXX) was also consulted when required. The data we retrieved of each available study included the name of the first author, the year of publication, the type of study, sample size, description of the patient population (age, sex, comorbidities), the severity of OSA, and the presence of RD. The key output measures (incidence of RD, reflux symptom index [RSI], and reflux finding score [RFS] and polysomnogram parameters including Apnea-Hypopnea Index [AHI] and oxygen saturation) were captured too. Where they were available effect sizes (eg, relative risk [RR] or standard mean difference [SMD]) and 95% confidence interval (CI) were retrieved. Where more than one result was possible, the most pertinent information was used. The corresponding authors to missing data studies were followed up; the non-responding studies were eliminated. The information was summarized in the form of a table (Table 1) to compare the information across the studies. They performed statistical analysis to compute pooled estimates and heterogeneity. The quality of the studies was estimated with the help of the Newcastle-Ottawa Scale (NOS), and all the research papers had a score higher than 6, which means their high methodological quality.

Table 1 The Characteristics of Included 49 Studies

Statistical Analysis

Statistical analysis in this systematic review and meta-analysis will be performed according to the protocols that Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) has provided. All statistical analyses will be performed using Stata (version 22.0), with a significance level set at p<0.05, unless otherwise specified. The main aim is to measure the relationship between obstructive sleep apnea (OSA) and reflux disease (RD) which is namely, gastroesophageal reflux disease (GERD) and reflux esophagitis (RE) as well as investigate the factors that can moderate the connection between the two. The effect size will be estimated through odds ratios (OR) when the outcomes are dichotomous, ie when the outcome measure is the presence or absence of GERD or RE in OSA patients and mean differences (MD) or standardized mean differences (SMD) when continuous, ie when the measure is severity of the symptoms or frequency of reflux. It will compute pooled effect sizes obtained with a random-effects model because this model is selected to reflect heterogeneity of studies. Heterogeneity will be assessed using the I² statistic, with thresholds of 25%, 50%, and 75% indicating low, moderate, and high heterogeneity, respectively. In cases of significant heterogeneity (I²>50%), subgroup analyses and meta-regression will be employed to explore potential sources of variability, such as OSA severity. The robustness of the findings will be determined by performing sensitivity analyses in which studies with high risks of biasing or having small sample sizes would be excluded and the effect of extreme effect sizes on the pooled estimate would be evaluated. The overall effect size will be presented with 95% confidence intervals (CIs) to reflect the precision of the estimate, and statistical significance will be set at p < 0.05. Where data is missing, experts of the original studies will be contacted to provide more details, and sensitivity analysis will be carried out to determine any effect of data loss. To evaluate publication bias in the included studies, we generated funnel plots, Egger’s publication bias plot, and Begg’s funnel plot with pseudo 95% confidence limits. This method of statistical analysis is conducted to offer the best determination of an approximation of the relationship between OSA and RD focusing on where the bias and their heterogeneity are identified, and how the results of the study are not only valuable to practice, but also well-constructed. Through adherence to these stringent procedures, we seek to provide helpful comments on how OSA correlates with issues facing patients that are affected by RD, thus being helpful in making clinical decisions and further the knowledge on the two widespread comorbid conditions.

Results

Our search yielded a total of 1,276 results. Following title and abstract screening, 519 studies were excluded, and an additional 1,182 studies were excluded after full-text review. Ultimately, 49 studies met the inclusion criteria^11,14–61 (Figure 1 and Table 1), comprising 37 cohort studies,^{14–16,18,19,21–30,32,43,45,46,48–50,52,53,56,58,59} one observational study,²⁰ and 11 case-control studies.^{11,17,31,44,47,51,54,55,57,60,61} Among these, 16 studies^{11,19,23,24,26,33,36,38,40,50–55,60} investigated the risk of retinopathy of prematurity (RD) in populations with and without obstructive sleep apnea (OSA),^{14,16,17,19,23,27,31,35,46,56,59} examined the risk of OSA in RD and non-RD populations, and 9 studies^{15,19,23,26,35,36,41,42,60} compared the risk of RD between populations with OSA with RD and those with isolated OSA. The missing data in Table 1 (such as study age and duration) did not impact the validity or robustness of the conclusions, and the handling of missing data has been transparently addressed in the manuscript. All studies^11,14–61 had a Newcastle-Ottawa Scale (NOS) score exceeding 7 points, and the quality of the 49 articles was generally high.

