Obstructive sleep apnea (OSA) is characterized by intermittent upper airway collapse during sleep, leading to transient hypopnea or apnea, consequent hypoxemia, hypercapnia, and sleep fragmentation (1). Clinically, OSA manifests as snoring, excessive daytime sleepiness, neurocognitive impairment, and erectile dysfunction, frequently comorbid with obesity and hypertension (2). Epidemiological data indicate a prevalence of 22% in males and 17% in females [apnea-hypopnea index (AHI) ≥ 5], with a significantly higher incidence observed in men (3). Notably, emerging evidence associates OSA with increased cardiovascular mortality (4). Furthermore, OSA has been implicated in various neurological disorders, including depression, attentional deficits, executive dysfunction, and verbal memory impairment (5). Given the paucity of reliable lipid biomarkers for OSA prediction, this study aims to identify and optimize lipid-based indices to predict OSA onset and mitigate its associated comorbidities.

Lipid metabolic dysregulation has been well-documented in OSA patients, marked by elevated low-density lipoprotein cholesterol (LDL), triglycerides (TG), and total cholesterol (TC), alongside reduced high-density lipoprotein cholesterol (HDL-C) (6, 7). These aberrations constitute independent risk factors for cardiovascular disease (8). A retrospective clinical analysis revealed a positive correlation between the TG/HDL-C ratio and OSA severity (9). Mechanistically, OSA-induced hyperlipidemia may arise from impaired lipoprotein clearance due to suppressed lipoprotein lipase (LpL) activity. Preclinical studies demonstrate that prolonged intermittent hypoxia inhibits triglyceride-rich lipoprotein (TRLP) catabolism, resulting in elevated TC and TG (10). Additionally, murine models of acute hypoxia exhibit downregulated PPAR-γ expression—a key regulator of lipid metabolism—leading to reduced LpL activity and consequent dyslipidemia (11).

Patients with OSA typically present with disorders of lipid metabolism, which include abnormalities of TG/HDL-C (9). Changes in TG/HDL-C values can cause a number of health problems. According to prior research, the TG/HDL-C correlates with insulin resistance and identifies patients with type 2 diabetes with poor glycemic control (12). TG/HDL-C is a new indicator of metabolic syndrome and cardiovascular disease (13). TG/HDL-C levels have also been reported to affect later cognitive function during acute ischemic stroke (14). The elevation in the value of TG/HDL-C possibly enhances the likelihood of fatty liver (15).

While prior investigations have predominantly examined individual lipid parameters in OSA, the diagnostic utility of composite lipid indices remains underexplored. This study comprehensively evaluates the association between TG/HDL-C and OSA severity, positing that OSA pathogenesis is influenced by synergistic lipid abnormalities. Compared to conventional lipid measures and the NHHR, TG/HDL-C may serve as a superior cardiovascular risk biomarker in OSA patients (16). These findings could inform personalized lipid-modifying therapies to attenuate OSA-related cardiovascular and neurological sequelae.

This large-scale cross-sectional study elucidated the association between the composite lipid marker TG/HDL-C and the incidence of OSA. TG/HDL-C was identified as an independent risk factor for OSA development [OR = 1.17; 95% CI: 1.00–1.35; p = 0.045]. Notably, elevated TG/HDL-C levels demonstrated a stronger association with OSA prevalence than individual lipid markers or NHHR. RCS regression revealed a nonlinear dose–response relationship between TG/HDL-C levels and OSA (p for nonlinear < 0.05). TG/HDL-C exhibited superior discriminative accuracy for OSA compared to TG and NHHR (AUC: 0.589 vs. 0.572–0.575). Furthermore, the relationship between TG/HDL-C and OSA varied by PIR, congestive heart failure, and angina. In conclusion, this study is an independent association study of the composite lipid index TG/HDL-C with OSA disease.

Numerous studies have established a link between OSA and dyslipidemia. However, the extant literature has predominantly focused on individual lipid parameters, overlooking the potential clinical utility of composite lipid indices. A study found that OSA patients had markedly elevated lipid levels (TG, TC, LDL) compared to healthy individuals (18). Supporting this observation, a prospective cohort study involving 93 persons further established positive correlations between TG, TC, and LDL and OSA severity (19). Notably, Barceló et al. conducted a targeted investigation, isolating LDL particles from 14 severe OSA patients and 13 matched controls. Their findings revealed a pronounced increase in thiobarbituric acid-reactive substances (TBARS) and aberrant lipid peroxidation profiles among OSA patients, suggesting that oxidative stress may underlie the dyslipidemia-OSA interplay (20).

While substantial evidence links dyslipidemia to OSA, notable inconsistencies exist across studies. A large cross-sectional analysis (n = 753 males) failed to demonstrate significant associations between OSA and conventional lipid parameters (TG, HDL-C, LDL-C, TC; all p > 0.05 after multivariable adjustment) (21). This null finding may reflect population homogenization, as shared regional lifestyle and dietary patterns among participants could attenuate true biological associations. Further complicating the evidence base, a case–control investigation (n = 32 OSA patients) reported no correlation between severe OSA and LDL, HDL-C levels (22). The clinical interpretation of these results is constrained by methodological limitations, including inadequate sample size and the absence of a matched control group. These discrepant findings underscore the critical importance of considering population characteristics and study design when evaluating the lipid-OSA relationship.

