Pericoronary Fat Attenuation: How TyG Index Links to OSA

Introduction The coexistence of type 2 diabetes mellitus (T2DM) and obstructive sleep apnea (OSA) poses a major global health challenge. Worldwide, T2DM affects over 537 million people,1 with T2DM having a significantly higher occurrence of OSA than those without diabetes.2 Studies suggest a mutual relationship: OSA raises the likelihood of insulin resistance, and T2DM patients with OSA experience increased cardiovascular mortality and coronary heart disease risks.3–5 The Stop-Bang questionnaire is a proven tool for screening OSA, offering substantial clinical benefits for individuals with T2DM.6 This tool quickly detects high-risk individuals by evaluating snoring, daytime sleepiness, high blood pressure, and a body mass index (BMI) over 35 kg/m2. Significantly, the STOP-Bang score was positively correlated with the apnea-hypopnea index (AHI) among T2DM patients (P<0.001), and scores of 5 or more independently indicated severe OSA in this population.7 Consequently, it facilitates early intervention and enables risk-based management to reduce the risk of cardiovascular disease. Insulin resistance (IR) is a shared pathophysiological mechanism for OSA, T2DM, and cardiovascular events.8,9 Quantified as In [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2], the triglyceride-glucose index (TyG) is a trustworthy indicator for assessing insulin resistance.10,11 With each tertile rise in the TyG index, the risk of OSA increased by 3.35 times (odds ratio [OR] = 3.35, 95% confidence interval [CI]: 1.08–10.37).12 In individuals with T2DM, there was a significant correlation between higher TyG index quartiles and an increased risk of major adverse cardiovascular events (MACE).13 Individuals in the top quartile exhibited a 4.32 times greater lifetime risk of cardiovascular disease (OR = 4.32, 95% CI 1.19–15.67; P = 0.026) compared to those in the bottom quartile.14 According to ROC analysis, TyG outperformed HOMA-IR in predicting lifetime CVD risk, as indicated by an AUC of 0.72 versus 0.65, with the difference being statistically significant (P<0.05),14 highlighting its key role in the pathophysiology of metabolic-sleep disorders. The fat attenuation index (FAI) measures inflammation in pericoronary adipose tissue (PCAT) using coronary computed tomography angiography (CCTA),15 offering new perspectives on the interaction between metabolism and inflammation. A multicenter study conducted over time involved 40,091 patients who underwent CCTA at eight hospitals in the UK, with a median follow-up period of 2.7 years. Patients with FAI scores in the highest quartile, as shown by an interquartile range (IQR) of 1.4–5.3, faced a 29.8-fold increase in cardiac mortality risk (hazard ratio [HR] = 29.8, 95% CI: 13.9–63.9; P<0.001) and a 12.6-fold increase in MACE risk (HR=12.6, 95% CI: 8.5–18.6; P<0.001).16 In T2DM patients, increased lesion-specific FAI is an independent predictor of MACE, especially in cases with moderate-to-severe coronary artery calcification.17 While researchers have identified independent links between the TyG index and cardiovascular risk in T2DM/OSA groups, the connection between FAI and OSA in T2DM, as well as FAI’s involvement in the TyG-OSA pathway, has not been defined. Therefore, this study aimed to investigate the correlations among TyG index, PCAT-FAI, and OSA risk, stratifying OSA risk via STOP-Bang scores, to inform preventive strategies for OSA-associated T2DM. Patients and Methods Study Design and Population This retrospective cross-sectional study investigated the interrelationships among OSA, coronary inflammation, and insulin resistance in T2DM cohorts. At Yan’an University Affiliated Hospital, we retrospectively screened consecutive patients with type 2 diabetes mellitus (T2DM) referred for clinically indicated coronary computed tomography angiography (CCTA) between January 2020 to August 2023. Inclusion and Exclusion Criteria The inclusion criteria were as follows: (i) age >18 years; (ii) active antihyperglycemic pharmacotherapy; and (iii) WHO ICD-10-coded T2DM diagnosis (E11). The exclusion criteria were as follows: (i) history of coronary revascularization (percutaneous coronary intervention [PCI] or coronary artery bypass