Hepatic Steatosis affects the Outcome of Anti- tuberculosis Treatment in Pulmonary Tuberculosis Patients

Hepatic Steatosis affects the Outcome of Anti- tuberculosis Treatment in Pulmonary Tuberculosis Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hepatic Steatosis affects the Outcome of Anti- tuberculosis Treatment in Pulmonary Tuberculosis Patients Zhi-xiang Du, Yuan Yang, Yu-xiang Gong, Xing Liu, Miao-yang Chen, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4306921/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Pulmonary Tuberculosis (PTB) remains the leading cause of death from infectious diseases worldwide. Few studies have investigated hepatic steatosis's effect on PTB's progression and prognosis. Methods: We selected 785 PTB patients in this study. And 174 PTB patients were enrolled in the statistical analysis. Patients with positive sputum smears for over eight weeks are clustered in the Extension Group. Conversely, the other patients are clustered in the Unextended Group. The clinical data were used to analyze the risk factors for the time of smear conversion. Results: Eleven different factors were obtained from the clinical data of PTB patients. According to clinical significance, correlation detection, collinearity diagnosis, and chest package score, six factors were enrolled into the model. The logistics regression analysis confirmed that Hepatic steatosis and IL-18 were positively correlated with the smear conversion (β= 2.401, OR =11.036, 95% CI = 2.489-48.934; β= 2.396, OR =10.984, 95% CI = 1.832-65.862). On the contrary, the T-SPOT was negatively correlated with the smear conversion (β= -4.199, OR =0.015, 95% CI = 0.001-0.172). Conclusions: Hepatic steatosis is confirmed as an independent risk factor affecting smear conversion time in PTB. The levels of serum cytokines, such as IL-6 and IL-18, are significantly related to the outcome of anti-tuberculosis treatment. Previous studies have confirmed that hepatic steatosis is associated with abnormal serum cytokines. Therefore, more attention is needed for the PTB patients with hepatic steatosis. Tuberculosis Hepatic steatosis Nonalcoholic fatty liver disease Smear conversion IL-6 IL-18 Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction According to the World Health Organization (WHO), tuberculosis (TB) remains the leading cause of death from infectious diseases worldwide[1]. The epidemic of TB is closely related to poverty[2]. Related research confirms that a high body mass index (BMI) is a protective factor against the infection of TB[3]. However, a high BMI is also a significant risk factor for metabolic diseases, such as type 2 diabetes mellitus (DM) and hepatic steatosis[4,5]. The International Union Against Tuberculosis and Lung Disease guidelines and the WHO state that patients with type2 DM should be systematically screened for TB in China[6]. The association between nutritional status or metabolic status and TB is extremely perplexing. Previous study indicates that (7.3–25.4%) pulmonary tuberculosis (PTB) patients have not achieved sputum conversion at the end of the intensive phase of anti-tuberculosis treatment[7]. Factors such as Age, Sex, BMI, smoking, and HIV affected the sputum conversion at two months of treatment[8]. However, the impact of hepatic steatosis on the treatment of tuberculosis has yet to receive sufficient attention. Hepatic steatosis is an early characteristic in the pathogenesis of non-alcoholic fatty liver disease (NAFLD), which is an umbrella term for a continuum of liver conditions that have very different rates of progression and clinical manifestations[9]. A recent meta-analysis indicates that China has over 240 million NAFLD patients and the highest annual NAFLD-related mortality rate[10]. Patients with NAFLD may be more prone to infection. Nseir’s study demonstrates that NAFLD is closely related to the recurrence of urinary infections [11]. Another retrospective study validated that patients with hepatic steatosis are more prone to develop pneumonia[12]. However, the exact mechanism remains unclear. Most Patients with steatosis without features of cirrhosis carry a shallow risk of clinically relevant adverse outcomes. In the past, hepatic steatosis was not considered a severe systemic disease in China. Correspondingly, the effect of NAFLD on the progression and prognosis of TB has yet to attract enough attention from clinicians[10]. Hereditary factors may have played a vital role in the initiation and progression of NAFLD in Chinese populations. Previous study indicated that elevated IL-6/STAT3 is associated with the progression of NAFLD[13]. In mice models, hepatic IL-6/STAT3 activation enhanced the fatty acid oxidation-associated genes in the liver[14]. It has been reported that the co-expression of IL-6 and STAT3 is associated with the dysfunction of T-cells in Tuberculosis patients[15]. The expression of inflammatory cytokines such as IL-6 and IL-10 play an essential role in promoting the progression of NAFLD[16]. Nevertheless, research on the interaction between NAFLD and TB has rarely been reported. In this study, we analyzed the clinical characteristics of pulmonary TB patients with and without extension of sputum conversion. Subsequently, we obtained the independent risk factors affecting the outcome of anti-tuberculosis treatment. Finally, a detailed analysis of the possible mechanism of outcome affected by hepatic steatosis in PTB patients was summarized in this study. 2. Materials and Methods 2.1 Study Individuals Seven hundred and fifty-eight active TB patients were enrolled from Taizhou People’s Hospital between January 2018 and December 2022. The diagnosis of TB was based on the guidelines of WHO (1). Inclusion criteria: All PTB patients had positive sputum smears of Mycobacterium tuberculosis, typical clinical manifestations (Cough, expectoration for more than two weeks, or blood in sputum or hemoptysis, night sweat, fatigue, intermittent or persistent afternoon low fever, anorexia, weight loss, etc.,), and imaging evidence (In mild cases, the main manifestations are patches, nodules and cords or tuberculomas or isolated cavities; in severe cases, there may be lobar infiltrates, caseous pneumonia, multiple cavity formation and bronchial spread.). Exclusion criteria: 1. Retreatment PTB, Multi-Drug Resistant PTB patients, and tuberculous pleurisy were excluded from this study; 2. Patients with underlying diseases such as diabetes, hypertension, autoimmune disease, tumors, chronic obstructive pulmonary disease, and bronchiectasis. 3. Patients with missing clinical data. Finally, one hundred and seventy-four initial-treatment patients with active PTB were enrolled in this study. This study obtained approval from the Clinical Research Ethics Committee of Taizhou People’s Hospital TZRY-LL-AF/SQ-014-2.0 (Protocol number KY201803901). 2.2 Data Collection and study design All patients with active PTB were given standardized anti-TB treatment consisting of oral rifampicin (R) 10 mg/kg/d, isoniazid (H) 5 mg/kg/d, pyrazinamide (Z) 25 mg/kg/d, and ethambutol (E) 20 mg/kg/d for two months (intensive phase), followed by daily isoniazid and rifampicin for four months-control period (2HRZE/4HR). If necessary, the infectious disease physician modified the regimen when there were adverse drug effects. Patients with positive sputum-smear for over eight weeks are clustered in the extension sputum conversion group (Extension Group). Conversely, patients who had sputum conversion within eight weeks are clustered in the Unextended Group. The study design of this study was presented in a flowchart in Figure 1. The informed consent of human clinical data was obtained from all subjects. The clinical baseline data included age, sex, Body Mass Index (BMI), anti-tuberculosis outcome, intensive period time, T-SPOT results, liver ultrasonogram (Identify liver diseases, including liver mass, liver fibrosis, and hepatic steatosis.), etc. Laboratory test data included Blood routine (RBC, NEU, etc.), liver function (TBIL, ALB, ALT, AST, etc.), and kidney function (BUN, CREA, B2MG, etc.). The laboratory test data also included tumors (CA-125, CA19-9) and inflammatory indicators (CRP, ESR, IL-6, IL-10, IL-18). The CA-125, CEA, and CA19-9 cutoff points were selected as 22 U/ml, 5.0 ng/ml, and 37 U/L, respectively. 2.3 Statistical Analysis The clinical and laboratory test data were expressed as the frequencies (n), percentages (%), mean ± standard deviations (SDs), and median (interquartile ranges) and were analyzed with R version 4.2.1. Count data were analyzed with the Fisher's chi-square test. The t-test analyzed the measurement data conformed to a normal distribution. The Mann–Whitney U-test was used to compare nonnormally distributed data. The correlation of the difference factors was analysis by the Spearman method. Chest_0.3.7. package for R were applied the change-in-effect estimate method to assess confounding effects in difference factors for anti-tuberculosis outcome[17]. Logistics regression analysis was used to analyze the relationships between the different factors and the time for outcome in PTB patients. p < 0.05 was considered statistically significant. 3. Results 3.1 Characteristics of Clinical Baseline Data A total of one hundred and seventy-four PTB patients were enrolled in this study. The body mass index (BMI) in the Extension group was 22.55±3.73 kg/m2, which was significantly higher than the 20.52±3.46 kg/m2 in the Unextended Group (p=0.004). The proportion of lesions in two lungs in the Extension group was significantly higher than in the Unextended Group (p<0.05). The time for the time of intensive period in the Extension group was 5.00 (4.00, 6.00) months, significantly higher than the 1.00 (1.00, 2.00) months in the Unextended Group (p< 0.05). The proportion of T-SPOT positive in Extension group was 72%, which was significantly lower than 99% in the Unextended Group (p<0.05). According to the liver ultrasonogram, the proportion of patients with hepatic steatosis in Extension group was 75%, significantly higher than 16% in Unextended Group (p 0.05) ( Table 1 ). 3.2 Characteristics of Laboratory tests The NEU level in the Extension group was 65.00 (36.00, 75.00) 10 9 /L, which was higher than 5.00 (3.00, 8.00) 10 9 /L in the Unextended Group (p<0.05). The LYM and MON levels in the Extension group were 15.00 (5.00, 26.00) and 7.35 (2.80, 8.33) 10 9 /L, which were significantly higher than 1.00 (1.00, 2.00) and 0.58 (0.40, 0.88) 10 9 /L in the Unextended Group (p<0.05). The two groups had no significant difference in other blood routine values ( Table 2 ). The BUN levels in the Extension group were 5.05 (4.20, 7.40) mmol/L, which was higher than 4.61 (3.72, 5.75) mmol/L in the Unextended Group (p<0.05). The CREA levels in the Extension group were 65.00 (58.00, 79.00) umol/L, which were significantly higher than 57.00 (50.00, 69.00) umol/L in the Unextended Group (p < 0.05). The two groups had no significant difference in other liver and kidney function values ( Table 3 ). 3.3 Characteristics of Tumor and Inflammatory indicators The IL-6 levels in the Extension group were 9.00 (5.00, 18.00) ng/ml, which was lower than 19.00 (9.00, 36.00) ng/ml in the Unextended Group (p<0.05). The IL-10 levels in the Extension group were 19.00 (8.00, 28.00) ng/ml, which was higher than 8.00 (4.00, 12.00) ng/ml in the Unextended Group (p<0.05). The IL-18 levels in the Extension group were 60.00 (46.00, 73.00) ng/ml, which was significantly higher than 19.00 (8.00, 38.00) ng/ml in the Unextended Group (p<0.05). The two groups had no significant difference in other tumor and inflammatory indicators ( Table 4 ). 3.4 The selection of different factors Nine different factors of continuous variable were obtained through the above statistical analysis. Pearson correlation analysis was applied to confirm the independence of the factors ( Figure 2 ). LYM was significantly correlated with MON and NEU (r＞0.84, P＜0.05). IL-10 was significantly correlated with IL-18, NEU, LYM, and MON (r＞0.50, P＜0.05). BUN was significantly correlated with CREA (r＞0.50, P＜0.05). The VIF values of four continuous variables (NEU, LYM, MON) were higher than five in the collinearity diagnostics ( TableS1 ). There was no significant multicollinearity between the other four continuous variables (VIF < 5, R 2 <1). We evaluated the confounding factors among eleven different factors by Chest package ( Figure3 ). The effect of the association between the exposure (difference factor) and the outcome (sputum conversion) in the plot was estimated at 95 % confidence intervals and changes in different steps (%). 3.5 Logistics regression analysis of the different factors between the two groups The logistic regression included six difference factors (Hepatic steatosis, T-SPOT, BUN, IL-6, IL-10, IL-18) to analyze the smear conversion impact in PTB patients. After multifactorial Logistics regression analysis, three factors were confirmed as an independent risk factors for the smear conversion ( Table5 and Figure4 ). Hepatic steatosis and IL-18 were positively correlated with the smear conversion (β= 2.401, OR =11.036, 95% CI = 2.489-48.934; β= 2.396, OR =10.984, 95% CI = 1.832-65.862). On the contrary, the T-SPOT was negatively correlated with the smear conversion (β= -4.199, OR =0.015, 95% CI = 0.001-0.172). 