This study identified significant linear associations between OSA and multiple lipid indices, including TG/HDL-C ratio [OR = 1.17; 95% CI: 1.00–1.35; p = 0.045], TG [OR = 1.18; 95% CI: 1.01–1.38; p = 0.035], and HDL-C [OR = 0.62; 95% CI: 0.45–0.85; p = 0.006]. These findings align with established pathophysiological mechanisms linking dyslipidemia to OSA. Experimental evidence from murine models demonstrates that high-fat diet-induced obesity downregulates PPAR-γ expression, triggering inflammatory cascades and oxidative stress that potentiate chronic intermittent hypoxia (23). Oxidative stress produces dysfunctional oxidized lipids while decreasing HDL-C levels through impaired reverse cholesterol transport (24). In addition, OSA-induced hypoxia activates the sympathetic nervous system (25), resulting in complex regulation of lipid metabolism. Inhibition of both beta and alpha receptors influences lipid profiles: specifically, antagonism of alpha-1 receptors elevates HDL levels and reduces blood triglycerides, whereas blockade of beta-adrenergic receptors yields contrary effects (26–28). These interacting mechanisms may explain the association between sympathetic overactivity and atherogenic dyslipidemia observed in patients with OSA.

ROC analysis and DeLong test demonstrated superior predictive performance of TG/HDL-C for OSA compared to conventional lipid markers and NHHR. This finding gains biological plausibility from genome-wide analyses identifying TG/HDL-C as a surrogate marker for insulin resistance (29), a metabolic derangement frequently comorbid with OSA (30–32). Louis et al. identified a study that subjected healthy individuals to intermittent hypoxia for 5 h, resulting in a decline in insulin sensitivity (33). Another study, selecting young healthy adults to selectively inhibit slow-wave sleep (SWS) at night without modifying overall sleep length, led to impaired insulin sensitivity and decreased glucose tolerance (34).

Subgroup analyses showed that the association of TG/HDL with OSA was more applicable to non-poor populations (PIR ≥ 1.00) without congestive heart failure and angina. The relationship between lipids and OSA in impoverished populations is likely to be affected by the BMI. Congestive heart failure and angina populations may develop OSA caused by extreme hypoxemia (35). In patients with severe OSA, fluctuations in lipid levels did not produce statistical differences (22).

The findings of this study demonstrate a high degree of reliability, as the conclusions align with prior research examining the relationship between OSA and dyslipidemia. The substantial sample size (n = 3,270) provides sufficient statistical power to support the observed correlation. However, several limitations must be acknowledged. First, the cross-sectional design precludes causal inference, and residual confounding may persist despite multivariate adjustments. OSA and dyslipidemia may exhibit a bidirectional relationship. For instance, patients with OSA may develop dyslipidemia, while pre-existing dyslipidemia beyond a certain threshold could potentially contribute to the onset or exacerbation of OSA. Second, OSA diagnosis relied on self-reported questionnaires, introducing potential response and recall biases that could reduce the specificity of ROC analyses. Incorporating polysomnography (PSG) in future studies would enhance diagnostic accuracy and improve generalizability. Third, the absence of long-term follow-up data limited our ability to assess survival outcomes, particularly cardiovascular mortality. A prospective cohort design with extended follow-up is warranted to validate these findings. Fourth, the interpretation of subgroup analyses for congestive heart failure and angina may be constrained by limited sample sizes. Given the relatively low global prevalence of these conditions—1–3% for heart failure (36) and 3.8% for myocardial infarction (37)—the statistical power of these comparisons was inherently restricted. Notably, in this study, CVD encompasses four major events (congestive heart failure, angina, coronary heart disease, and stroke), which collectively determine its clinical heterogeneity. Therefore, the observed relationships between the TG/HDL-C and OSA need to be interpreted in the broader context of composite CVD outcomes rather than isolated subgroups. Finally, the study population was restricted to U. S. adults; thus, external validation in diverse ethnic and geographic cohorts is necessary to ensure broader applicability.

The results of this research demonstrated the superiority of TG/HDL-C as a biomarker to predict OSA risk in adults compared to previous NHHR index predictions. The results of this study have therapeutic value, potentially allowing clinicians to apply early therapies for dyslipidemia as a predictor of OSA and reduce the likelihood of its development. This composite index is more sensitive to the balance of the patient’s blood lipids than the former single index and serves as a valuable reference for guiding therapeutic medication utilization. This study establishes the TG/HDL-C as a superior biomarker for predicting OSA risk in adult populations compared to the single lipid index and NHHR. The findings hold significant clinical relevance, as early identification of dyslipidemia patterns through TG/HDL-C monitoring may enable preemptive therapeutic interventions to mitigate OSA progression. Notably, the TG/HDL-C ratio demonstrates enhanced sensitivity and specificity in detecting lipid metabolic imbalances compared to single-parameter indices. This composite biomarker provides a more physiological representation of atherogenic dyslipidemia, thereby offering clinicians a valuable tool for risk stratification, therapeutic decision-making, and monitoring treatment efficacy in at-risk populations.

This study investigated the association between the TG/HDL-C and OSA risk. Multivariate logistic regression revealed that each unit increment in TG/HDL-C was independently associated with a 17% elevated OSA risk [OR = 1.17; 95% CI: 1.00–1.35; p = 0.045]. ROC analysis and DeLong test demonstrated superior predictive performance of TG/HDL-C compared to TG and NHHR. The results suggest that modulation of lipid homeostasis through TG/HDL-C monitoring could serve as a preventive strategy for healthcare providers to mitigate OSA incidence. These findings have important clinical implications for early OSA risk stratification in at-risk populations, personalized therapeutic approaches targeting lipid metabolism, and potential reduction of OSA-related cardiovascular and neurological complications. However, the reliance on questionnaire-based OSA diagnosis in our study population may introduce detection bias, potentially affecting the observed associations. Future studies incorporating polysomnography-confirmed diagnoses are warranted to validate these findings.