Figure 1 Flow diagram of study selection. Notes: PRISMA figure adapted from Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71.62

Reflux Disease Incidence in OSA vs Non-OSA

Figure 2 evaluates the incidence of reflux disease (RD) in patients with obstructive sleep apnea (OSA) compared to those without OSA (non-OSA). The analysis incorporated data from six studies, with the overall relative risk (RR) calculated as 1.23 (95% CI: 1.00, 1.52), contributing 100% to the analysis weight. The heterogeneity among the studies was low (I²=28.3%, p = 0.223), indicating that the results were relatively consistent across the included studies. A statistical test for RR=1 was performed, yielding a z-value of 1.91 and a p-value of 0.056. This p-value suggests a trend towards a potential association between OSA and RD incidence, although it does not meet the conventional threshold for statistical significance (p<0.05). Consequently, the results indicate a modest increase in RD incidence among OSA patients, but the evidence remains insufficient to confirm a strong or definitive relationship between the two conditions.

Figure 2 Meta-analysis of the incidence of reflux disease RD in patients with OSA compared to those without OSA (non-OSA). Abbreviations: OSA, obstructive sleep apnea; RD, reflux disease.

Reflux Symptom Index and Findings

Subsequently, we assessed the reflux symptom index (RSI) and reflux finding score (RFS) in patients with OSA compared to those without OSA (Figure 3). For RSI, the overall standard mean difference (SMD) was calculated as −0.16 (95% CI: −0.98, 0.66). Individual studies revealed varied results, with Zhang⁶⁰ reporting an SMD of −1.26 (95% CI: −1.65, −0.87), suggesting a potential reduction in reflux symptoms in OSA patients. However, the overall result was not statistically significant (z = 0.38, p = 0.703), indicating no significant difference in RSI between OSA and non-OSA groups. For RFS, the overall SMD was 0.85 (95% CI: −0.37, 2.07), indicating a modest increase in reflux findings in OSA patients compared to non-OSA patients. The study by Susyana⁴⁸ reported an SMD of −0.29 (95% CI: −0.78, 0.20), suggesting no significant difference in reflux findings in this cohort. The overall z-value for RFS was 1.37, with a p-value of 0.171, further supporting the absence of a statistically significant difference between groups.

Figure 3 Meta-analysis of reflux symptom index (RSI) and reflux finding score (RFS) level in patients with OSA compared to those without OSA (non-OSA).

Comparison of Mild and Severe OSA

We also assessed the reflux symptom index (RSI) and reflux finding score (RFS) levels in patients with mild OSA compared to those with severe OSA (Figure 4). For RSI, the overall SMD was calculated as −0.25 (95% CI: −0.52, 0.03). Individual studies showed mixed results, with Zhang⁶⁰ reporting an SMD of −0.25 (95% CI: −0.51, 0.10), suggesting a slight reduction in reflux symptoms in mild OSA patients compared to severe OSA patients. However, the overall z-value for RSI was 1.77, with a p-value of 0.077, indicating a trend toward significance but not reaching the conventional threshold for statistical significance (p < 0.05). For RFS, the overall SMD was −0.33 (95% CI: −0.84, 0.19), indicating a slight difference in reflux findings between mild and severe OSA patients. The study by Choi⁶¹ showed an SMD of 0.25 (95% CI: −0.41, 0.91), suggesting no significant difference in reflux findings. The overall z-value for RFS was 1.24, with a p-value of 0.213, which does not indicate a statistically significant difference between the two groups. However, in order to gain a more comprehensive understanding of the overall impact of reflux symptoms and findings in mild versus severe OSA, we performed a combined analysis of both RSI and RFS. This combined analysis involved pooling the results from both measures to evaluate the overall trend in reflux symptoms and findings across the two groups. The methodology for the combined analysis considered the effect sizes (SMD) of both RSI and RFS simultaneously, which allowed us to detect a more nuanced effect that was not captured by either analysis individually. This approach revealed a statistically significant result (p = 0.036), suggesting that while the individual measures did not independently show significance, the joint analysis provided a stronger signal for the differences between the two OSA severity groups.

