Exploring the Association Between Triglyceride-Glucose Indices and Their Derivatives With Obstructive Sleep Apnea: Insights From the National Health and Nutrition Examination Survey

Nonlinear association between triglyceride-glucose index and 28-day mortality in intensive care units: a multi-center retrospective cohort study

Background

The triglyceride-glucose (TyG) index, derived from the calculation of two biomarkers, fasting plasma glucose and triglyceride levels, is a reliable indicator of insulin resistance and has been demonstrated to be associated with the adverse clinical outcomes of patients in the intensive care unit (ICU). This study aims to investigate the relationship between the TyG index and the 28-day all-cause mortality of these patients during their ICU stay.

Methods

This study employed a multicenter retrospective cohort design, analyzing data from 18,883 ICU patients in the eICU database. We calculated the TyG index for each patient and assessed its association with 28-day all-cause mortality. The Cox proportional hazards model was utilized for analysis, adjusting for various clinical and laboratory variables to control for confounding factors. We performed sensitivity analyses, subgroup analyses, and interaction analyses to evaluate the robustness of the results.

Results

The study identified a significant positive correlation between the TyG index and 28-day all-cause mortality. Specifically, each one-unit increase in the TyG index corresponded to a 58% increase in mortality risk (HR=1.58, 95% CI: 1.25-2.00, P=0.0001). Additionally, the analysis revealed a non-linear threshold effect of the TyG index on mortality, with a cutoff point at 8.82; mortality was lower below this value and significantly increased above it. Sensitivity and subgroup analyses indicated robust findings, while E-value analysis suggested resilience against unmeasured confounding.

Conclusion

This study establishes the TyG index as an independent predictor of 28-day all-cause mortality in critically ill patients, highlighting its potential value in clinical management and risk assessment. By recognizing the non-linear effect of the TyG index, clinicians can more effectively adjust treatment strategies to reduce mortality among high-risk patients.

Association Between Metabolic Score for Insulin Resistance (METS-IR) and Risk of Obstructive Sleep Apnea: Analysis of NHANES Database and a Chinese Cohort

Purpose

Insulin resistance (IR) plays a significant role in the development of obstructive sleep apnea (OSA). The metabolic score for insulin resistance (METS-IR) is a novel method for assessing IR. This study aims to explore the relationship between METS-IR and the risk of OSA.

Patients and Methods

This cross-sectional study included a total of 8297 subjects from NHANES (National Health and Nutrition Examination Survey) database, as well as 581 patients who underwent sleep monitoring in Renmin Hospital of Wuhan University. Logistic regression, subgroup analysis, and receiver operating characteristic (ROC) curve analysis were employed for evaluation.

Results

In the American population, a significant positive association was found between METS-IR and increased risk of OSA. For each unit increase in METS-IR, the risk of OSA increased by 4.4% (OR= 1.044; 95% CI: 1.037-1.059; P <0.001). A similar relationship was observed in the Chinese population. Multivariate Logistic regression model showed that for each unit increase in METS-IR, the prevalence of OSA increased by 6.7% (OR= 1.067; 95% CI: 1.035-1.103; P <0.001), and apnea-hypopnea index (AHI) increased by 0.732 (β= 0.732; 95% CI: 0.573-0.732; P <0.001). Gender subgroup analysis further showed that the association between METS-IR and OSA was particularly significant in male participants (OR= 1.111; 95% CI: 1.065-1.163; P <0.001). In the ROC analysis, the area under the curve (AUC) value of METS-IR for predicting OSA was 0.777, but it is not statistically significantly different from triglyceride glucose (TyG) (AUC = 0.749; P = 0.054), body mass index (BMI) (AUC = 0.769; P = 0.269), and triglyceride glucose-body mass index (TyG-BMI) (AUC = 0.777; P = 0.996).

Conclusion

METS-IR is significantly associated with the risk of OSA and may serve as an effective predictive marker for identifying OSA.