Characterizing the tuberculosis and type 2 diabetes mellitus comorbidity in a South African cohort using untargeted GCxGC-TOFMS metabolomics

Characterizing the tuberculosis and type 2 diabetes mellitus comorbidity in a South African cohort using untargeted GCxGC-TOFMS metabolomics | 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 Characterizing the tuberculosis and type 2 diabetes mellitus comorbidity in a South African cohort using untargeted GCxGC-TOFMS metabolomics Karla Reinecke, Léanie Kleynhans, Katharina Ronacher, Du Toit Loots This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7739707/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Jan, 2026 Read the published version in Metabolomics → Version 1 posted 8 You are reading this latest preprint version Abstract Introduction Tuberculosis (TB) and type 2 diabetes mellitus (T2DM) are highly prevalent diseases resulting in high mortality rates globally. Furthermore, T2DM increases susceptibility to TB and vice versa, worsening disease outcomes. This comorbidity is, however, not well described or understood, despite its rising prevalence globally. Objectives This investigation aimed to better characterize the urinary metabolic profiles of patients with the TB and T2DM comorbidity in a South African cohort, to better understand its metabolic basis and associated clinical implications. Methods Using untargeted GCxGC-TOFMS metabolomics, urine samples from 17 patients with TB and T2DM and 34 healthy controls were analyzed and statistically compared to identify significantly altered urinary metabolites. Results TB-T2DM comorbid patients were characterized by altered metabolism of: 1) tryptophan and kynurenine (reduced kynurenic acid, anthranilic acid, picolinic acid) associated with changes to NAD + synthesis and a redox imbalance, 2) nucleotides (reduced 3-aminoisobutyric acid, orotic acid, thymine, β-alanine, adenine, hypoxanthine), 3) tyrosine (reduced 3,4-dihydroxyphenylglycol, 4-hydroxy-3-methoxyphenylglycol, hydroxyphenylpyruvate), 4) lipids (reduced dec-2-enedioate, adipic acid, methylmalonic acid), 5) reduced concentrations of various glycine conjugates associated with glycine depletion, and 6) reduced urinary concentrations of various gut microbial metabolites indicative of microbial dysbiosis. Conclusion These results indicate several metabolic disruptions to amino acids, nucleotides, lipids, NAD⁺ homeostasis and the host microbiome, in TB-T2DM patients, mainly driven by inflammation and oxidative stress. Overall, the findings indicate synergistic amplification of metabolic stress, associated with immune suppression and TB-T2DM disease progression, and subsequently suggests how TB increases T2DM susceptibility and vice versa, as foundation for further investigations. Tuberculosis Diabetes Comorbidity Metabolomics Urine Figures Figure 1 Figure 2 1. Introduction Tuberculosis (TB), caused by Mycobacterium tuberculosis ( M.tb ), is a major global epidemic, with approximately 10.8 million new cases and a resulting 1.25 million deaths, reported for 2023 (World Health Organization, 2024). Nearly a quarter of the global population is infected, making TB the leading cause of death from a single infectious agent worldwide (World Health Organization, 2024). Transmission occurs via inhalation of aerosolized droplets with M.tb reaching alveoli and triggering an immune response, resulting in M.tb internalization by resident macrophages, followed by granuloma formation to limit bacterial spread (Leung, 1999 ; Philips and Ernst, 2012 ). The infection may be eradicated or remain latent, depending on host’s immune competence (Alsayed and Gunosewoyo, 2023 ; Behr et al., 2021 ; Lin and Flynn, 2010 ). Immunocompromised individuals risk progression to active TB, presenting with cough, fever, and weight loss (Acharya et al., 2020 ; Leung, 1999 ), and risk latent TB reactivation (5% risk within two years following infection) (Menzies et al., 2018 ). Additional risk factors for contracting the disease include the occurrence of immunosuppressive diseases such as diabetes mellitus (DM) and AIDS (World Health Organization, 2024). On the other hand, DM affects approximately 589 million adults globally and resulted in 3.4 million deaths in 2024 globally (International Diabetes Federation, 2025 ). With 4.3 million South African cases (Diabetes Alliance, 2023 ), it is the African country with the highest prevalence. DM, defined by chronic hyperglycemia (HbA1c ≥ 6.5%), is classified into type 1 (T1DM), which is characterized by autoimmune β-cell destruction and reduction in insulin secretion (Knip and Siljander, 2008 ) and type 2 (T2DM), that is associated with insulin resistance developed due to poor lifestyle and obesity, leading to pancreatic damage and reduced insulin secretion (Banday et al., 2021 ; Wang et al., 2020 ). T2DM accounts for approximately 90% of all DM cases (Zhang et al., 2014 ) and is therefore the focus of this study. T2DM impairs the immune system and consequently increases the risk of contracting active TB disease by approximately three-fold (Niazi and Kalra, 2012 ; Sane Schepisi et al., 2018 ). Consequently, Workneh et al. ( 2017 ) reported approximately 16% of TB patients present with T2DM. T2DM also increases the likelihood for TB relapses after treatment, lower cure rates and increased morbidity (Adane et al., 2023 ; Habib et al., 2024 ; Lee et al., 2014 ). Furthermore, TB-DM comorbid patients also have a 3.8-fold increased risk of developing multiple drug-resistant TB (Evangelista et al., 2020 ). Anti-T2DM treatment also affects TB disease outcomes. Clinical research shows that high doses of dipeptidyl peptidase-4 inhibitor (DPP4) inhibitors increase TB risk (Chen et al., 2020 ), while metformin and sulphonylureas reduces TB incidence (Lin et al., 2020 ; Meregildo-Rodriguez et al., 2022 ; Zhang and He, 2020 ). Conversely, M.tb infection induces stress-associated hyperglycemia via pro-inflammatory cytokines and ROS production (Jeon and Murray, 2008 ; Niazi and Kalra, 2012 ; Shastri et al., 2018 ; Workneh et al., 2017 ; Yorke et al., 2017 ), which stimulate hepatic glucose release (Sharma et al., 2019 ) and contribute to metabolic dysregulation (Magee et al., 2018 ). A metabolomics study done by Du Preez and Loots ( 2013 ), showed a 10 fold increase in the concentrations of the norepinephrine derivative; normetanephrine, in TB-positive patients, explaining the associated glucose intolerance (additionally contributing to the increased D-gluconic acid d-lactone detected in their study), since elevated levels of normetanephrine are also associated with insulin resistance and impaired insulin secretion (Murabayashi et al., 2018 ). Anti-TB drugs also interact with T2DM medication. Rifampicin lowers plasma levels of biguanides and sulphonylureas (Niazi and Kalra, 2012 ), while isoniazid antagonizes sulphonylureas (Dartois and Rubin, 2022 ; Yorke et al., 2017 ). Metabolomics serves as a useful tool to better understand TB-T2DM, since T2DM is a metabolic disease (Li et al., 2009 ; Yen et al., 2023 ) and TB also results in sever metabolic changes (Du Preez and Loots, 2013 ; Isa et al., 2018 ; Luies and Loots, 2016 ; Vrieling et al., 2019 ). To date, as far as we are aware, only one such metabolomics study has been done describing the metabolic changes associated with TB-T2DM, using patient collected plasma, reporting and briefly describing reduced choline, citrulline, histidine, ornithine, and tryptophan in TB-T2DM patients when compared with healthy controls (Vrieling et al., 2019 ). Plasma metabolomics reflects the metabolic state of an individual at the exact time of sample collection (González-Domínguez et al., 2020 ), while urinary metabolomics on the other hand, captures metabolic fluctuations over time (Dunn and Ellis, 2005 ) and is also considered easier to collect and prepare due to its low protein content (Du Preez and Loots, 2013 ; Khamis et al., 2015 ; Zhang et al., 2012 ). Therefore, this study employed untargeted GCxGC-TOFMS urinary metabolomics to compare TB-T2DM patients to healthy controls with and without latent TB, to comprehensively characterize the metabolic profiles of TB-T2DM patients in a South African cohort. 2. Methods and materials 2.1. Participants Voluntarily participating study participants (n = 125; n = 97 healthy controls (HC) with and without latent TB and n = 28 TB-T2DM patients) were recruited from hospitals and community clinics situated in the Western Cape, South Africa. The participant cohort was refined into a final cohort (n = 51; n = 34 HC and n = 17 TB-T2DM) by application of various inclusion and exclusion criteria. The HC included individuals accompanying patients to the hospitals and clinics with (n = 15) and without (n = 19) latent TB. Individuals were excluded from the HC group if they had any acute respiratory tract infection in the 4 weeks prior to recruitment, suffered from chronic hyperglycemia, were previously or currently diagnosed with T2DM, tested positive for active TB disease by a GeneXpert and/or QuantiFERON test or were suffering from any severe systemic condition. Participants were included in the TB-T2DM group if they were either newly diagnosed with pulmonary TB, or recurrent TB with TB treatment completed at least 2 months prior to recruitment and diagnosed with T2DM and an HbA1c ≥ 6.5% (excluding gestational or steroid-induced diabetes) with- and without T2DM treatment. The TB diagnosis was confirmed by two separate positive sputum smears and/or a positive mycobacteria growth indicator tube culture, and/or positive polymerase chain reaction (PCR) for the presence of M.tb . Study participants were generally excluded if diagnosed with any alternative medical conditions (chronic bronchitis/emphysema/asthma, cancer, current HIV or HIV within a 3-month period), received steroid therapy within 6 months of recruitment, participated in any drug or vaccine trial, were pregnant, abused alcohol (> 3 alcoholic beverages per day) or illicit drugs and had no permanent address. Table 1 shows the biographical information of the study participants. Ethics approval for the larger scope of this study has been obtained from the Health Research Ethics Committee (HREC) of Stellenbosch University (reference number: N13/05/064; project ID: 4095). The current study (Ethics number: NWU-00096-23-A1-02) falls under a larger study at the North-West University with the title: “The characterization of tuberculosis-diabetes mellitus co-morbidity in a South African cohort using metabolomics” for which ethics approval has been obtained (Ethics number: NWU-00096-23-A1). Table 1 Sociodemographic characteristics of healthy control and tuberculosis-type 2 diabetes comorbid participants HC with latent TB (n = 19) HC without latent TB (n = 15) TB-T2DM (n = 17) Age (average ± standard deviation) 41.21 ± 10.02 32.60 ± 10.45 47.35 ± 9.45 Sex (% female/% male) 53/47 73/27 47/53 HbA1c (% average ± standard deviation) 5.42 ± 0.44 5.34 ± 0.43 9.49 ± 2.20 Patients on T2DM treatments: (%) No treatment - - 47.1 Only insulin - - 5.9 Only metformin - - 17.6 Metformin with other anti-T2DM drugs - - 23.5 Insulin and metformin - - 5.9 Duration of T2DM: (%) Less than 1 year - - 29.4 1–5 years - - 17.6 6–15 years - - 29.4 Undocumented - - 23.5 HC, healthy control; TB, tuberculosis; TB-T2DM, tuberculosis-type 2 diabetes mellitus; HbA1c, glycated hemoglobin; T2DM, type 2 diabetes mellitus. 2.2. Urine sampling and storage Urine sample collection, using standard urine collection vials, was done by trained healthcare professionals. The samples were initially stored at -80°C at Stellenbosch University, after which they were transported to North-West University and stored at -80°C until the commencement of the GCxGC-TOFMS metabolomic analysis. 2.3. Reagents and chemicals The following reagents were used: 3-phenylbutyric acid (internal standard), methoxyamine hydrochloride (MOX-HCl) in pyridine and N,O-bis(trimethylsilyl)trifuoroacetamide (BSTFA) with 1% trimethylsilyl chloride (TMCS) and acetonitrile from Burdick and Jackson brand (Honeywell International Inc., Muskegon, USA). 2.4. Sample preparation Equal amounts (20 µL) of all patient urine samples were used to compile a pooled quality control (QC) sample, from which aliquots were prepared to be extracted and analyzed with each sample batch (samples were randomly assigned to batches). Following creatinine normalization to 1 µmol, the corresponding urine volume was combined with 100 µL of internal standard solution (3-phenyl butyric acid, 50 ppm) and 300 µL of ice-cold acetonitrile, vortexed, incubated on ice for 10 min and centrifuged thereafter at 10 000 g for 10 min. The supernatant was then transferred to a 2 mL GC vial and dried under nitrogen at 40°C. Derivatization involved: 1) methoximation with 50 µL MOX-HCl (15 mg/mL) in pyridine at 60°C for 60 min, and 2) trimethylsilylation with 50 µL BSTFA-TMCS, at 60°C for 60 min. The final sample was transferred to a glass insert, placed in the GC vial and capped. 2.5. GCxGC-TOFMS analysis and processing Prepared samples were randomly selected and analyzed (alongside QC samples, extraction blank samples, system suitability test samples comprising of fatty acid methyl esters (FAMEs), placed intermittently throughout each batch) using Pegasus 4D GC×GC-TOFMS (Leco Africa (Pty) Ltd, Johannesburg, South Africa) equipped with an Agilent 7890 GC. A 1:3 split ratio was employed to inject 1µl of each sample with the front inlet temperature at 270ºC. Purified helium served as a carrier gas with a constant flow of 3 mL/min. First-dimensional chromatographical separation was achieved with a Restek Rxi-5Sil MS primary column (28.2 m; 250 µm internal diameter and 0.25 µm film thickness), with the primary oven ramping from 70°C (2 min hold) to 300°C (2 min hold) at 5°C/min. Second-dimensional separation was achieved by a Restek Rxi-17 capillary column (1.32 m × 250 µm diameter × 0.25 µm film thickness), with the secondary oven ramping from 85°C (2 min hold) to 310°C (4.5 min hold) at 5.5°C/min. The modulator was programmed to ramp from 100°C (2 min hold) to 310°C (12 min hold) at 5°C/min, with 0.5 s cold/hot nitrogen pulses every 3 s. A 350 s acquisition delay excluded solvent detection. Transfer line and ion source were set to 270°C and 200°C, respectively, with − 70 eV filament bias and 150 V detector voltage. Mass spectra were acquired over 50–950 m/z at 200 spectra per second. Data was processed using Leco Corporation ChromaTOF software (v4.32) with peak identification based on 70% spectral library match, signal-to-noise ratio of 200 and minimum of 3 apex peaks. Furthermore, the Statistical compare function was used for peak alignment based on similarity in mass spectra and retention times. 2.6. Data management Data clean-up was performed using Microsoft Excel prior to statistical analysis. Relative concentrations (mmol/mol creatinine) were calculated by normalization to the internal standard, 3-phenylbutyric acid. A 50% filter was applied to retain only compounds present in at least half of at least one of the two experimental groups. Batch correction (using quantile equating) and a coefficient of variation (CV) filter (retaining compounds with CV ≤ 50%) were applied using QC samples. Zero values detected for a compound were replaced with half the smallest detected value to reflect the lower detection limit (Luies and Loots, 2016 ). MetaboAnalyst 6.0, based on the statistical program, “R” (v4.3.2), was employed for data normalization with log transformation and autoscaling, as well as further statistical analysis. Principal component analysis (PCA) was performed on the final data set to determine if any natural separation between the HC and TB-T2DM groups exists. Biomarker selection was based on a multi-statistical approach using: 1) PLS-DA (VIP > 1), 2) t-test (p 0.8) (Du Preez and Loots, 2013 ). 3. Results Following data processing, cleaning, mass spectral deconvolution, peak identification and alignment, 2161 urinary metabolites were detected. Removal of unidentified compounds yielded a final data matrix of 280 metabolites. The PCA performed using all patient samples and all 280 identified metabolites, showed no natural separation between TB-T2DM and HC groups (S1 Fig. 1 a), likely due to demographical variation (age, sex, diet, treatment, etc.), introducing metabolic “noise”. Outliers may have arisen from this “noise”, preventing clear discrimination between the two experimental groups by PCA. The available clinical information did not explain the definite origin of the noise, though multiple factors may contribute to it. Therefore, a multi-statistical approach was employed for metabolite marker selection using: 1) PLS-DA (VIP > 1), 2) t-test (p |2|) (S1 Fig. 1 b). The 19 metabolites that satisfied all three criteria were included in a “noise-reduced” dataset. PCA of this noise-reduced dataset showed separation between the TB-T2DM and HC groups (S1 Fig. 1 c). Random forest analysis identified 4 outlier samples only from the HC group, which were removed from the dataset (Wu et al. , 2008). A PLS-DA model (Fig. 1 a) was constructed and evaluated by 10-fold cross-validation repeated 10 times. The model achieved an accuracy of 84.1 ± 1.8% with a predictive ability of Q² = 0.513 and R 2 = 0.975 (Szymańska et al., 2012 ). Variability observed within the TB-T2DM group likely reflects differences in T2DM treatment and duration as reported in Table 1 . These patients were still included to preserve cohort size, enhancing statistical power and avoiding the introduction of a degree of bias. Despite treatment, all TB-T2DM patients still presented with HbA1c > 6.5%, confirming persistent hyperglycemia and uncontrolled T2DM. Finally, a 2nd Venn diagram (Fig. 1 b) was constructed to illustrate the number of metabolites meeting each criterion and the overlap of common metabolites between each test. Only compounds satisfying all three criteria: VIP > 1 (PLS-DA, Fig. 1 b), p 0.8 (effect size), were included in the final list of biomarkers, listed in Table 2 . Table 2 Metabolite markers of the TB-T2DM comorbidity and the healthy controls Compound (ChemSpider ID) PLS-DA t-test Effect size Average relative concentration (mmol/mol creatinine); Standard deviation VIP p-value Cohen’s d Healthy control TB-T2DM Tryptophan metabolism/kynurenine pathway/NAD + Kynurenic acid (3712) 1.124 0.023 0.915 0.158 ; 0.38 0.038 ; 0.045 Anthranilic acid (222) 1.030 0.013 0.979 0.283 ; 0.559 0.157 ; 0.261 Picolinic acid (993) 1.075 0.023 0.851 0.533 ; 1.331 0.183 ; 0.155 Amino acid metabolism 2-Methylcrotonyl glycine (4945715) 1.048 0.018 0.892 1.710 ; 2.929 0.617 ; 0.756 N-(2-methyl-1-oxobutyl) glycine (168243) 1.042 0.016 0.929 0.071 ; 0.172 0.022 ; 0.034 Isobutyryl glycine (9030891) 1.116 0.005 1.137 0.215 ; 0.352 0.069 ; 0.102 Tyrosine metabolism 3,4-Dihydroxyphenylglycol (82648) 2.476 0.001 1.583 0.15 ; 0.302 0.003 ; 0.01 4-Hydroxy-3-methoxyphenylglycol (10348) 1.088 0.027 0.816 1.409 ; 1.684 0.791 ; 0.79 Hydroxyphenylpyruvate (954) 1.044 0.018 0.906 1.094 ; 1.996 0.309 ; 0.275 Pyrimidine metabolism Orotic acid (942) 1.052 0.024 0.907 0.593 ; 1.319 0.131 ; 0.170 3-Aminoisobutyric acid (58481) 1.044 0.029 0.857 4.676 ; 17.452 2.174 ; 5.453 β-Alanine (234) 1.060 0.041 0.806 0.156 ; 0.514 0.020 ; 0.026 Thymine (1103) 2.097 < 0.001 1.674 0.155 ; 0.391 0.010 ; 0.022 Purine metabolism Adenine (185) 1.046 0.028 0.879 0.278 ; 0.761 0.105 ; 0.159 Hypoxanthine (768) 1.033 0.029 0.862 2.385 ; 5.655 1.139 ; 1.627 Dicarboxylic / lipid metabolism Dec-2-enedioate (21237627) 1.912 < 0.001 1.589 0.025 ; 0.041 0.003 ; 0.003 Adipic acid (191) 1.045 0.018 0.897 1.66 ; 5.102 0.282 ; 0.396 Methylmalonic acid (473) 2.269 0.018 1.079 0.623 ; 2.642 0.002 ; 0.006 Gut microflora metabolism Phenylacetylglutamine (83292) 3.027 < 0.001 2.111 0.672 ; 2.019 0.124 ; 0.315 Indoxyl (45861) 1.087 0.028 0.808 0.525 ; 1.871 0.149 ; 0.169 3-(3-Hydroxyphenyl) propanoic acid (89) 1.049 0.023 0.849 0.400 ; 0.984 0.096 ; 0.089 Cyclohexylamine (7677) 1.081 0.023 0.844 12.039 ; 41.082 2.638 ; 2.347 Syringol (6774) 1.808 0.001 1.414 0.729 ; 3.471 0.028 ; 0.073 Syringic acid (10289) 1.070 0.016 0.983 32.022 ; 101.646 7.425 ; 10.644 2,6-Dihydroxybenzoic acid (8974) 1.364 0.003 1.266 0.027 ; 0.047 0.004 ; 0.006 Nicotine consumption Trans-3’-hydroxycotinine (97080) 1.067 0.026 0.926 0.182 ; 0.499 0.035 ; 0.048 4. Discussion Table 2 lists the selected metabolite markers that most significantly differ between the urinary metabolic profiles of the TB-T2DM patients and HC. The selected metabolite markers indicate perturbations in multiple metabolic pathways including the metabolism of tryptophan, glycine, tyrosine, nucleotides, dicarboxylic acids/lipids and gut microbiome. These perturbations in these metabolic pathways are discussed in the following sections and are illustrated in Fig. 2 . 