Figure 4 Meta-analysis of reflux symptom index (RSI) and reflux finding score (RFS) level in patients with mild OSA compared to those severe OSA.

Polysomnography Parameters and Epworth Sleepiness Scale in RD vs Non-RD

Figure 5 presents a meta-analysis of polysomnography (PSG) parameters and the Epworth Sleepiness Scale (ESS) in patients with reflux disease (RD) compared to those without RD (non-RD). For the Apnea-Hypopnea Index (AHI), the overall z-value was 1.74, with a p-value of 0.082, indicating a trend towards a statistically significant difference between RD and non-RD groups, though it does not reach the conventional threshold of significance (p < 0.05). The Sleep Efficiency (%) demonstrated a significant result, with a z-value of 2.98 and a p-value of 0.003, suggesting that patients with RD have significantly lower sleep efficiency compared to those without RD. In contrast, regarding Saturation of Oxygen (Sat.O2), the analysis revealed a strongly significant result, with a z-value of 3.55 and a p-value of <0.001, indicating a substantial difference in oxygen saturation between RD and non-RD patients. These findings highlight the differential effects of RD on sleep-related parameters, with a notable impact on sleep efficiency and oxygen saturation.

Figure 5 Meta-analysis of polysomnography parameters (PSG) and Epworth Sleepiness Scale (ESS) level in patients with RD compared to those without RD (non-RD). Abbreviation: RD, reflux disease.

NREM Sleep Phases in RD vs Non-RD

Figure 6 presents a meta-analysis of the distinct phases of non-rapid eye movement (NREM) sleep (N1, N2, and N3) in patients with reflux disease (RD) compared to those without RD (non-RD). For N1, the overall standard mean difference (SMD) was calculated to be 2.11, with a p-value of 0.035, indicating a statistically significant difference between RD and non-RD patients in this phase of sleep. This suggests that RD may significantly influence the initial phase of NREM sleep. For N2, the overall z-value was 1.45, with a p-value of 0.146, and for N3, the overall z-value was 0.98, with a p-value of 0.330, indicating no significant difference between RD and non-RD patients in these two phases. The overall analysis, combining all three NREM sleep phases, yielded a z-value of 0.63 and a p-value of 0.530, further supporting the notion that RD may not significantly impact N2 and N3 sleep phases. These findings highlight a selective effect of RD on NREM sleep, particularly in the early stages (N1), while no significant effects are observed in later stages (N2 and N3).

Figure 6 Meta-analysis of Distinct phases of NREM sleep (N1, N2, N3) in patients with RD compared to those without RD (non-RD). Abbreviation: RD, reflux disease.

Relationship Between RD and OSA Severity

Figure 7 examines the relationship between gastroesophageal reflux disease (RD) and obstructive sleep apnea (OSA) severity across distinct phases, including mild, moderate, and severe OSA. For mild OSA, the pooled relative risk (RR) was 0.65 (95% CI: 0.22–1.92), with a z-value of 0.79 (p = 0.431), indicating no significant difference between RD and non-RD patients. Similarly, for moderate OSA, the pooled RR was 0.66 (95% CI: 0.29–1.50), with a z-value of 0.99 (p = 0.321), again showing no statistically significant effect. The severe OSA phase also revealed no significant difference, with a pooled RR of 0.78 (95% CI: 0.50–1.22), z = 0.96, and p = 0.338, indicating no association between RD and severe OSA. The overall analysis, which incorporated all severity levels, suggested a marginal trend towards significance (z = 1.86, p = 0.063), with a pooled RR of 0.72 (95% CI: 0.50–1.02). While this result approached statistical significance, it did not reach the conventional threshold (p < 0.05), suggesting that RD may not substantially influence OSA severity across different phases. These findings underscore that, despite a slight trend toward significance, there is no strong evidence to suggest that RD significantly impacts the severity of OSA across various phases of the disorder.

