Metabolic Risk Assessment Using the Triglyceride–Glucose Index in Patients with Adrenal Insufficiency on Glucocorticoid Therapy

Abstract

Purpose:

Adrenal insufficiency (AI) is an endocrine condition requiring lifelong glucocorticoid replacement treatment. Long-term exposure to glucocorticoids may result in significant metabolic problems, such as insulin resistance, abdominal obesity, dyslipidemia, and increased cardiometabolic risk. The triglyceride–glucose (TyG) index is a simple, inexpensive marker of insulin resistance and metabolic syndrome (MetS), increasingly used in endocrine and metabolic research, but its role in AI remains insufficiently researched. This study aimed to assess the clinical utility of the TyG index in patients with AI.

Method:

This was a single-center, cross-sectional observational study including 58 patients with primary or secondary AI receiving glucocorticoid replacement therapy. Demographic, clinical, and anthropometric data were recorded, and body composition was assessed by bioelectrical impedance analysis. The TyG index was calculated using fasting triglyceride and glucose values. MetS was diagnosed according to the International Diabetes Federation 2009 criteria. The relationships between TyG and metabolic risk factors were examined, and TyG levels were investigated in the primary AI and secondary AI groups. The discriminative value of TyG for MetS was determined using receiver operating characteristic (ROC) analysis, including pairwise area under the curve (AUC) comparisons and the Youden-optimal threshold.

Results:

The mean age was 44.8 ± 15.6 years, and 28 patients (48.3%) were female. Secondary and primary AI were present in 53.4% and 46.6% of patients, respectively. MetS prevalence was 31.0%. TyG was higher in secondary AI compared with primary AI (8.8 ± 0.7 vs. 8.4 ± 0.5; p = 0.014). TyG values were higher in patients with elevated waist-to-height ratio (WHtR), high waist-to-hip ratio, obesity, and MetS (p < 0.05). ROC analysis showed that TyG had the strongest discriminative value for identifying MetS (AUC = 0.840, 95% confidence interval [CI]: 0.722–0.959; p < 0.001), with a cut-off of 8.49 yielding 94.4% sensitivity and 67.5% specificity, followed by WHtR (AUC = 0.826, 95% CI: 0.719–0.932, p < 0.001) and body mass index (AUC = 0.757, 95% CI: 0.631–0.883, p = 0.002).

Conclusion:

This study evaluates TyG in AI and indicates which it is a simple, low-cost marker associated with metabolic risk. TyG may support early identification and risk stratification in this vulnerable population.