4.1. Tryptophan metabolism, kynurenine pathway and NAD+ The kynurenine pathway is a major route of tryptophan catabolism in mammals (Mandi and Vecsei, 2012 ; Martin et al., 2020 ). Tryptophan is converted to kynurenine by the dioxygenases, tryptophan 2,3-dioxygenase (TDO) and indoleamine 2,3-dioxygenase-1 (IDO-1), along with kynurenine formamidase (Badawy, 2017 ; Martin et al., 2020 ) as seen in Fig. 2 . TDO primarily acts in the liver, while IDO-1 functions in extrahepatic tissues, particularly immune cells (Badawy, 2017 ; Pires et al., 2020 ). IDO-1 catalyzes the rate-limiting step of this pathway and is induced by pro-inflammatory cytokines interferon-γ (IFN-γ) and tumor necrosis factor-α (TNF-α) (González et al., 2008 ; Mandi and Vecsei, 2012 ). These inflammatory mediators are secreted by airway epithelial cells, dendritic cells, alveolar macrophages, type II pneumocytes, and CD4+/CD8 + T cells, promoting macrophage activation during pulmonary M. tb infection (Etna et al., 2014 ; Sharma et al., 2007 ). Elevated IDO-1 activity and higher kynurenine/tryptophan ratios have associated with an increased mortality in pulmonary TB, suggesting its potential prognostic use (Suzuki et al., 2012 ). Similarly, in T2DM, hyperglycemia has also been associated with increased TNF-α (Navarro-González and Mora-Fernández, 2008 ; Wang et al., 2020 ), further contributing to IDO-1 activation. IDO-1 activation shifts tryptophan catabolism toward kynurenine pathway in immune cells as a negative feedback mechanism to modulate inflammation (Mandi and Vecsei, 2012 ; Martin et al., 2020 ). The kynurenine pathway metabolites have also been shown to impair the immune response by inhibiting CD4⁺ T-cell activity, contributing to immunosuppression (Singhal and Cheng, 2018 ). Although not selected as a metabolite marker in this study using the strict selection criteria, the tryptophan catabolite, 5-hydroxyindoleacetic acid (Luies and Loots, 2016 ) was also significantly reduced in TB-T2DM urine samples of this study (0.357 vs. 0.993 mmol/mol creatinine, p = 0.009), aligning with Vrieling et al. ( 2019 ) reporting lower plasma tryptophan concentrations in TB-T2DM patients compared to healthy controls. The concentrations of kynurenic acid, anthranilic acid and picolinic acid were furthermore significantly reduced in TB-T2DM patients when compared to HC in this study. Although these metabolites are associated with the kynurenine pathway (Badawy, 2017 ; Martin et al., 2020 ), their reductions indicate a flux towards elevated NAD + -synthesis (Badawy, 2017 ; Mandi and Vecsei, 2012 ; Martin et al., 2020 ) as illustrated in Fig. 2 , as a means to correct for the typically disrupted NAD + /NADH ratio associated with both TB and diabetes (Ido et al., 1997 ; Pajuelo et al., 2018 ; Sun et al., 2015 ; Williamson et al., 1993 ). Further confirmation of this are the reduced urinary concentrations of nicotinamide (NAM) (0.062 vs. 0.167 mmol/mol creatinine, p = 0.037), though not selected as a metabolite marker using the strict selection criteria. In a salvage pathway, nicotinamide phosphoribosyl transferase (NAMPT), a rate-limiting enzyme, catalyzes the conversion of NAM to nicotinamide mononucleotide (NMN) (Gallí et al., 2010 ; Singhal and Cheng, 2018 ), illustrated in Fig. 2 . Activated immune cells exhibit upregulated gene expression of NAMPT (Gallí et al., 2010 ). This upregulation has also been observed in vascular endothelial cells, alveolar epithelial cells, inflammatory cells and other cells in individuals with acute lung injury and pulmonary inflammation (Zhang et al., 2011 ). The release of pro-inflammatory cytokines, including IL-1β, which has also been seen to be elevated in TB-T2DM patients, has been implicated in the upregulation of NAMPT, which in turn, results in elevated IL-8 secretion by pulmonary A549 cells (Ronacher et al., 2015 ; Zhang et al., 2011 ). The final step in NAD⁺ biosynthesis is the conversion of NMN to NAD⁺ (Fukushima and Lopaschuk, 2016 ). The aforementioned imbalanced NAD⁺/NADH ratio, affects fatty acid, glucose, TCA cycle metabolism, energy production (Xie et al., 2020 ), DNA repair (Hou et al., 2018 ; Wilk et al., 2020 ), and oxidative stress (Sultana et al., 2016 ; Wang et al., 2014 ), and is associated with T2DM progression (Sultana et al., 2016 ; Williamson et al., 1993 ) and fatty liver disease (Akie et al., 2015 ). The latter is not only strongly associated with T2DM (Kumar et al., 2022 ), but also TB (Amarapurkar and Ghansar, 2007 ), and hence the susceptibility of TB patients for developing T2DM. Kynurenic acid also functions as noncompetitive N-methyl-D-aspartate receptor (NMDAR) antagonist (Mandi and Vecsei, 2012 ). In mice, NMDAR overactivation led to insulin resistance and hyperlipidemia, while its inhibition reversed these effects (Huang et al., 2021 ). Furthermore, Huang et al. ( 2021 ) proposed that activation of pancreatic NMDARs initiates a cascade of events resulting in mitochondria-mediated apoptosis of the pancreatic β-cells: firstly, the increase in the intracellular Ca²⁺ concentration depolarizes mitochondrial membrane potential, impairment of mitochondrial oxidative phosphorylation, increase in oxidative stress/ROS production and ultimately, reduction in pancreatic insulin secretion. Thus, the observed reduction in kynurenic acid concentration, due to TB (Asp et al., 2011 ; Etna et al., 2014 ; Sharma et al., 2007 ), would activate NMDAR (Asp et al., 2011 ), contributing to reduced insulin secretion (Huang et al., 2017 ) that naturally results in the T2DM progression (Peterson and Shulman, 2018 ). In summary, the upregulation of NAD⁺ biosynthesis via the kynurenine pathway in the TB-T2DM patients, likely represents an attempt to correct the redox balance by restoring the disrupted NAD⁺/NADH ratio, which is perturbed by several pathophysiological mechanisms associated with both TB and T2DM (Singhal and Cheng, 2018 ). Poor glycemic control (associated to T2DM as well as TB) promotes the upregulation of glycolysis, fatty acid oxidation, the TCA cycle (in insulin-independent tissues) and the polyol pathway – resulting in a decreased NAD⁺/NADH ratio (Garg and Gupta, 2022 ). Furthermore, oxidative stress induced by inflammation associated with both TB and DM (Amaral et al., 2021 ; Pasupuleti et al., 2020 ), damages mitochondrial proteins, impairing mitochondrial function and disrupting NAD⁺ recycling via the electron transport chain (Zhao et al., 2021 ). The decreased NAD⁺/NADH ratio furthermore, impairs the immune response mediated mainly via TNF by sirtuins, NAD + dependent deacetylases (Gallí et al., 2010 ), a likely mechanism by which diabetes increases the susceptibility for TB infection. 4.2. Oxidative stress and glycine metabolism Hyperglycemia promotes ROS production via glucose auto-oxidation, glycation of antioxidant enzymes, and non-enzymatic glycation reactions, all of which impair anti-oxidative mechanisms (Kaneto et al., 2010 ; Pasupuleti et al., 2020 ). In insulin-independent cells, excess intracellular glucose surpasses cellular glycolytic ability and enters alternative pathways that further elevate ROS (Li et al., 2008 ). In both T2DM and TB, increased lipolysis and fatty acid oxidation contribute to mitochondrial ROS generation, particularly via electron leakage at complexes I and III of the OXPHOS system (Las et al., 2020a ; Li et al., 2008 ). Furthermore, a TB metabolomics study done by Du Preez and Loots ( 2013 ), showed elevated glucose oxidation which results in the production of H 2 O 2 in TB. Pancreatic β-cells, due to their low antioxidant capacity, are especially vulnerable to oxidative damage and ROS-induced damage induces β-cell apoptosis, impairing insulin secretion (Las et al., 2020b ; Wang et al., 2020 ). Vrieling et al. ( 2019 ) reported significantly reduced plasma glycine in TB-T2DM individuals compared to healthy controls. This is substantiated by observed reduction in the concentrations of glycine conjugates in this study. Glycine is essential for synthesizing glutathione (GSH), the most abundant intracellular antioxidant (Lu, 2013 ). GSH synthesis begins with ligation of glutamate and cysteine by glutamate-cysteine ligase (GCL), composed of catalytic (GCLC) and modifier (GCLM) subunits (Lu, 2013 ). GCLM mediates feedback inhibition of GCL by GSH to regulate levels, and GSH synthetase then adds glycine to form GSH (Fig. 2 ) (Lu, 2013 ). In T2DM, reduced glycine and GSH levels (Sekhar et al., 2011 ) reflect impaired antioxidant capacity and elevated oxidative stress. GSH mitigates oxidative stress by reducing oxidative species and forming glutathione disulfide (GSSG) (Butkowski and Jelinek, 2016 ). In TB-T2DM, elevated ROS accelerates GSH oxidation to GSSG, resulting in feedback inhibition on GCL and promoting GSH synthesis from glycine, cysteine, and glutamate (Lu, 2013 ). This increased GSH demand likely contributes to glycine depletion previously observed in TB-T2DM patients (Vrieling et al., 2019 ). In this study, the aforementioned reduced glycine conjugates: 2-methylcrotonyl glycine, N-(2-methyl-1-oxobutyl) glycine and isobutyryl glycine in the TB-T2DM patients, confirms the previously observed glycine reduction. 2-Methylcrotonyl glycine is formed by conjugating glycine with 2-methylcrotonyl-CoA, an intermediate in isoleucine degradation (Fontaine et al., 1996 ), while, N-(2-methyl-1-oxobutyl) glycine and isobutyryl glycine are derived from 2-methylbutanoyl-CoA and isobutyryl-CoA respectively, metabolites involved in catabolism of branched-chain amino acids (BCAAs), isoleucine, valine, and leucine (Guda et al., 2007 ; Shibata and Sakamoto, 2016 ). T2DM is reportedly associated with elevated urinary BCAA concentrations (Siddik and Shin, 2019 ; Theron et al., 2024 ), suggesting the reduction in their degradation, and our results also confirming the latter. 4.3. Tyrosine metabolism NE is synthesized from tyrosine via dopamine (DA) (Fig. 2 ) and binds postjunctional receptors to mediate various physiological responses (Alaniz et al., 1999 ). Excess NE is either reabsorbed or metabolized (Linares et al., 1987 ). TB has been associated with increased norepinephrine (NE) secretion (Du Preez and Loots, 2013 ). In this study, urinary 3,4-dihydroxyphenylglycol and 4-hydroxy-3-methoxyphenylglycol, two NE catabolites (Denfeld et al., 2018 ; Peaston and Weinkove, 2004 ), were significantly reduced in TB-T2DM patients, suggesting increased NE receptor binding and utilization. Homovanillic acid, a DA catabolite (Irwin et al., 1988 ), though not selected as a biomarker, was also markedly decreased (0.565 vs. 1.087 mmol/mol creatinine, p = 0.028), supporting enhanced DA utilization. Additionally, reduced concentrations of hydroxyphenylpyruvate, an intermediate in tyrosine catabolism (Gertsman et al., 2015 ; Irwin et al., 1988 ), further indicate altered tyrosine metabolism in the TB-T2DM group. This suggests a mechanism by which TB patients are more susceptible to developing diabetes and diabetes progression/severity (Du Preez and Loots, 2013 ). 4.4. Nucleotide metabolism Glutamine is crucial for de novo purine and pyrimidine synthesis, as seen in Fig. 2 (Parveen and Bishai, 2024 ). M.tb reprograms host glutamine metabolism toward energy production via glutaminolysis, depleting glutamine and impairing nucleotide de novo synthesis (Koeken et al., 2019 ; Parveen and Bishai, 2024 ). To compensate, nucleotide salvage pathways are upregulated to preserve nucleotide availability (Chandel, 2021 ). The TB-T2DM patients in this study showed significantly reduced orotic acid, an intermediate in the pyrimidine de novo synthesis pathway (Chandel, 2021 ), likely due to glutamine depletion (Koeken et al., 2019 ). The decreased concentrations of other pyrimidine catabolites in this study; 3-aminoisobutyric acid, thymine and β-alanine (Chandel, 2021 ), support enhanced nucleotide salvage activity. Similarly, the purine degradation products; adenine and hypoxanthine (Chandel, 2021 ), were also reduced in TB-T2DM, indicative of impaired de novo purine synthesis due to aforementioned glutamine and glycine depletion, and compensatory purine salvage pathway activation (Fig. 2 ) (Ducati et al., 2011 ). Given that TB-T2DM exhibits reduced glycolysis and TCA flux in insulin-dependent cells as well as a compromised mitochondrial function, salvage pathways, requiring less ATP than de novo synthesis (Villela et al., 2011 ), are preferentially employed (Chandel, 2021 ), explaining the reduced purine catabolite excretion. Purine metabolism also supports cyclic adenosine monophosphate (cAMP) synthesis from adenine, essential for insulin secretion (Carvalho et al., 2018 ). Considering this, the compromised nucleotide synthesis induced by TB contributes to susceptibility of these patients to getting T2DM and would further contribute to diabetes progression/severity. 4.5. Dicarboxylic acid/lipid metabolism In T2DM, production various TCA cycle intermediates are impaired in insulin-dependent cells (Peterson and Shulman, 2018 ). To compensate, dicarboxylic acids like dec-2-enedioate and adipic acid may be oxidized to generate acetyl-CoA and succinyl-CoA, supplementing the TCA cycle, as seen in Fig. 2 (Mingrone et al., 2013 ). Methylmalonic acid, derived from propionyl-CoA (Chalmers and Lawson, 1982 ), also supplements TCA cycle via succinyl-CoA (Peterson and Shulman, 2018 ). The significantly reduced urinary concentrations of these metabolites in TB-T2DM patients supports their increased metabolic utilization to sustain energy production. Moreover, their enhanced oxidation may also elevate oxidative stress, potentially exacerbating hyperinsulinemia (Las et al., 2020b ). 4.6. Gut microbial metabolism TB is associated with altered gut microbial composition and reduced microbial diversity (Liu et al., 2021 ). TB is reported to reduce microbial populations of Bifidobacteria , Lactobacillus and Bacteroides (Hu et al., 2019 ; Negi et al., 2020 ). T2DM, additionally, is associated with reductions in both oral and gut microbiota (Shillitoe et al., 2012 ). Furthermore, diabetic enteropathy, a T2DM complication, further alters the gastrointestinal (GI) tract, causing diarrhea, constipation, and abdominal discomfort (Zhong et al., 2016 ). The decreased urinary excretion of the observed microbial metabolites in TB-T2DM patients in this study further supports the presence of the above-mentioned gut dysbiosis. Phenylacetylglutamine and indoxyl are derived from microbial metabolism of tyrosine and tryptophan, respectively (Barrios et al., 2016 ). 3-(3-Hydroxyphenyl) propanoic acid results from microbial breakdown of procyanidins (Ou et al., 2014 ) and cyclohexylamine from cyclamate (Drasar et al., 1972 ). Syringol and syringic acid originate from microbial fermentation of lignin and anthocyanins respectively (Hidalgo et al., 2012 ; Ohra-aho et al., 2016 ), while unabsorbed aspirin may be converted to 2,6-dihydroxybenzoic acid by intestinal microbes (Sankaranarayanan et al., 2020 ). The overall reduction of these urinary microbial metabolites supports decreased microbial diversity and activity due to TB- and T2DM-associated gut microbiota disturbances. 4.7. Nicotine consumption Trans-3’-hydroxycotinine, a nicotine catabolite (Bergen et al., 2015 ), was significantly reduced. While participant smoking habits were unavailable, TB-related pulmonary symptoms such as persistent coughing and chest discomfort (Storla et al., 2008 ), may have discouraged nicotine use, and patients are also advised to stop smoking, explaining the decline. 5. Conclusion This investigation provides a characterization of the urinary metabolic profile of the TB-T2DM comorbidity using untargeted GC×GC-TOFMS metabolomics in a South African cohort. The findings show that metabolic disturbances associated with TB-T2DM are driven by inflammatory responses and oxidative stress with substantial downstream effects on amino acid metabolism, NAD + /NADH balance, nucleotide biosynthesis and lipid oxidation. This investigation observed the activation of the kynurenine pathway, likely mediated by release of the pro-inflammatory cytokines IFN-γ and TNF-α in response to M.tb infection, resulting in the activation of NMDARs. This elevates mitochondrial ROS production and causes pancreatic dysfunction, due to limited antioxidants present in pancreatic β-cells, impairing insulin secretion, thereby facilitating T2DM pathogenesis in TB and the TB-T2DM patients. Additionally, the hyperglycemia seen in T2DM, depletes cellular NAD⁺ pools due to the reduction in mitochondrial NAD + recycling due to mitochondrial damage caused by ROS, resulting in a compensatory upregulation of NAD + synthesis via the kynurenine pathway and elevated glycolysis, fatty acid oxidation, the TCA cycle in insulin-independent tissues and the polyol pathway. This NAD + depletion and compensatory synthesis upregulation is further substantiated by the reduction in NAM excretion, from which NAD + is synthesized. A compromised NAD⁺/NADH ratio weakens host immunity, particularly TNF-driven responses mediated by NAD + -dependent sirtuins, thereby increasing susceptibility to TB infection or reactivation in T2DM patients. The elevated oxidative stress is further substantiated by reduced urinary excretion of glycine conjugates, suggesting increased glutathione synthesis in response to ROS. Several detected metabolites in this investigation points to a disruption in nucleotide metabolism in TB-T2DM patients. This study shows that the TB-associated glutamine depletion impairs de novo purine and pyrimidine synthesis, initiating a shift to the salvage pathways. This is reflected in reduced concentrations of nucleotide degradation products in urine. Furthermore, this investigation identified significant perturbations in the gut microbiome of TB-T2DM patients, indicative of a reduction in microbial diversity. Overall, the findings of this study, support previous hypotheses regarding the mechanisms by which TB patients have an increased susceptibility to developing diabetes and vice versa, and highlights the disease mechanisms in each which would have a compounding effect towards and increase disease severity. Abbreviations DM Diabetes mellitus GCxGC-TOFMS Two-dimensional gas chromatography combined with time-of-flight mass spectrometry HC Healthy control group IDO-1 Indole amine 2,3-dioxygenase-1 IFN-γ Interferon-γ M.tb Mycobacterium tuberculosis NAD + Nicotinamide adenine dinucleotide NAM Nicotinamide ROS Reactive oxygen species T2DM Type 2 diabetes mellitus TB Tuberculosis TB-T2DM Tuberculosis-type 2 diabetes mellitus TNF-α Tumor necrosis factor-α Declarations Funding This work was supported by funding from the National Research foundation (NRF) of South Africa [grant number: SRUG2204062208] in consideration that the