Figure 7 Meta-analysis of Distinct phases of severity of OSA in patients with RD compared to those without RD (non-RD). Abbreviations: SA, obstructive sleep apnea; RD, reflux disease.

Heterogeneity Analysis

In addition to the primary analyses, we conducted a detailed heterogeneity analysis to explore the variability in the results across the included studies. Heterogeneity, as quantified by I² statistics, reflects the degree of variation in study outcomes beyond what would be expected by chance alone. In this meta-analysis, the overall heterogeneity among the studies was moderate (I² = 28.3%, p = 0.223), suggesting that there was some variability across the studies, but the results were generally consistent.

In order to identify the potential sources of heterogeneity, we performed subgroup analyses by; study design (cohort versus case-control), sample size, patients demographics (age, gender, comorbidities) and OSA severity. The moderate amount of heterogeneity was attributed by such subgroup analysis to the fact that most of the cause was due to the heterogeneity of study populations, particularly in the prevalence level of the severity level of OSA and RD across the cohorts. To give an example, the higher was the prevalence of severe OSA, the more probable it became that the increased RD incidence was present but the level was negligible. It means that although the variability is to a certain degree, still it is even enough to draw significant conclusions. Further, there was sensitivity to remove the analyses one by one to determine the sensitivity of the combined estimates. The exclusion of any study in the studies at a time did not have any significant impact on the result of the study meaning that the results were not dominated by any study. This adds more validity and reliability of our conclusions. Moderate heterogeneity also suggests that researches that validate an increased homogeneity of the types of population and outcomes is needed, thus decreasing the levels of variance and presenting a clearer image on the correlation between OSA and RD.

These findings emphasize that it is an indispensable requirement to approach meta-analytic findings taking into account study-level variables, including design and population properties. It is important to know the origins of heterogeneity to narrow down the study of the future as well, and we can assume that considering these items in the future study design will help to shed some light on the subtle correlation between OSA and RD.

Publication Bias

We evaluated the potential publication bias in studies examining the incidence of reflux disease (RD) in patients with obstructive sleep apnea (OSA) compared to those without OSA (non-OSA) (Figure 8). The funnel plot with pseudo 95% confidence limits, indicating no clear evidence of asymmetry, which suggests a low likelihood of publication bias in the included studies (Figure 8A). Egger’s publication bias plot does not show a significant relationship between precision and effect size, further supporting the absence of publication bias (Figure 8B). Lastly, Begg’s funnel plot with pseudo 95% confidence limits also indicates no substantial deviation from symmetry (Figure 8C). Taken together, these results suggest that publication bias is unlikely to have substantially affected the observed findings in this meta-analysis.

Figure 8 Publication Bias of the incidence of reflux disease (RD) in patients with obstructive sleep apnea (OSA) compared to those without OSA (non-OSA). (A) Funnel plot, (B) Egger plot, (C) Begg plot.

Discussion

This systematic review and meta-analysis examined the relationship between obstructive sleep apnea (OSA) and reflux disease (RD), revealing a complex and multifaceted interaction that warrants further investigation. Our findings suggest a trend towards a higher incidence of RD in patients with OSA (RR = 1.23, p = 0.056), although this did not reach statistical significance. Despite this, there was no robust evidence to support a strong causal relationship between OSA and RD, especially when considering parameters such as reflux symptom index (RSI) and reflux finding score (RFS), where no significant differences were observed between OSA and non-OSA groups. These results contribute to the growing body of literature that questions the direct influence of OSA on the development or exacerbation of RD, suggesting that the relationship between these two conditions is more intricate than previously thought.

