Obstructive sleep apnea (OSA) is a recurrent chronic illness marked by partial or total blockage of the upper respiratory tract while you sleep [1] [2] . It usually presents with nonspecific symptoms such as snoring, excessive daytime sleepiness or fatigue, and apnea [3] . These respiratory disturbances lead to periodic decreases or cessation of ventilation, which results in intermittent hypoxemia, hypercapnia, sleep fragmentation, recurrent nocturnal sleep awakenings, and activation of the sympathetic nervous system [4] . There is a close interplay between OSA and the oral cavity. As an important part of the upper respiratory tract, structural and functional abnormalities in the oral cavity may increase the risk of OSA, including anatomical abnormalities [5] , mouth breathing habits [6] , sleep grinding [7] , and genetic factors [8] . In turn, OSA may adversely affect oral health, such as symptoms of congestion, masticatory muscle hypertrophy, and dry mouth [9] , and long-term OSA may also aggravate diseases such as periodontitis [10] .

An epidemiological investigation and a seminal article on the natural history of sleep cardiorespiratory disorders were published in 1993 by the researcher. This study revealed the high prevalence of untreated obstructive sleep apnea (OSA), which is important in the field of public health [11] . Since 2000, the number of people treated for OSA has continued to increase globally; In 2015, the United States, grappling with a widespread incidence of OSA, incurred an estimated $12.4 billion in expenses related to the diagnosis and management of the condition, with half of the cost spent on therapeutic oral appliances and positive airway pressure (PAP) therapy, and the other half spent on surgical aspects of treatment [12] . Recent findings reveal that OSA has a widespread impact, afflicting close to one billion individuals worldwide, with an estimated 936 million adults grappling with varying degrees of this condition, from mild to severe [13] , which poses a serious threat to public health worldwide. In addition, if left untreated, OSA may eventually lead to serious complications such as hypertension, cardiovascular disease, metabolic syndrome, and diabetes [14] - [18] . Currently, in clinical practice we widely consider polysomnography (PSG) as the gold standard for diagnosing OSAHS [19] , however obtaining PSG results can be challenging in some settings. For example, PSG monitoring equipment and sleep monitoring rooms may not be available in primary health care settings, and results may not be returned promptly when the subjects are children, as PSG examinations require them to sleep through the night. Therefore, finding a simple, easily accessible biomarker that is consistent with PSG results is critical for the early diagnosis of OSA, which can help clinicians make an initial assessment of the condition and plan subsequent treatment.

High-density lipoprotein cholesterol (HDL-C) is believed to have anti-inflammatory and anti-oxidative stress properties, which can help remove oxidative stress products and inflammatory mediators from the body [20] - [23] . In addition, HDL-C plays a crucial role throughout the cardiovascular system with anti-atherosclerotic, antiplatelet, and antithrombotic effects, protecting our cardiovascular system through a series of complex mechanisms [24] - [28] . A significant correlation exists between the severity of obstructive sleep apnea (OSA) and levels of HDL-C [29] [30] . The calculation of non-high-density lipoprotein (NHDL) cholesterol involves subtracting HDL-C from the total cholesterol (TC), providing a measure of the cholesterol content not carried by HDL particles and includes all cholesterol present in lipoprotein particles thought to cause atherosclerosis: triglyceride-rich (TG) lipoproteins, low-density lipoproteins, very low-density lipoproteins (VLDL), chyme residue, and intermediate-density lipoprotein (IDL) [31] . The non-high-density lipoprotein (NHDL) cholesterol to high-density lipoprotein cholesterol (HDL-C) ratio (NHHR) has been identified as a novel atherosclerosis risk assessment index [32] [33] . In addition, a multitude of research has demonstrated that NHHR outperforms traditional lipid metrics in forecasting the likelihood of various diseases, showcasing its superior diagnostic precision, including atherosclerosis, abdominal aortic aneurysm, periodontitis, depression, non-alcoholic-fatty-liver-disease, diabetes, and metabolic syndrome [32] [34] - [39] . With these findings, we postulated whether NHHR could be utilized for early OSA diagnosis in high-risk individuals, allowing for the early implementation of interventions aimed at slowing the disease’s advancement.

To the best of our knowledge, the potential connection between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and obstructive sleep apnea (OSA) remains uncharted territory. We hypothesized that NHHR might be linked to OSA and that its regulation could play a role in the prevention and management of the condition. To explore this intriguing hypothesis, we initiated a cross-sectional investigation, utilizing data from the NHANES 2015-2018. We aimed to delve into the association between NHHR and the prevalence of obstructive sleep apnea within the non-pregnant United States population.

In this cross-sectional study involving 5113 participants, we observed a significant and independent positive association between NHHR and the prevalence of obstructive sleep apnea (OSA). Importantly, this relationship persisted after adjustment for a wide range of potential confounders, suggesting that NHHR may serve as a promising biomarker for identifying individuals at higher risk of OSA. The association remained robust across multiple subgroup and interaction analyses, including among non-Hispanic Black participants, females, individuals with a poverty-to-income ratio (PIR) below 3.23, those with a BMI less than 25, and participants without a history of hypertension or diabetes. These findings suggest that the relationship between NHHR and OSA is relatively consistent across various demographic and metabolic contexts. Notably, significant interactions were identified between PIR, diabetes status, and NHHR in relation to OSA risk. Socioeconomic factors, as reflected by PIR, may influence health behaviors, healthcare accessibility, and baseline metabolic risk, thereby modifying the NHHR-OSA association. Diabetes, characterized by chronic inflammation and dyslipidemia, could synergistically interact with elevated NHHR to exacerbate OSA risk. These results emphasize the importance of incorporating metabolic and socioeconomic factors into OSA risk stratification and prevention strategies. Furthermore, smoothed curve fitting and threshold effect analyses revealed a nonlinear relationship, with an inflection point at an NHHR value of 3.65. Below this threshold, higher NHHR was significantly associated with an increased risk of OSA, whereas above this point, the association plateaued and was no longer statistically significant (p = 0.2043). This suggests a possible saturation phenomenon in lipid-related pathways, where further increases in NHHR do not confer additional OSA risk due to ceiling effects in inflammatory or oxidative stress mechanisms. Clinically, this implies that moderate elevations in NHHR may warrant heightened surveillance for OSA, while extremely high levels might not further increase risk.

