A previous study assessed relationship between the Tyg index and OSA by using the NHANES 2005–2008 dataset. However, prior research has focused mainly on investigating OSA as a standalone condition, thus neglecting the impact of the TyG index on the clinical presentations of OSA.

In this study, weighted linear regression and logistic regression analyses were employed to explore the correlation between the TyG index and OSA, encompassing OSA-related symptoms. Furthermore, a receiver operating characteristic curve was constructed to predict the risk of OSA. Following this design, a restricted cubic spline analysis was utilized to evaluate the relationship between the TyG index and OSA, along with its associated symptoms.

The primary finding of this study was that the TyG index exhibited a strong ability to predict OSA, with an AUC of 0.701 (95% CI: 0.6619–0.688). A higher AUC indicated that the model was more accurate at predicting the correct class, with 1 representing the optimal score. A model showing an AUC ranging between 0.7 and 0.8 signifies commendable performance. Additionally, the TyG index demonstrated a close association with OSA in both the crude model (OR = 0.24; 95% CI: 0.22–0.26) and Model 2 (OR = 0.02; 95% CI: 0.00–0.04). A higher TyG index indicated an increased risk of OSA (OR = 1.45; 95% CI: 1.02–2.42). Another significant finding was the correlation between the TyG index and symptoms in OSA patients experiencing breathing cessation, although its association with other symptoms remains inconclusive.

Insulin resistance is characterized by compromised glucose uptake, diminished glycogen synthesis, and reduced inhibition of lipid oxidation. Hence, when defining insulin resistance, it is crucial to consider not only triglycerides but also glucose levels. The TyG index is calculated based on fasting triglyceride and glucose levels. The incorporation of both lipid and sugar metabolism provides a straightforward approach for evaluating insulin resistance and the risk of metabolic syndrome. Moreover, several composite lipid indices are linked to OSA.

The lipid accumulation product (LAP), which was introduced in 2005, integrates waist circumference with triglyceride levels to provide insights into metabolic health and fat accumulation. Its robust ability to predict cardiovascular risks and diverse metabolic conditions is notable. However, the complexity of LAP measurements, which rely on accurate waist circumference data, poses challenges. The reliability of these measurements can be influenced by factors such as measurement technique, operator skill, and physiological variations such as postprandial distension. Although LAP emphasizes lipid metabolism, it may place less emphasis on sugar metabolism than the TyG index.

In 2010, the Visceral Adiposity Index (VAI) was acknowledged for its substantial correlations with cardiovascular diseases and metabolic syndrome [32]. The visceral fat index is computed by incorporating measurements such as waist circumference, weight, height, triglycerides, and systolic blood pressure. Its main objective is to evaluate the level of visceral fat accumulation (specifically, the degree of abdominal obesity). Research into the relationship between the VAI and OSA, including studies conducted by Mazzuca and a comprehensive Chinese study, has not identified a significant connection [33, 34]. The calculation of the VAI is more difficult than that of the TyG index. Therefore, the VAI is considered to be inferior to the TyG index.

The atherogenic index of plasma (AIP), which is a marker that reflects the esterification rate of HDL particles, provides a comprehensive understanding of the relationship between HDL-C and triglyceride levels [35]. The AIP is calculated by using easily accessible parameters, and studies have shown that the AIP is elevated in patients with OSA and is linked to disease severity [36]. Nevertheless, research suggests that the AIP increases in OSA only in individuals with moderate to severe disease and in those who have concurrent hypertension and diabetes [37]. In specific populations, such as individuals with exceptionally high or low triglyceride levels, the accuracy of the AIP may be compromised. Consequently, the clinical applicability of this marker could be limited [38].

The TyG index primarily evaluates insulin resistance and the risk of metabolic syndrome. The VAI predominantly assesses visceral fat accumulation and abdominal obesity. Moreover, the AIP is mainly associated with the risk of atherosclerosis. The LAP is primarily correlated with insulin resistance, metabolic syndrome, and the risk of cardiovascular disease.

Hence, although other indices demonstrate strong predictive abilities for OSA, the TyG index retains unique advantages. A recent meta-analysis aligns with this viewpoint, thus suggesting that the diagnostic accuracy of the TyG index is similar to that of other anthropometric indices [23].