4. Discussion Nutritional status is an established risk factor for the development of active tuberculosis. It has been demonstrated that the decrease in BMI, mid-upper arm circumference (MUAC), skin-fold thicknesses, and muscle mass are significantly associated with increased mortality and relapse of active TB[18]. The serum cholesterol levels are confirmed to be significantly lower in active TB patients. Nutritional supplementation can reduce the risk of adverse drug reactions and improve outcomes during anti-tuberculosis chemotherapy[19]. However, the studies about hepatic steatosis and the outcome of anti-tuberculosis treatment are poorly reported. A retrospective cohort study conducted in Taiwan showed that the relationship between abnormal lipids metabolism and TB incidence is complex and nonlinear[20]. Another study confirmed that elevated serum cholesterol impairs the adaptive immunity to TB[21]. Therefore, a better understanding of the interaction between host metabolism and the immunology of TB infection may guide new prevention strategies for the TB epidemic. Nonalcoholic fatty liver disease (NAFLD) is a metabolic disease. Experts reached a consensus that “MAFLD” is suggested as a more appropriate term for fatty liver associated with metabolic dysfunction[22]. NAFLD or MAFLD individuals have elements of metabolic disease, including insulin resistance and hypertension, which have been previously demonstrated as risk factors for various infections [23,24]. However, few studies have investigated the effect of hepatic steatosis, an early characteristic in the pathogenesis of NAFLD, on the progression and prognosis of pulmonary TB (PTB). In this study, eleven different factors were obtained from the clinical data of one hundred and seventy-four PTB patients. According to clinical significance, correlation detection, collinearity diagnosis, and chest package score, six different factors (hepatic steatosis: TB patients with hepatic steatosis, T-SPOT, BUN, IL-6, IL-10, IL18) were enrolled into the model. According to the logistics regression analysis, Hepatic steatosis and IL-18 were positively correlated with the smear conversion (β= 2.401, OR =11.036, 95% CI = 2.489-48.934; β= 2.396, OR =10.984, 95% CI = 1.832-65.862). On the contrary, the T-SPOT was negatively correlated with the smear conversion (β= -4.199, OR =0.015, 95% CI = 0.001-0.172). These results indicate that hepatic steatosis appreciably affects anti-tuberculosis chemotherapy's curative effect for TB patients. T-SPOT, a test for interferon-γ (IFN-γ) release assays (IGRAs), is currently endorsed by the WHO as a diagnosis for TB infection. Previous study confirms that the levels of IFN-γ are correlated with smear conversion results of clinical specimens[25]. Therefore, T-SPOT could be used to monitor response to treatment of anti-TB[26]. Intricacies or subsequent disease severity in TB infection are usually connected with cytokine storm. The levels of IL-6 in extension group are significantly lower than those in Unextended Group. The levels of IL-10 and IL-18 in extension group are significantly higher than those in Unextended Group. Previous studies have indicated that Interleukin (IL-6) is crucial in the immune response to TB. Elevated IL-6 can lead to a complex combination with membrane-bound interleukin-6 receptor (mIL-6R) to act on glycoprotein 130 (gp130). Gp130 regulates the concentrations of monocytes chemoattractant protein-1(MCP-1) and granulocyte-macrophage colony-stimulating factor (GM-CSF) through the JAK-STAT pathways[27]. In our test results, low levels of IL-6 in PTB patients are associated with a longer time of smear conversion. Inerleukin-10 (IL-10) is also demonstrated to be critical for defending against TB infections. IL-10 can inhibit the expression of MHC-II in M.tuberculosis -infected macrophages[28]. And the IL-10 is regulated by TLR3 in response to TB infection through the PI3K/AKT signaling pathway[29]. The absence of IL-10 might benefit the transition from immune evasion to immune protection and the early clearance of TB[30]. However, a recent study confirms that high levels of IL-10 impair the unextendedof M .tuberculosis growth only before the onset of the T-cell response. During the early stages of M .tuberculosis infection, CD4 + T cells are recruited to the lungs by high levels of serum IL-10. Nevertheless, the actived CD4 + T cells are not migrate into the parenchyma[31]. The IL-10 overexpression in PTB patients with NAFLD primed the CD4 + T cells accumulate in the vasculature and did not migrate into the lung tissues infected with M .tuberculosis. Research indicated that IL-6 and IL-10 expression levels are consistently maintained at a high level in TB patients. Moreover, aberrant expressions of IL-6/IL10 are correlated with high pSTAT3 levels. The constitutive pSTAT3 and high SOCS3 expression are influential factors that indicate impaired T-cell functions in tuberculosis patients[15]. IL-1B, produced by alveolar macrophages, is related to tissue necrosis in lung lesions of active TB patients[32]. IL-18 is also a cytokine, including the IL-1 family, and is found to cause injury in the lung tissue of TB infected hosts [33].CCL20 and CCL2 are small chemotactic chemokines expressed from M.tuberculosis -infected macrophages. However, the correlation between CCL20 or CCL2 and the severity of PTB may not have a cause-effect relationship[34]. A study from India has identified that plasma chemokine CXCL1 is associated with a decreased incidence of unfavorable outcomes in anti-TB treatment[35]. For chronic PTB patients, CSF3 plays a vital role in innate immunity and represents a promising cytokine for anti-TB immunotherapy[36]. As mentioned above, the function of the hub genes obtained from TB and NAFLD is to regulate the production of macrophage cytokines. And PI3K/AKT signaling pathway probably plays a crucial role in regulating macrophage function. A recent study indicated that adipocyte death induces liver injury and inflammation by activating CCR2+ macrophages[37]. Mitogen-activated protein kinase 14 (MAPK14) increases simple steatosis and ameliorates oxidative stress in the pathogenesis of NASH[38]. Mycobacterium tuberculosis lysates induce IL-27 expression in human macrophages by activating MAPK[39]. The polymorphism of oligomerization domain-containing (NOD)2 (Arg702Trp) is likely to be the protective factor for active TB[40]. And NOD2 can mitigate the steatosis and fibrosis of the liver during NAFLD progression[41]. Furthermore, receptor interacting-serine/threonine-protein kinase 2 (RIPK2) is a critical adapter protein for signal propagation of NOD2 and might be a risk factor for TB infection[42]. Evidence is obtained implicating Growth arrest and DNA damage-inducible 45β (GADD45β) against lipid accumulation and insulin resistance in NAFLD mice. In the innate immune system, GADD45β is linked to the chemotaxis of macrophages in response to lipopolysaccharide[43]. Collectively, the abnormal production of macrophage cytokines has a significant impact on the outcome of anti-tuberculosis treatment. In addition, the effect of hepatic steatosis on anti-TB drug metabolism is still relatively unknown. Previous studies confirmed that NAFLD affects the function of the mitochondrion. Cytochrome P450 2E1 (CYP2E1), located within liver mitochondria, is correlated with the degree of steatosis. CYP2E1 is an important factor leading to oxidative stress[44]. Another study confirmed that Rifampicin can induce the high expression of CYP450 in steatosis hepatocytes, which will increase the risk of Drug lug interactions (DDIs). Further study is needed to analysis the mechanism of hepatic steatosis impact on TB[45]. Limitations This study contains a retrospective clinical analysis. The sample size we obtained was small, and there was significant statistical bias. We will continue to expand the sample size to continue the study in depth in the future. Conclusions In this work, hepatic steatosis is confirmed as an independent risk factor affecting smear conversion time in pulmonary tuberculosis patients (PTB). The levels of serum cytokines, such as IL-6 and IL-18, are significantly related to the outcome of anti-tuberculosis treatment. Previous studies have confirmed that hepatic steatosis is associated with abnormal serum cytokines. Therefore, more attention is needed for the PTB patients with hepatic steatosis. Declarations Ethics approval and consent to participate All pulmonary tuberculosis patients were treated with standard care without intervention from this study. All data were obtained via electronic medical records, and a database review were acquired (the patient’s name was replaced with an identification code, and the patient’s private information was deleted before the analysis) to protect patient privacy. This study obtained approval from the Clinical Research Ethics Committee of Taizhou People’s Hospital TZRY-LL-AF/SQ-014-2.0 (Protocol number KY201803901). The informed consent of human clinical data was obtained from all subjects. All experimental serum samples were obtained from the remaining clinical samples of the pulmonary tuberculosis patients. Consent for publication Not applicable Availability of data and materials The collinearity diagnostic results of differential factors were given in( Table S1 ). Competing interests The authors declare there are no conflicts of interest. Funding This work was supported by the National Natural Science Foundation of China (81970454). AUTHOR CONTRIBUTIONS DZX, YY, and GYX contributed equally to the study's design. DZX completed this article's data sorting and writing; LX and CMY contributed significantly to analysis and manuscript preparation; CMY, WL, LHL, ZY, and HMD helped perform the analysis and constructive discussions. HCM, LY, and YYF contributed to the design of the study. All authors provided original data and participated in the article design. All authors have read and agreed to the published version of the manuscript. Acknowledgements Thank you for the data provided by the Department of Infectious Diseases of Taizhou People 's Hospital. References WHO. Global tuberculosis report 2021 [Internet]. [cited 2022 Sep 25]. Available from: https://www.who.int/news-room/fact-sheets/detail/tuberculosis Tuberculosis - PubMed [Internet]. [cited 2022 Sep 25]. Available from: https://pubmed.ncbi.nlm.nih.gov/30904262/ Lönnroth K, Williams BG, Cegielski P, Dye C. A consistent log-linear relationship between tuberculosis incidence and body mass index. Int J Epidemiol. 2010;39:149–55. Huang PL. A comprehensive definition for metabolic syndrome. Dis Model Mech. 2009;2:231–7. Abdullah A, Peeters A, de Courten M, Stoelwinder J. The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies. Diabetes Res Clin Pract. 2010;89:309–19. Mushtaq A. Tuberculosis in diabetes: insidious and neglected. Lancet Respir Med. 2019;7:483. Calderwood CJ, Wilson JP, Fielding KL, Harris RC, Karat AS, Mansukhani R, et al. Dynamics of sputum conversion during effective tuberculosis treatment: A systematic review and meta-analysis. PLoS Med. 2021;18:e1003566. Chaves Torres NM, Quijano Rodríguez JJ, Porras Andrade PS, Arriaga MB, Netto EM. Factors predictive of the success of tuberculosis treatment: A systematic review with meta-analysis. PLoS One. 2019;14:e0226507. Friedman SL, Neuschwander-Tetri BA, Rinella M, Sanyal AJ. Mechanisms of NAFLD development and therapeutic strategies. Nat Med. 2018;24:908–22. Zhou J, Zhou F, Wang W, Zhang X, Ji Y, Zhang P, et al. Epidemiological Features of NAFLD From 1999 to 2018 in China. Hepatology [Internet]. 2020 [cited 2022 Sep 27];71:1851–64. Available from: https://onlinelibrary.wiley.com/doi/10.1002/hep.31150 Nseir W, Taha H, Khateeb J, Grosovski M, Assy N. Fatty liver is associated with recurrent bacterial infections independent of metabolic syndrome. Dig Dis Sci. 2011;56:3328–34. Fisher-Hoch SP, Mathews CE, McCormick JB. Obesity, diabetes and pneumonia: the menacing interface of non-communicable and infectious diseases. Trop Med Int Health. 2013;18:1510–9. Park J, Zhao Y, Zhang F, Zhang S, Kwong AC, Zhang Y, et al. IL6/STAT3 axis dictates the PNPLA3-mediated susceptibility to non-alcoholic fatty liver disease. J Hepatol. 2022;S0168-8278(22)03053-7. Miller AM, Wang H, Bertola A, Park O, Horiguchi N, Ki SH, et al. Inflammation-associated interleukin-6/signal transducer and activator of transcription 3 activation ameliorates alcoholic and nonalcoholic fatty liver diseases in interleukin-10-deficient mice. Hepatology. 2011;54:846–56. Harling K, Adankwah E, Güler A, Afum-Adjei Awuah A, Adu-Amoah L, Mayatepek E, et al. Constitutive STAT3 phosphorylation and IL-6/IL-10 co-expression are associated with impaired T-cell function in tuberculosis patients. Cell Mol Immunol. 2019;16:275–87. Tutunchi H, Ostadrahimi A, Saghafi-Asl M, Roshanravan N, Shakeri-Bavil A, Asghari-Jafarabadi M, et al. Expression of NF-κB, IL-6, and IL-10 genes, body composition, and hepatic fibrosis in obese patients with NAFLD-Combined effects of oleoylethanolamide supplementation and calorie restriction: A triple-blind randomized controlled clinical trial. J Cell Physiol. 2021;236:417–26. Greenland S, Pearce N. Statistical Foundations for Model-Based Adjustments. Annual Review of Public Health [Internet]. 2015 [cited 2023 Oct 22];36:89–108. Available from: https://doi.org/10.1146/annurev-publhealth-031914-122559 Kant S, Gupta H, Ahluwalia S. Significance of nutrition in pulmonary tuberculosis. Crit Rev Food Sci Nutr. 2015;55:955–63. A cholesterol-rich diet accelerates bacteriologic sterilization in pulmonary tuberculosis - PubMed [Internet]. [cited 2022 Oct 5]. Available from: https://pubmed.ncbi.nlm.nih.gov/15706008/ Lin H-H, Wu C-Y, Wang C-H, Fu H, Lönnroth K, Chang Y-C, et al. Association of Obesity, Diabetes, and Risk of Tuberculosis: Two Population-Based Cohorts. Clin Infect Dis. 2018;66:699–705. Martens GW, Arikan MC, Lee J, Ren F, Vallerskog T, Kornfeld H. Hypercholesterolemia impairs immunity to tuberculosis. Infect Immun. 2008;76:3464–72. Eslam M, Sanyal AJ, George J, International Consensus Panel. MAFLD: A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease. Gastroenterology. 2020;158:1999-2014.e1. Obesity and risk of infections: results from men and women in the Swedish National March Cohort - PubMed [Internet]. [cited 2022 Oct 6]. Available from: https://pubmed.ncbi.nlm.nih.gov/31292615/ Harpsøe MC, Nielsen NM, Friis-Møller N, Andersson M, Wohlfahrt J, Linneberg A, et al. Body Mass Index and Risk of Infections Among Women in the Danish National Birth Cohort. Am J Epidemiol. 2016;183:1008–17. Kobashi Y, Mouri K, Yagi S, Obase Y, Miyashita N, Oka M. Transitional changes in T-cell responses to Mycobacterium tuberculosis-specific antigens during treatment. J Infect. 2009;58:197–204. Bosshard V, Roux-Lombard P, Perneger T, Metzger M, Vivien R, Rochat T, et al. Do results of the T-SPOT.TB interferon-gamma release assay change after treatment of tuberculosis? Respir Med. 2009;103:30–4. Boni FG, Hamdi I, Koundi LM, Shrestha K, Xie J. Cytokine storm in tuberculosis and IL-6 involvement. Infection, Genetics and Evolution [Internet]. 2022 [cited 2022 Oct 7];97:105166. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1567134821004664 Interleukin-10-induced MARCH1 mediates intracellular sequestration of MHC class II in monocytes - PubMed [Internet]. [cited 2022 Oct 7]. Available from: https://pubmed.ncbi.nlm.nih.gov/18389477/ Bai W, Liu H, Ji Q, Zhou Y, Liang L, Zheng R, et al. TLR3 regulates mycobacterial RNA-induced IL-10 production through the PI3K/AKT signaling pathway. Cell Signal. 2014;26:942–50. Abdalla AE, Lambert N, Duan X, Xie J. Interleukin-10 Family and Tuberculosis: An Old Story Renewed. Int J Biol Sci [Internet]. 2016 [cited 2022 Oct 7];12:710–7. Available from: http://www.ijbs.com/v12p0710.htm Ferreira CM, Barbosa AM, Barreira-Silva P, Silvestre R, Cunha C, Carvalho A, et al. Early IL-10 promotes vasculature-associated CD4+ T cells unable to control Mycobacterium tuberculosis infection. JCI Insight [Internet]. 2021 [cited 2022 Nov 15];6:e150060. Available from: https://insight.jci.org/articles/view/150060 Wu S, Wang Y, Zhang M, Shrestha SS, Wang M, He J-Q. Genetic Polymorphisms of IL1B, IL6, and TNF α in a Chinese Han Population with Pulmonary Tuberculosis. BioMed Research International [Internet]. 2018 [cited 2022 Oct 7];2018:1–10. Available from: https://www.hindawi.com/journals/bmri/2018/3010898/ Vecchié A, Bonaventura A, Toldo S, Dagna L, Dinarello CA, Abbate A. IL‐18 and infections: Is there a role for targeted therapies? J Cell Physiol [Internet]. 2021 [cited 2022 Oct 8];236:1638–57. Available from: https://onlinelibrary.wiley.com/doi/10.1002/jcp.30008 CCL20 is overexpressed in Mycobacterium tuberculosis-infected monocytes and inhibits the production of reactive oxygen species (ROS) - PubMed [Internet]. [cited 2022 Oct 7]. Available from: https://pubmed.ncbi.nlm.nih.gov/20819093/ Kumar NP, Moideen K, Nancy A, Viswanathan V, Thiruvengadam K, Nair D, et al. Plasma Chemokines Are Baseline Predictors of Unfavorable Treatment Outcomes in Pulmonary Tuberculosis. Clin Infect Dis. 2021;73:e3419–27. Chuang Y-M, He L, Pinn ML, Tsai Y-C, Cheng MA, Farmer E, et al. Albumin fusion with granulocyte-macrophage colony-stimulating factor acts as an immunotherapy against chronic tuberculosis. Cell Mol Immunol [Internet]. 2021 [cited 2022 Oct 8];18:2393–401. Available from: https://www.nature.com/articles/s41423-020-0439-2 Adipocyte Death Preferentially Induces Liver Injury and Inflammation Through the Activation of Chemokine (C-C Motif) Receptor 2-Positive Macrophages and Lipolysis - PubMed [Internet]. [cited 2022 Oct 9]. Available from: https://pubmed.ncbi.nlm.nih.gov/30681731/ Hwang S, Wang X, Rodrigues RM, Ma J, He Y, Seo W, et al. Protective and Detrimental Roles of p38α Mitogen-Activated Protein Kinase in Different Stages of Nonalcoholic Fatty Liver Disease. Hepatology. 2020;72:873–91. Zhang J, Qian X, Ning H, Eickhoff CS, Hoft DF, Liu J. Transcriptional suppression of IL-27 production by Mycobacterium tuberculosis-activated p38 MAPK via inhibition of AP-1 binding. J Immunol. 2011;186:5885–95. NOD2 polymorphisms and pulmonary tuberculosis susceptibility: a systematic review and meta-analysis - PubMed [Internet]. [cited 2022 Oct 9]. Available from: https://pubmed.ncbi.nlm.nih.gov/24391456/ NOD2 in hepatocytes engages a liver-gut axis to protect against steatosis, fibrosis, and gut dysbiosis during fatty liver disease in mice - PubMed [Internet]. [cited 2022 Oct 9]. Available from: https://pubmed.ncbi.nlm.nih.gov/32516028/ Song J, Liu T, Jiao L, Zhao Z, Hu X, Wu Q, et al. RIPK2 polymorphisms and susceptibility to tuberculosis in a Western Chinese Han population. Infect Genet Evol. 2019;75:103950. Salerno DM, Tront JS, Hoffman B, Liebermann DA. Gadd45a and Gadd45b modulate innate immune functions of granulocytes and macrophages by differential regulation of p38 and JNK signaling. J Cell Physiol. 2012;227:3613–20. Rey-Bedon C, Banik P, Gokaltun A, Hofheinz O, Yarmush ML, Uygun MK, et al. CYP450 drug inducibility in NAFLD via an in vitro hepatic model: Understanding drug-drug interactions in the fatty liver. Biomed Pharmacother. 2022;146:112377. Aubert J, Begriche K, Knockaert L, Robin MA, Fromenty B. Increased expression of cytochrome P450 2E1 in nonalcoholic fatty liver disease: mechanisms and pathophysiological role. Clin Res Hepatol Gastroenterol. 2011;35:630–7. Tables Table 1. The clinical baseline data of 174 PTB patients Variable Unextended Group Extension Group Statistics(χ 2 / F /z) P-value n 142 32 Hepatic steatosis 23 (16%) 24 (75%) 45.802 <0.001 Sex 0.081 0.800 female 39 (27%) 8 (25%) male 103 (73%) 24 (75%) Age (year) 58 (39, 68) 57 (44, 69) 0.700 BMI (kg/m2) 20.52±3.46 22.55±3.73 2.949 0.004 Infected lung 16.274 <0.001 one 109 (77%) 13 (41%) two 33 (23%) 19 (59%) Outcome 36.614 <0.001 failure 1 (0.7%) 9 (28%) success 141 (99%) 23 (72%) Intensive period time (mouth) 1.00 (1.00, 2.00) 5.00 (4.00, 6.00) 19.395 <0.001 T-SPOT 3.883 <0.001 negative 1 (0.7%) 9 (28%) positive 141 (99%) 23 (72%) Note:BMI: Body Mass Index; Outcome: the outcome of anti-tuberculosis treatment；n (%); Median (IQR); Statistical method: chi-square test, t-test, Mann-Whitney U-test, Pearson's Chi-squared test; Fisher's exact test. Table 2. The characteristics of blood routine in 174 PTB patients Variable Unextended Group Extension Group Statistics(χ 2 /z) P-value n 142 32 RBC （10 9 /L） 4.19±0.65 4.13±0.75 0.500 0.643 HB （g/L） 121.56±20.13 119.5±22 0.513 0.994 WBC （10 9 /L） 7.08±2.86 7.77±2.78 1.224 0.888 NEU （10 9 /L） 5.00 (3.00, 8.00) 65.00 (36.00, 75.00) 6.293 <0.001 LYM （10 9 /L） 1.00 (1.00, 2.00) 15.00 (5.00, 26.00) 4.639 <0.001 MON （10 9 /L） 0.58 (0.40, 0.88) 7.35 (2.80, 8.33) 5.958 <0.001 PLT （10 9 /L） 219 (176, 299) 215 (160, 306) 1.377 0.168 Note: RBC: red blood cell; HB: hemoglobin; NEU: neutrophile granulocyte; LYM: lymphocyte; MON: monocyte; PLT: blood platelet. Statistical methods: independent sample t-test, a nonparametric test, chi-square test. Table 3. The characteristics of liver and kidney function in 174 PTB patients. Variable Unextended Group Extension Group Statistics(χ 2 /F/z) P-value n 142 32 TBIL （umol/L） 10.00 (7.00, 15.00) 10.00 (7.00, 12.00) 0.287 0.400 DBIL （umol/L） 2.30 (1.40, 3.78) 1.95 (1.30, 3.15) 0.299 0.300 TP （g/L） 66.30±8.62 65.54±8.58 0.454 0.523 ALB （g/L） 34.91±6.22 35.25±7.08 0.273 0.616 ALT （U/L） 13.00 (8.00, 22.00) 18.00 (11.00, 27.00) 2.866 0.100 AST （U/L） 20.00 (16.00, 26.00) 21.00 (17.00, 31.00) 1.340 0.180 ALP （U/L） 90 (74, 109) 85 (64, 106) 1.308 0.191 GGT （U/L） 27 (18, 44) 29 (22, 41) 0.480 0.631 BUN （mmol/L） 4.61 (3.72, 5.75) 5.05 (4.20, 7.40) 1.973 0.048 CREA （umol/L） 57.00 (50.00, 69.00) 65.00 (58.00, 79.00) 2.232 0.026 B 2 MG （mg/L） 2.16 (1.59, 2.89) 2.35 (1.82, 3.44) 1.511 0.131 Note: TBIL: total bilirubin; DBIL: direct bilirubin; TP: total protein; ALB: albumin; ALT: alanine aminotransferase; AST: aspartate aminotransferase; ALP: serum alkaline phosphatase; GGT: gamma-glutamyl transpeptidase; BUN: urea nitrogen; CREA: creatinine; UA: uric acid; Statistical methods: independent sample t-test, nonparametric test. Table 4. The characteristics of Tumor and Inflammatory indicators in 174 PTB patients. Variable Unextended Group Extension Group Statistics(χ 2 /F/z) P-value n 142 32 CRP(mg/l) 14 (2, 40) 9 (3, 31) 0.074 0.941 ESR(mm/h) 18 (5, 44) 21 (9, 43) 0.884 0.4 CEA (mg/L) 2.54 (1.65, 4.46) 2.30 (1.52, 3.15) 1.247 0.2 CA-125 (mg/L) 37 (23, 63) 22 (12, 70) 1.371 0.2 CA19-9 (mg/L) 7 (4, 13) 9 (6, 14) 1.154 0.2 IL-6 (ng/ml) 19 (9, 36) 9 (5, 18) 3.048 0.002 IL-10 (ng/ml) 8 (4, 12) 19 (8, 28) 4.207 <0.001 IL-18 (ng/ml) 19 (8, 38) 60 (46, 73) 6.192 <0.001 Note: Statistical methods: nonparametric test. Table 5. Logistics regression analysis of the different factors Values β P-value OR 95.0% CI β P-value OR 95.0% CI Univariable lower upper Multivariable lower upper Hepatic Steatosis 2.742 <0.001 15.520 6.210 38.800 2.401 0.002 11.036 2.489 48.934 T-SPOT -3.912 <0.001 0.020 0.000 0.150 -4.199 0.001 0.015 0.001 0.172 BUN 2.747 0.052 15.590 0.970 249.740 1.656 0.409 5.239 0.103 267.065 IL-6 -1.715 0.002 0.180 0.060 0.530 -0.732 0.335 0.481 0.109 2.128 IL-10 2.980 <0.001 19.680 4.740 81.710 -1.009 0.378 0.365 0.039 3.427 IL-18 3.961 <0.001 52.510 9.220 299.030 2.396 0.009 10.984 1.832 65.862 Note: Hepatic Steatosis:Tb patients with Hepatic Steatosis; OR: odds ratio; CI: confidence interval. Statistical methods: Univariate and Multivariable logistics regression. Additional Declarations No competing interests reported. Supplementary Files Tables1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4306921","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294488510,"identity":"aa8ac44c-6d9b-473a-9fe4-70fdf49909bb","order_by":0,"name":"Zhi-xiang Du","email":"","orcid":"","institution":"Department of infectious disease and liver disease, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhi-xiang","middleName":"","lastName":"Du","suffix":""},{"id":294488511,"identity":"d17d0ae1-c79e-4ba9-a20d-6a065622e300","order_by":1,"name":"Yuan Yang","email":"","orcid":"","institution":"Department of infectious disease and liver disease, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Yang","suffix":""},{"id":294488512,"identity":"918b0e56-2e3f-445a-b4c1-8ae0e43d855c","order_by":2,"name":"Yu-xiang Gong","email":"","orcid":"","institution":"Department of infectious disease and liver disease, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yu-xiang","middleName":"","lastName":"Gong","suffix":""},{"id":294488513,"identity":"83fdf937-919b-4307-a0c7-d97eb11d95df","order_by":3,"name":"Xing Liu","email":"","orcid":"","institution":"Department of infectious disease and liver disease, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xing","middleName":"","lastName":"Liu","suffix":""},{"id":294488514,"identity":"c355153f-2235-4c18-a44a-67a6681886f8","order_by":4,"name":"Miao-yang Chen","email":"","orcid":"","institution":"Department of infectious disease and liver disease, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Miao-yang","middleName":"","lastName":"Chen","suffix":""},{"id":294488515,"identity":"f31c6f98-b865-4541-8882-107e4a93fa95","order_by":5,"name":"Li Wang","email":"","orcid":"","institution":"Department of infectious disease and liver disease, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Wang","suffix":""},{"id":294488516,"identity":"b911fd69-aa19-45ab-a6aa-fa2e1ed7dccb","order_by":6,"name":"Hong-li Liu","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Hong-li","middleName":"","lastName":"Liu","suffix":""},{"id":294488536,"identity":"ac39a1d3-f78e-4f76-bfbd-9cb28280235d","order_by":7,"name":"Yu Zhang","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhang","suffix":""},{"id":294488538,"identity":"a8f3f255-baec-460c-90ff-7d39d8f2943a","order_by":8,"name":"Chun-mei Hu","email":"","orcid":"","institution":"Department of Tuberculosis, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, 210003, China.","correspondingAuthor":false,"prefix":"","firstName":"Chun-mei","middleName":"","lastName":"Hu","suffix":""},{"id":294488540,"identity":"04f73764-7579-4923-96b1-43d536da75f0","order_by":9,"name":"Jiang-hua YANG","email":"","orcid":"","institution":"Yijishan Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jiang-hua","middleName":"","lastName":"YANG","suffix":""},{"id":294488544,"identity":"406ad77c-8b48-4faf-bb2a-71bb308e1016","order_by":10,"name":"Ming-da Hao","email":"","orcid":"","institution":"The Affiliated Taizhou People's Hospital of Nanjing Medical University, Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ming-da","middleName":"","lastName":"Hao","suffix":""},{"id":294488546,"identity":"66d3abcb-0fdb-49b0-b9f6-22654a190043","order_by":11,"name":"Yang Li","email":"","orcid":"","institution":"The Affiliated Taizhou People's Hospital of Nanjing Medical University, Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Li","suffix":""},{"id":294488547,"identity":"dd365bb0-2ca0-4687-95ba-3324d271697f","order_by":12,"name":"Yong-feng Yang","email":"data:image/png;base64,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","orcid":"","institution":"Department of infectious disease and liver disease, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yong-feng","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-04-22 15:34:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4306921/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4306921/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55630529,"identity":"ba1c8d35-47f9-42b5-bbed-8f45c773b0d2","added_by":"auto","created_at":"2024-04-30 19:35:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":471247,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of patient inclusion and exclusion and study design. \u003c/strong\u003e(PTB: pulmonary tuberculosis. Patients with positive sputum-smear for over eight weeks are clustered in the extension sputum conversion group (Extension Group). Conversely, patients who had sputum conversion within eight weeks are clustered in the control group.)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4306921/v1/d49f21c35a6043b3e6891ce1.png"},{"id":55630528,"identity":"89593b07-ba78-448b-9f3a-a138847e0a93","added_by":"auto","created_at":"2024-04-30 19:35:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":704450,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis of difference factors. \u003c/strong\u003e(Green=Extension group, Red=Unextended Group. The correlation of the difference factors was analysis by the Spearman method. The correlation of the difference factors was analysis by the Spearman method.)\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4306921/v1/c8a6734e9e3be4080f36d6d7.png"},{"id":55631502,"identity":"4f2b0ed8-a1bd-47db-8635-b358625ac70d","added_by":"auto","created_at":"2024-04-30 19:43:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":117082,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe confounding factors among eleven different factors by Chest package. \u003c/strong\u003e(Chest_0.3.7. package for R were applied the change-in-effect estimate method to assess confounding effects in difference factors for anti-tuberculosis outcome.)