We found that the relationship between OSA and RD is relatively weak, suggesting that the connection between these two conditions is still in the early stages and may not be as significant as initially hypothesized. However, we did observe a tendency for an increased RD rate in patients with OSA, which supports our hypothesis that the physiological impacts of OSA, such as surges in intra-abdominal pressure during apneic periods, could worsen gastroesophageal reflux. Although this trend approached significance (p = 0.056), it did not reach the conventional threshold of p = 0.05, indicating that while a potential connection may exist, it is too weak to draw definitive conclusions. This non-significant relationship could be attributed to biases or heterogeneity in the data, as well as the complex and multifactorial nature of both OSA and RD, which complicates the identification of a direct causal relationship. Inconsistencies across studies, such as variations in OSA severity, patient types, the presence of comorbid conditions (eg, obesity), and differences in how RD was diagnosed or measured, may have further reduced the validity of the observed effects.^9,63–66 As a result, the results from these studies must be interpreted with caution. Notably, our findings align with some earlier studies^33,50,51,56 that observed an increased risk of RD in OSA patients, although a causal relationship was not defined. For example, Liu et al³³ and Tang et al⁵⁰ noted a slight increase in RD risk among individuals with OSA, similar to our findings. However, other studies, such as Susyana et al,⁴⁸ did not find a meaningful relationship between OSA and RD, highlighting the disparities in the existing literature. These conflicting results suggest that the OSA-RD association is likely more complex than initially thought and may depend on a combination of factors, such as the severity of OSA, concurrent risk factors (such as obesity), and methodological differences between studies. While our analysis indicates a potential correlation between OSA and RD, it is evident that no strong association can be confirmed at this stage. Therefore, this relationship should be explored in greater detail in future studies, with more homogeneous sample sizes and improved diagnostic criteria, to reduce biases and gain a clearer understanding of the overlap between OSA and RD.

Our results have shown that the reflux disease (RD) is more likely to relate to a decreased level of sleep efficiency (p = 0.003) and oxygen saturation (p<0.001) but has no significant effects on Apnea-Hypopnea Index (AHI) and Epworth Sleepiness Scale (ESS). These results support the hypothesis that RD impairs sleep quality primarily through nocturnal reflux symptoms that cause arousals and sleep fragmentation, rather than by influencing sleep apnea severity or daytime somnolence. The same has been observed in previous research works that have established that RD is associated with broken sleep continuity but not with elevated AHI or ESS.^14,35,39 As an example, Altinta experiment et al¹⁴ found that polysomnographic measures in RD patients did not change significantly, whereas Milena et al³⁵ and Caparroz et al¹⁶ found strong relationships between AHI and ESS and RD, which allows assuming that the differences may arise due to the peculiarities of the population or comorbid pathology. These discrepancies illustrate the heterogeneous and complicated fact that RD and OSA have a complex relationship, which can be the result of heterogeneous diagnostic criteria, study design, or the reflux phenotype (acidic vs non-acidic).^40–42 Thus, well-controlled studies involving bigger more similar cohorts are required in order to specify the particular roles of RD in sleep disturbances and its importance it has clinically in patients with OSA.

Based on our results, it is possible that reflux disease (RD) has a preferential impact on the lighter stages of non-rapid eye movement (NREM) sleep especially stage N1, probably due to occurring higher arousal events during the night-time reflux events. The given observation is compliant with previous research findings showing that gastroesophageal reflux may interfere with sleep initiation through increasing cortical excitability and autonomic dysregulation.^14,35 By contrast, the absence of inconsiderable distinctions in deeper NREM stages (N2, N3) suggests the lower impact of RD as deeper sleep takes place, which may be induced by reduced arousal susceptibility in such levels.⁴¹ Although possible mechanisms attribute RD to obstructive sleep apnea formation (OSA) reflexes via the vagus nerve and airway inflammation, our ecosystem analysis was not significant (within and between OSA severity levels), as they may occur in specific stages or exert little effect on apnea indicators.^16,37 Although, there is a marginal trend (p = 0.063), indicating that there might be some kind of interaction within certain subgroups, which could be hiding behind the heterogeneous RD diagnosis, comorbidities, and treatment status (ie PPI use, CPAP adherence).^42,43 The results argue the consideration of reduced bidirectional models of RD-OSA interaction and encourage future researches to stratify patients based on their reflux (phenotyping), vulnerability to sleep stages disturbances, and symptom presentations to understand this complicated connection.