opinions, findings and conclusions or recommendations expressed is that of the authors alone, and that the NRF accepts no liability whatsoever in this regard. Acknowledgements We would like to thank the healthcare professionals for the recruitment of participants into the study. We would also want to extend our gratitude to the research personnel at Stellenbosch University for patient data collection and sample collection, storage and transport. Author contributions DTL conceived and designed the original research study. KR prepared the samples for analysis and performed data analysis. KR and DTL interpreted the results. KR wrote the manuscript, and the manuscript underwent revision and editing by DTL. All authors read and approved of the final manuscript. Data availability The raw data, supporting the findings of this study, can be acquired from the author upon reasonable request. Ethical approval Ethics approval for the larger scope of this study has been obtained from the Health Research Ethics Committee (HREC) of the Stellenbosch University (reference number: N13/05/064; project ID: 4095). The current study (Ethics number: NWU-00096-23-A1-02) falls under a larger study at the North-West University with the title: “The characterization of tuberculosis-diabetes mellitus co-morbidity in a South African cohort using metabolomics” for which ethics approval has been obtained (Ethics number: NWU-00096-23-A1). All procedures performed in this study that involved human participants were in accordance with the ethical standards of the Health Research Ethics Committee (HREC) of the Stellenbosch University and North-West University. Informed consent Informed consent was obtained from all individual participants included in this study. Conflict of interest The authors declare that they have no conflict of interest References Acharya, B., Acharya, A., Gautam, S., Ghimire, S.P., Mishra, G., Parajuli, N. and Sapkota, B. (2020) Advances in diagnosis of Tuberculosis: an update into molecular diagnosis of Mycobacterium tuberculosis. Molecular Biology Reports 47, 4065-4075. https://doi.org/10.1007/s11033-020-05413-7. Adane, H.T., Howe, R.C., Wassie, L. and Magee, M.J. (2023) Diabetes mellitus is associated with an increased risk of unsuccessful treatment outcomes among drug-susceptible tuberculosis patients in Ethiopia: A prospective health facility-based study. J Clin Tuberc Other Mycobact Dis 31, 100368. https://doi.org/10.1016/j.jctube.2023.100368. Akie, T.E., Liu, L., Nam, M., Lei, S. and Cooper, M.P. (2015) OXPHOS-Mediated Induction of NAD+ Promotes Complete Oxidation of Fatty Acids and Interdicts Non-Alcoholic Fatty Liver Disease. PLOS ONE 10, e0125617. https://doi.org/10.1371/journal.pone.0125617. Alaniz, R.C., Thomas, S.A., Perez-Melgosa, M., Mueller, K., Farr, A.G., Palmiter, R.D. and Wilson, C.B. (1999) Dopamine beta-hydroxylase deficiency impairs cellular immunity. Proceedings of the National Academy of Sciences of the United States of America 96, 2274-2278. https://doi.org/10.1073/pnas.96.5.2274. Alsayed, S.S.R. and Gunosewoyo, H. (2023) Tuberculosis: Pathogenesis, Current Treatment Regimens and New Drug Targets. International Journal of Molecular Sciences 24 . https://doi.org/10.3390/ijms24065202. Amaral, E.P., Vinhaes, C.L., Oliveira-de-Souza, D., Nogueira, B., Akrami, K.M. and Andrade, B.B. (2021) The Interplay Between Systemic Inflammation, Oxidative Stress, and Tissue Remodeling in Tuberculosis. Antioxidants and Redox Signaling 34, 471-485. https://doi.org/10.1089/ars.2020.8124. Amarapurkar, A. and Ghansar, T. (2007) Fatty liver: Experience from western India. Annals of Hepatology 6, 37-40. https://doi.org/10.1016/S1665-2681(19)31951-9. Asp, L., Johansson, A.S., Mann, A., Owe-Larsson, B., Urbanska, E.M., Kocki, T., Kegel, M., Engberg, G., Lundkvist, G.B.S. and Karlsson, H. (2011) Effects of pro-inflammatory cytokines on expression of kynurenine pathway enzymes in human dermal fibroblasts. Journal of Inflammation 8 . https://doi.org/10.1186/1476-9255-8-25. Badawy, A.A.B. (2017) Tryptophan availability for kynurenine pathway metabolism across the life span: Control mechanisms and focus on aging, exercise, diet and nutritional supplements. Neuropharmacology 112, 248-263. http://dx.doi.org/10.1016/j.neuropharm.2015.11.015. Banday, M.Z., Sameer, A.S. and Nissar, S. (2021) Pathophysiology of diabetes: An overview. Avicenna Journal of Medicine 10, 174-188. https://doi.org/10.4103/ajm.ajm_53_20. Barrios, C., Spector, T.D. and Menni, C. (2016) Blood, urine and faecal metabolite profiles in the study of adult renal disease. Archives of Biochemistry and Biophysics 589, 81-92. https://doi.org/10.1016/j.abb.2015.10.006. Behr, M.A., Kaufmann, E., Duffin, J., Edelstein, P.H. and Ramakrishnan, L. (2021) Latent Tuberculosis: Two Centuries of Confusion. American Journal of Respiratory and Critical Care Medicine 204, 142-148. https://doi.org/10.1164/rccm.202011-4239PP. Bergen, A.W., Michel, M., Nishita, D., Krasnow, R., Javitz, H.S., Conneely, K.N., Lessov-Schlaggar, C.N., Hops, H., Zhu, A.Z., Baurley, J.W., McClure, J.B., Hall, S.M., Baker, T.B., Conti, D.V., Benowitz, N.L., Lerman, C., Tyndale, R.F. and Swan, G.E. (2015) Drug Metabolizing Enzyme and Transporter Gene Variation, Nicotine Metabolism, Prospective Abstinence, and Cigarette Consumption. Plos One 10, e0126113. https://doi.org/10.1371/journal.pone.0126113. Butkowski, E.G. and Jelinek, H.F. (2016) Hyperglycaemia, oxidative stress and inflammatory markers. Redox Report 22, 257-264. https://doi.org/10.1080/13510002.2016.1215643. Carvalho, D.S., De Almeida, A.A., Borges, A.F. and Vannucci Campos, D. (2018) Treatments for diabetes mellitus type II: New perspectives regarding the possible role of calcium and cAMP interaction. European Journal of Pharmacology 830, 9-16. https://doi.org/10.1016/j.ejphar.2018.04.002. Chalmers, R.A. and Lawson, A.M. (1982) Disorders of propionate and methylmalonate metabolism. Organic acids in Man , 296-331. https://doi.org/10.1007/978-94-009-5778-7_11. Chandel, N.S. (2021) Nucleotide Metabolism. Cold Spring Harbor Perspectives in Biology 13 . https://doi.org/10.1101/cshperspect.a040592. Chen, H.H., Hsieh, M.C., Ho, C.W., Chen, C.C., Hsu, S.P., Lin, C.L. and Kao, C.H. (2020) Effects of dipeptidyl peptidase-4 inhibitor treatment doses on tuberculosis in patients with diabetes: a long-term nationwide population-based cohort study. Ann Palliat Med 9, 2817-2825. https://doi.org/10.21037/apm-20-278. Dartois, V.A. and Rubin, E.J. (2022) Anti- tuberculosis treatment strategies and drug development: challenges and priorities. Nature Reviewe Microbiology 20, 685-701. https://doi.org/10.1038/s41579-022-00731-y. Denfeld, Q.E., Habecker, B.A. and Woodward, W.R. (2018) Measurement of plasma norepinephrine and 3,4-dihydroxyphenylglycol: method development for a translational research study. BMC Research Notes 11, 248. https://doi.org/10.1186/s13104-018-3352-3. Diabetes Alliance (2023) 2023 Diabetes Summit Report 2023. https://www.diabetesalliance.org.za/diabetes-summit-2023/. Drasar, B.S., Renwick, A.G. and Williams, R.T. (1972) The role of the gut flora in the metabolism of cyclamate. Biochemical Journal 129, 881-890. https://doi.org/10.1042/bj1290881. Du Preez, I. and Loots, D.T. (2013) New sputum metabolite markers implicating adaptations of the host to Mycobacterium tuberculosis, and vice versa. Tuberculosis 93, 330-337. http://dx.doi.org/10.1016/j.tube.2013.02.008. Ducati, R.G., Breda, A. and Basso, L.A. (2011) Purine Salvage Pathway in Mycobacterium tuberculosis. Current Medicinal Chemistry 18, 1258–1275. https://doi.org/10.2174/092986711795029627. Dunn, W.B. and Ellis, D.I. (2005) Metabolomics: Current analytical platforms and methodologies. TrAC Trends in Analytical Chemistry 24, 285-294. https://doi.org/10.1016/j.trac.2004.11.021. Etna, M.P., Giacomini, E., Severa, M. and Coccia, E.M. (2014) Pro- and anti-inflammatory cytokines in tuberculosis: A two-edged sword in TB pathogenesis. Seminars in Immunology 26, 543-551. http://dx.doi.org/10.1016/j.smim.2014.09.011. Evangelista, M., Maia, R., Toledo, J.P., Abreu, R.G. and Barreira, D. (2020) Tuberculosis associated with diabetes mellitus by age group in Brazil: a retrospective cohort study, 2007-2014. The Brazilian Journal of Infectious Diseases 24, 130-136. https://doi.org/10.1016/j.bjid.2020.03.005. Fontaine, M., Briand, G., Ser, N., Armelin, I., Rolland, M.O., Degand, P. and Vamec, J. (1996) Metabolic studies in twin brothers with 2-methylacetoacetyl-CoA thiolase deficiency. Clinica Chimica Acta 255, 67-83. https://doi.org/10.1016/0009-8981(96)06391-7. Fukushima, A. and Lopaschuk, G.D. (2016) Acetylation control of cardiac fatty acid β-oxidation and energy metabolism in obesity, diabetes, and heart failure. Biochimica et Biophysica Acta 1862, 2211-2220. http://dx.doi.org/10.1016/j.bbadis.2016.07.020. Gallí, M., Van Gool, F., Rongvaux, A., Andris, F. and Leo, O. (2010) The nicotinamide phosphoribosyltransferase: A molecular link between metabolism, inflammation, and cancer. Cancer Research 70 . https://doi.org/10.1158/0008-5472.CAN-09-2465. Garg, S.S. and Gupta, J. (2022) Polyol pathway and redox balance in diabetes. Pharmacological Research 182, 106326. https://doi.org/10.1016/j.phrs.2022.106326. Gertsman, I., Gangoiti, J.A., Nyhan, W.L. and Barshop, B.A. (2015) Perturbations of tyrosine metabolism promote the indolepyruvate pathway via tryptophan in host and microbiome. Molecular, Genetics and Metabolism 114, 431-7. https://doi.org/10.1016/j.ymgme.2015.01.005. González-Domínguez, R., González-Domínguez, Á., Sayago, A. and Fernández-Recamales, Á. (2020) Recommendations and Best Practices for Standardizing the Pre-Analytical Processing of Blood and Urine Samples in Metabolomics. Metabolites 10, 229. https://doi.org/10.3390/metabo10060229. González, A., Varo, N., Alegre, E., Díaz, A. and Melero, I. (2008) Immunosuppression routed via the kynurenine pathway: a biochemical and pathophysiologic approach. Adv Clin Chem 45, 155-97. https://doi.org/10.1016/s0065-2423(07)00007-8. Guda, P., Guda, C. and Subramaniam, S. (2007) Reconstruction of pathways associated with amino acid metabolism in human mitochondria. Genomics Proteomics Bioinformatics 5, 166-76. https://doi.org/10.1016/S1672-0229(08)60004-2. Habib, M.A., Afrin, K., Efa, S.S., Hossain, M.D., Islam, M.R., Rahman, M.M., Islam, N., Afroz, F., Rahim, M.A. and Hossain, M.D. (2024) Effects of diabetes mellitus on retreatment of Tuberculosis: A multi-centered case-control study from Bangladesh. Journal of Clinical Tuberculosis and Other Mycobacterial Diseases 36, 100450. https://doi.org/10.1016/j.jctube.2024.100450. Hidalgo, M., Oruna-Concha, M.J., Kolida, S., Walton, G.E., Kallithraka, S., Spencer, J.P.E., Gibson, G.R. and De Pascual-Teresa, S. (2012) Metabolism of Anthocyanins by Human Gut Microflora and Their Influence on Gut Bacterial Growth. Journal of Agricultural and Food Chemistry 60, 3882-3890. https://doi.org/10.1021/jf3002153. Hou, Y., Lautrup, S., Cordonnier, S., Wang, Y., Croteau, D.L., Zavala, E., Zhang, Y., Moritoh, K., O’Connell, J.F., Baptiste, B.A., Stevnsner, T.V., Mattson, M.P. and Bohr, V.A. (2018) NAD+ supplementation normalizes key Alzheimer’s features and DNA damage responses in a new AD mouse model with introduced DNA repair deficiency. Proceedings of the National Academy of Sciences 115, E1876-E1885. https://doi.org/10.1073/pnas.1718819115. Hu, Y., Yang, Q., Liu, B., Dong, J., Sun, L., Zhu, Y., Su, H., Yang, J., Yang, F., Chen, X. and Jin, Q. (2019) Gut microbiota associated with pulmonary tuberculosis and dysbiosis caused by anti-tuberculosis drugs. J Infect 78, 317-322. https://doi.org/10.1016/j.jinf.2018.08.006. Huang, X.T., Yang, J.X., Wang, Z., Zhang, C.Y., Luo, Z.Q., Liu, W. and Tang, S.Y. (2021) Activation of N-methyl-D-aspartate receptor regulates insulin sensitivity and lipid metabolism. Theranostics 11, 2247-2262. https://doi.org/10.7150/thno.51666. Huang, X.T., Yue, S.J., Li, C., Huang, Y.H., Cheng, Q.M., Li, X.H., Hao, C.X., Wang, L.Z., Xu, J.P., Ji, M., Chen, C., Feng, D.D. and Luo, Z.Q. (2017) A Sustained Activation of Pancreatic NMDARs Is a Novel Factor of beta-Cell Apoptosis and Dysfunction. Endocrinology 158, 3900-3913. https://doi.org/10.1210/en.2017-00366. Ido, Y., Kilo, C. and Williamson, J.R. (1997) Cytosolic NADH/NAD+, free radicals, and vascular dysfunction in early diabetes mellitus. Diabetologia 40 Suppl 2, S115-7. https://doi.org/10.1007/s001250051422. International Diabetes Federation (2025) IDF Diabetes Atlas. https://diabetesatlas.org/data/en/. Irwin, J., Kopin, J.H., White and Bankiewicz, K. (1988) A new approach to biochemical evaluation of brain dopamine metabolism. Cellular and Molecular Neurobiology 8, 171-179. https://doi.org/10.1007/bf00711243. Isa, F., Collins, S., Hee Lee, M., Decome, D., Dorvil, N., Joseph, P., Smith, L., Salerno, S., Wells, M.T., Fischer, S., Bean, J.M., Pape, J.W., Johnson, W.D., Fitzgerald, D.W. and Rhee, K.Y. (2018) Mass Spectrometric Identification of Urinary Biomarkers of Pulmonary Tuberculosis. EBioMedecine 31, 157-165. https://doi.org/10.1016/j.ebiom.2018.04.014. Jeon, C.Y. and Murray, M.B. (2008) Diabetes mellitus increases the risk of active tuberculosis: a systematic review of 13 observational studies. PLoS Med 5, e152. https://doi.org/10.1371/journal.pmed.0050152. Kaneto, H., Katakami, N., Matsuhisa, M. and Matsuoka, T.A. (2010) Role of reactive oxygen species in the progression of type 2 diabetes and atherosclerosis. Mediators Inflammation 2010, 453892. https://doi.org/10.1155/2010/453892. Khamis, M.M., Adamko, D.J. and El-Aneed, A. (2015) Mass spectrometric based approaches in urine metabolomics and biomarker discovery. Mass Spectrometry Reviews 36, 115–134. https://doi.org/10.1002/mas.21455. Knip, M. and Siljander, H. (2008) Autoimmune mechanisms in type 1 diabetes. Autoimmun Rev 7, 550-7. https://doi.org/10.1016/j.autrev.2008.04.008. Koeken, V.A.C.M., Lachmandas, E., Riza, A., Matzaraki, V., Li, Y., Kumar, V., Oosting, M., Joosten, L.A.B., Netea, M.G. and Van Crevel, R. (2019) Role of Glutamine Metabolism in Host Defense Against Mycobacterium tuberculosis Infection. The Journal of Infectious Diseases 219, 1662-1670. https://doi.org/10.1093/infdis/jiy709. Kumar, P., Rawat, S., Kakar, A. and Sinha, A.K. (2022) Prevalence of non-alcoholic fatty liver disease among diabetes, prediabetes and healthy population. J Family Med Prim Care 11, 7640-7643. https://doi.org/10.4103/jfmpc.jfmpc_856_22. Las, G., Oliveira, M.F. and Shirihai, O.S. (2020a) Emerging roles of beta-cell mitochondria in type-2-diabetes. Molecular Aspects of Medicine 71, 100843. https://doi.org/10.1016/j.mam.2019.100843. Las, G., Oliveira, M.F. and Shirihai, O.S. (2020b) Emerging roles of β-cell mitochondria in type-2-diabetes. Molecular Aspects of Medicine 71 . https://doi.org/10.1016/j.mam.2019.100843. Lee, P.H., Lin, H.C., Huang, A.S., Wei, S.H., Lai, M.S. and Lin, H.H. (2014) Diabetes and risk of tuberculosis relapse: nationwide nested case-control study. PLoS One 9, e92623. https://doi.org/10.1371/journal.pone.0092623. Leung, A.N. (1999) Pulmonary Tuberculosis: The Essentials. Radiology 210, 307-322. https://doi.org/10.1148/radiology.210.2.r99ja34307. Li, N., Frigerio, F. and Maechler, P. (2008) The sensitivity of pancreatic beta-cells to mitochondrial injuries triggered by lipotoxicity and oxidative stress. Biochemical Society Transactions 36, 930-4. https://doi.org/10.1042/BST0360930. Li, X., Xu, Z., Lu, X., Yang, X., Yin, P., Kong, H., Yu, Y. and Xu, G. (2009) Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry for metabonomics: Biomarker discovery for diabetes mellitus. Anal Chim Acta 633, 257-62. https://doi.org/10.1016/j.aca.2008.11.058. Lin, K., Luo, C.W., Chen, S.P., Tu, D.G., Lin, M.S. and Kuan, Y.H. (2020) α-Glucosidase Inhibitor Can Effectively Inhibit the Risk of Tuberculosis in Patients with Diabetes: A Nested Case-Control Study. BioMed Research International 2020, 1-12. https://doi.org/10.1155/2020/8085106. Lin, P.L. and Flynn, J.L. (2010) Understanding Latent Tuberculosis: A Moving Target. The journal of immunology 185, 15-22. https://doi.org/10.4049/jimmunol.0903856. Linares, O.A., Jacquez, J.A., Zech, L.A., Smith, M.J., Sanfield, J.A., Morrow, L.A., Rosen, S.G. and Halter, J.B. (1987) Norepinephrine metabolism in humans. Kinetic analysis and model. The Journal of Clinical Investigation 80, 1332-1341. https://doi.org/10.1172/JCI113210. Liu, Y., Wang, J. and Wu, C. (2021) Microbiota and Tuberculosis: A Potential Role of Probiotics, and Postbiotics. Frontiers in Nutrition 8, 626254. https://doi.org/10.3389/fnut.2021.626254. Lu, S.C. (2013) Glutathione synthesis. Biochimica et Biophysica Acta 1830, 3143-53. https://doi.org/10.1016/j.bbagen.2012.09.008. Luies, L. and Loots, D.T. (2016) Tuberculosis metabolomics reveals adaptations of man and microbe in order to outcompete and survive. Metabolomics 12 . https://doi.org/10.1007/s11306-016-0969-x. Magee, M.J., Salindri, A.D., Kyaw, N.T.T., Auld, S.C., Haw, J.S. and Umpierrez, G.E. (2018) Stress Hyperglycemia in Patients with Tuberculosis Disease: Epidemiology and Clinical Implications. Current Diabetes Reports 18, 71. https://doi.org/10.1007/s11892-018-1036-y. Mandi, Y. and Vecsei, L. (2012) The kynurenine system and immunoregulation. Basic neuroscience, genetics and immunology 119, 197-209. https://doi.org/10.1007/s00702-011-0681-y. Martin, K.S., Azzolini, M. and Ruas, J.L. (2020) The kynurenine connection: how exercise shifts muscle tryptophan metabolism and affects energy homeostasis, the immune system, and the brain. American Journal of Physiology-Cell physiology 318, 818-830. https://doi.org/10.1152/ajpcell.00580.2019. Menzies, N.A., Wolf, E., Connors, D., Bellerose, M., Sbarra, A.N., Cohen, T., Hill, A.N., Yaesoubi, R., Galer, K., White, P.J., Abubakar, I. and Salomon, J.A. (2018) Progression from latent infection to active disease in dynamic tuberculosis transmission models: a systematic review of the validity of modelling assumptions. The Lancet Infectious Diseases 18, https://doi.org/10.1016/S1473-3099(18)30134-8 Meregildo-Rodriguez, E.D., Asmat-Rubio, M.G., Zavaleta-Alaya, P. and Vásquez-Tirado, G.A. (2022) Effect of Oral Antidiabetic Drugs on Tuberculosis Risk and Treatment Outcomes: Systematic Review and Meta-Analysis. Tropical Medicine and Infectious Disease 7, 343. https://doi.org/10.3390/tropicalmed7110343. Mingrone, G., Castagneto-Gissey, L. and Mace, K. (2013) Use of dicarboxylic acids in type 2 diabetes. British Journal of Clinical Pharmacology 75, 671-6. https://doi.org/10.1111/j.1365-2125.2012.04177.x. Murabayashi, M., Daimon, M., Murakami, H., Kamba, A. and Mizushiri, S. (2018) Association of Urinary Normetanephrine Levels with Increased Insulin Resistance in a General Population. Diabetes 67 . https://doi.org/10.2337/db18-1655-P. Navarro-González, J. and Mora-Fernández, C. (2008) The Role of Inflammatory Cytokines in Diabetic Nephropathy. Journal of the American Society of Nephrology (JASN) 19, 433-442. https://doi.org/10.1681/ASN.2007091048. Negi, S., Pahari, S., Bashir, H. and Agrewala, J.N. (2020) Intestinal microbiota disruption limits the isoniazid mediated clearance of Mycobacterium tuberculosis in mice. European Journal of Immunology 50, 1976-1987. https://doi.org/10.1002/eji.202048556. Niazi, A.K. and Kalra, S. (2012) Diabetes and tuberculosis: a review of the role of optimal glycemic control. Journal of Diabetes & Metabolic Disorders 11 . https://doi.org/10.1186/2251-6581-11-28. Ohra-aho, T., Niemi, P., Aura, A.M., Orlandi, M., Poutanen, K., Buchert, J. and Tamminen, T. (2016) Structure of Brewer’s Spent Grain Lignin and Its Interactions with Gut Microbiota in Vitro. Journal of Agricultural and Food Chemistry 64, 812-820. https://doi.org/10.1021/acs.jafc.5b05535. Ou, K., Sarnoski, P., Schneider, K.R., Song, K., Khoo, C. and Gu, L. (2014) Microbial catabolism of procyanidins by human gut microbiota. Molecular Nutrition and Food Research 58, 2196-205. https://doi.org/10.1002/mnfr.201400243. Pajuelo, D., Gonzalez-Juarbe, N., Tak, U., Sun, J., Orihuela, C.J. and Niederweis, M. (2018) NAD(+) Depletion Triggers Macrophage Necroptosis, a Cell Death Pathway Exploited by Mycobacterium tuberculosis. Cell Rep 24, 429-440. https://doi.org/10.1016/j.celrep.2018.06.042. Parveen, S. and Bishai, W.R. (2024) Role of glutamine metabolism in tuberculosis pathogenesis: a mini review. Frontiers in Tuberculosis 2 . https://doi.org/10.3389/ftubr.2024.1432880. Pasupuleti, V.R., Arigela, C.S., Gan, S.H., Salam, S.K.N., Krishnan, K.T., Rahman, N.A. and Jeffree, M.S. (2020) A Review on Oxidative Stress, Diabetic Complications, and the Roles of Honey Polyphenols. Oxidative Medicine and Cellular Longevity 2020, 8878172. https://doi.org/10.1155/2020/8878172. Peaston, R.T. and Weinkove, C. (2004) Measurement of catecholamines and their metabolites. Ann Clin Biochem 41, 17-38. https://doi.org/10.1258/000456304322664663. Peterson, M.C. and Shulman, G.I. (2018) Mechanisms of insulin action and insulin resistance. Physiological Reviews 98, 2133-2223. https://doi.org/10.1152/physrev.00063.2017. Philips, P.A. and Ernst, J.D. (2012) Tuberculosis pathogenesis and immunity. Annual Review of Pathology: Mechanisms of Disease 7, 353-384. https://doi.org/10.1146/annurev-pathol-011811-132458. Pires, A.S., Tan, V.X., Heng, B., Guillemin, G.J. and Latini, A. (2020) Kynurenine and Tetrahydrobiopterin Pathways Crosstalk in Pain Hypersensitivity. Frontiers in Neuroscience 14 . https://doi.org/10.3389/fnins.2020.00620. Ronacher, K., Joosten, S.A., Van Crevel, R., Dockrell, H.M., Walzl, G. and Ottenhoff, T.H.M. (2015) Acquired immunodeficiencies and tuberculosis: focus on HIV/AIDS and diabetes mellitus. Immonological Reviews 264, 121-137. https://doi.org/10.1111/imr.12257. Sane Schepisi, M., Navarra, A., Altet Gomez, M.N., Dudnyk, A., Dyrhol-Riise, A.M., Esteban, J., Giorgetti, P.F., Gualano, G., Guglielmetti, L., Heyckendorf, J., Kaluzhenina, A., Lange, B., Lange, C., Manika, K., Miah, J., Nanovic, Z., Pontali, E., Prego, M.R., Solovic, I., Tiberi, S., Palmieri, F. and Girardi, E. (2018) Burden and Characteristics of the Comorbidity Tuberculosis—Diabetes in Europe: TBnet Prevalence Survey and Case-Control Study. Open Forum Infectious Diseases 6 . https://doi.org/10.1093/ofid/ofy337. Sankaranarayanan, R., Kumar, D.R., Patel, J. and Bhat, G.J. (2020) Do Aspirin and Flavonoids Prevent Cancer through a Common Mechanism Involving Hydroxybenzoic Acids?