grafting [CABG]), (ii) chronic total occlusion (CTO) lesions, and (iii) non-diagnostic CCTA studies secondary to respiratory motion artifacts or arrhythmias. Following screening, 420 eligible participants comprised the analytical cohort (Figure 1). Ethical Considerations The Institutional Review Board of Yan’an University Affiliated Hospital approved our study protocol, which adheres to the Declaration of Helsinki (2013 revision), granting exemption from full ethics review and waiving written informed consent requirements under Chinese national regulations for retrospective analyses of anonymized data. STOP-BANG Risk Stratification We assessed obstructive sleep apnea (OSA) risk using the validated STOP-Bang questionnaire.18,19 Consistent with prior research, a STOP-Bang score of 3 or higher demonstrated high diagnostic accuracy (>0.80) for moderate-to-severe OSA (defined as AHI ≥15 events/hour).18 CCTA Acquisition and Analysis Imaging Protocol We acquired all coronary computed tomography angiography (CCTA) examinations using a 256-slice dual-source CT scanner (SOMATOM Definition Flash; Siemens Healthineers, Forchheim, Germany). Imaging optimization protocols included: patients maintained resting respiration during image acquisition; target heart rate maintained at <70 beats per minute (bpm); patients exceeding threshold received oral metoprolol (25–75 mg) 1 h pre-scan; intravenous access established via 20-gauge cannula in the right antecubital vein; iodinated contrast (iodixanol, 320 mgI/mL; 60–80 mL) injected at 4.5–6.5 mL/s using dual-barrel injector, with contrast volume titrated by body weight (≥75 kg: 80 mL). Scan Parameters We employed retrospective electrocardiographic (ECG) gating with the following optimized parameters: tube voltage, 120 kV; tube current, 250–800 mA (automated dose modulation via CARE Dose4D); rotation time, 270 ms; collimation, 128×0.625 mm; reconstruction, 0.75 mm slice thickness at 0.5 mm increments; matrix size, 512×512 pixels.20,21 PCAT Analysis PCAT attenuation was quantified using deep learning-based software (CT-FAI V1.2, ShuKun Technology Co., Ltd., Beijing, China).21 Vessel segments analyzed: left anterior descending (LAD) and left circumflex (LCX) arteries: proximal 40-mm segments; right coronary artery (RCA): proximal 10–50 mm segments. The Segment Technical workflow included: 1. Bolus tracking in the ascending aorta (trigger threshold: 120 Hounsfield units [HU]);2. Automated vessel segmentation via centerline extraction; 3. PCAT definition: adipose tissue within a radial distance ≤3 Mm from the vessel wall;4. Attenuation measurement: mean HU values within the −190 to −30 HU range. Two board-certified cardiovascular radiologists, blinded to the clinical and OSA statuses, independently analyzed all CCTA datasets. TyG Index Calculation and Covariate Definitions TyG Index Derivation The TyG index (TyG) was derived using the following validated formula: TyG = ln[fasting triglycerides (mg/dL) × fasting plasma glucose (mg/dL)/2]. We converted all original laboratory measurements from mmol/L to mg/dL using standardized international conversion factors: fasting plasma glucose at 1 mmol/L = 18.018 mg/dL and triglycerides at 1 mmol/L = 88.57 mg/dL. Diagnostic Criteria T2DM was diagnosed according to American Diabetes Association (ADA) criteria, requiring fulfillment of ≥1 diagnostic criterion: Fasting plasma glucose ≥7.0 mmol/L;2-hour oral glucose tolerance test ≥11.1 mmol/L; Glycated hemoglobin (HbA1c) ≥6.5%;22 Current glucose-lowering pharmacotherapy; Documented physician diagnosis. ②We classified hypertension (HTN) according to the 2024 Chinese Guidelines for Hypertension Management (CGH-2024), defining it as systolic blood pressure (SBP) ≥140 mmHg, diastolic blood pressure (DBP) ≥90 mmHg, or current use of prescribed antihypertensive medication.23 We defined smoking status based on cumulative tobacco exposure exceeding five pack-years, equivalent to at least six months of sustained smoking at ≥20 cigarettes per day. Statistical Analysis We utilized SPSS software, version 26.0, from IBM Corp. in Armonk, NY, USA, to conduct all statistical analyses. According to Shapiro–Wilk normality tests, continuous variables with a normal distribution were presented as mean ± standard