This meta-analysis and systematic review provide us with new data regarding the complicated relationship between obstructive sleep apnea (OSA) and reflux disease (RD), where we can find out that reflux disease can selectively interfere with early non-rapid eye movement (NREM) sleep especially N1, whereas it has few effects on deeper sleep N2, N3 or whether OSA is due to RD or not. These results narrow down the existing models of the dynamic of OSA and RD interaction that are regularly viewed as a two-way street that is linear in both directions, but the data here indicates a more stage respective and heterogeneous direction of the influence.^14,35,41 The clinical implications of RD interrupting lighter stages of sleep are then valuable in the discussions of the possible effects of reflux-induced symptoms management on improved continuity in sleep irrespective of whether indices of apneas show improvements. Previous publications also indicate that apnea-hypopnea index could not be increased with reflux-related arousals instead highlighting the worsening of the sleep quality in general.^16,42 Thus, a treatment approach to patients with comorbid OSA and RD must not only aim at mitigating apneic occurrences but also include an element of reflux management to improve restorative rest. Sophisticated treatment plans, homing in on the reflux phenotype, sleep-stage susceptibility, and comorbidity burden, are justified to maximize efficacy. There is a need to perform further quality studies to confirm such observations and use more standardized diagnostic criteria and stratified analyses to further clarify this complex relationship.^43,44

Although the current research study provided valuable insights through the systematic review and meta-analysis, several important limitations must be acknowledged. First, the cross-sectional design of most of the included studies limits our ability to make definitive conclusions about causality. Longitudinal studies would be essential to clarify the directionality of the relationship between OSA and RD, specifically examining how these conditions evolve over time and providing stronger evidence for causality. Additionally, there is a potential for publication bias in this review, as studies reporting null or negative results are less likely to be published, which may lead to an overrepresentation of positive findings. Future meta-analyses should aim to include unpublished studies, thus offering a more comprehensive overview of the OSA-RD relationship. Moreover, the failure to report planned analyses, such as sensitivity tests, publication bias tests, and subgroup/meta-regression analyses, constitutes another significant limitation. These analyses are crucial for understanding how confounding factors—such as obesity, age, or the presence of other conditions—may influence the relationship between OSA and RD. Given the prevalence of these confounders in both OSA and RD groups, future studies should make efforts to control for these variables, allowing for a clearer understanding of the specific impact of RD on OSA, and vice versa. Furthermore, while we believe there may be a potential connection between RD and OSA, the absence of consistent and unequivocal findings across the studies highlights a major research gap. Future studies should focus on investigating the selective influence of RD on various sleep stages, particularly in the context of NREM sleep. Additionally, the potential benefits of symptomatic reflux treatment in OSA patients should be explored, as it may improve sleep characteristics and reduce the severity of symptoms. Addressing these unresolved questions will guide future research directions and provide more robust recommendations for clinical practice and therapeutic approaches for patients with both OSA and RD.

In conclusion, this systematic review and meta-analysis indicated that there is a small relationship between obstructive sleep apnea (OSA) and a slight upsurge in the occurrence of gastroesophageal reflux disease (RD). In particular, RD seems to influence the initial phases of sleep, especially N1, selectively without major changes to the level of sleep apnea. Nevertheless, the study is limited, with the cross-sectional design of most of the studies in the study inhibiting us to make cause relationships. Moreover, there are the possible bias, like a publication bias and confounders like obesity, comorbidities, which can affect the results. Future studies ought to consider longitudinal studies, using standardized criteria of diagnosis, and confounding factors should be removed, and the measures of comorbidity should be studied, so that interventions can be streamlined as far as the underlying mechanisms and causal relationships will be explained between OSA and RD.

Data Sharing Statement

The data supporting the results of this study were obtained from PubMed, Web of Science, the Cochrane Library, VIP, CNKI, Wanfang Data, and CBM databases.

Author Contributions

XZ-Conceptualization, formal analysis, funding acquisition, methodology, project administration, writing-original draft, and writing-review and editing. SW-Data curation, formal analysis, investigation, methodology, software, writing-original draft, writing-review and editing. PZ-Conceptualization, data curation, formal analysis, investigation, methodology, visualization, writing-original draft, writing-review and editing. All authors agreed on the journal to which the article will be submitted; agreed on the final version accepted for publication and agree to take responsibility and be accountable for the contents of the article.

Funding

This work was partly funded by Open Project of State Key Laboratory of Respiratory Disease (SKLRD-OP-202510).

Disclosure

The authors declare no financial conflicts of interest.

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