Obstructive sleep apnea (OSA) is a prevalent sleep disorder marked by recurring instances of partial or complete blockage of the upper airway during sleep, which leads to interruptions in sleep and episodes of intermittent hypoxia (1, 2). This condition impacts a considerable segment of the adult population, with estimates indicating that more than 25% of adults may experience mild to moderate OSA (3, 4). OSA is linked to numerous serious health issues, including an elevated risk of cardiovascular disease, hypertension, type 2 diabetes, and metabolic syndrome (5–8). The chronic nature of OSA, along with the likelihood of many cases remaining undiagnosed (9), emphasizes the necessity for early identification and intervention to reduce its detrimental effects.

In clinical research and practice, composite indices have emerged as valuable tools for capturing complex physiological phenomena through integrated metrics. These indices combine multiple biomarkers or clinical parameters into a single quantitative measure, offering a more holistic assessment than individual variables alone. Particularly in endocrinology and metabolic medicine, composite indices like HOMA-IR for insulin resistance or TyG (triglyceride glucose) index have proven instrumental in evaluating multifaceted conditions (10, 11). Their strength lies in synthesizing interrelated biological processes, enhancing predictive capacity, and simplifying clinical decision-making. However, such indices also present limitations: they may obscure the relative contribution of individual components, vary in performance across populations, and sometimes lack standardized cutoff values. Despite these challenges, when properly validated, composite indices remain powerful tools for uncovering systemic relationships in metabolic health.

The Cardiometabolic Index (CMI) serves as a critical tool for assessing both cardiac function and metabolic status (12, 13). By integrating various parameters—such as body weight, waist circumference, and biochemical markers (14), CMI provides a comprehensive evaluation of an individual’s cardiovascular risk, thus allowing healthcare providers to identify at-risk individuals and implement timely preventive strategies (15, 16). Understanding the significance of CMI is crucial not just within clinical practice but also in research settings, as it has the potential to uncover novel associations between metabolic health and sleep disorders.

Recent studies have begun to explore the complex relationship between CMI and OSA, revealing that elevated CMI may predispose individuals to OSA. Factors such as increased adiposity can exacerbate upper airway obstruction, while systemic inflammation frequently seen in metabolic dysregulation may also play a pivotal role (17–20). Conversely, the metabolic changes associated with OSA, including insulin resistance and dyslipidemia, can further heighten the risks posed by a high CMI, suggesting a bidirectional interplay between the two conditions (21–23). The interplay between CMI and OSA is complex and multifactorial, involving several biological and behavioral mechanisms that warrant further exploration. Therefore, understanding the nuanced connection between CMI and OSA is not merely an academic exercise; it has far-reaching implications for public health strategies. A clearer understanding of how CMI and OSA interact could pave the way for targeted interventions that address both metabolic health and sleep quality, ultimately aiming to reduce the prevalence and impact of OSA in individuals with elevated CMI.

This study aims to elucidate the association between CMI and OSA, contributing to a more comprehensive understanding of their interconnected roles in cardiovascular and metabolic health. We hypothesize that elevated CMI values will demonstrate a positive association with OSA prevalence. Furthermore, investigating potential threshold effects in this relationship represents a critical research objective. Additionally, we aim to examine whether specific mediating factors influence the CMI-OSA association.

The findings of this study reveal a significant positive nonlinear relationship between elevated CMI and the incidence of OSA in the adult population of the United States. The “L” shape relationship found through threshold effect analysis indicates that maintaining an appropriate CMI level is critical to mitigating the risk of developing OSA. Specifically, our analysis demonstrated that for each unit increase in CMI, the likelihood of OSA increases by 31% in the fully adjusted model [OR (95% CI): 1.31 (1.21, 1.42), p < 0.001]. Notably, smoking was found to mediate the association between CMI and OSA, highlighting its role as a confounding factor that exacerbates this risk.

In practical terms, these results suggest that CMI could be effectively integrated into existing risk stratification frameworks for OSA screening. Healthcare providers can utilize CMI as a quantifiable metric to identify individuals at heightened risk for OSA, allowing for timely interventions. By incorporating CMI into routine assessments, clinicians could enhance their ability to stratify patients based on risk and customize preventive strategies tailored to individuals’ profiles. For example, patients with high CMI scores could undergo more rigorous screening for OSA, enabling earlier diagnosis and targeted management, potentially including lifestyle modifications or therapeutic interventions. Overall, adopting CMI in clinical practice as part of risk stratification for OSA could significantly improve patient outcomes and empower healthcare providers to implement proactive measures against this common sleep disorder.

Obstructive Sleep Apnea (OSA) is increasingly acknowledged as an important public health concern due to its high prevalence and serious health consequences. Research estimates that about 25% of adult males and 10% of adult females in the United States are impacted by OSA (28, 29), with these rates continuing to rise alongside increasing obesity rates (30). This sleep disorder is marked by repeated instances of either complete or partial blockage of the upper airway during sleep, which results in intermittent hypoxia and frequent awakenings. The consequences of untreated OSA are significant, leading to various adverse health outcomes, such as cardiovascular disease, hypertension, type 2 diabetes, and an increased risk of stroke (31–36). Additionally, OSA is associated with decreased quality of life, increased healthcare utilization, and economic burdens, underscoring the urgent need for effective screening and intervention strategies.

The cardiometabolic Index (CMI) has emerged as a valuable tool for assessing cardiovascular and metabolic health. It integrates multiple physiological parameters, such as waist-to-height ratio (used to reflect abdominal obesity), and lipid levels (used to reflect lipid metabolism in the body), to provide a comprehensive evaluation of an individual’s cardiac health (37–39). Its significance lies in its potential to identify individuals at increased risk for cardiovascular complications, thereby enabling proactive management of metabolic syndrome (15, 16, 40, 41). The development of CMI reflects a growing awareness of the interactions between metabolic health and cardiovascular disease, highlighting the importance of adopting a holistic approach to patient care.