\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4306921/v1/31ed1f1b24dd5f340b3f9a6f.png"},{"id":55630532,"identity":"4ee3b383-b8c8-4015-8da1-fe766f133ff1","added_by":"auto","created_at":"2024-04-30 19:35:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":129190,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe forest map of logistics regression results. \u003c/strong\u003e(After multifactorial Logistics regression analysis, three factors were confirmed as an independent risk factor for the smear conversion. Hepatic steatosis and IL-18 were positively correlated with the smear conversion (β= 2.401, OR =11.036, 95% CI = 2.489-48.934; β= 2.396, OR =10.984, 95% CI = 1.832-65.862). On the contrary, the T-SPOT was negatively correlated with the smear conversion (β= -4.199, OR =0.015, 95% CI = 0.001-0.172).)\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4306921/v1/3c632875f076f41d51e0f770.png"},{"id":67367157,"identity":"da50ae43-eddb-425e-a39e-43ead8800803","added_by":"auto","created_at":"2024-10-24 07:25:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2250321,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4306921/v1/8f2f0387-7d84-47f9-ac42-1bbc51f91eca.pdf"},{"id":55630531,"identity":"7e200cb5-123f-488a-87f1-806831be4c45","added_by":"auto","created_at":"2024-04-30 19:35:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12977,"visible":true,"origin":"","legend":"","description":"","filename":"Tables1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4306921/v1/16b095543191c438a9bc5dbd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hepatic Steatosis affects the Outcome of Anti- tuberculosis Treatment in Pulmonary Tuberculosis Patients","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccording to the World Health Organization (WHO), tuberculosis (TB) remains the leading cause of death from infectious diseases worldwide[1]. The epidemic of TB is closely related to poverty[2]. Related research confirms that a high body mass index (BMI) is a protective factor against the infection of TB[3]. However, a high BMI is also a significant risk factor for metabolic diseases, such as type 2 diabetes mellitus (DM) and hepatic steatosis[4,5]. The International Union Against Tuberculosis and Lung Disease guidelines and the WHO state that patients with type2 DM should be systematically screened for TB in China[6].\u0026nbsp;The association between nutritional status or metabolic status and TB is extremely perplexing.\u003c/p\u003e\n\u003cp\u003ePrevious study indicates that (7.3\u0026ndash;25.4%) pulmonary tuberculosis (PTB) patients have not achieved sputum conversion at the end of the intensive phase of anti-tuberculosis treatment[7]. Factors such as Age, Sex, BMI, smoking, and HIV affected the sputum conversion at two months of treatment[8]. However, the impact of hepatic steatosis on the treatment of tuberculosis has yet to receive sufficient attention. Hepatic steatosis is an early characteristic in the pathogenesis of non-alcoholic fatty liver disease (NAFLD), which is an umbrella term for a continuum of liver conditions that have very different rates of progression and clinical manifestations[9]. A recent meta-analysis indicates that China has over 240 million NAFLD patients and the highest annual NAFLD-related mortality rate[10]. Patients with NAFLD may be more prone to infection. Nseir\u0026rsquo;s study demonstrates that NAFLD is closely related to the recurrence of urinary infections\u0026nbsp;[11]. Another retrospective study validated that patients with hepatic steatosis are more prone to develop pneumonia[12]. However, the exact mechanism remains unclear.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMost Patients with steatosis without features of cirrhosis carry a shallow risk of clinically relevant adverse outcomes. In the past, hepatic steatosis was not considered a severe systemic disease in China. Correspondingly, the effect of NAFLD on the progression and prognosis of TB has yet to attract enough attention from clinicians[10]. Hereditary factors may have played a vital role in the initiation and progression of NAFLD in Chinese populations. Previous study indicated that elevated IL-6/STAT3 is associated with the progression of NAFLD[13]. In mice models, hepatic IL-6/STAT3 activation enhanced the fatty acid oxidation-associated genes in the liver[14]. It has been reported that the co-expression of IL-6 and STAT3 is associated with the dysfunction of T-cells in Tuberculosis patients[15]. The expression of inflammatory cytokines such as IL-6 and IL-10 play an essential role in promoting the progression of NAFLD[16]. Nevertheless, research on the interaction between NAFLD and TB has rarely been reported.\u003c/p\u003e\n\u003cp\u003eIn this study, we analyzed the clinical characteristics of pulmonary TB patients with and without extension of sputum conversion. Subsequently, we obtained the independent risk factors affecting the outcome of anti-tuberculosis treatment. Finally, a detailed analysis of the possible mechanism of outcome affected by hepatic steatosis in PTB patients was summarized in this study.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Individuals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeven hundred and fifty-eight active TB patients were enrolled from Taizhou People\u0026rsquo;s Hospital between January 2018 and December 2022. The diagnosis of TB was based on the guidelines of WHO (1). Inclusion criteria: All PTB patients had positive sputum smears of Mycobacterium tuberculosis, typical clinical manifestations (Cough, expectoration for more than two weeks, or blood in sputum or hemoptysis, night sweat, fatigue, intermittent or persistent afternoon low fever, anorexia, weight loss, etc.,), and imaging evidence (In mild cases, the main manifestations are patches, nodules and cords or tuberculomas or isolated cavities; in severe cases, there may be lobar infiltrates, caseous pneumonia, multiple cavity formation and bronchial spread.). Exclusion criteria: 1. Retreatment PTB, Multi-Drug Resistant PTB patients, and tuberculous pleurisy were excluded from this study; 2. Patients with underlying diseases such as diabetes, hypertension, autoimmune disease, tumors, chronic obstructive pulmonary disease, and bronchiectasis. 3. Patients with missing clinical data. Finally, one hundred and seventy-four initial-treatment patients with active PTB were enrolled in this study. This study obtained approval from the Clinical Research Ethics Committee of Taizhou People\u0026rsquo;s Hospital TZRY-LL-AF/SQ-014-2.0 (Protocol number KY201803901).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Data Collection and study design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients with active PTB were given standardized anti-TB treatment consisting of oral rifampicin (R) 10\u0026thinsp;mg/kg/d, isoniazid (H) 5\u0026thinsp;mg/kg/d, pyrazinamide (Z) 25\u0026thinsp;mg/kg/d, and ethambutol (E) 20\u0026thinsp;mg/kg/d for two\u0026thinsp;months (intensive phase), followed by daily isoniazid and rifampicin for four\u0026thinsp;months-control period (2HRZE/4HR). If necessary, the infectious disease physician modified the regimen when there were adverse drug effects. Patients with positive sputum-smear for over eight weeks are clustered in the extension sputum conversion group (Extension Group). Conversely, patients who had sputum conversion within eight weeks are clustered in the Unextended Group. The study design of this study was presented in a flowchart in \u003cstrong\u003eFigure 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe informed consent of human clinical data was obtained from all subjects. The clinical baseline data included age, sex, Body Mass Index (BMI), anti-tuberculosis outcome, intensive period time, T-SPOT results, liver ultrasonogram (Identify liver diseases, including liver mass, liver fibrosis, and hepatic steatosis.), etc. Laboratory test data included Blood routine (RBC, NEU, etc.), liver function (TBIL, ALB, ALT, AST, etc.), and kidney function (BUN, CREA, B2MG, etc.). The laboratory test data also included tumors (CA-125, CA19-9) and inflammatory indicators (CRP, ESR, IL-6, IL-10, IL-18). The CA-125, CEA, and CA19-9 cutoff points were selected as 22\u0026thinsp;U/ml, 5.0\u0026thinsp;ng/ml, and 37\u0026thinsp;U/L, respectively.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.3 Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eThe clinical and laboratory test data were expressed as the frequencies (n), percentages (%), mean \u0026plusmn; standard deviations (SDs), and median (interquartile ranges) and were analyzed with R version 4.2.1. Count data were analyzed with the Fisher\u0026apos;s chi-square test. The t-test analyzed the measurement data conformed to a normal distribution. The Mann\u0026ndash;Whitney U-test was used to compare nonnormally distributed data. The correlation of the difference factors was analysis by the Spearman method. Chest_0.3.7. package for R were applied the change-in-effect estimate method to assess confounding effects in difference factors for anti-tuberculosis outcome[17]. Logistics regression analysis was used to analyze the relationships between the different factors and the time for outcome in PTB patients. p \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch3\u003e3.1 Characteristics of Clinical Baseline Data\u003c/h3\u003e\n\u003cp\u003eA total of one hundred and seventy-four PTB patients were enrolled in this study. The body mass index (BMI) in the Extension group was 22.55\u0026plusmn;3.73 kg/m2, which was significantly higher than the 20.52\u0026plusmn;3.46 kg/m2 in the Unextended Group (p=0.004). The proportion of lesions in two lungs in the Extension group was significantly higher than in the Unextended Group (p\u0026lt;0.05). The time for the time of intensive period in the Extension group was 5.00 (4.00, 6.00) months, significantly higher than the 1.00 (1.00, 2.00) months in the Unextended Group (p\u0026lt; 0.05). The proportion of T-SPOT positive in Extension group was 72%, which was significantly lower than 99% in the Unextended Group (p\u0026lt;0.05). According to the liver ultrasonogram, the proportion of patients with hepatic steatosis in Extension group was 75%, significantly higher than 16% in Unextended Group (p\u0026lt; 0.05). There were no significant differences in the other values between the two groups (p \u0026gt; 0.05) (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003ch3\u003e3.2 Characteristics of Laboratory tests\u003c/h3\u003e\n\u003cp\u003eThe NEU level in the Extension group was\u0026nbsp;65.00 (36.00, 75.00) 10\u003csup\u003e9\u003c/sup\u003e/L, which was higher than 5.00 (3.00, 8.00) 10\u003csup\u003e9\u003c/sup\u003e/L in the\u0026nbsp;Unextended Group\u0026nbsp;(p\u0026lt;0.05). The LYM and MON levels\u0026nbsp;in the Extension group were\u0026nbsp;15.00 (5.00, 26.00) and 7.35 (2.80, 8.33) 10\u003csup\u003e9\u003c/sup\u003e/L, which were significantly higher than 1.00 (1.00, 2.00) and 0.58 (0.40, 0.88) 10\u003csup\u003e9\u003c/sup\u003e/L in the\u0026nbsp;Unextended Group\u0026nbsp;(p\u0026lt;0.05). The two groups had no significant difference in other blood routine values (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe BUN levels in the\u0026nbsp;Extension group\u0026nbsp;were\u0026nbsp;5.05 (4.20, 7.40)\u0026nbsp;mmol/L, which was higher than\u0026nbsp;4.61 (3.72, 5.75) mmol/L\u0026nbsp;in the\u0026nbsp;Unextended Group\u0026nbsp;(p\u0026lt;0.05). The CREA levels in the\u0026nbsp;Extension group\u0026nbsp;were\u0026nbsp;65.00 (58.00, 79.00) umol/L, which were significantly higher than\u0026nbsp;57.00 (50.00, 69.00) umol/L\u0026nbsp;in the\u0026nbsp;Unextended Group\u0026nbsp;(p \u0026lt; 0.05). The two groups had no significant difference in other liver and kidney function values (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u003c/p\u003e\n\u003ch3\u003e3.3 Characteristics of\u0026nbsp;Tumor and Inflammatory indicators\u003c/h3\u003e\n\u003cp\u003eThe IL-6 levels in the\u0026nbsp;Extension group\u0026nbsp;were\u0026nbsp;9.00 (5.00, 18.00)\u0026nbsp;ng/ml, which was lower than\u0026nbsp;19.00 (9.00, 36.00)\u0026nbsp;ng/ml\u0026nbsp;in the\u0026nbsp;Unextended Group\u0026nbsp;(p\u0026lt;0.05). The IL-10 levels in the\u0026nbsp;Extension group\u0026nbsp;were\u0026nbsp;19.00 (8.00, 28.00)\u0026nbsp;ng/ml, which was higher than\u0026nbsp;8.00 (4.00, 12.00)\u0026nbsp;ng/ml\u0026nbsp;in the\u0026nbsp;Unextended Group\u0026nbsp;(p\u0026lt;0.05). The IL-18 levels in the\u0026nbsp;Extension group\u0026nbsp;were\u0026nbsp;60.00 (46.00, 73.00)\u0026nbsp;ng/ml, which was significantly higher than\u0026nbsp;19.00 (8.00, 38.00)\u0026nbsp;ng/ml\u0026nbsp;in the\u0026nbsp;Unextended Group\u0026nbsp;(p\u0026lt;0.05). The two groups had no significant difference in other\u0026nbsp;tumor and inflammatory indicators\u0026nbsp;(\u003cstrong\u003eTable 4\u003c/strong\u003e).\u003c/p\u003e\n\u003ch3\u003e3.4 The selection of different factors\u003c/h3\u003e\n\u003cp\u003eNine different factors of continuous variable were obtained through the above statistical analysis. Pearson correlation analysis was applied to confirm the independence of the factors (\u003cstrong\u003eFigure 2\u003c/strong\u003e).\u0026nbsp;LYM was significantly correlated with MON and NEU (r＞0.84, P＜0.05). IL-10 was significantly correlated with IL-18, NEU, LYM, and MON (r＞0.50, P＜0.05). BUN was significantly correlated with CREA (r＞0.50, P＜0.05). The VIF values of four continuous variables (NEU, LYM, MON) were higher than five in the collinearity diagnostics (\u003cstrong\u003eTableS1\u003c/strong\u003e). There was no significant multicollinearity between the other four continuous variables (VIF \u0026lt; 5, R\u003csup\u003e2\u003c/sup\u003e\u0026lt;1). We evaluated the confounding factors among eleven different factors by Chest package (\u003cstrong\u003eFigure3\u003c/strong\u003e). The effect of the association between the exposure (difference factor) and the outcome (sputum conversion) in the plot was estimated at 95 % confidence intervals and changes in different steps (%).\u003c/p\u003e\n\u003ch3\u003e3.5 Logistics regression analysis of the different factors between the two groups\u003c/h3\u003e\n\u003cp\u003eThe logistic regression included six difference factors (Hepatic steatosis, T-SPOT, BUN, IL-6, IL-10, IL-18) to analyze the smear conversion impact in PTB patients. After multifactorial Logistics regression analysis, three factors were confirmed as an independent risk factors for the smear conversion (\u003cstrong\u003eTable5\u0026nbsp;\u003c/strong\u003eand\u0026nbsp;\u003cstrong\u003eFigure4\u003c/strong\u003e). Hepatic steatosis and IL-18 were positively correlated with the smear conversion (\u0026beta;= 2.401, OR =11.036, 95% CI = 2.489-48.934; \u0026beta;= 2.396, OR =10.984, 95% CI = 1.832-65.862). On the contrary, the T-SPOT was negatively correlated with the smear conversion (\u0026beta;= -4.199, OR =0.015, 95% CI = 0.001-0.172).