-The Metabolite Hypothesis. Molecules 25 . https://doi.org/10.3390/molecules25092243. Sekhar, R.V., McKay, S.V., Patel, S.G., Guthikonda, A.P., Reddy, V.T., Balasubramanyam, A. and Jahoor, F. (2011) Glutathione Synthesis Is Diminished in Patients With Uncontrolled Diabetes and Restored by Dietary Supplementation With Cysteine and Glycine. Diabetes Care 34, 162-167. https://doi.org/10.2337/dc10-1006. Sharma, M., Sharma, S., Roy, S., Varma, S. and Bose, M. (2007) Pulmonary epithelial cells are a source of interferon-gamma in response to Mycobacterium tuberculosis infection. Immunol Cell Biol 85, 229-37. https://doi.org/10.1038/sj.icb.7100037. Sharma, S., Krishnappa, D., Singh, A.D., Sinha, S., Ammini, A.C. and Soneja, M. (2019) Impact of tuberculosis on glycaemic status: A neglected association. Indian Journal of Medical Research 149 . https://doi.org/10.4103/ijmr.IJMR_1927_17. Shastri, M.D., Shukla, S.D., Chong, W.C., Dua, K., Peterson, G.M., Patel, R.P., Hansbro, P.M., Eri, R., O’Toole, R.F. and De Miranda, A.S. (2018) Role of Oxidative Stress in the Pathology and Management of Human Tuberculosis. Oxidative Medicine and Cellular Longevity 2018 . https://doi.org/10.1155/2018/7695364. Shibata, K. and Sakamoto, M. (2016) Urinary branched-chain 2-oxo acids as a biomarker for function of B-group vitamins in humans. Journal of Nutritional Science and Vitaminology 62, 220-228. https://doi.org/10.3177/jnsv.62.220. Shillitoe, E., Weinstock, R., Kim, T., Simon, H., Planer, J., Noonan, S. and Cooney, R. (2012) The oral microflora in obesity and type-2 diabetes. Journal of Oral Microbiology 4 . https://doi.org/10.3402/jom.v4i0.19013. Siddik, M.A.D. and Shin, A.C. (2019) Recent Progress on Branched-Chain Amino Acids in Obesity, Diabetes, and Beyond. Endocrinology and Metabolism 34 . https://doi.org/10.3803/EnM.2019.34.3.234. Singhal, A. and Cheng, C.Y. (2018) Host NAD+ metabolism and infections: therapeutic implications. International Immunology 31, 59-67. https://doi.org/10.1093/intimm/dxy068. Storla, D.G., Yimer, S. and Bjune, G.A. (2008) A systematic review of delay in the diagnosis and treatment of tuberculosis. BMC Public Health 8, 15. https://doi.org/10.1186/1471-2458-8-15. Sultana, M.R., Bagul, P.K., Katare, P.B., Anwar Mohammed, S., Padiya, R. and Banerjee, S.K. (2016) Garlic activates SIRT-3 to prevent cardiac oxidative stress and mitochondrial dysfunction in diabetes. Life Sci 164, 42-51. https://doi.org/10.1016/j.lfs.2016.08.030. Sun, J., Siroy, A., Lokareddy, R.K., Speer, A., Doornbos, K.S., Cingolani, G. and Niederweis, M. (2015) The tuberculosis necrotizing toxin kills macrophages by hydrolyzing NAD. Nature Structural & Molecular Biology 22, 672-678. https://doi.org/10.1038/nsmb.3064. Suzuki, Y., Suda, T., Asada, K., Miwa, S., Suzuki, M., Fujie, M., Furuhashi, K., Nakamura, Y., Inui, N., Shirai, T., Hayakawa, H., Nakamura, H. and Chidaa, K. (2012) Serum indoleamine 2,3-dioxygenase cctivity predicts prognosis of pulmonary tuberculosis. Clinical and Vaccine Immunology 19, 436-442. https://doi.org/10.1128/CVI.05402-11. Szymańska, E., Saccenti, E., Smilde, A.K. and Westerhuis, J.A. (2012) Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies. Metabolomics 8, 3-16. https://doi.org/10.1007/s11306-011-0330-3. Theron, I.J., Mason, S., van Reenen, M., Stander, Z., Kleynhans, L., Ronacher, K. and Loots, D.T. (2024) Characterizing poorly controlled type 2 diabetes using (1)H-NMR metabolomics. Metabolomics 20, 54. https://doi.org/10.1007/s11306-024-02127-w. Villela, A.D., Sánchez-Quitian, Z.A., Ducati, G., Santos, D.S. and Basso, L.A. (2011) Pyrimidine salvage pathway in Mycobacterium tuberculosis. Current Medicinal Chemistry 18, 1286-1298. https://doi.org/10.2174/092986711795029555. Vrieling, F., Alisjahbana, B., Sahiratmadja, E., van Crevel, R., Harms, A.C., Hankemeier, T., Ottenhoff, T.H.M. and Joosten, S.A. (2019) Plasma metabolomics in tuberculosis patients with and without concurrent type 2 diabetes at diagnosis and during antibiotic treatment. Scientific Reports 9, 18669. https://doi.org/10.1038/s41598-019-54983-5. Wang, B., Ma, Y., Kong, X., Ding, X., Gu, H., Chu, T. and Ying, W. (2014) NAD(+) administration decreases doxorubicin-induced liver damage of mice by enhancing antioxidation capacity and decreasing DNA damage. Chem Biol Interact 212, 65-71. https://doi.org/10.1016/j.cbi.2014.01.013. Wang, M., Tan, Y., Shi, Y., Wang, X., Liao, Z. and Wei, P. (2020) Diabetes and sarcopenic obesity: pathogenesis, diagnosis, and treatments. Frontiers in Endocrinology 11 . https://doi.org/10.3389/fendo.2020.00568. Wilk, A., Hayat, F., Cunningham, R., Li, J., Garavaglia, S., Zamani, L., Ferraris, D.M., Sykora, P., Andrews, J., Clark, J., Davis, A., Chaloin, L., Rizzi, M., Migaud, M. and Sobol, R.W. (2020) Extracellular NAD+ enhances PARP-dependent DNA repair capacity independently of CD73 activity. Scientific Reports 10, 651. https://doi.org/10.1038/s41598-020-57506-9. Williamson, J.R., Chang, K., Frangos, M., Hasan, K.S., Ido, Y., Kawamura, T., Nyengaard, J.R., van den Enden, M., Kilo, C. and Tilton, R.G. (1993) Hyperglycemic pseudohypoxia and diabetic complications. Diabetes 42, 801-13. https://doi.org/10.2337/diab.42.6.801. Workneh, M.H., Bjune, G.A. and Yimer, S.A. (2017) Prevalence and associated factors of tuberculosis and diabetes mellitus comorbidity: A systematic review. PLOS ONE 12, e0175925. https://doi.org/10.1371/journal.pone.0175925. World Health Organisation (2024) Global tuberculosis report 2024. https://www.who.int/teams/global-tuberculosis-programme/tb-reports. Wu, S.H., D., D. and Wang, L.C. (2008) A Study of Outlier Analysis Techniques for Delay Testing. IEEE International Test Conference . https://doi.org/10.1109/TEST.2008.4700548. Xie, N., Zhang, L., Gao, W., Huang, C., Huber, P.E., Zhou, X., Li, C., Shen, G. and Zou, B. (2020) NAD(+) metabolism: pathophysiologic mechanisms and therapeutic potential. Signal Transduction and Targeted Therapy 5, 227. https://doi.org/10.1038/s41392-020-00311-7. Yen, N.T.H., Anh, N.K., Jayanti, R.P., Phat, N.K., Vu, D.H., Ghim, J.L., Ahn, S., Shin, J.G., Oh, J.Y., Long, N.P. and Kim, D.H. (2023) Multimodal plasma metabolomics and lipidomics in elucidating metabolic perturbations in tuberculosis patients with concurrent type 2 diabetes. Biochimie 211, 153-163. https://doi.org/10.1016/j.biochi.2023.04.009. Yorke, E., Atiase, Y., Akpalu, J., Sarfo-Kantanka, O., Boima, V. and Dey, I.D. (2017) The Bidirectional Relationship between Tuberculosis and Diabetes. Tuberc Res Treat 2017, 1702578. https://doi.org/10.1155/2017/1702578. Zhang, A., Qiu, S., Xu, H., Sun, H. and Wang, X. (2014) Metabolomics in diabetes. Clinica Chimica Acta 429, 106-110. http://dx.doi.org/10.1016/j.cca.2013.11.037. Zhang, A., Sun, H., Wu, X. and Wang, X. (2012) Urine metabolomics. Clinica Chimica Acta 414, 65-69. http://dx.doi.org/10.1016/j.cca.2012.08.016. Zhang, L.Q., Heruth, D.P. and Ye, S.Q. (2011) Nicotinamide Phosphoribosyltransferase in Human Diseases. Journal of Bioanalysis and Biomedicine 3, 13-25. https://doi.org/10.4172/1948-593X.1000038. Zhang, M. and He, J.Q. (2020) Impacts of metformin on tuberculosis incidence and clinical outcomes in patients with diabetes: a systematic review and meta-analysis. European Journal of Clinical Pharmacololgy 76, 149-159. https://doi.org/10.1007/s00228-019-02786-y. Zhao, M., Wang, Y., Li, L., Liu, S., Wang, C., Yuan, Y., Yang, G., Chen, Y., Cheng, J., Lu, Y. and Liu, J. (2021) Mitochondrial ROS promote mitochondrial dysfunction and inflammation in ischemic acute kidney injury by disrupting TFAM-mediated mtDNA maintenance. Theranostics 11, 1845-1863. https://doi.org/10.7150/thno.50905. Zhong, H.J., Yuan, Y., Xie, W.R., Chen, M.H. and He, X.X. (2016) Type 2 Diabetes Mellitus Is Associated with More Serious Small Intestinal Mucosal Injuries. Plos One 11, e0162354. https://doi.org/10.1371/journal.pone.0162354. Additional Declarations No competing interests reported. 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02:37:00","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":278230,"visible":true,"origin":"","legend":"","description":"","filename":"4e04c18fa36b4e3a8c02d03c9ec981b41structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7739707/v1/b3b5419c28c05706b5b07f5b.xml"},{"id":93543776,"identity":"27d1e483-9708-4c8b-93ea-4da3660afb08","added_by":"auto","created_at":"2025-10-15 02:45:00","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":292378,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7739707/v1/98396a66183c93470527ff89.html"},{"id":93542147,"identity":"8af8e8a6-6e74-4eb2-88f4-bc0a6be69865","added_by":"auto","created_at":"2025-10-15 02:36:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79573,"visible":true,"origin":"","legend":"\u003cp\u003e(a) PLS-DA plot of the TB-T2DM and healthy control group showing group separation. (b) Venn diagram illustrating the number of metabolites that met each criterium as well as the overlap of common metabolites, forming the list of biomarkers.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7739707/v1/439df3493e8c28bdeeaf893b.png"},{"id":93542145,"identity":"b153c57c-4615-4b53-a10a-29fbc2161ee5","added_by":"auto","created_at":"2025-10-15 02:36:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":278313,"visible":true,"origin":"","legend":"\u003cp\u003eA schematic summary of the metabolic changes associated with the tuberculosis and type 2 diabetes mellitus observed in this investigation. The directional change in the urinary concentrations of the metabolites indicated to be reduced (↓), relative to the healthy control group. UMP, uridine monophosphate; UDP, uridine diphosphate; UTP, uridine triphosphate; CTP, cytidine triphosphate; PRPP, phosphoribosyl pyrophosphate; GAR, Glycine amide ribonucleotide; FGAR, Formyl glycinamide ribonucleotide; IMP, inosine monophosphate; AMP, adenosine monophosphate; ADP, adenosine diphosphate; ATP, adenosine triphosphate; GSH, glutathione; GSSG, glutathione disulfide; NAD\u003csup\u003e+\u003c/sup\u003e, nicotinamide adenine dinucleotide; NADH, reduced nicotinamide adenine dinucleotide; NAM, nicotinamide \u0026nbsp;NMN, nicotinamide mononucleotide; TDO, tryptophan-2,3-dioxygenase; IDO, indoleamine-2,3-dioxygenase. *Metabolites associated with microbial metabolism and not human metabolism.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7739707/v1/6870db65c0c876a7b9a105e3.png"},{"id":101152127,"identity":"48ad864f-2c6a-4949-a39d-563a1826c8a6","added_by":"auto","created_at":"2026-01-26 16:10:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1819014,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7739707/v1/8a3f1a0c-13b6-42a0-b1f8-6e84a59d08b3.pdf"},{"id":93543773,"identity":"427d7793-cbfb-4f6e-a347-fed2c02bc7be","added_by":"auto","created_at":"2025-10-15 02:44:59","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":68608,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7739707/v1/42f8cb4993e41fa1ce699777.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterizing the tuberculosis and type 2 diabetes mellitus comorbidity in a South African cohort using untargeted GCxGC-TOFMS metabolomics","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTuberculosis (TB), caused by \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (\u003cem\u003eM.tb\u003c/em\u003e), is a major global epidemic, with approximately 10.8\u0026nbsp;million new cases and a resulting 1.25\u0026nbsp;million deaths, reported for 2023 (World Health Organization, 2024). Nearly a quarter of the global population is infected, making TB the leading cause of death from a single infectious agent worldwide (World Health Organization, 2024). Transmission occurs via inhalation of aerosolized droplets with \u003cem\u003eM.tb\u003c/em\u003e reaching alveoli and triggering an immune response, resulting in \u003cem\u003eM.tb\u003c/em\u003e internalization by resident macrophages, followed by granuloma formation to limit bacterial spread (Leung, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Philips and Ernst, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The infection may be eradicated or remain latent, depending on host\u0026rsquo;s immune competence (Alsayed and Gunosewoyo, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Behr et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lin and Flynn, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Immunocompromised individuals risk progression to active TB, presenting with cough, fever, and weight loss (Acharya et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Leung, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), and risk latent TB reactivation (5% risk within two years following infection) (Menzies et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additional risk factors for contracting the disease include the occurrence of immunosuppressive diseases such as diabetes mellitus (DM) and AIDS (World Health Organization, 2024).\u003c/p\u003e\u003cp\u003eOn the other hand, DM affects approximately 589\u0026nbsp;million adults globally and resulted in 3.4\u0026nbsp;million deaths in 2024 globally (International Diabetes Federation, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). With 4.3\u0026nbsp;million South African cases (Diabetes Alliance, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), it is the African country with the highest prevalence. DM, defined by chronic hyperglycemia (HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%), is classified into type 1 (T1DM), which is characterized by autoimmune β-cell destruction and reduction in insulin secretion (Knip and Siljander, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and type 2 (T2DM), that is associated with insulin resistance developed due to poor lifestyle and obesity, leading to pancreatic damage and reduced insulin secretion (Banday et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). T2DM accounts for approximately 90% of all DM cases (Zhang et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and is therefore the focus of this study.\u003c/p\u003e\u003cp\u003eT2DM impairs the immune system and consequently increases the risk of contracting active TB disease by approximately three-fold (Niazi and Kalra, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sane Schepisi et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Consequently, Workneh et al. (\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported approximately 16% of TB patients present with T2DM. T2DM also increases the likelihood for TB relapses after treatment, lower cure rates and increased morbidity (Adane et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Habib et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Furthermore, TB-DM comorbid patients also have a 3.8-fold increased risk of developing multiple drug-resistant TB (Evangelista et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Anti-T2DM treatment also affects TB disease outcomes. Clinical research shows that high doses of dipeptidyl peptidase-4 inhibitor (DPP4) inhibitors increase TB risk (Chen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while metformin and sulphonylureas reduces TB incidence (Lin et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Meregildo-Rodriguez et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang and He, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conversely, \u003cem\u003eM.tb\u003c/em\u003e infection induces stress-associated hyperglycemia via pro-inflammatory cytokines and ROS production (Jeon and Murray, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Niazi and Kalra, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Shastri et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Workneh et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yorke et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which stimulate hepatic glucose release (Sharma et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and contribute to metabolic dysregulation (Magee et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A metabolomics study done by Du Preez and Loots (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), showed a 10 fold increase in the concentrations of the norepinephrine derivative; normetanephrine, in TB-positive patients, explaining the associated glucose intolerance (additionally contributing to the increased D-gluconic acid d-lactone detected in their study), since elevated levels of normetanephrine are also associated with insulin resistance and impaired insulin secretion (Murabayashi et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Anti-TB drugs also interact with T2DM medication. Rifampicin lowers plasma levels of biguanides and sulphonylureas (Niazi and Kalra, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), while isoniazid antagonizes sulphonylureas (Dartois and Rubin, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yorke et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMetabolomics serves as a useful tool to better understand TB-T2DM, since T2DM is a metabolic disease (Li et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Yen et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and TB also results in sever metabolic changes (Du Preez and Loots, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Isa et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Luies and Loots, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vrieling et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To date, as far as we are aware, only one such metabolomics study has been done describing the metabolic changes associated with TB-T2DM, using patient collected plasma, reporting and briefly describing reduced choline, citrulline, histidine, ornithine, and tryptophan in TB-T2DM patients when compared with healthy controls (Vrieling et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Plasma metabolomics reflects the metabolic state of an individual at the exact time of sample collection (Gonz\u0026aacute;lez-Dom\u0026iacute;nguez et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while urinary metabolomics on the other hand, captures metabolic fluctuations over time (Dunn and Ellis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and is also considered easier to collect and prepare due to its low protein content (Du Preez and Loots, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Khamis et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, this study employed untargeted GCxGC-TOFMS urinary metabolomics to compare TB-T2DM patients to healthy controls with and without latent TB, to comprehensively characterize the metabolic profiles of TB-T2DM patients in a South African cohort.