A QuantiFERON®-TB Gold Plus-Based Model for Predicting Latent Tuberculosis Infection in Patients with Diabetes

Jiajun Zhou,1 Xiaoqing Lin,1 Yuhui Yu,2 Yiyang Zhang,3 Liufeng Qiu,3 Zhiruo Lin,3 Xiangao Jiang,1 Jichan Shi1 1Department of Infectious Disease, The Wenzhou Central Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China; 2Department of Endocrinology, The Wenzhou Central Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China; 3The Wenzhou Central Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of ChinaCorrespondence: Xiangao Jiang, Department of Infectious Disease, The Wenzhou Central Affiliated Hospital of Wenzhou Medical University, No. 252 Baili East Road, Lucheng District, Wenzhou, Zhejiang, 325000, People’s Republic of China, Tel +860577-88882121, Email [email protected] Jichan Shi, Department of Infectious Disease, The Wenzhou Central Affiliated Hospital of Wenzhou Medical University, No. 252 Baili East Road, Lucheng District, Wenzhou, Zhejiang, 325000, People’s Republic of China, Tel +860577-88882121, Email [email protected]: Diabetes mellitus (DM) is a well-established independent risk factor for tuberculosis (TB), and the prevalence of latent tuberculosis infection (LTBI) among patients with DM remains a critical public health concern. This study aims to investigate the prevalence and predictive factors of LTBI in patients with DM using the QuantiFERON®-TB Gold Plus (QFT-PLUS) test and to develop a clinically applicable predictive model.Methods: A cross-sectional study was conducted involving 195 participants (96 with DM and 99 healthy controls) at Wenzhou Central Hospital between June and December 2023. LTBI was diagnosed using the QFT-PLUS test, and data on patient characteristics, laboratory findings and computed tomography scans were gathered. A predictive model was constructed through univariate and multivariate logistic regression analyses.Results: Among individuals with DM, 28 (29.2%) were identified as having LTBI compared with 16.2% in healthy controls. The final predictive model included age (> 60 vs ≤ 60 years, odds ratio [OR]: 4.994, 95% CI: 1.583– 17.610), gender (male vs female, OR: 26.886, 95% CI: 5.224– 231.700), duration of diabetes (> 10 vs ≤ 10 years, OR: 3.450, 95% CI: 1.000– 13.240) and presence of hyperlipidaemia (yes vs no, OR: 10.637, 95% CI: 2.299– 82.340) as significant factors, yielding an area under the curve of 0.847 (95% CI: 0.765– 0.928).Conclusion: This QFT-PLUS-based predictive model, incorporating readily available clinical parameters, demonstrates potential clinical utility for LTBI risk stratification and targeted TB screening in patients with DM. Future multicentre external validation studies with larger sample sizes are warranted before clinical implementation.Keywords: diabetes, latent tuberculosis infection, QFT-PLUS, risk factor, predictive model