deviation (SD), while those without a normal distribution were displayed as median with interquartile range (IQR). We displayed categorical variables in terms of frequencies and percentages. For analyses with two groups, we used Student’s t-test for normally distributed data and the Mann–Whitney U-test for non-normally distributed data. One-way ANOVA was used to analyze normally distributed data to compare multiple groups, while the Kruskal–Wallis test was applied to non-normally distributed data. We developed multivariable logistic regression models to assess the associations between metabolic markers and OSA risk, taking into account predetermined factors such as age, sex, BMI, and medication history. We used the Akaike information criterion (AIC) to optimize and fit restricted cubic splines (RCS) with four knots to explore potential nonlinear relationships between STOP-Bang scores, RCA-FAI, and the TyG index with high-risk OSA. The TyG index’s mediation effects were significant, as assessed using Hayes’ PROCESS macro (Model 4), to evaluate the role of RCA-FAI in mediating the TyG-OSA relationship.24,25 We assessed the statistical significance of indirect mediation effects by generating 95% confidence intervals using 1000 bias-corrected bootstrap resamples. All statistical tests were conducted as two-tailed, with a P-value of less than 0.05 indicating statistical significance. Study Objectives The primary objective was to evaluate the exposure-response relationships between OSA severity and coronary inflammation, as quantified by FAI, and insulin resistance, assessed via the TyG index, in T2DM patients. The secondary objective was to evaluate the mediating role of coronary inflammation in the association between the TyG index and the severity of OSA. Results Baseline Characteristics The analytical cohort comprised 420 consecutively enrolled patients with T2DM, stratified by STOP-Bang questionnaire scores into low-risk (n = 126, 30.0%), intermediate-risk (n = 148, 35.2%), and high-risk groups (n = 146, 34.8%). As detailed in Table 1, we observed statistically significant intergroup differences (p < 0.05) in metabolic parameters: body mass index (BMI), systolic and diastolic blood pressure (SBP/DBP), STOP-Bang scores, hypertension prevalence, fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride-glucose (TyG) index. Conversely, we detected no statistically significant variations in demographic characteristics (age, sex distribution), clinical profiles (smoking history, disease duration), or medication regimens (hypoglycemic agents, lipid-lowering drugs, antihypertensive agents, antiplatelet therapy) between groups (all P > 0.05). Table 1 Stratification Baseline Characteristics Stratified by OSA Risk 3.2 Distribution of research subjects based on OSA risk stratification and peri-crown fat attenuation index characteristics. Figure 2 visually demonstrates significantly higher PCAT-FA values in all three coronary arteries (LAD, LCX, and RCA) among patients with elevated OSA risk (intermediate- and high-risk groups) compared to the low-risk group (P < 0.05). Multivariable Regression Analysis for High-Risk OSA To evaluate the independent associations between pericoronary adipose tissue inflammation and OSA risk, we developed multivariable logistic regression models employing progressive adjustment strategies (Table 2): Model 1 (Unadjusted): Base model without covariates; Model 2 (Partially Adjusted): Adjusted for demographic confounders (age, sex); Model 3 (Fully Adjusted): Adjusted for clinical covariates: hypertension status, smoking history, body mass index (BMI), glycated hemoglobin (HbA1c), and medication classes (glucose-lowering agents, lipid-modifying therapy, antiplatelet agents). After full adjustment (Model 3): RCA-FAI: OR 1.06 (95% CI: 1.01–1.10; P = 0.01). The TyG index (OR 3.30 [95% CI: 1.92–5.67; P < 0.01) was significantly associated with a high risk of OSA. We identified no significant associations between perivascular fat attenuation indices—specifically LAD-FAI, LCX-FAI, or culprit vessel FAI—and high-risk OSA.