\u003c/p\u003e"},{"header":"2. Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Participants\u003c/h2\u003e\u003cp\u003eVoluntarily participating study participants (n\u0026thinsp;=\u0026thinsp;125; n\u0026thinsp;=\u0026thinsp;97 healthy controls (HC) with and without latent TB and n\u0026thinsp;=\u0026thinsp;28 TB-T2DM patients) were recruited from hospitals and community clinics situated in the Western Cape, South Africa. The participant cohort was refined into a final cohort (n\u0026thinsp;=\u0026thinsp;51; n\u0026thinsp;=\u0026thinsp;34 HC and n\u0026thinsp;=\u0026thinsp;17 TB-T2DM) by application of various inclusion and exclusion criteria. The HC included individuals accompanying patients to the hospitals and clinics with (n\u0026thinsp;=\u0026thinsp;15) and without (n\u0026thinsp;=\u0026thinsp;19) latent TB. Individuals were excluded from the HC group if they had any acute respiratory tract infection in the 4 weeks prior to recruitment, suffered from chronic hyperglycemia, were previously or currently diagnosed with T2DM, tested positive for active TB disease by a GeneXpert and/or QuantiFERON test or were suffering from any severe systemic condition. Participants were included in the TB-T2DM group if they were either newly diagnosed with pulmonary TB, or recurrent TB with TB treatment completed at least 2 months prior to recruitment and diagnosed with T2DM and an HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5% (excluding gestational or steroid-induced diabetes) with- and without T2DM treatment. The TB diagnosis was confirmed by two separate positive sputum smears and/or a positive mycobacteria growth indicator tube culture, and/or positive polymerase chain reaction (PCR) for the presence of \u003cem\u003eM.tb\u003c/em\u003e. Study participants were generally excluded if diagnosed with any alternative medical conditions (chronic bronchitis/emphysema/asthma, cancer, current HIV or HIV within a 3-month period), received steroid therapy within 6 months of recruitment, participated in any drug or vaccine trial, were pregnant, abused alcohol (\u0026gt;\u0026thinsp;3 alcoholic beverages per day) or illicit drugs and had no permanent address. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the biographical information of the study participants. Ethics approval for the larger scope of this study has been obtained from the Health Research Ethics Committee (HREC) of Stellenbosch University (reference number: N13/05/064; project ID: 4095). The current study (Ethics number: NWU-00096-23-A1-02) falls under a larger study at the North-West University with the title: \u0026ldquo;The characterization of tuberculosis-diabetes mellitus co-morbidity in a South African cohort using metabolomics\u0026rdquo; for which ethics approval has been obtained (Ethics number: NWU-00096-23-A1).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSociodemographic characteristics of healthy control and tuberculosis-type 2 diabetes comorbid participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHC with latent TB\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHC without latent TB\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTB-T2DM\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003cp\u003e(average\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.21\u0026thinsp;\u0026plusmn;\u0026thinsp;10.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.60\u0026thinsp;\u0026plusmn;\u0026thinsp;10.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.35\u0026thinsp;\u0026plusmn;\u0026thinsp;9.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (% female/% male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53/47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73/27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47/53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c\u003c/p\u003e\u003cp\u003e(% average\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePatients on T2DM treatments: (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnly insulin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnly metformin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetformin with other anti-T2DM drugs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsulin and metformin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDuration of T2DM: (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 1 year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;5 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u0026ndash;15 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUndocumented\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eHC, healthy control; TB, tuberculosis; TB-T2DM, tuberculosis-type 2 diabetes mellitus; HbA1c, glycated hemoglobin; T2DM, type 2 diabetes mellitus.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Urine sampling and storage\u003c/h2\u003e\u003cp\u003eUrine sample collection, using standard urine collection vials, was done by trained healthcare professionals. The samples were initially stored at -80\u0026deg;C at Stellenbosch University, after which they were transported to North-West University and stored at -80\u0026deg;C until the commencement of the GCxGC-TOFMS metabolomic analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Reagents and chemicals\u003c/h2\u003e\u003cp\u003eThe following reagents were used: 3-phenylbutyric acid (internal standard), methoxyamine hydrochloride (MOX-HCl) in pyridine and N,O-bis(trimethylsilyl)trifuoroacetamide (BSTFA) with 1% trimethylsilyl chloride (TMCS) and acetonitrile from Burdick and Jackson brand (Honeywell International Inc., Muskegon, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Sample preparation\u003c/h2\u003e\u003cp\u003eEqual amounts (20 \u0026micro;L) of all patient urine samples were used to compile a pooled quality control (QC) sample, from which aliquots were prepared to be extracted and analyzed with each sample batch (samples were randomly assigned to batches). Following creatinine normalization to 1 \u0026micro;mol, the corresponding urine volume was combined with 100 \u0026micro;L of internal standard solution (3-phenyl butyric acid, 50 ppm) and 300 \u0026micro;L of ice-cold acetonitrile, vortexed, incubated on ice for 10 min and centrifuged thereafter at 10 000 g for 10 min. The supernatant was then transferred to a 2 mL GC vial and dried under nitrogen at 40\u0026deg;C. Derivatization involved: 1) methoximation with 50 \u0026micro;L MOX-HCl (15 mg/mL) in pyridine at 60\u0026deg;C for 60 min, and 2) trimethylsilylation with 50 \u0026micro;L BSTFA-TMCS, at 60\u0026deg;C for 60 min. The final sample was transferred to a glass insert, placed in the GC vial and capped.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. GCxGC-TOFMS analysis and processing\u003c/h2\u003e\u003cp\u003ePrepared samples were randomly selected and analyzed (alongside QC samples, extraction blank samples, system suitability test samples comprising of fatty acid methyl esters (FAMEs), placed intermittently throughout each batch) using Pegasus 4D GC\u0026times;GC-TOFMS (Leco Africa (Pty) Ltd, Johannesburg, South Africa) equipped with an Agilent 7890 GC. A 1:3 split ratio was employed to inject 1\u0026micro;l of each sample with the front inlet temperature at 270\u0026ordm;C. Purified helium served as a carrier gas with a constant flow of 3 mL/min. First-dimensional chromatographical separation was achieved with a Restek Rxi-5Sil MS primary column (28.2 m; 250 \u0026micro;m internal diameter and 0.25 \u0026micro;m film thickness), with the primary oven ramping from 70\u0026deg;C (2 min hold) to 300\u0026deg;C (2 min hold) at 5\u0026deg;C/min. Second-dimensional separation was achieved by a Restek Rxi-17 capillary column (1.32 m \u0026times; 250 \u0026micro;m diameter \u0026times; 0.25 \u0026micro;m film thickness), with the secondary oven ramping from 85\u0026deg;C (2 min hold) to 310\u0026deg;C (4.5 min hold) at 5.5\u0026deg;C/min. The modulator was programmed to ramp from 100\u0026deg;C (2 min hold) to 310\u0026deg;C (12 min hold) at 5\u0026deg;C/min, with 0.5 s cold/hot nitrogen pulses every 3 s. A 350 s acquisition delay excluded solvent detection. Transfer line and ion source were set to 270\u0026deg;C and 200\u0026deg;C, respectively, with \u0026minus;\u0026thinsp;70 eV filament bias and 150 V detector voltage. Mass spectra were acquired over 50\u0026ndash;950 m/z at 200 spectra per second. Data was processed using Leco Corporation ChromaTOF software (v4.32) with peak identification based on 70% spectral library match, signal-to-noise ratio of 200 and minimum of 3 apex peaks. Furthermore, the Statistical compare function was used for peak alignment based on similarity in mass spectra and retention times.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Data management\u003c/h2\u003e\u003cp\u003eData clean-up was performed using Microsoft Excel prior to statistical analysis. Relative concentrations (mmol/mol creatinine) were calculated by normalization to the internal standard, 3-phenylbutyric acid. A 50% filter was applied to retain only compounds present in at least half of at least one of the two experimental groups. Batch correction (using quantile equating) and a coefficient of variation (CV) filter (retaining compounds with CV\u0026thinsp;\u0026le;\u0026thinsp;50%) were applied using QC samples. Zero values detected for a compound were replaced with half the smallest detected value to reflect the lower detection limit (Luies and Loots, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). MetaboAnalyst 6.0, based on the statistical program, \u0026ldquo;R\u0026rdquo; (v4.3.2), was employed for data normalization with log transformation and autoscaling, as well as further statistical analysis. Principal component analysis (PCA) was performed on the final data set to determine if any natural separation between the HC and TB-T2DM groups exists. Biomarker selection was based on a multi-statistical approach using: 1) PLS-DA (VIP\u0026thinsp;\u0026gt;\u0026thinsp;1), 2) t-test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1), and 3) effect size (Cohen\u0026rsquo;s d\u0026thinsp;\u0026gt;\u0026thinsp;0.8) (Du Preez and Loots, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eFollowing data processing, cleaning, mass spectral deconvolution, peak identification and alignment, 2161 urinary metabolites were detected. Removal of unidentified compounds yielded a final data matrix of 280 metabolites. The PCA performed using all patient samples and all 280 identified metabolites, showed no natural separation between TB-T2DM and HC groups (S1 Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), likely due to demographical variation (age, sex, diet, treatment, etc.), introducing metabolic \u0026ldquo;noise\u0026rdquo;. Outliers may have arisen from this \u0026ldquo;noise\u0026rdquo;, preventing clear discrimination between the two experimental groups by PCA. The available clinical information did not explain the definite origin of the noise, though multiple factors may contribute to it. Therefore, a multi-statistical approach was employed for metabolite marker selection using: 1) PLS-DA (VIP\u0026thinsp;\u0026gt;\u0026thinsp;1), 2) t-test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.5), and 3) fold change (log2 (FC) \u0026gt;|2|) (S1 Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The 19 metabolites that satisfied all three criteria were included in a \u0026ldquo;noise-reduced\u0026rdquo; dataset. PCA of this noise-reduced dataset showed separation between the TB-T2DM and HC groups (S1 Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Random forest analysis identified 4 outlier samples only from the HC group, which were removed from the dataset (Wu \u003cem\u003eet al.\u003c/em\u003e, 2008).\u003c/p\u003e\u003cp\u003eA PLS-DA model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) was constructed and evaluated by 10-fold cross-validation repeated 10 times. The model achieved an accuracy of 84.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8% with a predictive ability of Q\u0026sup2; = 0.513 and R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.975 (Szymańska et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Variability observed within the TB-T2DM group likely reflects differences in T2DM treatment and duration as reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These patients were still included to preserve cohort size, enhancing statistical power and avoiding the introduction of a degree of bias. Despite treatment, all TB-T2DM patients still presented with HbA1c\u0026thinsp;\u0026gt;\u0026thinsp;6.5%, confirming persistent hyperglycemia and uncontrolled T2DM. Finally, a 2nd Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) was constructed to illustrate the number of metabolites meeting each criterion and the overlap of common metabolites between each test. Only compounds satisfying all three criteria: VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 (PLS-DA, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (t-test), and d\u0026thinsp;\u0026gt;\u0026thinsp;0.8 (effect size), were included in the final list of biomarkers, listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMetabolite markers of the TB-T2DM comorbidity and the healthy controls\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCompound (ChemSpider ID)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePLS-DA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003et-test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEffect size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eAverage relative concentration (mmol/mol creatinine); Standard deviation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVIP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCohen\u0026rsquo;s d\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHealthy control\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTB-T2DM\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eTryptophan metabolism/kynurenine pathway/NAD\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKynurenic acid (3712)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.158 ; 0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.038 ; 0.045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnthranilic acid (222)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.283 ; 0.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.157 ; 0.261\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePicolinic acid (993)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.533 ; 1.331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.183 ; 0.155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAmino acid metabolism\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2-Methylcrotonyl glycine (4945715)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.710 ; 2.929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.617 ; 0.756\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN-(2-methyl-1-oxobutyl) glycine (168243)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.071 ; 0.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.022 ; 0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIsobutyryl glycine (9030891)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.215 ; 0.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.069 ; 0.102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTyrosine metabolism\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3,4-Dihydroxyphenylglycol (82648)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.15 ; 0.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.003 ; 0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4-Hydroxy-3-methoxyphenylglycol (10348)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.409 ; 1.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.791 ; 0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHydroxyphenylpyruvate (954)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.094 ; 1.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.309 ; 0.275\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePyrimidine metabolism\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrotic acid (942)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.593 ; 1.319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.131 ; 0.170\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3-Aminoisobutyric acid (58481)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.676 ; 17.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.174 ; 5.453\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eβ-Alanine (234)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.156 ; 0.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.020 ; 0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThymine (1103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.155 ; 0.391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.010 ; 0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePurine metabolism\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdenine (185)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.278 ; 0.761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.105 ; 0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypoxanthine (768)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.385 ; 5.655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.139 ; 1.627\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDicarboxylic / lipid metabolism\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDec-2-enedioate (21237627)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.025 ; 0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.003 ; 0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdipic acid (191)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.66 ; 5.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.282 ; 0.396\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethylmalonic acid (473)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.623 ; 2.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002 ; 0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGut microflora metabolism\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhenylacetylglutamine (83292)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.672 ; 2.