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eNutritional status is an established risk factor for the development of active tuberculosis. It has been demonstrated that the decrease in BMI, mid-upper arm circumference (MUAC), skin-fold thicknesses, and muscle mass are significantly associated with increased mortality and relapse of active TB[18]. The serum cholesterol levels are confirmed to be significantly lower in active TB patients. Nutritional supplementation can reduce the risk of adverse drug reactions and improve outcomes during anti-tuberculosis chemotherapy[19]. However, the studies about hepatic steatosis and the outcome of anti-tuberculosis treatment are poorly reported.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA retrospective cohort study conducted in Taiwan showed that the relationship between abnormal lipids metabolism and TB incidence is complex and nonlinear[20]. Another study confirmed that elevated serum cholesterol impairs the adaptive immunity to TB[21]. Therefore, a better understanding of the interaction between host metabolism and the immunology of TB infection may guide new prevention strategies for the TB epidemic. Nonalcoholic fatty liver disease (NAFLD) is a metabolic disease. Experts reached a consensus that \u0026ldquo;MAFLD\u0026rdquo; is suggested as a more appropriate term for fatty liver associated with metabolic dysfunction[22]. NAFLD or MAFLD individuals have elements of metabolic disease, including insulin resistance and hypertension, which have been previously demonstrated as risk factors for various infections\u0026nbsp;[23,24]. However, few studies have investigated the effect of hepatic steatosis, an early characteristic in the pathogenesis of NAFLD, on the progression and prognosis of pulmonary TB (PTB). In this study,\u0026nbsp;eleven different factors were obtained\u0026nbsp;from the clinical data of one hundred and seventy-four PTB patients. According to clinical significance, correlation detection, collinearity diagnosis, and chest package score,\u0026nbsp;six different factors (hepatic steatosis: TB patients with hepatic steatosis, T-SPOT, BUN, IL-6, IL-10, IL18) were enrolled\u0026nbsp;into the model.\u0026nbsp;According to the logistics regression analysis, Hepatic steatosis and IL-18 were positively correlated with the smear conversion (\u0026beta;= 2.401, OR =11.036, 95% CI = 2.489-48.934; \u0026beta;= 2.396, OR =10.984, 95% CI = 1.832-65.862). On the contrary, the T-SPOT was negatively correlated with the smear conversion (\u0026beta;= -4.199, OR =0.015, 95% CI = 0.001-0.172). These results indicate that hepatic steatosis appreciably affects anti-tuberculosis chemotherapy\u0026apos;s curative effect for TB patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT-SPOT, a test for interferon-\u0026gamma; (IFN-\u0026gamma;) release assays (IGRAs), is currently endorsed by the WHO as a diagnosis for TB infection. Previous study confirms that the levels of IFN-\u0026gamma; are correlated with smear conversion results of clinical specimens[25]. Therefore, T-SPOT could be used to monitor response to treatment of anti-TB[26]. Intricacies or subsequent disease severity in TB infection are usually connected with cytokine storm.\u0026nbsp;The levels of IL-6 in extension group are significantly lower than those in\u0026nbsp;Unextended Group. The levels of IL-10 and IL-18 in extension group are significantly higher than those in\u0026nbsp;Unextended Group.\u0026nbsp;Previous studies have indicated that Interleukin (IL-6) is crucial in the immune response to TB. Elevated IL-6 can lead to a complex combination with membrane-bound interleukin-6 receptor (mIL-6R) to act on glycoprotein 130 (gp130). Gp130 regulates the concentrations of monocytes chemoattractant protein-1(MCP-1) and granulocyte-macrophage colony-stimulating factor (GM-CSF) through the JAK-STAT pathways[27]. In our test results, low levels of IL-6 in PTB patients are associated with a longer time of smear conversion. Inerleukin-10 (IL-10) is also demonstrated to be critical for defending against TB infections. IL-10 can inhibit the expression of MHC-II in \u003cem\u003eM.tuberculosis\u003c/em\u003e-infected macrophages[28]. And the IL-10 is regulated by TLR3 in response to TB infection through the PI3K/AKT signaling pathway[29].\u0026nbsp;The absence of IL-10 might benefit the transition from immune evasion to immune protection and the early clearance of TB[30]. However, a recent study confirms that high levels of IL-10 impair the unextendedof \u003cem\u003eM\u003c/em\u003e.tuberculosis growth only before the onset of the T-cell response. During the early stages of \u003cem\u003eM\u003c/em\u003e.tuberculosis infection, CD4\u003csup\u003e+\u003c/sup\u003eT cells are recruited to the lungs by high levels of serum IL-10. Nevertheless, the actived CD4\u003csup\u003e+\u003c/sup\u003eT cells are not migrate into the parenchyma[31]. The IL-10 overexpression in PTB patients with NAFLD primed the CD4\u003csup\u003e+\u003c/sup\u003eT cells accumulate in the vasculature and did not migrate into the lung tissues infected with \u003cem\u003eM\u003c/em\u003e.tuberculosis. Research indicated that IL-6 and IL-10 expression levels are consistently maintained at a high level in TB patients. Moreover, aberrant expressions of IL-6/IL10 are correlated with high pSTAT3 levels. The constitutive pSTAT3 and high SOCS3 expression are influential factors that indicate impaired T-cell functions in tuberculosis patients[15].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;IL-1B, produced by alveolar macrophages, is related to tissue necrosis in lung lesions of active TB patients[32]. IL-18 is also a cytokine, including the IL-1 family, and is found to cause injury in the lung tissue of TB infected hosts\u0026nbsp;[33].CCL20 and CCL2 are small chemotactic chemokines expressed from \u003cem\u003eM.tuberculosis\u003c/em\u003e-infected macrophages. However, the correlation between CCL20 or CCL2 and the severity of PTB may not have a cause-effect relationship[34]. A study from India has identified that plasma chemokine CXCL1 is associated with a decreased incidence of unfavorable outcomes in anti-TB treatment[35]. For chronic PTB patients, CSF3 plays a vital role in innate immunity and represents a promising cytokine for anti-TB immunotherapy[36]. As mentioned above, the function of the hub genes obtained from TB and NAFLD is to regulate the production of macrophage cytokines. And PI3K/AKT signaling pathway probably plays a crucial role in regulating macrophage function.\u003c/p\u003e\n\u003cp\u003eA recent study indicated that adipocyte death induces liver injury and inflammation by activating CCR2+ macrophages[37]. Mitogen-activated protein kinase 14 (MAPK14) increases simple steatosis and ameliorates oxidative stress in the pathogenesis of NASH[38]. \u003cem\u003eMycobacterium tuberculosis\u0026nbsp;\u003c/em\u003elysates induce IL-27 expression in human macrophages by activating MAPK[39]. The polymorphism of oligomerization domain-containing (NOD)2 (Arg702Trp) is likely to be the protective factor for active TB[40]. And NOD2 can mitigate the steatosis and fibrosis of the liver during NAFLD progression[41]. Furthermore, receptor interacting-serine/threonine-protein kinase 2 (RIPK2) is a critical adapter protein for signal propagation of NOD2 and might be a risk factor for TB infection[42]. Evidence is obtained implicating Growth arrest and DNA damage-inducible 45\u0026beta; (GADD45\u0026beta;) against lipid accumulation and insulin resistance in NAFLD mice. In the innate immune system, GADD45\u0026beta; is linked to the chemotaxis of macrophages in response to lipopolysaccharide[43]. Collectively, the abnormal production of macrophage cytokines has a significant impact on the outcome of anti-tuberculosis treatment. \u0026nbsp; In addition, the effect of hepatic steatosis on anti-TB drug metabolism is still relatively unknown. Previous studies confirmed that NAFLD affects the function of the mitochondrion. Cytochrome P450 2E1 (CYP2E1), located within liver mitochondria, is correlated with the degree of steatosis. CYP2E1 is an important factor leading to oxidative stress[44]. Another study confirmed that Rifampicin can induce the high expression of CYP450 in steatosis hepatocytes, which will increase the risk of Drug lug interactions (DDIs). Further study is needed to analysis the mechanism of hepatic steatosis impact on TB[45].\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThis study contains a retrospective clinical analysis. The sample size we obtained was small, and there was significant statistical bias. We will continue to expand the sample size to continue the study in depth in the future.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this work, hepatic steatosis is confirmed as an independent risk factor affecting smear conversion time in pulmonary tuberculosis patients (PTB). The levels of serum cytokines, such as IL-6 and IL-18, are significantly related to the outcome of anti-tuberculosis treatment. Previous studies have confirmed that hepatic steatosis is associated with abnormal serum cytokines. Therefore, more attention is needed for the PTB patients with hepatic steatosis.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll pulmonary tuberculosis patients were treated with standard care without intervention from this study. All data were obtained via electronic medical records, and a database review were acquired (the patient\u0026rsquo;s name was replaced with an identification code, and the patient\u0026rsquo;s private information was deleted before the analysis) to protect patient privacy. This study obtained approval from the Clinical Research Ethics Committee of Taizhou People\u0026rsquo;s Hospital TZRY-LL-AF/SQ-014-2.0 (Protocol number KY201803901). The informed consent of human clinical data was obtained from all subjects. All experimental serum samples were obtained from the remaining clinical samples of the pulmonary tuberculosis patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe collinearity diagnostic results of differential factors were given in(\u003cstrong\u003eTable S1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare there are no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (81970454).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDZX, YY, and GYX contributed equally to the study\u0026apos;s design. DZX completed this article\u0026apos;s data sorting and writing; LX and CMY contributed significantly to analysis and manuscript preparation; CMY, WL, LHL, ZY, and HMD helped perform the analysis and constructive discussions. HCM, LY, and YYF contributed to the design of the study. All authors provided original data and participated in the article design. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThank you for the data provided by the Department of Infectious Diseases of Taizhou People \u0026apos;s Hospital.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. Global tuberculosis report 2021 [Internet]. [cited 2022 Sep 25]. Available from: https://www.who.int/news-room/fact-sheets/detail/tuberculosis\u003c/li\u003e\n\u003cli\u003eTuberculosis - PubMed [Internet]. [cited 2022 Sep 25]. Available from: https://pubmed.ncbi.nlm.nih.gov/30904262/\u003c/li\u003e\n\u003cli\u003eL\u0026ouml;nnroth K, Williams BG, Cegielski P, Dye C. A consistent log-linear relationship between tuberculosis incidence and body mass index. Int J Epidemiol. 2010;39:149\u0026ndash;55.\u003c/li\u003e\n\u003cli\u003eHuang PL. A comprehensive definition for metabolic syndrome. Dis Model Mech. 2009;2:231\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eAbdullah A, Peeters A, de Courten M, Stoelwinder J. The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies. Diabetes Res Clin Pract. 2010;89:309\u0026ndash;19.\u003c/li\u003e\n\u003cli\u003eMushtaq A. Tuberculosis in diabetes: insidious and neglected. Lancet Respir Med. 2019;7:483.\u003c/li\u003e\n\u003cli\u003eCalderwood CJ, Wilson JP, Fielding KL, Harris RC, Karat AS, Mansukhani R, et al. Dynamics of sputum conversion during effective tuberculosis treatment: A systematic review and meta-analysis. PLoS Med. 2021;18:e1003566.\u003c/li\u003e\n\u003cli\u003eChaves Torres NM, Quijano Rodr\u0026iacute;guez JJ, Porras Andrade PS, Arriaga MB, Netto EM. Factors predictive of the success of tuberculosis treatment: A systematic review with meta-analysis. PLoS One. 2019;14:e0226507.\u003c/li\u003e\n\u003cli\u003eFriedman SL, Neuschwander-Tetri BA, Rinella M, Sanyal AJ. Mechanisms of NAFLD development and therapeutic strategies. Nat Med. 2018;24:908\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eZhou J, Zhou F, Wang W, Zhang X, Ji Y, Zhang P, et al. Epidemiological Features of NAFLD From 1999 to 2018 in China. Hepatology [Internet]. 2020 [cited 2022 Sep 27];71:1851\u0026ndash;64. Available from: https://onlinelibrary.wiley.com/doi/10.1002/hep.31150\u003c/li\u003e\n\u003cli\u003eNseir W, Taha H, Khateeb J, Grosovski M, Assy N. Fatty liver is associated with recurrent bacterial infections independent of metabolic syndrome. Dig Dis Sci. 2011;56:3328\u0026ndash;34.\u003c/li\u003e\n\u003cli\u003eFisher-Hoch SP, Mathews CE, McCormick JB. Obesity, diabetes and pneumonia: the menacing interface of non-communicable and infectious diseases. Trop Med Int Health. 2013;18:1510\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003ePark J, Zhao Y, Zhang F, Zhang S, Kwong AC, Zhang Y, et al. IL6/STAT3 axis dictates the PNPLA3-mediated susceptibility to non-alcoholic fatty liver disease. J Hepatol. 2022;S0168-8278(22)03053-7.\u003c/li\u003e\n\u003cli\u003eMiller AM, Wang H, Bertola A, Park O, Horiguchi N, Ki SH, et al. Inflammation-associated interleukin-6/signal transducer and activator of transcription 3 activation ameliorates alcoholic and nonalcoholic fatty liver diseases in interleukin-10-deficient mice. Hepatology. 2011;54:846\u0026ndash;56.