(all P > 0.05). Nonlinear Associations Between Coronary Inflammation and Insulin Resistance Restricted cubic spline analyses demonstrated significant overall associations between the RCA-FAI and TyG index with OSA risk (Poverall < 0.001; Figure 3). Specifically, RCA-FAI showed a nonlinear relationship with high OSA risk (Pnonlinearity = 0.007), with a threshold effect at −80 HU. Above this threshold, each 10-HU increment conferred a 1.8- to 4.2-fold elevated risk of OSA (95% CI [1.5, 4.5]; Ptrend = 0.002). TyG index exhibited no significant nonlinear association (Pnonlinearity = 0.051). Insulin Resistance-Mediated Effect of Coronary Inflammation on the High Risk of OSA Mediation analysis demonstrated that the RCA-FAI significantly mediated the association between the TyG index and high-risk OSA (Figure 4). The mediation proportion was 11.78%, with a 95% CI of 2.3–21.1 (P = 0.03). Discussions In this cross-sectional study of 420 patients with T2DM, we investigated the relationship between the TyG index, FAI, and OSA risk. Consistent with prior research, the OSA group demonstrated a significantly elevated TyG index compared to non-OSA controls.11 Our study reveals significant differences in pericoronary adipose tissue FAI across OSA risk stratifications among individuals with T2DM, establishing substantial correlations between the TyG index, FAI, and elevated OSA risk. Mechanistically, we identified FAI as an essential mediator of the TyG index-OSA risk association, accounting for 11.8% of the total effect (β = 0.32, 95% CI 0.18–0.47). This work provides the first evidence establishing the TyG-FAI pathway as a novel mechanism of OSA susceptibility, specifically in T2DM populations. Building on the established predictive value of the TyG index for cardiovascular disease (CVD) in T2DM, recent evidence has highlighted its significant association with OSA severity and related cardiovascular risks. A systematic review and meta-analysis demonstrated that the TyG index significantly enhances the predictive power of traditional risk models for CVD in T2DM patients.26 Furthermore, research indicates that the TyG index and its derivatives (TyG-BMI and TyG-WC) increase progressively with worsening OSA severity. This link extends to specific cardiovascular outcomes.27 A cross-sectional study of 1059 patients with OSA revealed that each unit increase in the TyG index was associated with a 1.98-fold higher risk of coronary heart disease (OR = 1.98, 95% CI: 1.42–2.80, p < 0.001). The same study also found a significant nonlinear positive correlation between the TyG index and the severity of coronary atherosclerosis, as measured by the Gensini score (nonlinear test p = 0.003).28 These findings collectively suggest that the TyG index serves as an effective metabolic marker for stratifying coronary heart disease risk and assessing coronary lesions in patients with OSA, confirming the association between metabolic dysregulation and OSA, and indicating a dose-response relationship between the TyG index and OSA severity. Our results are consistent with this established relationship. After multivariate adjustment, we observed an independent positive correlation between the TyG index and high OSA risk. This finding reinforces the conclusion from a recent meta-analysis, which reported a significantly higher TyG index in OSA groups compared to controls (SMD = 0.86, 95% CI: 0.58–1.13).29 Our analysis revealed significantly elevated FAI in the RCA, LAD, LCX, and culprit vessels among intermediate- and high-risk OSA groups compared to the low-risk group. Notably, the association between RCA-FAI and OSA risk persisted after comprehensive multivariable adjustment, indicating its predictive independence from traditional metabolic confounders such as BMI, blood pressure, and lipid profiles. This observation aligns with prior evidence that the RCA is the optimal anatomical site for assessing coronary artery inflammation,30,31 and corroborates findings from Liu et al, who identified occult vascular inflammation in high-risk T2DM cohorts with coronary heart disease.17 Collectively, these results suggest FAI may serve as a complementary imaging biomarker for OSA risk stratification beyond conventional metabolic metrics. Furthermore, mediation analysis demonstrated that RCA-FAI mediated 11.78% (β = 0.118) of the TyG index–OSA risk association, supporting a plausible pathological pathway wherein metabolic dysregulation