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.124 ; 0.315\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndoxyl (45861)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.525 ; 1.871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.149 ; 0.169\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3-(3-Hydroxyphenyl) propanoic acid (89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.400 ; 0.984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.096 ; 0.089\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCyclohexylamine (7677)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.039 ; 41.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.638 ; 2.347\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSyringol (6774)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.729 ; 3.471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.028 ; 0.073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSyringic acid (10289)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.022 ; 101.646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.425 ; 10.644\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2,6-Dihydroxybenzoic acid (8974)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.027 ; 0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004 ; 0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNicotine consumption\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrans-3\u0026rsquo;-hydroxycotinine (97080)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.182 ; 0.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.035 ; 0.048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e lists the selected metabolite markers that most significantly differ between the urinary metabolic profiles of the TB-T2DM patients and HC. The selected metabolite markers indicate perturbations in multiple metabolic pathways including the metabolism of tryptophan, glycine, tyrosine, nucleotides, dicarboxylic acids/lipids and gut microbiome. These perturbations in these metabolic pathways are discussed in the following sections and are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Tryptophan metabolism, kynurenine pathway and NAD+\u003c/h2\u003e\u003cp\u003eThe kynurenine pathway is a major route of tryptophan catabolism in mammals (Mandi and Vecsei, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Martin et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Tryptophan is converted to kynurenine by the dioxygenases, tryptophan 2,3-dioxygenase (TDO) and indoleamine 2,3-dioxygenase-1 (IDO-1), along with kynurenine formamidase (Badawy, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Martin et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. TDO primarily acts in the liver, while IDO-1 functions in extrahepatic tissues, particularly immune cells (Badawy, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pires et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). IDO-1 catalyzes the rate-limiting step of this pathway and is induced by pro-inflammatory cytokines interferon-γ (IFN-γ) and tumor necrosis factor-α (TNF-α) (Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Mandi and Vecsei, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These inflammatory mediators are secreted by airway epithelial cells, dendritic cells, alveolar macrophages, type II pneumocytes, and CD4+/CD8\u0026thinsp;+\u0026thinsp;T cells, promoting macrophage activation during pulmonary \u003cem\u003eM. tb\u003c/em\u003e infection (Etna et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Elevated IDO-1 activity and higher kynurenine/tryptophan ratios have associated with an increased mortality in pulmonary TB, suggesting its potential prognostic use (Suzuki et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Similarly, in T2DM, hyperglycemia has also been associated with increased TNF-α (Navarro-Gonz\u0026aacute;lez and Mora-Fern\u0026aacute;ndez, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), further contributing to IDO-1 activation. IDO-1 activation shifts tryptophan catabolism toward kynurenine pathway in immune cells as a negative feedback mechanism to modulate inflammation (Mandi and Vecsei, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Martin et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The kynurenine pathway metabolites have also been shown to impair the immune response by inhibiting CD4⁺ T-cell activity, contributing to immunosuppression (Singhal and Cheng, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough not selected as a metabolite marker in this study using the strict selection criteria, the tryptophan catabolite, 5-hydroxyindoleacetic acid (Luies and Loots, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) was also significantly reduced in TB-T2DM urine samples of this study (0.357 vs. 0.993 mmol/mol creatinine, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), aligning with Vrieling et al. (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reporting lower plasma tryptophan concentrations in TB-T2DM patients compared to healthy controls. The concentrations of kynurenic acid, anthranilic acid and picolinic acid were furthermore significantly reduced in TB-T2DM patients when compared to HC in this study. Although these metabolites are associated with the kynurenine pathway (Badawy, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Martin et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), their reductions indicate a flux towards elevated NAD\u003csup\u003e+\u003c/sup\u003e-synthesis (Badawy, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mandi and Vecsei, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Martin et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, as a means to correct for the typically disrupted NAD\u003csup\u003e+\u003c/sup\u003e/NADH ratio associated with both TB and diabetes (Ido et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Pajuelo et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Williamson et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Further confirmation of this are the reduced urinary concentrations of nicotinamide (NAM) (0.062 vs. 0.167 mmol/mol creatinine, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037), though not selected as a metabolite marker using the strict selection criteria. In a salvage pathway, nicotinamide phosphoribosyl transferase (NAMPT), a rate-limiting enzyme, catalyzes the conversion of NAM to nicotinamide mononucleotide (NMN) (Gall\u0026iacute; et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Singhal and Cheng, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Activated immune cells exhibit upregulated gene expression of NAMPT (Gall\u0026iacute; et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This upregulation has also been observed in vascular endothelial cells, alveolar epithelial cells, inflammatory cells and other cells in individuals with acute lung injury and pulmonary inflammation (Zhang et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The release of pro-inflammatory cytokines, including IL-1β, which has also been seen to be elevated in TB-T2DM patients, has been implicated in the upregulation of NAMPT, which in turn, results in elevated IL-8 secretion by pulmonary A549 cells (Ronacher et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The final step in NAD⁺ biosynthesis is the conversion of NMN to NAD⁺ (Fukushima and Lopaschuk, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The aforementioned imbalanced NAD⁺/NADH ratio, affects fatty acid, glucose, TCA cycle metabolism, energy production (Xie et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), DNA repair (Hou et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wilk et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and oxidative stress (Sultana et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and is associated with T2DM progression (Sultana et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Williamson et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) and fatty liver disease (Akie et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The latter is not only strongly associated with T2DM (Kumar et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but also TB (Amarapurkar and Ghansar, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and hence the susceptibility of TB patients for developing T2DM.\u003c/p\u003e\u003cp\u003eKynurenic acid also functions as noncompetitive N-methyl-D-aspartate receptor (NMDAR) antagonist (Mandi and Vecsei, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In mice, NMDAR overactivation led to insulin resistance and hyperlipidemia, while its inhibition reversed these effects (Huang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, Huang et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) proposed that activation of pancreatic NMDARs initiates a cascade of events resulting in mitochondria-mediated apoptosis of the pancreatic β-cells: firstly, the increase in the intracellular Ca\u0026sup2;⁺ concentration depolarizes mitochondrial membrane potential, impairment of mitochondrial oxidative phosphorylation, increase in oxidative stress/ROS production and ultimately, reduction in pancreatic insulin secretion. Thus, the observed reduction in kynurenic acid concentration, due to TB (Asp et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Etna et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), would activate NMDAR (Asp et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), contributing to reduced insulin secretion (Huang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) that naturally results in the T2DM progression (Peterson and Shulman, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn summary, the upregulation of NAD⁺ biosynthesis via the kynurenine pathway in the TB-T2DM patients, likely represents an attempt to correct the redox balance by restoring the disrupted NAD⁺/NADH ratio, which is perturbed by several pathophysiological mechanisms associated with both TB and T2DM (Singhal and Cheng, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Poor glycemic control (associated to T2DM as well as TB) promotes the upregulation of glycolysis, fatty acid oxidation, the TCA cycle (in insulin-independent tissues) and the polyol pathway \u0026ndash; resulting in a decreased NAD⁺/NADH ratio (Garg and Gupta, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, oxidative stress induced by inflammation associated with both TB and DM (Amaral et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pasupuleti et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), damages mitochondrial proteins, impairing mitochondrial function and disrupting NAD⁺ recycling via the electron transport chain (Zhao et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The decreased NAD⁺/NADH ratio furthermore, impairs the immune response mediated mainly via TNF by sirtuins, NAD\u003csup\u003e+\u003c/sup\u003e dependent deacetylases (Gall\u0026iacute; et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), a likely mechanism by which diabetes increases the susceptibility for TB infection.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Oxidative stress and glycine metabolism\u003c/h2\u003e\u003cp\u003eHyperglycemia promotes ROS production via glucose auto-oxidation, glycation of antioxidant enzymes, and non-enzymatic glycation reactions, all of which impair anti-oxidative mechanisms (Kaneto et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Pasupuleti et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In insulin-independent cells, excess intracellular glucose surpasses cellular glycolytic ability and enters alternative pathways that further elevate ROS (Li et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In both T2DM and TB, increased lipolysis and fatty acid oxidation contribute to mitochondrial ROS generation, particularly via electron leakage at complexes I and III of the OXPHOS system (Las et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Furthermore, a TB metabolomics study done by Du Preez and Loots (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), showed elevated glucose oxidation which results in the production of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e in TB. Pancreatic β-cells, due to their low antioxidant capacity, are especially vulnerable to oxidative damage and ROS-induced damage induces β-cell apoptosis, impairing insulin secretion (Las et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eVrieling et al. (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported significantly reduced plasma glycine in TB-T2DM individuals compared to healthy controls. This is substantiated by observed reduction in the concentrations of glycine conjugates in this study. Glycine is essential for synthesizing glutathione (GSH), the most abundant intracellular antioxidant (Lu, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). GSH synthesis begins with ligation of glutamate and cysteine by glutamate-cysteine ligase (GCL), composed of catalytic (GCLC) and modifier (GCLM) subunits (Lu, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). GCLM mediates feedback inhibition of GCL by GSH to regulate levels, and GSH synthetase then adds glycine to form GSH (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (Lu, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In T2DM, reduced glycine and GSH levels (Sekhar et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) reflect impaired antioxidant capacity and elevated oxidative stress. GSH mitigates oxidative stress by reducing oxidative species and forming glutathione disulfide (GSSG) (Butkowski and Jelinek, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In TB-T2DM, elevated ROS accelerates GSH oxidation to GSSG, resulting in feedback inhibition on GCL and promoting GSH synthesis from glycine, cysteine, and glutamate (Lu, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This increased GSH demand likely contributes to glycine depletion previously observed in TB-T2DM patients (Vrieling et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, the aforementioned reduced glycine conjugates: 2-methylcrotonyl glycine, N-(2-methyl-1-oxobutyl) glycine and isobutyryl glycine in the TB-T2DM patients, confirms the previously observed glycine reduction. 2-Methylcrotonyl glycine is formed by conjugating glycine with 2-methylcrotonyl-CoA, an intermediate in isoleucine degradation (Fontaine et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), while, N-(2-methyl-1-oxobutyl) glycine and isobutyryl glycine are derived from 2-methylbutanoyl-CoA and isobutyryl-CoA respectively, metabolites involved in catabolism of branched-chain amino acids (BCAAs), isoleucine, valine, and leucine (Guda et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Shibata and Sakamoto, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). T2DM is reportedly associated with elevated urinary BCAA concentrations (Siddik and Shin, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Theron et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), suggesting the reduction in their degradation, and our results also confirming the latter.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Tyrosine metabolism\u003c/h2\u003e\u003cp\u003eNE is synthesized from tyrosine via dopamine (DA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and binds postjunctional receptors to mediate various physiological responses (Alaniz et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Excess NE is either reabsorbed or metabolized (Linares et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). TB has been associated with increased norepinephrine (NE) secretion (Du Preez and Loots, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In this study, urinary 3,4-dihydroxyphenylglycol and 4-hydroxy-3-methoxyphenylglycol, two NE catabolites (Denfeld et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Peaston and Weinkove, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), were significantly reduced in TB-T2DM patients, suggesting increased NE receptor binding and utilization. Homovanillic acid, a DA catabolite (Irwin et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), though not selected as a biomarker, was also markedly decreased (0.565 vs. 1.087 mmol/mol creatinine, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), supporting enhanced DA utilization. Additionally, reduced concentrations of hydroxyphenylpyruvate, an intermediate in tyrosine catabolism (Gertsman et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Irwin et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), further indicate altered tyrosine metabolism in the TB-T2DM group. This suggests a mechanism by which TB patients are more susceptible to developing diabetes and diabetes progression/severity (Du Preez and Loots, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Nucleotide metabolism\u003c/h2\u003e\u003cp\u003eGlutamine is crucial for de novo purine and pyrimidine synthesis, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (Parveen and Bishai, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). \u003cem\u003eM.tb\u003c/em\u003e reprograms host glutamine metabolism toward energy production via glutaminolysis, depleting glutamine and impairing nucleotide de novo synthesis (Koeken et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Parveen and Bishai, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To compensate, nucleotide salvage pathways are upregulated to preserve nucleotide availability (Chandel, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The TB-T2DM patients in this study showed significantly reduced orotic acid, an intermediate in the pyrimidine de novo synthesis pathway (Chandel, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), likely due to glutamine depletion (Koeken et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The decreased concentrations of other pyrimidine catabolites in this study; 3-aminoisobutyric acid, thymine and β-alanine (Chandel, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), support enhanced nucleotide salvage activity.