\u003c/li\u003e\n\u003cli\u003eHarling K, Adankwah E, G\u0026uuml;ler A, Afum-Adjei Awuah A, Adu-Amoah L, Mayatepek E, et al. Constitutive STAT3 phosphorylation and IL-6/IL-10 co-expression are associated with impaired T-cell function in tuberculosis patients. Cell Mol Immunol. 2019;16:275\u0026ndash;87.\u003c/li\u003e\n\u003cli\u003eTutunchi H, Ostadrahimi A, Saghafi-Asl M, Roshanravan N, Shakeri-Bavil A, Asghari-Jafarabadi M, et al. Expression of NF-\u0026kappa;B, IL-6, and IL-10 genes, body composition, and hepatic fibrosis in obese patients with NAFLD-Combined effects of oleoylethanolamide supplementation and calorie restriction: A triple-blind randomized controlled clinical trial. J Cell Physiol. 2021;236:417\u0026ndash;26.\u003c/li\u003e\n\u003cli\u003eGreenland S, Pearce N. Statistical Foundations for Model-Based Adjustments. Annual Review of Public Health [Internet]. 2015 [cited 2023 Oct 22];36:89\u0026ndash;108. Available from: https://doi.org/10.1146/annurev-publhealth-031914-122559\u003c/li\u003e\n\u003cli\u003eKant S, Gupta H, Ahluwalia S. Significance of nutrition in pulmonary tuberculosis. Crit Rev Food Sci Nutr. 2015;55:955\u0026ndash;63.\u003c/li\u003e\n\u003cli\u003eA cholesterol-rich diet accelerates bacteriologic sterilization in pulmonary tuberculosis - PubMed [Internet]. [cited 2022 Oct 5]. Available from: https://pubmed.ncbi.nlm.nih.gov/15706008/\u003c/li\u003e\n\u003cli\u003eLin H-H, Wu C-Y, Wang C-H, Fu H, L\u0026ouml;nnroth K, Chang Y-C, et al. Association of Obesity, Diabetes, and Risk of Tuberculosis: Two Population-Based Cohorts. Clin Infect Dis. 2018;66:699\u0026ndash;705.\u003c/li\u003e\n\u003cli\u003eMartens GW, Arikan MC, Lee J, Ren F, Vallerskog T, Kornfeld H. Hypercholesterolemia impairs immunity to tuberculosis. Infect Immun. 2008;76:3464\u0026ndash;72.\u003c/li\u003e\n\u003cli\u003eEslam M, Sanyal AJ, George J, International Consensus Panel. MAFLD: A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease. Gastroenterology. 2020;158:1999-2014.e1.\u003c/li\u003e\n\u003cli\u003eObesity and risk of infections: results from men and women in the Swedish National March Cohort - PubMed [Internet]. [cited 2022 Oct 6]. Available from: https://pubmed.ncbi.nlm.nih.gov/31292615/\u003c/li\u003e\n\u003cli\u003eHarps\u0026oslash;e MC, Nielsen NM, Friis-M\u0026oslash;ller N, Andersson M, Wohlfahrt J, Linneberg A, et al. Body Mass Index and Risk of Infections Among Women in the Danish National Birth Cohort. Am J Epidemiol. 2016;183:1008\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003eKobashi Y, Mouri K, Yagi S, Obase Y, Miyashita N, Oka M. Transitional changes in T-cell responses to Mycobacterium tuberculosis-specific antigens during treatment. J Infect. 2009;58:197\u0026ndash;204.\u003c/li\u003e\n\u003cli\u003eBosshard V, Roux-Lombard P, Perneger T, Metzger M, Vivien R, Rochat T, et al. Do results of the T-SPOT.TB interferon-gamma release assay change after treatment of tuberculosis? Respir Med. 2009;103:30\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eBoni FG, Hamdi I, Koundi LM, Shrestha K, Xie J. Cytokine storm in tuberculosis and IL-6 involvement. Infection, Genetics and Evolution [Internet]. 2022 [cited 2022 Oct 7];97:105166. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1567134821004664\u003c/li\u003e\n\u003cli\u003eInterleukin-10-induced MARCH1 mediates intracellular sequestration of MHC class II in monocytes - PubMed [Internet]. [cited 2022 Oct 7]. Available from: https://pubmed.ncbi.nlm.nih.gov/18389477/\u003c/li\u003e\n\u003cli\u003eBai W, Liu H, Ji Q, Zhou Y, Liang L, Zheng R, et al. TLR3 regulates mycobacterial RNA-induced IL-10 production through the PI3K/AKT signaling pathway. Cell Signal. 2014;26:942\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eAbdalla AE, Lambert N, Duan X, Xie J. Interleukin-10 Family and Tuberculosis: An Old Story Renewed. Int J Biol Sci [Internet]. 2016 [cited 2022 Oct 7];12:710\u0026ndash;7. Available from: http://www.ijbs.com/v12p0710.htm\u003c/li\u003e\n\u003cli\u003eFerreira CM, Barbosa AM, Barreira-Silva P, Silvestre R, Cunha C, Carvalho A, et al. Early IL-10 promotes vasculature-associated CD4+ T cells unable to control Mycobacterium tuberculosis infection. JCI Insight [Internet]. 2021 [cited 2022 Nov 15];6:e150060. Available from: https://insight.jci.org/articles/view/150060\u003c/li\u003e\n\u003cli\u003eWu S, Wang Y, Zhang M, Shrestha SS, Wang M, He J-Q. Genetic Polymorphisms of \u003cem\u003eIL1B, IL6,\u003c/em\u003e and \u003cem\u003eTNF \u003c/em\u003e\u0026alpha; in a Chinese Han Population with Pulmonary Tuberculosis. BioMed Research International [Internet]. 2018 [cited 2022 Oct 7];2018:1\u0026ndash;10. Available from: https://www.hindawi.com/journals/bmri/2018/3010898/\u003c/li\u003e\n\u003cli\u003eVecchi\u0026eacute; A, Bonaventura A, Toldo S, Dagna L, Dinarello CA, Abbate A. IL‐18 and infections: Is there a role for targeted therapies? J Cell Physiol [Internet]. 2021 [cited 2022 Oct 8];236:1638\u0026ndash;57. Available from: https://onlinelibrary.wiley.com/doi/10.1002/jcp.30008\u003c/li\u003e\n\u003cli\u003eCCL20 is overexpressed in Mycobacterium tuberculosis-infected monocytes and inhibits the production of reactive oxygen species (ROS) - PubMed [Internet]. [cited 2022 Oct 7]. Available from: https://pubmed.ncbi.nlm.nih.gov/20819093/\u003c/li\u003e\n\u003cli\u003eKumar NP, Moideen K, Nancy A, Viswanathan V, Thiruvengadam K, Nair D, et al. Plasma Chemokines Are Baseline Predictors of Unfavorable Treatment Outcomes in Pulmonary Tuberculosis. Clin Infect Dis. 2021;73:e3419\u0026ndash;27.\u003c/li\u003e\n\u003cli\u003eChuang Y-M, He L, Pinn ML, Tsai Y-C, Cheng MA, Farmer E, et al. Albumin fusion with granulocyte-macrophage colony-stimulating factor acts as an immunotherapy against chronic tuberculosis. Cell Mol Immunol [Internet]. 2021 [cited 2022 Oct 8];18:2393\u0026ndash;401. Available from: https://www.nature.com/articles/s41423-020-0439-2\u003c/li\u003e\n\u003cli\u003eAdipocyte Death Preferentially Induces Liver Injury and Inflammation Through the Activation of Chemokine (C-C Motif) Receptor 2-Positive Macrophages and Lipolysis - PubMed [Internet]. [cited 2022 Oct 9]. Available from: https://pubmed.ncbi.nlm.nih.gov/30681731/\u003c/li\u003e\n\u003cli\u003eHwang S, Wang X, Rodrigues RM, Ma J, He Y, Seo W, et al. Protective and Detrimental Roles of p38\u0026alpha; Mitogen-Activated Protein Kinase in Different Stages of Nonalcoholic Fatty Liver Disease. Hepatology. 2020;72:873\u0026ndash;91.\u003c/li\u003e\n\u003cli\u003eZhang J, Qian X, Ning H, Eickhoff CS, Hoft DF, Liu J. Transcriptional suppression of IL-27 production by Mycobacterium tuberculosis-activated p38 MAPK via inhibition of AP-1 binding. J Immunol. 2011;186:5885\u0026ndash;95.\u003c/li\u003e\n\u003cli\u003eNOD2 polymorphisms and pulmonary tuberculosis susceptibility: a systematic review and meta-analysis - PubMed [Internet]. [cited 2022 Oct 9]. Available from: https://pubmed.ncbi.nlm.nih.gov/24391456/\u003c/li\u003e\n\u003cli\u003eNOD2 in hepatocytes engages a liver-gut axis to protect against steatosis, fibrosis, and gut dysbiosis during fatty liver disease in mice - PubMed [Internet]. [cited 2022 Oct 9]. Available from: https://pubmed.ncbi.nlm.nih.gov/32516028/\u003c/li\u003e\n\u003cli\u003eSong J, Liu T, Jiao L, Zhao Z, Hu X, Wu Q, et al. RIPK2 polymorphisms and susceptibility to tuberculosis in a Western Chinese Han population. Infect Genet Evol. 2019;75:103950.\u003c/li\u003e\n\u003cli\u003eSalerno DM, Tront JS, Hoffman B, Liebermann DA. Gadd45a and Gadd45b modulate innate immune functions of granulocytes and macrophages by differential regulation of p38 and JNK signaling. J Cell Physiol. 2012;227:3613\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eRey-Bedon C, Banik P, Gokaltun A, Hofheinz O, Yarmush ML, Uygun MK, et al. CYP450 drug inducibility in NAFLD via an in vitro hepatic model: Understanding drug-drug interactions in the fatty liver. Biomed Pharmacother. 2022;146:112377.\u003c/li\u003e\n\u003cli\u003eAubert J, Begriche K, Knockaert L, Robin MA, Fromenty B. Increased expression of cytochrome P450 2E1 in nonalcoholic fatty liver disease: mechanisms and pathophysiological role. Clin Res Hepatol Gastroenterol. 2011;35:630\u0026ndash;7.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eThe clinical baseline data of 174 PTB patients\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.91304347826087%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnextended Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtension Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistics(\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e/\u003cstrong\u003eF\u003c/strong\u003e/z)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.782608695652176%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.86111111111111%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.145833333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.743055555555557%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.86111111111111%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHepatic steatosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e23 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e24 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.145833333333332%\"\u003e\n \u003cp\u003e45.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.743055555555557%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.86111111111111%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.145833333333332%\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.743055555555557%\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.86111111111111%\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e39 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e8 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.145833333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.743055555555557%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.86111111111111%\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e103 (73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e24 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.145833333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.743055555555557%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.86111111111111%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e(year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e58 (39, 68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e57 (44, 69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.145833333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.743055555555557%\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.545454545454547%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e(kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.55944055944056%\"\u003e\n \u003cp\u003e20.52\u0026plusmn;3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.734265734265735%\"\u003e\n \u003cp\u003e22.55\u0026plusmn;3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.25874125874126%\"\u003e\n \u003cp\u003e2.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.902097902097903%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.86111111111111%\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfected lung\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.145833333333332%\"\u003e\n \u003cp\u003e16.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.743055555555557%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.86111111111111%\"\u003e\n \u003cp\u003eone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e109 (77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e13 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.145833333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.743055555555557%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.86111111111111%\"\u003e\n \u003cp\u003etwo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e33 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e19 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.145833333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.743055555555557%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.86111111111111%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.145833333333332%\"\u003e\n \u003cp\u003e36.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.743055555555557%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.86111111111111%\"\u003e\n \u003cp\u003efailure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e9 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.145833333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.743055555555557%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.86111111111111%\"\u003e\n \u003cp\u003esuccess\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e141 (99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e23 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.145833333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.743055555555557%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.86111111111111%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntensive period time\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(mouth)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.00 (1.00, 2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e5.00 (4.00, 6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.145833333333332%\"\u003e\n \u003cp\u003e19.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.743055555555557%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.86111111111111%\"\u003e\n \u003cp\u003e\u003cstrong\u003eT-SPOT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.145833333333332%\"\u003e\n \u003cp\u003e3.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.743055555555557%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.86111111111111%\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e9 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.145833333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.743055555555557%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.86111111111111%\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e141 (99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e23 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.145833333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.743055555555557%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote:BMI: Body Mass Index; Outcome: the outcome of anti-tuberculosis treatment；n (%); Median (IQR); Statistical method: chi-square test, t-test, Mann-Whitney U-test, Pearson\u0026apos;s Chi-squared test; Fisher\u0026apos;s exact test.