The NHHR is a novel composite index that has recently been developed and used to assess aspects of atherosclerosis extent [32] , far surpassing traditional lipid parameters [48] . A growing body of evidence recognizes the NHHR as an accurate indicator of the risk of lipid-related diseases, with greater predictive power for non-alcoholic-fatty-liver-disease(NAFLD) compared to other lipid indicators [36] [49] , and as a reliable diagnostic tool for the assessment of insulin resistance and the metabolic syndrome [35] , while at the same time, the metric has demonstrated greater predictive accuracy [32] . Several cross-sectional studies have demonstrated significant independent associations between NHHR and diseases such as periodontitis, kidney stones, and depression [34] [38] [39] [50] . In conclusion, NHHR, as a widely used test, shows great promise for clinical implementation due to its noninvasive, easily accessible, and cost-effective nature.

Although the role of NHHR in OSA has not been previously explored, existing literature has documented significant disruptions in lipid metabolism among individuals with OSA. Patients with OSA often exhibit decreased levels of high-density lipoprotein cholesterol (HDL-C) alongside elevated total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C), indicating a pronounced dyslipidemic profile [51] - [53] . In addition, non-traditional lipid indices such as the TG/HDL-C ratio, TC/HDL-C ratio, LDL-C/HDL-C ratio, and non-HDL cholesterol (NHDL-C) have been positively correlated with OSA severity [54] . These alterations may be driven by intermittent hypoxia, oxidative stress, and systemic inflammation inherent in OSA. Conversely, dyslipidemia—particularly elevated non-HDL cholesterol—can promote endothelial dysfunction and upper airway collapsibility, potentially exacerbating OSA. NHHR, as a composite index reflecting both atherogenic (non-HDL) and protective (HDL) lipid components, may capture the bidirectional interactions between lipid metabolism and OSA pathophysiology. Supporting this, previous studies have proposed that OSA-induced hypoxia and sleep fragmentation can worsen lipid profiles, while dyslipi-demia itself may contribute to the development and progression of OSA [51] [55] - [57] . Therefore, future longitudinal and mechanistic studies are warranted to investigate the causal pathways linking NHHR and OSA.

From a clinical perspective, NHHR could serve as a readily available biomarker to facilitate early OSA risk assessment, particularly in primary care settings where polysomnography resources are limited. Incorporating NHHR into routine lipid screening may help identify individuals at higher risk of OSA, enabling timely referral for diagnostic evaluation and intervention. This approach supports personalized treatment strategies and proactive disease management, potentially reducing the burden of OSA-related complications.

The data for this study came from the NHANES database, which represents the civilian and non-institutionalized national population, making our results more representative and convincing. Also, we analyzed different types of variables and adjusted for covariates to ensure the credibility and generalizability of our findings. However, there are some limitations of this study. First, the self-reports of the participants were used to identify OSA, which raised the likelihood of interviewer bias, recording errors, and recall bias. Second, the cholesterol data analyzed in this study were fasting measurements, while non-fasting data were not examined, potentially introducing bias due to laboratory testing variations. Third, although multiple confounders were considered, influenced by the 2015-2018 NHANES database, we still could not exclude the effect of all potential confounders, and the results may have some bias. Lastly, given that this study draws from cross-sectional data, we must acknowledge the potential for selection bias. This inherent limitation complicates the task of ascertaining a direct cause-and-effect link between the non-HDL to HDL ratio (NHHR) and obstructive sleep apnea (OSA). Therefore, larger-scale prospective cohort studies are needed to test the clinical value of the NHHR marker as a prognostic tool for assessing the progression of OSA.

In conclusion, the current study demonstrated that there is a non-linear and independent correlation between NHHR and OSA. This implies that NHHR may be a new biomarker for OSA incidence, which could help with personalized treatment and clinical decision-making. Nevertheless, extensive prospective studies are required to confirm the validity of NHHR as a prognostic marker.

This study was supported by the Guizhou Provincial Science and Technology Program Project ([2022] 272) and ([2022] 196).

The survey protocol for the NHANES was approved by the CDC’s National Center for Health Statistics Institutional Research Ethics Review Board ( https://www.cdc.gov/nchs/nhanes/irba98.htm ).

The datasets generated during and/or analyzed during the current study are publicly available and can be found at https://www.cdc.gov/nchs/nhanes/index.htm .

This manuscript has been read by all authors who have confirmed their consent for publication.

This study used participant data from the NHANES public database; thus, participant statements were unavailable.

Data curation: Meiyan Rong; Formal analysis: Jiangling Sun; Investigation: Meiyan Rong; Methodology: Jiangling Sun, Cheng Niu; Project administration: Xubo Duan; Software: Zhengfen Li; Writing-original draft: Meiyan Rong; Writing-review & editing: Wei Yang.