\u003c/p\u003e\u003cp\u003eSimilarly, the purine degradation products; adenine and hypoxanthine (Chandel, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), were also reduced in TB-T2DM, indicative of impaired de novo purine synthesis due to aforementioned glutamine and glycine depletion, and compensatory purine salvage pathway activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (Ducati et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Given that TB-T2DM exhibits reduced glycolysis and TCA flux in insulin-dependent cells as well as a compromised mitochondrial function, salvage pathways, requiring less ATP than de novo synthesis (Villela et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), are preferentially employed (Chandel, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), explaining the reduced purine catabolite excretion. Purine metabolism also supports cyclic adenosine monophosphate (cAMP) synthesis from adenine, essential for insulin secretion (Carvalho et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Considering this, the compromised nucleotide synthesis induced by TB contributes to susceptibility of these patients to getting T2DM and would further contribute to diabetes progression/severity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Dicarboxylic acid/lipid metabolism\u003c/h2\u003e\u003cp\u003eIn T2DM, production various TCA cycle intermediates are impaired in insulin-dependent cells (Peterson and Shulman, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To compensate, dicarboxylic acids like dec-2-enedioate and adipic acid may be oxidized to generate acetyl-CoA and succinyl-CoA, supplementing the TCA cycle, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (Mingrone et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Methylmalonic acid, derived from propionyl-CoA (Chalmers and Lawson, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), also supplements TCA cycle via succinyl-CoA (Peterson and Shulman, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The significantly reduced urinary concentrations of these metabolites in TB-T2DM patients supports their increased metabolic utilization to sustain energy production. Moreover, their enhanced oxidation may also elevate oxidative stress, potentially exacerbating hyperinsulinemia (Las et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.6. Gut microbial metabolism\u003c/h2\u003e\u003cp\u003eTB is associated with altered gut microbial composition and reduced microbial diversity (Liu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). TB is reported to reduce microbial populations of \u003cem\u003eBifidobacteria\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e (Hu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Negi et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). T2DM, additionally, is associated with reductions in both oral and gut microbiota (Shillitoe et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Furthermore, diabetic enteropathy, a T2DM complication, further alters the gastrointestinal (GI) tract, causing diarrhea, constipation, and abdominal discomfort (Zhong et al., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe decreased urinary excretion of the observed microbial metabolites in TB-T2DM patients in this study further supports the presence of the above-mentioned gut dysbiosis. Phenylacetylglutamine and indoxyl are derived from microbial metabolism of tyrosine and tryptophan, respectively (Barrios et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). 3-(3-Hydroxyphenyl) propanoic acid results from microbial breakdown of procyanidins (Ou et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and cyclohexylamine from cyclamate (Drasar et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). Syringol and syringic acid originate from microbial fermentation of lignin and anthocyanins respectively (Hidalgo et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ohra-aho et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), while unabsorbed aspirin may be converted to 2,6-dihydroxybenzoic acid by intestinal microbes (Sankaranarayanan et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The overall reduction of these urinary microbial metabolites supports decreased microbial diversity and activity due to TB- and T2DM-associated gut microbiota disturbances.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.7. Nicotine consumption\u003c/h2\u003e\u003cp\u003eTrans-3\u0026rsquo;-hydroxycotinine, a nicotine catabolite (Bergen et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), was significantly reduced. While participant smoking habits were unavailable, TB-related pulmonary symptoms such as persistent coughing and chest discomfort (Storla et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), may have discouraged nicotine use, and patients are also advised to stop smoking, explaining the decline.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis investigation provides a characterization of the urinary metabolic profile of the TB-T2DM comorbidity using untargeted GC\u0026times;GC-TOFMS metabolomics in a South African cohort. The findings show that metabolic disturbances associated with TB-T2DM are driven by inflammatory responses and oxidative stress with substantial downstream effects on amino acid metabolism, NAD\u003csup\u003e+\u003c/sup\u003e/NADH balance, nucleotide biosynthesis and lipid oxidation. This investigation observed the activation of the kynurenine pathway, likely mediated by release of the pro-inflammatory cytokines IFN-γ and TNF-α in response to \u003cem\u003eM.tb\u003c/em\u003e infection, resulting in the activation of NMDARs. This elevates mitochondrial ROS production and causes pancreatic dysfunction, due to limited antioxidants present in pancreatic β-cells, impairing insulin secretion, thereby facilitating T2DM pathogenesis in TB and the TB-T2DM patients. Additionally, the hyperglycemia seen in T2DM, depletes cellular NAD⁺ pools due to the reduction in mitochondrial NAD\u003csup\u003e+\u003c/sup\u003e recycling due to mitochondrial damage caused by ROS, resulting in a compensatory upregulation of NAD\u003csup\u003e+\u003c/sup\u003e synthesis via the kynurenine pathway and elevated glycolysis, fatty acid oxidation, the TCA cycle in insulin-independent tissues and the polyol pathway. This NAD\u003csup\u003e+\u003c/sup\u003e depletion and compensatory synthesis upregulation is further substantiated by the reduction in NAM excretion, from which NAD\u003csup\u003e+\u003c/sup\u003e is synthesized. A compromised NAD⁺/NADH ratio weakens host immunity, particularly TNF-driven responses mediated by NAD\u003csup\u003e+\u003c/sup\u003e-dependent sirtuins, thereby increasing susceptibility to TB infection or reactivation in T2DM patients. The elevated oxidative stress is further substantiated by reduced urinary excretion of glycine conjugates, suggesting increased glutathione synthesis in response to ROS. Several detected metabolites in this investigation points to a disruption in nucleotide metabolism in TB-T2DM patients. This study shows that the TB-associated glutamine depletion impairs de novo purine and pyrimidine synthesis, initiating a shift to the salvage pathways. This is reflected in reduced concentrations of nucleotide degradation products in urine. Furthermore, this investigation identified significant perturbations in the gut microbiome of TB-T2DM patients, indicative of a reduction in microbial diversity.\u003c/p\u003e\u003cp\u003eOverall, the findings of this study, support previous hypotheses regarding the mechanisms by which TB patients have an increased susceptibility to developing diabetes and vice versa, and highlights the disease mechanisms in each which would have a compounding effect towards and increase disease severity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGCxGC-TOFMS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eTwo-dimensional gas chromatography combined with time-of-flight mass spectrometry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eHealthy control group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIDO-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eIndole amine 2,3-dioxygenase-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIFN-\u0026gamma;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eInterferon-\u0026gamma;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eM.tb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003e\u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNAD\u003csup\u003e+\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eNicotinamide adenine dinucleotide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNAM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eNicotinamide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eROS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eReactive oxygen species\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2DM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eType 2 diabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eTuberculosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTB-T2DM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eTuberculosis-type 2 diabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNF-\u0026alpha;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eTumor necrosis factor-\u0026alpha;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eThis work was supported by funding from the National Research foundation (NRF) of South Africa [grant number: SRUG2204062208] in consideration that the opinions, findings and conclusions or recommendations expressed is that of the authors alone, and that the NRF accepts no liability whatsoever in this regard.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eWe would like to thank the healthcare professionals for the recruitment of participants into the study. We would also want to extend our gratitude to the research personnel at Stellenbosch University for patient data collection and sample collection, storage and transport.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eDTL conceived and designed the original research study. KR prepared the samples for analysis and performed data analysis. KR and DTL interpreted the results. KR wrote the manuscript, and the manuscript underwent revision and editing by DTL. All authors read and approved of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eThe raw data, supporting the findings of this study, can be acquired from the author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eEthics approval for the larger scope of this study has been obtained from the Health Research Ethics Committee (HREC) of the Stellenbosch University (reference number: N13/05/064; project ID: 4095). The current study (Ethics number: NWU-00096-23-A1-02) falls under a larger study at the North-West University with the title: “The characterization of tuberculosis-diabetes mellitus co-morbidity in a South African cohort using metabolomics” for which ethics approval has been obtained (Ethics number: NWU-00096-23-A1). All procedures performed in this study that involved human participants were in accordance with the ethical standards of the Health Research Ethics Committee (HREC) of the Stellenbosch University and North-West University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all individual participants included in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflict of interest\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcharya, B., Acharya, A., Gautam, S., Ghimire, S.P., Mishra, G., Parajuli, N. and Sapkota, B. (2020) Advances in diagnosis of Tuberculosis: an update into molecular diagnosis of Mycobacterium tuberculosis. \u003cem\u003eMolecular Biology Reports\u003c/em\u003e \u003cstrong\u003e47,\u003c/strong\u003e 4065-4075. https://doi.org/10.1007/s11033-020-05413-7. \u003c/li\u003e\n\u003cli\u003eAdane, H.T., Howe, R.C., Wassie, L. and Magee, M.J. (2023) Diabetes mellitus is associated with an increased risk of unsuccessful treatment outcomes among drug-susceptible tuberculosis patients in Ethiopia: A prospective health facility-based study. \u003cem\u003eJ Clin Tuberc Other Mycobact Dis\u003c/em\u003e \u003cstrong\u003e31,\u003c/strong\u003e 100368. https://doi.org/10.1016/j.jctube.2023.100368. \u003c/li\u003e\n\u003cli\u003eAkie, T.E., Liu, L., Nam, M., Lei, S. and Cooper, M.P. (2015) OXPHOS-Mediated Induction of NAD+ Promotes Complete Oxidation of Fatty Acids and Interdicts Non-Alcoholic Fatty Liver Disease. \u003cem\u003ePLOS ONE\u003c/em\u003e \u003cstrong\u003e10,\u003c/strong\u003e e0125617. https://doi.org/10.1371/journal.pone.0125617. \u003c/li\u003e\n\u003cli\u003eAlaniz, R.C., Thomas, S.A., Perez-Melgosa, M., Mueller, K., Farr, A.G., Palmiter, R.D. and Wilson, C.B. (1999) Dopamine beta-hydroxylase deficiency impairs cellular immunity. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e \u003cstrong\u003e96,\u003c/strong\u003e 2274-2278. https://doi.org/10.1073/pnas.96.5.2274. \u003c/li\u003e\n\u003cli\u003eAlsayed, S.S.R. and Gunosewoyo, H. (2023) Tuberculosis: Pathogenesis, Current Treatment Regimens and New Drug Targets. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e. https://doi.org/10.3390/ijms24065202. \u003c/li\u003e\n\u003cli\u003eAmaral, E.P., Vinhaes, C.L., Oliveira-de-Souza, D., Nogueira, B., Akrami, K.M. and Andrade, B.B. (2021) The Interplay Between Systemic Inflammation, Oxidative Stress, and Tissue Remodeling in Tuberculosis. \u003cem\u003eAntioxidants and Redox Signaling\u003c/em\u003e \u003cstrong\u003e34,\u003c/strong\u003e 471-485. https://doi.org/10.1089/ars.2020.8124. \u003c/li\u003e\n\u003cli\u003eAmarapurkar, A. and Ghansar, T. (2007) Fatty liver: Experience from western India. \u003cem\u003eAnnals of Hepatology\u003c/em\u003e \u003cstrong\u003e6,\u003c/strong\u003e 37-40. https://doi.org/10.1016/S1665-2681(19)31951-9. \u003c/li\u003e\n\u003cli\u003eAsp, L., Johansson, A.S., Mann, A., Owe-Larsson, B., Urbanska, E.M., Kocki, T., Kegel, M., Engberg, G., Lundkvist, G.B.S. and Karlsson, H. (2011) Effects of pro-inflammatory cytokines on expression of kynurenine pathway enzymes in human dermal fibroblasts. \u003cem\u003eJournal of Inflammation\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e. https://doi.org/10.1186/1476-9255-8-25. \u003c/li\u003e\n\u003cli\u003eBadawy, A.A.B. (2017) Tryptophan availability for kynurenine pathway metabolism across the life span: Control mechanisms and focus on aging, exercise, diet and nutritional supplements. \u003cem\u003eNeuropharmacology\u003c/em\u003e \u003cstrong\u003e112,\u003c/strong\u003e 248-263. http://dx.doi.org/10.1016/j.neuropharm.2015.11.015. \u003c/li\u003e\n\u003cli\u003eBanday, M.Z., Sameer, A.S. and Nissar, S. (2021) Pathophysiology of diabetes: An overview. \u003cem\u003eAvicenna Journal of Medicine\u003c/em\u003e \u003cstrong\u003e10,\u003c/strong\u003e 174-188. https://doi.org/10.4103/ajm.ajm_53_20. \u003c/li\u003e\n\u003cli\u003eBarrios, C., Spector, T.D. and Menni, C. (2016) Blood, urine and faecal metabolite profiles in the study of adult renal disease. \u003cem\u003eArchives of Biochemistry and Biophysics\u003c/em\u003e \u003cstrong\u003e589,\u003c/strong\u003e 81-92. https://doi.org/10.1016/j.abb.2015.10.006. \u003c/li\u003e\n\u003cli\u003eBehr, M.A., Kaufmann, E., Duffin, J., Edelstein, P.H. and Ramakrishnan, L. (2021) Latent Tuberculosis: Two Centuries of Confusion. \u003cem\u003eAmerican Journal of Respiratory and Critical Care Medicine\u003c/em\u003e \u003cstrong\u003e204,\u003c/strong\u003e 142-148. https://doi.org/10.1164/rccm.202011-4239PP. \u003c/li\u003e\n\u003cli\u003eBergen, A.W., Michel, M., Nishita, D., Krasnow, R., Javitz, H.S., Conneely, K.N., Lessov-Schlaggar, C.N., Hops, H., Zhu, A.Z., Baurley, J.W., McClure, J.B., Hall, S.M., Baker, T.B., Conti, D.V., Benowitz, N.L., Lerman, C., Tyndale, R.F. and Swan, G.E. (2015) Drug Metabolizing Enzyme and Transporter Gene Variation, Nicotine Metabolism, Prospective Abstinence, and Cigarette Consumption. \u003cem\u003ePlos One\u003c/em\u003e \u003cstrong\u003e10,\u003c/strong\u003e e0126113. https://doi.org/10.1371/journal.pone.0126113. \u003c/li\u003e\n\u003cli\u003eButkowski, E.G. and Jelinek, H.F. (2016) Hyperglycaemia, oxidative stress and inflammatory markers. \u003cem\u003eRedox Report\u003c/em\u003e \u003cstrong\u003e22,\u003c/strong\u003e 257-264. https://doi.org/10.1080/13510002.2016.1215643. \u003c/li\u003e\n\u003cli\u003eCarvalho, D.S., De Almeida, A.A., Borges, A.F. and Vannucci Campos, D. (2018) Treatments for diabetes mellitus type II: New perspectives regarding the possible role of calcium and cAMP interaction. \u003cem\u003eEuropean Journal of Pharmacology\u003c/em\u003e \u003cstrong\u003e830,\u003c/strong\u003e 9-16. https://doi.org/10.1016/j.ejphar.2018.04.002.\u003c/li\u003e\n\u003cli\u003eChalmers, R.A. and Lawson, A.M. (1982) Disorders of propionate and methylmalonate metabolism. \u003cem\u003eOrganic acids in Man\u003c/em\u003e\u003cstrong\u003e,\u003c/strong\u003e 296-331. https://doi.org/10.1007/978-94-009-5778-7_11. \u003c/li\u003e\n\u003cli\u003eChandel, N.S. (2021) Nucleotide Metabolism. \u003cem\u003eCold Spring Harbor Perspectives in Biology\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e. https://doi.org/10.1101/cshperspect.a040592. \u003c/li\u003e\n\u003cli\u003eChen, H.H., Hsieh, M.C., Ho, C.W., Chen, C.C., Hsu, S.P., Lin, C.L. and Kao, C.H. (2020) Effects of dipeptidyl peptidase-4 inhibitor treatment doses on tuberculosis in patients with diabetes: a long-term nationwide population-based cohort study. \u003cem\u003eAnn Palliat Med\u003c/em\u003e \u003cstrong\u003e9,\u003c/strong\u003e 2817-2825. https://doi.org/10.21037/apm-20-278. \u003c/li\u003e\n\u003cli\u003eDartois, V.A. and Rubin, E.J. (2022) Anti- tuberculosis treatment strategies and drug development: challenges and priorities. \u003cem\u003eNature Reviewe Microbiology\u003c/em\u003e \u003cstrong\u003e20,\u003c/strong\u003e 685-701. https://doi.org/10.1038/s41579-022-00731-y. \u003c/li\u003e\n\u003cli\u003eDenfeld, Q.E., Habecker, B.A. and Woodward, W.R. (2018) Measurement of plasma norepinephrine and 3,4-dihydroxyphenylglycol: method development for a translational research study. \u003cem\u003eBMC Research Notes\u003c/em\u003e \u003cstrong\u003e11,\u003c/strong\u003e 248. https://doi.org/10.1186/s13104-018-3352-3. \u003c/li\u003e\n\u003cli\u003eDiabetes Alliance (2023) 2023 Diabetes Summit Report 2023. https://www.diabetesalliance.org.za/diabetes-summit-2023/.