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e The characteristics of blood routine in 174 PTB patients\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"572\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.015761821366024%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.066549912434326%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnextended Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.444833625218916%\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtension Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.563922942206656%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistics(\u0026chi;\u003csup\u003e2\u003c/sup\u003e/z)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.908931698774081%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.015761821366024%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.066549912434326%\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.444833625218916%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.563922942206656%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.908931698774081%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.015761821366024%\"\u003e\n \u003cp\u003e\u003cem\u003eRBC\u003c/em\u003e（10\u003csup\u003e9\u003c/sup\u003e/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.066549912434326%\"\u003e\n \u003cp\u003e4.19\u0026plusmn;0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.444833625218916%\"\u003e\n \u003cp\u003e4.13\u0026plusmn;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.563922942206656%\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.908931698774081%\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.015761821366024%\"\u003e\n \u003cp\u003e\u003cem\u003eHB\u003c/em\u003e（g/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.066549912434326%\"\u003e\n \u003cp\u003e121.56\u0026plusmn;20.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.444833625218916%\"\u003e\n \u003cp\u003e119.5\u0026plusmn;22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.563922942206656%\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.908931698774081%\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.015761821366024%\"\u003e\n \u003cp\u003e\u003cem\u003eWBC\u003c/em\u003e（10\u003csup\u003e9\u003c/sup\u003e/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.066549912434326%\"\u003e\n \u003cp\u003e7.08\u0026plusmn;2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.444833625218916%\"\u003e\n \u003cp\u003e7.77\u0026plusmn;2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.563922942206656%\"\u003e\n \u003cp\u003e1.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.908931698774081%\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.015761821366024%\"\u003e\n \u003cp\u003e\u003cem\u003eNEU\u003c/em\u003e（10\u003csup\u003e9\u003c/sup\u003e/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.066549912434326%\"\u003e\n \u003cp\u003e5.00 (3.00, 8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.444833625218916%\"\u003e\n \u003cp\u003e65.00 (36.00, 75.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.563922942206656%\"\u003e\n \u003cp\u003e6.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.908931698774081%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.015761821366024%\"\u003e\n \u003cp\u003e\u003cem\u003eLYM\u003c/em\u003e（10\u003csup\u003e9\u003c/sup\u003e/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.066549912434326%\"\u003e\n \u003cp\u003e1.00 (1.00, 2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.444833625218916%\"\u003e\n \u003cp\u003e15.00 (5.00, 26.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.563922942206656%\"\u003e\n \u003cp\u003e4.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.908931698774081%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.015761821366024%\"\u003e\n \u003cp\u003e\u003cem\u003eMON\u003c/em\u003e（10\u003csup\u003e9\u003c/sup\u003e/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.066549912434326%\"\u003e\n \u003cp\u003e0.58 (0.40, 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.444833625218916%\"\u003e\n \u003cp\u003e7.35 (2.80, 8.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.563922942206656%\"\u003e\n \u003cp\u003e5.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.908931698774081%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.015761821366024%\"\u003e\n \u003cp\u003e\u003cem\u003ePLT\u003c/em\u003e（10\u003csup\u003e9\u003c/sup\u003e/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.066549912434326%\"\u003e\n \u003cp\u003e219 (176, 299)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.444833625218916%\"\u003e\n \u003cp\u003e215 (160, 306)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.563922942206656%\"\u003e\n \u003cp\u003e1.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.908931698774081%\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: RBC: red blood cell; HB: hemoglobin; NEU: neutrophile granulocyte; LYM: lymphocyte; MON: monocyte; PLT: blood platelet. Statistical methods: independent sample t-test, a nonparametric test, chi-square test.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eThe characteristics of liver and kidney function in 174 PTB patients.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"582\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.010291595197256%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.013722126929675%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnextended Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.641509433962263%\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtension Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.612349914236706%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistics(\u0026chi;\u003csup\u003e2\u003c/sup\u003e/F/z)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.010291595197256%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.013722126929675%\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.641509433962263%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.612349914236706%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.010291595197256%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTBIL\u003c/strong\u003e（umol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.013722126929675%\"\u003e\n \u003cp\u003e10.00 (7.00, 15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.641509433962263%\"\u003e\n \u003cp\u003e10.00 (7.00, 12.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.612349914236706%\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.010291595197256%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDBIL\u003c/strong\u003e（umol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.013722126929675%\"\u003e\n \u003cp\u003e2.30 (1.40, 3.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.641509433962263%\"\u003e\n \u003cp\u003e1.95 (1.30, 3.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.612349914236706%\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.010291595197256%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTP\u003c/strong\u003e（g/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.013722126929675%\"\u003e\n \u003cp\u003e66.30\u0026plusmn;8.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.641509433962263%\"\u003e\n \u003cp\u003e65.54\u0026plusmn;8.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.612349914236706%\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.010291595197256%\"\u003e\n \u003cp\u003e\u003cstrong\u003eALB\u003c/strong\u003e（g/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.013722126929675%\"\u003e\n \u003cp\u003e34.91\u0026plusmn;6.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.641509433962263%\"\u003e\n \u003cp\u003e35.25\u0026plusmn;7.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.612349914236706%\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.010291595197256%\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT\u003c/strong\u003e（U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.013722126929675%\"\u003e\n \u003cp\u003e13.00 (8.00, 22.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.641509433962263%\"\u003e\n \u003cp\u003e18.00 (11.00, 27.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.612349914236706%\"\u003e\n \u003cp\u003e2.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.010291595197256%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAST\u003c/strong\u003e（U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.013722126929675%\"\u003e\n \u003cp\u003e20.00 (16.00, 26.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.641509433962263%\"\u003e\n \u003cp\u003e21.00 (17.00, 31.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.612349914236706%\"\u003e\n \u003cp\u003e1.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.010291595197256%\"\u003e\n \u003cp\u003e\u003cstrong\u003eALP\u003c/strong\u003e（U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.013722126929675%\"\u003e\n \u003cp\u003e90 (74, 109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.641509433962263%\"\u003e\n \u003cp\u003e85 (64, 106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.612349914236706%\"\u003e\n \u003cp\u003e1.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.010291595197256%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGGT\u003c/strong\u003e（U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.013722126929675%\"\u003e\n \u003cp\u003e27 (18, 44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.641509433962263%\"\u003e\n \u003cp\u003e29 (22, 41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.612349914236706%\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.010291595197256%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBUN\u003c/strong\u003e（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.013722126929675%\"\u003e\n \u003cp\u003e4.61 (3.72, 5.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.641509433962263%\"\u003e\n \u003cp\u003e5.05 (4.20, 7.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.612349914236706%\"\u003e\n \u003cp\u003e1.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.010291595197256%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCREA\u003c/strong\u003e（umol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.013722126929675%\"\u003e\n \u003cp\u003e57.00 (50.00, 69.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.641509433962263%\"\u003e\n \u003cp\u003e65.00 (58.00, 79.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.612349914236706%\"\u003e\n \u003cp\u003e2.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.010291595197256%\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003csub\u003e2\u003c/sub\u003eMG\u003c/strong\u003e（mg/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.013722126929675%\"\u003e\n \u003cp\u003e2.16 (1.59, 2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.641509433962263%\"\u003e\n \u003cp\u003e2.35 (1.82, 3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.612349914236706%\"\u003e\n \u003cp\u003e1.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: TBIL: total bilirubin; DBIL: direct bilirubin; TP: total protein; ALB: albumin; ALT: alanine aminotransferase; AST: aspartate aminotransferase; ALP: serum alkaline phosphatase; GGT: gamma-glutamyl transpeptidase; BUN: urea nitrogen; CREA: creatinine; UA: uric acid; \u0026nbsp;Statistical methods: independent sample t-test, nonparametric test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eThe characteristics of Tumor and Inflammatory indicators in 174 PTB patients.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"585\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.94871794871795%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnextended Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.564102564102566%\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtension Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.025641025641026%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistics(\u0026chi;\u003csup\u003e2\u003c/sup\u003e/F/z)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.94871794871795%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.564102564102566%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.025641025641026%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.94871794871795%\"\u003e\n \u003cp\u003eCRP(mg/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e14 (2, 40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.564102564102566%\"\u003e\n \u003cp\u003e9 (3, 31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.025641025641026%\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.94871794871795%\"\u003e\n \u003cp\u003eESR(mm/h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e18 (5, 44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.564102564102566%\"\u003e\n \u003cp\u003e21 (9, 43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.025641025641026%\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.94871794871795%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCEA\u003c/strong\u003e(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e2.54 (1.65, 4.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.564102564102566%\"\u003e\n \u003cp\u003e2.30 (1.52, 3.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.025641025641026%\"\u003e\n \u003cp\u003e1.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.94871794871795%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCA-125\u003c/strong\u003e(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e37 (23, 63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.564102564102566%\"\u003e\n \u003cp\u003e22 (12, 70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.025641025641026%\"\u003e\n \u003cp\u003e1.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.94871794871795%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCA19-9\u003c/strong\u003e(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e7 (4, 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.564102564102566%\"\u003e\n \u003cp\u003e9 (6, 14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.025641025641026%\"\u003e\n \u003cp\u003e1.