promotes coronary inflammation, thereby increasing OSA susceptibility. Nevertheless, we emphasize that our cross-sectional design cannot infer causality between TyG and OSA. Collectively, our findings advance the conceptualization of a “metabolic-inflammatory-hypoxia axis” in T2DM. We demonstrated that glycolipid metabolic dysregulation, reflected by an elevated TyG index, drives proinflammatory phenotypic transformation in PCAT, aligning with Liu et al’s observations of coronary inflammation in T2DM.17 Intermittent hypoxia (IH) secondary to OSA exacerbates insulin resistance through two key inflammatory pathways in coronary adipose tissue. First, IH activates hypoxia-inducible factor (HIF) signaling, which increases leptin secretion and macrophage infiltration.32 Second, IH promotes macrophage ferroptosis and M1 polarization while triggering endoplasmic reticulum stress and NLRP3 inflammasome activation.33,34 These processes collectively drive adipose tissue inflammation and impair insulin signaling via leptin overexpression and IL-6/TNF-α-mediated pathways.32,33 The resulting vascular dysfunction manifests as impaired vasodilation and increased perivascular inflammation34,35 (Figure S1). Building on the FAI’s role as a validated quantifier of coronary inflammation for cardiovascular risk assessment, our findings extend its clinical utility to the OSA prediction.15 We demonstrated for the first time that the TyG index mediates the relationship between metabolic dysfunction and OSA, providing mechanistic validation for Sotak et al’s adipose inflammation hypothesis.36 Notably, the dose-response relationship between TyG and coronary risk, as well as the corresponding FAI gradient changes, reveals an inflammatory threshold phenomenon in metabolic dysregulation. The OSA risk escalated nonlinearly when the RCA-FAI was higher than −80 HU. These clinically actionable thresholds support the development of a targeted screening protocol, wherein we recommend immediate polysomnography (PSG) referral for patients with T2DM who meet the predefined high-risk criteria to facilitate timely OSA diagnosis and early intervention in the future. Limitations and Future Direction This study has several limitations. First, the cross-sectional design precluded definitive causal inference of the sequence of the TyG-FAI-OSA pathway. Second, although we adjusted for key confounding factors, we cannot exclude residual confounding (eg, unmeasured genetic predisposition or gut microbiome dysbiosis). Third, OSA diagnosis relied on the Stop-BANG questionnaire rather than PSG, introducing OSA severity misclassification bias. Finally, two constraints merit emphasis: the sample size (N = 420) restricts the statistical power for subgroup analyses, and ethnic homogeneity reduces the external validity for diverse populations. Directions: To address these gaps, three research priorities merit investigation: (i) implementing longitudinal cohorts and Mendelian randomization designs to elucidate genetic associations between TyG-index traits and OSA susceptibility; (ii) integrating PSG with bioelectrical impedance analysis to delineate OSA-body composition-adipose distribution interactions; and (iii) constructing artificial intelligence frameworks integrating coronary CTA with multi-omics datasets for precision OSA risk stratification. Conclusion In this cohort of patients with T2DM, we demonstrate for the first time that FAI—a radiological marker of vascular inflammation—is significantly associated with high-risk OSA. The TyG index, an established indicator of insulin resistance, independently predicted high-risk OSA, with RCA-FAI mediating 11.8% of this association. Crucially, we identified a nonlinear threshold effect: OSA risk escalated when RCA-FAI exceeded −80 HU. These findings unveil pathogenic cross-system interactions linking T2DM, coronary inflammation, and OSA, while establishing RCA-FAI as a metabolic imaging biomarker for OSA risk stratification. Therefore, integrating RCA-FAI (≥ −80 HU) and TyG index into routine T2DM risk assessment workflows may enable precision stratification of OSA-related cardiovascular risk, guiding targeted interventions. Future prospective studies are warranted to validate this multi-marker screening strategy.