Xuan Zhang,1,2,* Haiyan Fu,2,3,* Jie Li,1,2 Junfang Yan,1 Jingjing Huang,4 Zhaoyuan Xu,1,2 Mingwu Li,2,5 Mengni Qian,3 Lifeng Wang,3 Hongjuan Li,2,3 Yingrong Du1,2 

1Cardiology Department, The 3rd People’s Hospital of Kunming, Kunming, Yunnan, People’s Republic of China; 2Yunnan Infectious Disease Clinical Medical Center, Kunming, Yunnan, People’s Republic of China; 3Hospice Care Center, The 3rd People’s Hospital of Kunming, Kunming, Yunnan, People’s Republic of China; 4Medical Record Department, The 3rd People’s Hospital of Kunming, Kunming, Yunnan, People’s Republic of China; 5Tuberculosis Department, The 3rd People’s Hospital of Kunming, Kunming, Yunnan, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Hongjuan Li, The 3rd People’s Hospital of Kunming, No. 319 WuJing Road GuanDu area, Kunming, People’s Republic of China, Tel +86 871-63510928, Email 56624140@qq.com; Yingrong Du, The 3rd People’s Hospital of Kunming, No. 319 WuJing Road GuanDu area, Kunming, People’s Republic of China, Tel +86 871-63543252, Email dyr_km@163.com

Aim: Exploring the value of inflammatory markers in diagnosing active pulmonary tuberculosis in diabetics.
Patients and Methods: Routine clinical indicators and a range of inflammatory markers were assessed in 276 diabetic patients (DM) and 276 patients with diabetes mellitus combined with active tuberculosis (DM-PTB) from Kunming, Yunnan Province, China. Differences between indicators were compared between the two groups, and factors influencing the susceptibility of diabetic patients to active tuberculosis were analyzed. A novel predictive model was constructed by combining inflammatory and lipid markers using R-Studio in a pioneering manner, and the efficacy of the predictive model was assessed using Calibration Curve and other methods in a multifaceted manner.
Results: Univariate analysis showed that clinical markers including triglycerides, leukocytes, neutrophils, lymphocytes, monocytes, and platelets; inflammatory markers including the neutrophil-to-lymphocyte ratio (NLR), neutrophil to high-density lipoprotein ratio (NHR), platelet-to-lymphocyte ratio (PLR), platelet-to-neutrophil ratio (PNR), platelet-to-monocyte ratio (PMR), monocyte to high-density lipoprotein ratio (MHR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), aggregate inflammation systemic index (AISI), neutrophil-to-monocyte ratio (NMR), and lymphocyte-to-monocyte ratio (LMR) showed significant differences. Specifically, triglyceride, PNR, PMR, MHR, and MLR are risk factors for the development of PTB in DM patients. The model for predicting DM-PTB using a combination of indicators has a high sensitivity (75.0%) and specificity (81.9%).
Conclusion: Triglycerides, PNR, PMR, MHR, and MLR were identified as influential factors in the progression to PTB in diabetic patients. The combined application of these indicators provides an economical, convenient and direct method for early identification of diabetic patients susceptible to Mycobacterium tuberculosis infection.

Keywords: inflammatory markers, diabetes mellitus, active pulmonary tuberculosis, influencing factors, predictive modeling