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.94871794871795%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL-6\u003c/strong\u003e(ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e19 (9, 36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.564102564102566%\"\u003e\n \u003cp\u003e9 (5, 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.025641025641026%\"\u003e\n \u003cp\u003e3.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.94871794871795%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL-10\u003c/strong\u003e(ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e8 (4, 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.564102564102566%\"\u003e\n \u003cp\u003e19 (8, 28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.025641025641026%\"\u003e\n \u003cp\u003e4.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.94871794871795%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL-18\u003c/strong\u003e(ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e19 (8, 38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.564102564102566%\"\u003e\n \u003cp\u003e60 (46, 73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.025641025641026%\"\u003e\n \u003cp\u003e6.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Statistical methods: nonparametric test.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e Logistics regression analysis of the different factors\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"574\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.608695652173912%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eValues\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.956521739130435%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26086956521739%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6521739130434785%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOR\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.869565217391305%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.0% \u003cem\u003eCI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.782608695652174%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.478260869565218%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOR\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.0% \u003cem\u003eCI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.54703832752613%\" colspan=\"5\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.536585365853659%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003elower\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.18815331010453%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eupper\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.306620209059233%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.536585365853659%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003elower\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.885017421602788%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eupper\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.020905923344948%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHepatic Steatosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.581881533101045%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e2.742\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.278745644599303%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.665505226480836%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.520\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.536585365853659%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.210\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.18815331010453%\" valign=\"bottom\"\u003e\n \u003cp\u003e38.800\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.013937282229966%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.401\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.801393728222996%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.491289198606272%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.036\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.536585365853659%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.489\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.885017421602788%\" valign=\"bottom\"\u003e\n \u003cp\u003e48.934\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.020905923344948%\"\u003e\n \u003cp\u003e\u003cstrong\u003eT-SPOT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.581881533101045%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e-3.912\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.278745644599303%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.665505226480836%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.020\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.536585365853659%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.18815331010453%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.150\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.013937282229966%\" valign=\"bottom\"\u003e\n \u003cp\u003e-4.199\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.801393728222996%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.491289198606272%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.015\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.536585365853659%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.885017421602788%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.172\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.020905923344948%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBUN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.581881533101045%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e2.747\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.278745644599303%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.052\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.665505226480836%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.590\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.536585365853659%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.970\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.18815331010453%\" valign=\"bottom\"\u003e\n \u003cp\u003e249.740\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.013937282229966%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.656\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.801393728222996%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.409\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.491289198606272%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.239\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.536585365853659%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.103\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.885017421602788%\" valign=\"bottom\"\u003e\n \u003cp\u003e267.065\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.020905923344948%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL-6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.581881533101045%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.715\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.278745644599303%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.665505226480836%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.180\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.536585365853659%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.060\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.18815331010453%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.530\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.013937282229966%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.732\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.801393728222996%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.335\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.491289198606272%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.481\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.536585365853659%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.109\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.885017421602788%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.128\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.020905923344948%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL-10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.581881533101045%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e2.980\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.278745644599303%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.665505226480836%\" valign=\"bottom\"\u003e\n \u003cp\u003e19.680\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.536585365853659%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.740\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.18815331010453%\" valign=\"bottom\"\u003e\n \u003cp\u003e81.710\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.013937282229966%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.801393728222996%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.378\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.491289198606272%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.365\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.536585365853659%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.039\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.885017421602788%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.427\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.020905923344948%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL-18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.581881533101045%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e3.961\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.278745644599303%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.665505226480836%\" valign=\"bottom\"\u003e\n \u003cp\u003e52.510\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.536585365853659%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.220\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.18815331010453%\" valign=\"bottom\"\u003e\n \u003cp\u003e299.030\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.013937282229966%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.396\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.801393728222996%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.491289198606272%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.984\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.536585365853659%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.832\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.885017421602788%\" valign=\"bottom\"\u003e\n \u003cp\u003e65.862\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.714770797962649%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.5466893039049237%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.791171477079796%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.0169779286927%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.979626485568761%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.31918505942275%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.99830220713073%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.809847198641766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.979626485568761%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.31918505942275%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.99830220713073%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Hepatic Steatosis:Tb patients with Hepatic Steatosis; OR: odds ratio; CI: confidence interval. Statistical methods: Univariate and Multivariable \u0026nbsp;logistics regression.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis, Hepatic steatosis, Nonalcoholic fatty liver disease, Smear conversion, IL-6, IL-18","lastPublishedDoi":"10.21203/rs.3.rs-4306921/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4306921/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground:\u003c/p\u003e\n\u003cp\u003ePulmonary Tuberculosis (PTB) remains the leading cause of death from infectious diseases worldwide. Few studies have investigated hepatic steatosis's effect on PTB's progression and prognosis.\u003c/p\u003e\n\u003cp\u003eMethods: We selected 785 PTB patients in this study. And 174 PTB patients were enrolled in the statistical analysis. Patients with positive sputum smears for over eight weeks are clustered in the Extension Group. Conversely, the other patients are clustered in the Unextended Group. The clinical data were used to analyze the risk factors for the time of smear conversion.\u003c/p\u003e\n\u003cp\u003eResults:\u003c/p\u003e\n\u003cp\u003eEleven different factors were obtained from the clinical data of PTB patients. According to clinical significance, correlation detection, collinearity diagnosis, and chest package score, six factors were enrolled into the model. The logistics regression analysis confirmed that Hepatic steatosis and IL-18 were positively correlated with the smear conversion (β= 2.401, OR =11.036, 95% CI = 2.489-48.934; β= 2.396, OR =10.984, 95% CI = 1.832-65.862). On the contrary, the T-SPOT was negatively correlated with the smear conversion (β= -4.199, OR =0.015, 95% CI = 0.001-0.172).\u003c/p\u003e\n\u003cp\u003eConclusions:\u003c/p\u003e\n\u003cp\u003eHepatic steatosis is confirmed as an independent risk factor affecting smear conversion time in PTB. The levels of serum cytokines, such as IL-6 and IL-18, are significantly related to the outcome of anti-tuberculosis treatment. Previous studies have confirmed that hepatic steatosis is associated with abnormal serum cytokines. Therefore, more attention is needed for the PTB patients with hepatic steatosis.\u003c/p\u003e","manuscriptTitle":"Hepatic Steatosis affects the Outcome of Anti- tuberculosis Treatment in Pulmonary Tuberculosis Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-30 19:35:45","doi":"10.21203/rs.3.rs-4306921/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8cb08a4c-27d7-4962-a7e0-b9158cd6f238","owner":[],"postedDate":"April 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-24T07:24:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-30 19:35:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4306921","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4306921","identity":"rs-4306921","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}