The results of this study are consistent with those of a study conducted in Korea showing that an elevated TyG index is associated with an increased risk of developing OSA [39]. Nevertheless, a separate cross-sectional study suggested that the TyG index may not have a significant association with OSA [24]. This discrepancy could be attributed to the sample size; specifically, a larger sample size enhances the statistical power of a study, thus increasing its ability to accurately detect effects. In contrast, a small sample size may result in the study failing to detect significant relationships or differences, thus potentially leading to false-negative outcomes. In such instances, the study could underestimate the true associations between variables. A larger sample size allows for more precise estimations of population parameters, such as the mean, ratio, and effect size. Conversely, a smaller sample size may introduce larger sampling errors, thus causing sample statistics to deviate significantly from population parameters and reducing the credibility of the study results [40]. In contrast, this discrepancy could be linked to the definition of OSA. The NHANES database comprises a wide range of questionnaires, thus leading different researchers to choose varying questionnaires based on diverse standards, which could potentially introduce biases into the results. The diagnostic criteria for OSA that were employed in this study were carefully formulated by considering a range of clinical symptoms and other factors, thus ensuring a high level of credibility [25, 26]. Research by Andras Bikov et al. [41] indicated that the TyG index independently influences OSA. In This study, even after accounting for covariates, the TyG index maintained a strong correlation with OSA, thus confirming its independent effect. Additionally, findings from Andras Bikov’s study underscore a significant link between BMI and the TyG index, which is particularly prominent in individuals with higher BMIs. This study conducted a BMI-stratified analysis, and the observed results were consistent with the abovementioned research, thus emphasizing a stronger association between the TyG index and OSA among individuals in the higher BMI range. Among the clinical symptoms of OSA, the TyG index exhibited a stronger correlation with breathing cessation than with snoring and daytime sleepiness. Breathing cessation, which is a key clinical manifestation of OSA, has a profound impact on patients and is characterized by episodes of intermittent hypoxia [42]. Lin et al. [43] reported that in nonobese individuals, nocturnal hypoxia was notably linked to elevated triglyceride levels compared to those in the control group, which is consistent with the results of the current study. Additionally, in animal studies conducted by Li and colleagues, a marked increase in the expression levels of crucial transcription factors was observed in lean mice exposed to intermittent hypoxia. These transcription factors play essential roles in triglyceride biosynthesis, thus suggesting a potential mechanism through which intermittent hypoxia disrupts lipid metabolism [44]. Several animal studies have highlighted a potential association between intermittent hypoxia and the development of insulin resistance in lean mice. Insulin plays a crucial role in inhibiting triglyceride synthesis in the liver. The severity of intermittent hypoxia correlates with the degree of insulin resistance, thus resulting in a higher TyG index. Another study suggested that intermittent hypoxia can trigger insulin resistance and glucose intolerance, thus aggravating lipid accumulation in liver tissues [45]. In a small clinical trial involving human subjects, individuals with OSA exhibited dysregulated triglyceride metabolism, which improved with continuous positive airway pressure treatment [46]. Another study also indicated that intermittent hypoxia could impact the TyG index. In addition to affecting circulating cholesterol levels, OSA may regulate lipid metabolism by stimulating the generation of oxidatively stressed dysfunctional lipids, thus consequently influencing the TyG index [47].

This study has significant implications for clinical practice, given the increasing annual incidence of cardiovascular and cerebrovascular diseases attributed to OSA [48]. OSA has emerged as a significant health concern affecting human well-being. However, diagnosing OSA is often time-consuming, labor-intensive, and financially burdensome for patients. Hence, there is an urgent clinical need to identify a convenient and efficient diagnostic method for OSA. The TyG index, which is a cost-effective and easily measurable indicator, effectively meets these clinical requirements. This study’s findings offer valuable guidance to health care providers in promptly and conveniently assessing patients' risks of developing OSA. Given the limited data on the relationship between the TyG index and OSA risk in the field of sleep medicine, this research could offer essential insights that are applicable to high-risk adult populations for OSA.

Subgroup analysis plays a crucial role in scientific research, thus offering valuable insights into specific population subsets and enhancing the understanding of complex relationships within the data [49]. In this study, age, sex, and race were employed as stratification variables for subgroup analysis. The results suggested that younger Caucasian females may be at a greater risk for obstructive sleep apnea. (OSA) Age was identified as being a significant factor influencing OSA incidence, particularly within the 18–34 age group, where the triglyceride-glucose (TyG) index exhibited the most pronounced impact on OSA incidence.

This study possessed several strengths that enhance its validity and reliability. First, the utilization of the NHANES database ensured sample diversity and representativeness, with strict adherence to the database guidelines. Second, the study employed linear, nonlinear, and logistic regression models to explore the relationship between OSA and the TyG index. Third, rigorous statistical adjustment techniques were applied to effectively control for any residual confounding factors potentially influencing the TyG index. Fourth, the use of the ROC curve illustrated that the TyG score serves as a robust predictive indicator for OSA. Fifth, the incorporation of subgroup analysis and interaction tests significantly bolstered the validity and reliability of the research findings. Notably, the subgroup analysis demonstrated that a younger average age was linked to a more substantial impact of the TyG index on OSA incidence.

Although this study had numerous strengths, the identification of certain limitations is crucial. First, the analytical and cross-sectional design of this study may weaken the evidence for the association between exposure and outcome. Future follow-up investigations are warranted to validate these findings. Additionally, diagnosing OSA by utilizing features from the NHANES database may introduce some bias. Furthermore, incomplete consideration of certain confounding factors, such as medication use, raises concerns. Finally, the generalizability of these findings to other regions is uncertain, thus highlighting the necessity for additional research.