In this study, we identified a significant positive correlation between the CMI and OSA. This finding suggests that cardiac metabolic health may play an important role in the occurrence and progression of OSA. Several potential mechanisms could explain this correlation. First, CMI is derived from a combination of factors such as waist circumference, height, triglycerides, and high-density lipoprotein levels, which collectively reflect an individual’s metabolic status and fat distribution. A higher CMI is typically indicative of abdominal obesity and metabolic dysfunction (14, 42). These factors can lead to fat deposition in the upper airway tissues (18, 43), increasing airway resistance and contributing to the development of OSA. Additionally, abdominal obesity may impair diaphragm function (44), further disrupting normal nighttime respiration. Second, elevated CMI is often closely linked with the development of insulin resistance, chronic inflammation, and metabolic syndrome (15, 45–48). Insulin resistance can lead to abnormal adipocyte function and the production of pro-inflammatory mediators (49–52), which not only affect cardiac metabolic health but may also exacerbate OSA by interfering with neural control of respiration (53, 54). Researches have shown that chronic inflammation is associated with upper airway dysfunction and dysregulation of neural control, both of which can worsen OSA (20). Moreover, the deterioration of cardiovascular health may also exacerbate the symptoms of OSA by affecting the balance of the autonomic nervous system. An increase in CMI is often accompanied by elevated blood pressure and greater cardiac workload, changes that may interfere with the neural regulation of breathing (55, 56), resulting in increased frequency of nocturnal apneas. Recent studies have shown that Glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter-2 (SGLT2) inhibitors have emerged as promising candidates. Both classes of medications, initially designed for T2D and obesity management, offer pleiotropic cardiovascular benefits, including weight reduction, blood pressure lowering, and improvements in endothelial function. Clinical studies suggest that GLP-1RAs can decrease OSA severity and enhance daytime alertness by mitigating upper airway fat accumulation and enhancing respiratory control. Similarly, SGLT2 inhibitors may exert beneficial cardiovascular effects through their actions on myocardial energetics and renal function (57–59). These mechanisms provide further insight into the complex pathology of OSA and emphasize the need for consideration of cardiac metabolic health in the clinical management of this condition. Future research should explore these mechanisms in greater depth to offer new strategies for the early identification and intervention of OSA.

Additionally, we found that smoking serves as a mediating factor, exacerbating the positive correlation between CMI and OSA. This may be attributed to the fact that smoking not only induces metabolic disturbances but also compromises respiratory health, thereby elevating the risk of OSA in individuals with high CMI (60–62). These results highlight the significance of focusing on metabolic health and lifestyle factors, such as smoking cessation, in the strategies for preventing and managing OSA (63).

We believe that this study, which specifically investigates the relationship between the CMI and OSA, presents a novel and significant contribution to the field. Throughout the research, we employed appropriate statistical methods and ensured continuity and logical flow by using the results from previous analyses to inform subsequent ones. The findings from the threshold effect and mediation effect analyses also offer valuable insights for clinical practice and daily life. However, since this research adopts a cross-sectional study design, the data collection is focused on a specific point in time, thereby limiting the ability to establish causal relationships among the variables. The nature of cross-sectional research prevents the identification of longitudinal trends and potential temporal relationships, which are essential for understanding the dynamics of the factors examined. Therefore, the limitations of this specific dataset hinder our ability to infer causal relationships between CMI and OSA, and to ascertain the causal role of smoking in the mediating effects between the two. Second, the constraints of covariates (key cardiovascular metabolic drugs, especially newer drugs such as GLP-1 receptor agonists and SGLT2 inhibitors), may simultaneously affect CMI and OSA. Unfortunately, due to the lack of data on the use of these drugs in the NHANES database, we were unable to include them as covariates in this study. Third, in our study, the diagnosis of OSA primarily relied on self-reported symptoms evaluated through the Sleep Questionnaire within the NHANES dataset. While this approach provides valuable insights into the prevalence of OSA, it inherently lacks the rigor of objective diagnostic methods, such as polysomnography (PSG) or assessing apnea-hypopnea index (AHI) scores. The reliance on subjective reporting may result in misclassification bias, as individuals may overreport or underreport their symptoms, leading to an inaccurate assessment of OSA prevalence. Consequently, this limitation underscores the need for a multidimensional evaluation that integrates both subjective symptom assessment and objective diagnostic criteria. Future investigations should aim to incorporate objective measures alongside self-reported questionnaires to enhance the accuracy of OSA diagnosis and to better understand its implications for public health and clinical practice. In addition, potential biases from self-reported questionnaires, including recall bias and loss to follow-up, may have affected the results. Finally, it is important to note that the study sample consists exclusively of American adults, which may restrict the generalizability of the results to other populations. Variations in culture, socioeconomic status, and behavior across different demographic groups could affect the study variables, suggesting that the findings may not be relevant to individuals from outside this demographic. Future studies should further investigate longitudinal effects and related intervention strategies to improve our comprehension of the mechanisms that connect CMI to OSA and to refine the experimental findings.

Our study reveals a positive nonlinear relationship between the CMI and OSA in the adult U.S. population. Threshold analysis indicates that keeping the CMI within a specific range may significantly lower the likelihood of developing OSA. Additionally, lifestyle factors like smoking may further influence this relationship. These findings highlight the importance of a comprehensive approach to screening and intervention that considers cardiometabolic and sleep health. Future research should focus on understanding the mechanisms linking CMI and OSA to improve prevention and treatment strategies for at-risk individuals.