\u003c/li\u003e\n\u003cli\u003eDrasar, B.S., Renwick, A.G. and Williams, R.T. (1972) The role of the gut flora in the metabolism of cyclamate. \u003cem\u003eBiochemical Journal\u003c/em\u003e \u003cstrong\u003e129,\u003c/strong\u003e 881-890. https://doi.org/10.1042/bj1290881. \u003c/li\u003e\n\u003cli\u003eDu Preez, I. and Loots, D.T. (2013) New sputum metabolite markers implicating adaptations of the host to Mycobacterium tuberculosis, and vice versa. \u003cem\u003eTuberculosis\u003c/em\u003e \u003cstrong\u003e93,\u003c/strong\u003e 330-337. http://dx.doi.org/10.1016/j.tube.2013.02.008. \u003c/li\u003e\n\u003cli\u003eDucati, R.G., Breda, A. and Basso, L.A. (2011) Purine Salvage Pathway in Mycobacterium tuberculosis. \u003cem\u003eCurrent Medicinal Chemistry\u003c/em\u003e \u003cstrong\u003e18,\u003c/strong\u003e 1258\u0026ndash;1275. https://doi.org/10.2174/092986711795029627. \u003c/li\u003e\n\u003cli\u003eDunn, W.B. and Ellis, D.I. (2005) Metabolomics: Current analytical platforms and methodologies. \u003cem\u003eTrAC Trends in Analytical Chemistry\u003c/em\u003e \u003cstrong\u003e24,\u003c/strong\u003e 285-294. https://doi.org/10.1016/j.trac.2004.11.021. \u003c/li\u003e\n\u003cli\u003eEtna, M.P., Giacomini, E., Severa, M. and Coccia, E.M. (2014) Pro- and anti-inflammatory cytokines in tuberculosis: A two-edged sword in TB pathogenesis. \u003cem\u003eSeminars in Immunology\u003c/em\u003e \u003cstrong\u003e26,\u003c/strong\u003e 543-551. http://dx.doi.org/10.1016/j.smim.2014.09.011. \u003c/li\u003e\n\u003cli\u003eEvangelista, M., Maia, R., Toledo, J.P., Abreu, R.G. and Barreira, D. (2020) Tuberculosis associated with diabetes mellitus by age group in Brazil: a retrospective cohort study, 2007-2014. \u003cem\u003eThe Brazilian Journal of Infectious Diseases\u003c/em\u003e \u003cstrong\u003e24,\u003c/strong\u003e 130-136. https://doi.org/10.1016/j.bjid.2020.03.005. \u003c/li\u003e\n\u003cli\u003eFontaine, M., Briand, G., Ser, N., Armelin, I., Rolland, M.O., Degand, P. and Vamec, J. (1996) Metabolic studies in twin brothers with 2-methylacetoacetyl-CoA thiolase deficiency. \u003cem\u003eClinica Chimica Acta\u003c/em\u003e \u003cstrong\u003e255,\u003c/strong\u003e 67-83. https://doi.org/10.1016/0009-8981(96)06391-7. \u003c/li\u003e\n\u003cli\u003eFukushima, A. and Lopaschuk, G.D. (2016) Acetylation control of cardiac fatty acid \u0026beta;-oxidation and energy metabolism in obesity, diabetes, and heart failure. \u003cem\u003eBiochimica et Biophysica Acta\u003c/em\u003e \u003cstrong\u003e1862,\u003c/strong\u003e 2211-2220. http://dx.doi.org/10.1016/j.bbadis.2016.07.020. \u003c/li\u003e\n\u003cli\u003eGall\u0026iacute;, M., Van Gool, F., Rongvaux, A., Andris, F. and Leo, O. (2010) The nicotinamide phosphoribosyltransferase: A molecular link between metabolism, inflammation, and cancer. \u003cem\u003eCancer Research\u003c/em\u003e \u003cstrong\u003e70\u003c/strong\u003e. https://doi.org/10.1158/0008-5472.CAN-09-2465. \u003c/li\u003e\n\u003cli\u003eGarg, S.S. and Gupta, J. (2022) Polyol pathway and redox balance in diabetes. \u003cem\u003ePharmacological Research\u003c/em\u003e \u003cstrong\u003e182,\u003c/strong\u003e 106326. https://doi.org/10.1016/j.phrs.2022.106326. \u003c/li\u003e\n\u003cli\u003eGertsman, I., Gangoiti, J.A., Nyhan, W.L. and Barshop, B.A. (2015) Perturbations of tyrosine metabolism promote the indolepyruvate pathway via tryptophan in host and microbiome. \u003cem\u003eMolecular, Genetics and Metabolism\u003c/em\u003e \u003cstrong\u003e114,\u003c/strong\u003e 431-7. https://doi.org/10.1016/j.ymgme.2015.01.005. \u003c/li\u003e\n\u003cli\u003eGonz\u0026aacute;lez-Dom\u0026iacute;nguez, R., Gonz\u0026aacute;lez-Dom\u0026iacute;nguez, \u0026Aacute;., Sayago, A. and Fern\u0026aacute;ndez-Recamales, \u0026Aacute;. (2020) Recommendations and Best Practices for Standardizing the Pre-Analytical Processing of Blood and Urine Samples in Metabolomics. \u003cem\u003eMetabolites\u003c/em\u003e \u003cstrong\u003e10,\u003c/strong\u003e 229. https://doi.org/10.3390/metabo10060229. \u003c/li\u003e\n\u003cli\u003eGonz\u0026aacute;lez, A., Varo, N., Alegre, E., D\u0026iacute;az, A. and Melero, I. (2008) Immunosuppression routed via the kynurenine pathway: a biochemical and pathophysiologic approach. \u003cem\u003eAdv Clin Chem\u003c/em\u003e \u003cstrong\u003e45,\u003c/strong\u003e 155-97. https://doi.org/10.1016/s0065-2423(07)00007-8. \u003c/li\u003e\n\u003cli\u003eGuda, P., Guda, C. and Subramaniam, S. (2007) Reconstruction of pathways associated with amino acid metabolism in human mitochondria. \u003cem\u003eGenomics Proteomics Bioinformatics\u003c/em\u003e \u003cstrong\u003e5,\u003c/strong\u003e 166-76. https://doi.org/10.1016/S1672-0229(08)60004-2. \u003c/li\u003e\n\u003cli\u003eHabib, M.A., Afrin, K., Efa, S.S., Hossain, M.D., Islam, M.R., Rahman, M.M., Islam, N., Afroz, F., Rahim, M.A. and Hossain, M.D. (2024) Effects of diabetes mellitus on retreatment of Tuberculosis: A multi-centered case-control study from Bangladesh. \u003cem\u003eJournal of Clinical Tuberculosis and Other Mycobacterial Diseases\u003c/em\u003e \u003cstrong\u003e36,\u003c/strong\u003e 100450. https://doi.org/10.1016/j.jctube.2024.100450. \u003c/li\u003e\n\u003cli\u003eHidalgo, M., Oruna-Concha, M.J., Kolida, S., Walton, G.E., Kallithraka, S., Spencer, J.P.E., Gibson, G.R. and De Pascual-Teresa, S. (2012) Metabolism of Anthocyanins by Human Gut Microflora and Their Influence on Gut Bacterial Growth. \u003cem\u003eJournal of Agricultural and Food Chemistry\u003c/em\u003e \u003cstrong\u003e60,\u003c/strong\u003e 3882-3890. https://doi.org/10.1021/jf3002153. \u003c/li\u003e\n\u003cli\u003eHou, Y., Lautrup, S., Cordonnier, S., Wang, Y., Croteau, D.L., Zavala, E., Zhang, Y., Moritoh, K., O\u0026rsquo;Connell, J.F., Baptiste, B.A., Stevnsner, T.V., Mattson, M.P. and Bohr, V.A. (2018) NAD+ supplementation normalizes key Alzheimer\u0026rsquo;s features and DNA damage responses in a new AD mouse model with introduced DNA repair deficiency. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e115,\u003c/strong\u003e E1876-E1885. https://doi.org/10.1073/pnas.1718819115. \u003c/li\u003e\n\u003cli\u003eHu, Y., Yang, Q., Liu, B., Dong, J., Sun, L., Zhu, Y., Su, H., Yang, J., Yang, F., Chen, X. and Jin, Q. (2019) Gut microbiota associated with pulmonary tuberculosis and dysbiosis caused by anti-tuberculosis drugs. \u003cem\u003eJ Infect\u003c/em\u003e \u003cstrong\u003e78,\u003c/strong\u003e 317-322. https://doi.org/10.1016/j.jinf.2018.08.006. \u003c/li\u003e\n\u003cli\u003eHuang, X.T., Yang, J.X., Wang, Z., Zhang, C.Y., Luo, Z.Q., Liu, W. and Tang, S.Y. (2021) Activation of N-methyl-D-aspartate receptor regulates insulin sensitivity and lipid metabolism. \u003cem\u003eTheranostics\u003c/em\u003e \u003cstrong\u003e11,\u003c/strong\u003e 2247-2262. https://doi.org/10.7150/thno.51666. \u003c/li\u003e\n\u003cli\u003eHuang, X.T., Yue, S.J., Li, C., Huang, Y.H., Cheng, Q.M., Li, X.H., Hao, C.X., Wang, L.Z., Xu, J.P., Ji, M., Chen, C., Feng, D.D. and Luo, Z.Q. (2017) A Sustained Activation of Pancreatic NMDARs Is a Novel Factor of beta-Cell Apoptosis and Dysfunction. \u003cem\u003eEndocrinology\u003c/em\u003e \u003cstrong\u003e158,\u003c/strong\u003e 3900-3913. https://doi.org/10.1210/en.2017-00366. \u003c/li\u003e\n\u003cli\u003eIdo, Y., Kilo, C. and Williamson, J.R. (1997) Cytosolic NADH/NAD+, free radicals, and vascular dysfunction in early diabetes mellitus. \u003cem\u003eDiabetologia\u003c/em\u003e \u003cstrong\u003e40 Suppl 2,\u003c/strong\u003e S115-7. https://doi.org/10.1007/s001250051422. \u003c/li\u003e\n\u003cli\u003eInternational Diabetes Federation (2025) IDF Diabetes Atlas. https://diabetesatlas.org/data/en/. \u003c/li\u003e\n\u003cli\u003eIrwin, J., Kopin, J.H., White and Bankiewicz, K. (1988) A new approach to biochemical evaluation of brain dopamine metabolism. \u003cem\u003eCellular and Molecular Neurobiology\u003c/em\u003e \u003cstrong\u003e8,\u003c/strong\u003e 171-179. https://doi.org/10.1007/bf00711243. \u003c/li\u003e\n\u003cli\u003eIsa, F., Collins, S., Hee Lee, M., Decome, D., Dorvil, N., Joseph, P., Smith, L., Salerno, S., Wells, M.T., Fischer, S., Bean, J.M., Pape, J.W., Johnson, W.D., Fitzgerald, D.W. and Rhee, K.Y. (2018) Mass Spectrometric Identification of Urinary Biomarkers of Pulmonary Tuberculosis. \u003cem\u003eEBioMedecine\u003c/em\u003e \u003cstrong\u003e31,\u003c/strong\u003e 157-165. https://doi.org/10.1016/j.ebiom.2018.04.014. \u003c/li\u003e\n\u003cli\u003eJeon, C.Y. and Murray, M.B. (2008) Diabetes mellitus increases the risk of active tuberculosis: a systematic review of 13 observational studies. \u003cem\u003ePLoS Med\u003c/em\u003e \u003cstrong\u003e5,\u003c/strong\u003e e152. https://doi.org/10.1371/journal.pmed.0050152. \u003c/li\u003e\n\u003cli\u003eKaneto, H., Katakami, N., Matsuhisa, M. and Matsuoka, T.A. (2010) Role of reactive oxygen species in the progression of type 2 diabetes and atherosclerosis. \u003cem\u003eMediators Inflammation\u003c/em\u003e \u003cstrong\u003e2010,\u003c/strong\u003e 453892. https://doi.org/10.1155/2010/453892. \u003c/li\u003e\n\u003cli\u003eKhamis, M.M., Adamko, D.J. and El-Aneed, A. (2015) Mass spectrometric based approaches in urine metabolomics and biomarker discovery. \u003cem\u003eMass Spectrometry Reviews\u003c/em\u003e \u003cstrong\u003e36,\u003c/strong\u003e 115\u0026ndash;134. https://doi.org/10.1002/mas.21455.\u003c/li\u003e\n\u003cli\u003eKnip, M. and Siljander, H. (2008) Autoimmune mechanisms in type 1 diabetes. \u003cem\u003eAutoimmun Rev\u003c/em\u003e \u003cstrong\u003e7,\u003c/strong\u003e 550-7. https://doi.org/10.1016/j.autrev.2008.04.008. \u003c/li\u003e\n\u003cli\u003eKoeken, V.A.C.M., Lachmandas, E., Riza, A., Matzaraki, V., Li, Y., Kumar, V., Oosting, M., Joosten, L.A.B., Netea, M.G. and Van Crevel, R. (2019) Role of Glutamine Metabolism in Host Defense Against Mycobacterium tuberculosis Infection. \u003cem\u003eThe Journal of Infectious Diseases\u003c/em\u003e \u003cstrong\u003e219,\u003c/strong\u003e 1662-1670. https://doi.org/10.1093/infdis/jiy709. \u003c/li\u003e\n\u003cli\u003eKumar, P., Rawat, S., Kakar, A. and Sinha, A.K. (2022) Prevalence of non-alcoholic fatty liver disease among diabetes, prediabetes and healthy population. \u003cem\u003eJ Family Med Prim Care\u003c/em\u003e \u003cstrong\u003e11,\u003c/strong\u003e 7640-7643. https://doi.org/10.4103/jfmpc.jfmpc_856_22. \u003c/li\u003e\n\u003cli\u003eLas, G., Oliveira, M.F. and Shirihai, O.S. (2020a) Emerging roles of beta-cell mitochondria in type-2-diabetes. \u003cem\u003eMolecular Aspects of Medicine\u003c/em\u003e \u003cstrong\u003e71,\u003c/strong\u003e 100843. https://doi.org/10.1016/j.mam.2019.100843. \u003c/li\u003e\n\u003cli\u003eLas, G., Oliveira, M.F. and Shirihai, O.S. (2020b) Emerging roles of \u0026beta;-cell mitochondria in type-2-diabetes. \u003cem\u003eMolecular Aspects of Medicine\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e. https://doi.org/10.1016/j.mam.2019.100843. \u003c/li\u003e\n\u003cli\u003eLee, P.H., Lin, H.C., Huang, A.S., Wei, S.H., Lai, M.S. and Lin, H.H. (2014) Diabetes and risk of tuberculosis relapse: nationwide nested case-control study. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e9,\u003c/strong\u003e e92623. https://doi.org/10.1371/journal.pone.0092623.\u003c/li\u003e\n\u003cli\u003eLeung, A.N. (1999) Pulmonary Tuberculosis: The Essentials. \u003cem\u003eRadiology\u003c/em\u003e \u003cstrong\u003e210,\u003c/strong\u003e 307-322. https://doi.org/10.1148/radiology.210.2.r99ja34307.\u003c/li\u003e\n\u003cli\u003eLi, N., Frigerio, F. and Maechler, P. (2008) The sensitivity of pancreatic beta-cells to mitochondrial injuries triggered by lipotoxicity and oxidative stress. \u003cem\u003eBiochemical Society Transactions\u003c/em\u003e \u003cstrong\u003e36,\u003c/strong\u003e 930-4. https://doi.org/10.1042/BST0360930.\u003c/li\u003e\n\u003cli\u003eLi, X., Xu, Z., Lu, X., Yang, X., Yin, P., Kong, H., Yu, Y. and Xu, G. (2009) Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry for metabonomics: Biomarker discovery for diabetes mellitus. \u003cem\u003eAnal Chim Acta\u003c/em\u003e \u003cstrong\u003e633,\u003c/strong\u003e 257-62. https://doi.org/10.1016/j.aca.2008.11.058.\u003c/li\u003e\n\u003cli\u003eLin, K., Luo, C.W., Chen, S.P., Tu, D.G., Lin, M.S. and Kuan, Y.H. (2020) \u0026alpha;-Glucosidase Inhibitor Can Effectively Inhibit the Risk of Tuberculosis in Patients with Diabetes: A Nested Case-Control Study. \u003cem\u003eBioMed Research International\u003c/em\u003e \u003cstrong\u003e2020,\u003c/strong\u003e 1-12. https://doi.org/10.1155/2020/8085106.\u003c/li\u003e\n\u003cli\u003eLin, P.L. and Flynn, J.L. (2010) Understanding Latent Tuberculosis: A Moving Target. \u003cem\u003eThe journal of immunology\u003c/em\u003e \u003cstrong\u003e185,\u003c/strong\u003e 15-22. https://doi.org/10.4049/jimmunol.0903856.\u003c/li\u003e\n\u003cli\u003eLinares, O.A., Jacquez, J.A., Zech, L.A., Smith, M.J., Sanfield, J.A., Morrow, L.A., Rosen, S.G. and Halter, J.B. (1987) Norepinephrine metabolism in humans. Kinetic analysis and model. \u003cem\u003eThe Journal of Clinical Investigation\u003c/em\u003e \u003cstrong\u003e80,\u003c/strong\u003e 1332-1341. https://doi.org/10.1172/JCI113210.\u003c/li\u003e\n\u003cli\u003eLiu, Y., Wang, J. and Wu, C. (2021) Microbiota and Tuberculosis: A Potential Role of Probiotics, and Postbiotics. \u003cem\u003eFrontiers in Nutrition\u003c/em\u003e \u003cstrong\u003e8,\u003c/strong\u003e 626254. https://doi.org/10.3389/fnut.2021.626254.\u003c/li\u003e\n\u003cli\u003eLu, S.C. (2013) Glutathione synthesis. \u003cem\u003eBiochimica et Biophysica Acta\u003c/em\u003e \u003cstrong\u003e1830,\u003c/strong\u003e 3143-53. https://doi.org/10.1016/j.bbagen.2012.09.008.\u003c/li\u003e\n\u003cli\u003eLuies, L. and Loots, D.T. (2016) Tuberculosis metabolomics reveals adaptations of man and microbe in order to outcompete and survive. \u003cem\u003eMetabolomics\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e. https://doi.org/10.1007/s11306-016-0969-x.\u003c/li\u003e\n\u003cli\u003eMagee, M.J., Salindri, A.D., Kyaw, N.T.T., Auld, S.C., Haw, J.S. and Umpierrez, G.E. (2018) Stress Hyperglycemia in Patients with Tuberculosis Disease: Epidemiology and Clinical Implications. \u003cem\u003eCurrent Diabetes Reports\u003c/em\u003e \u003cstrong\u003e18,\u003c/strong\u003e 71. https://doi.org/10.1007/s11892-018-1036-y.\u003c/li\u003e\n\u003cli\u003eMandi, Y. and Vecsei, L. (2012) The kynurenine system and immunoregulation. \u003cem\u003eBasic neuroscience, genetics and immunology\u003c/em\u003e \u003cstrong\u003e119,\u003c/strong\u003e 197-209. https://doi.org/10.1007/s00702-011-0681-y.\u003c/li\u003e\n\u003cli\u003eMartin, K.S., Azzolini, M. and Ruas, J.L. (2020) The kynurenine connection: how exercise shifts muscle tryptophan metabolism and affects energy homeostasis, the immune system, and the brain. \u003cem\u003eAmerican Journal of Physiology-Cell physiology\u003c/em\u003e \u003cstrong\u003e318,\u003c/strong\u003e 818-830. https://doi.org/10.1152/ajpcell.00580.2019.\u003c/li\u003e\n\u003cli\u003eMenzies, N.A., Wolf, E., Connors, D., Bellerose, M., Sbarra, A.N., Cohen, T., Hill, A.N., Yaesoubi, R., Galer, K., White, P.J., Abubakar, I. and Salomon, J.A. (2018) Progression from latent infection to active disease in dynamic tuberculosis transmission models: a systematic review of the validity of modelling assumptions. \u003cem\u003eThe Lancet Infectious Diseases\u003c/em\u003e \u003cstrong\u003e18,\u003c/strong\u003e https://doi.org/10.1016/S1473-3099(18)30134-8 \u003c/li\u003e\n\u003cli\u003eMeregildo-Rodriguez, E.D., Asmat-Rubio, M.G., Zavaleta-Alaya, P. and V\u0026aacute;squez-Tirado, G.A. (2022) Effect of Oral Antidiabetic Drugs on Tuberculosis Risk and Treatment Outcomes: Systematic Review and Meta-Analysis. \u003cem\u003eTropical Medicine and Infectious Disease\u003c/em\u003e \u003cstrong\u003e7,\u003c/strong\u003e 343. https://doi.org/10.3390/tropicalmed7110343.\u003c/li\u003e\n\u003cli\u003eMingrone, G., Castagneto-Gissey, L. and Mace, K. (2013) Use of dicarboxylic acids in type 2 diabetes. \u003cem\u003eBritish Journal of Clinical Pharmacology\u003c/em\u003e \u003cstrong\u003e75,\u003c/strong\u003e 671-6. https://doi.org/10.1111/j.1365-2125.2012.04177.x.\u003c/li\u003e\n\u003cli\u003eMurabayashi, M., Daimon, M., Murakami, H., Kamba, A. and Mizushiri, S. (2018) Association of Urinary Normetanephrine Levels with Increased Insulin Resistance in a General Population. \u003cem\u003eDiabetes\u003c/em\u003e \u003cstrong\u003e67\u003c/strong\u003e. https://doi.org/10.2337/db18-1655-P.\u003c/li\u003e\n\u003cli\u003eNavarro-Gonz\u0026aacute;lez, J. and Mora-Fern\u0026aacute;ndez, C. (2008) The Role of Inflammatory Cytokines in Diabetic Nephropathy. \u003cem\u003eJournal of the American Society of Nephrology (JASN)\u003c/em\u003e \u003cstrong\u003e19,\u003c/strong\u003e 433-442. https://doi.org/10.1681/ASN.2007091048.\u003c/li\u003e\n\u003cli\u003eNegi, S., Pahari, S., Bashir, H. and Agrewala, J.N. (2020) Intestinal microbiota disruption limits the isoniazid mediated clearance of Mycobacterium tuberculosis in mice. \u003cem\u003eEuropean Journal of Immunology\u003c/em\u003e \u003cstrong\u003e50,\u003c/strong\u003e 1976-1987. https://doi.org/10.1002/eji.202048556.\u003c/li\u003e\n\u003cli\u003eNiazi, A.K. and Kalra, S. (2012) Diabetes and tuberculosis: a review of the role of optimal glycemic control. \u003cem\u003eJournal of Diabetes \u0026amp; Metabolic Disorders\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e. https://doi.org/10.1186/2251-6581-11-28.\u003c/li\u003e\n\u003cli\u003eOhra-aho, T., Niemi, P., Aura, A.M., Orlandi, M., Poutanen, K., Buchert, J. and Tamminen, T. (2016) Structure of Brewer\u0026rsquo;s Spent Grain Lignin and Its Interactions with Gut Microbiota in Vitro. \u003cem\u003eJournal of Agricultural and Food Chemistry\u003c/em\u003e \u003cstrong\u003e64,\u003c/strong\u003e 812-820. https://doi.org/10.1021/acs.jafc.5b05535. \u003c/li\u003e\n\u003cli\u003eOu, K., Sarnoski, P., Schneider, K.R., Song, K., Khoo, C. and Gu, L. (2014) Microbial catabolism of procyanidins by human gut microbiota. \u003cem\u003eMolecular Nutrition and Food Research\u003c/em\u003e \u003cstrong\u003e58,\u003c/strong\u003e 2196-205. https://doi.org/10.1002/mnfr.201400243. \u003c/li\u003e\n\u003cli\u003ePajuelo, D., Gonzalez-Juarbe, N., Tak, U., Sun, J., Orihuela, C.J. and Niederweis, M. (2018) NAD(+) Depletion Triggers Macrophage Necroptosis, a Cell Death Pathway Exploited by Mycobacterium tuberculosis. \u003cem\u003eCell Rep\u003c/em\u003e \u003cstrong\u003e24,\u003c/strong\u003e 429-440. https://doi.org/10.1016/j.celrep.2018.06.042.\u003c/li\u003e\n\u003cli\u003eParveen, S. and Bishai, W.R. (2024) Role of glutamine metabolism in tuberculosis pathogenesis: a mini review. \u003cem\u003eFrontiers in Tuberculosis\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e. https://doi.org/10.3389/ftubr.2024.1432880.\u003c/li\u003e\n\u003cli\u003ePasupuleti, V.R., Arigela, C.S., Gan, S.H., Salam, S.K.N., Krishnan, K.T., Rahman, N.A. and Jeffree, M.S. (2020) A Review on Oxidative Stress, Diabetic Complications, and the Roles of Honey Polyphenols. \u003cem\u003eOxidative Medicine and Cellular Longevity\u003c/em\u003e \u003cstrong\u003e2020,\u003c/strong\u003e 8878172. https://doi.org/10.1155/2020/8878172.\u003c/li\u003e\n\u003cli\u003ePeaston, R.T. and Weinkove, C. (2004) Measurement of catecholamines and their metabolites. \u003cem\u003eAnn Clin Biochem\u003c/em\u003e \u003cstrong\u003e41,\u003c/strong\u003e 17-38. https://doi.org/10.1258/000456304322664663. \u003c/li\u003e\n\u003cli\u003ePeterson, M.C. and Shulman, G.I. (2018) Mechanisms of insulin action and insulin resistance. \u003cem\u003ePhysiological Reviews\u003c/em\u003e \u003cstrong\u003e98,\u003c/strong\u003e 2133-2223. https://doi.org/10.1152/physrev.00063.2017.\u003c/li\u003e\n\u003cli\u003ePhilips, P.A. and Ernst, J.D. (2012) Tuberculosis pathogenesis and immunity. \u003cem\u003eAnnual Review of Pathology: Mechanisms of Disease\u003c/em\u003e \u003cstrong\u003e7,\u003c/strong\u003e 353-384. https://doi.org/10.1146/annurev-pathol-011811-132458. \u003c/li\u003e\n\u003cli\u003ePires, A.S., Tan, V.X., Heng, B., Guillemin, G.J. and Latini, A. (2020) Kynurenine and Tetrahydrobiopterin Pathways Crosstalk in Pain Hypersensitivity. \u003cem\u003eFrontiers in Neuroscience\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e. https://doi.org/10.3389/fnins.2020.00620.\u003c/li\u003e\n\u003cli\u003eRonacher, K., Joosten, S.A., Van Crevel, R., Dockrell, H.M., Walzl, G. and Ottenhoff, T.H.M. (2015) Acquired immunodeficiencies and tuberculosis: focus on HIV/AIDS and diabetes mellitus. \u003cem\u003eImmonological Reviews\u003c/em\u003e \u003cstrong\u003e264,\u003c/strong\u003e 121-137. https://doi.org/10.1111/imr.12257.\u003c/li\u003e\n\u003cli\u003eSane Schepisi, M., Navarra, A., Altet Gomez, M.N., Dudnyk, A., Dyrhol-Riise, A.M., Esteban, J., Giorgetti, P.F., Gualano, G., Guglielmetti, L., Heyckendorf, J., Kaluzhenina, A., Lange, B., Lange, C., Manika, K., Miah, J., Nanovic, Z., Pontali, E., Prego, M.R., Solovic, I., Tiberi, S., Palmieri, F. and Girardi, E. (2018) Burden and Characteristics of the Comorbidity Tuberculosis\u0026mdash;Diabetes in Europe: TBnet Prevalence Survey and Case-Control Study. \u003cem\u003eOpen Forum Infectious Diseases\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e. https://doi.org/10.1093/ofid/ofy337.\u003c/li\u003e\n\u003cli\u003eSankaranarayanan, R., Kumar, D.R., Patel, J. and Bhat, G.J. (2020) Do Aspirin and Flavonoids Prevent Cancer through a Common Mechanism Involving Hydroxybenzoic Acids?-The Metabolite Hypothesis. \u003cem\u003eMolecules\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e. https://doi.org/10.3390/molecules25092243.\u003c/li\u003e\n\u003cli\u003eSekhar, R.V., McKay, S.V., Patel, S.G., Guthikonda, A.P., Reddy, V.T., Balasubramanyam, A. and Jahoor, F. (2011) Glutathione Synthesis Is Diminished in Patients With Uncontrolled Diabetes and Restored by Dietary Supplementation With Cysteine and Glycine. \u003cem\u003eDiabetes Care\u003c/em\u003e \u003cstrong\u003e34,\u003c/strong\u003e 162-167. https://doi.org/10.2337/dc10-1006.\u003c/li\u003e\n\u003cli\u003eSharma, M., Sharma, S., Roy, S., Varma, S. and Bose, M. (2007) Pulmonary epithelial cells are a source of interferon-gamma in response to Mycobacterium tuberculosis infection. \u003cem\u003eImmunol Cell Biol\u003c/em\u003e \u003cstrong\u003e85,\u003c/strong\u003e 229-37. https://doi.org/10.1038/sj.icb.7100037.\u003c/li\u003e\n\u003cli\u003eSharma, S., Krishnappa, D., Singh, A.D., Sinha, S., Ammini, A.C. and Soneja, M. (2019) Impact of tuberculosis on glycaemic status: A neglected association. \u003cem\u003eIndian Journal of Medical Research\u003c/em\u003e \u003cstrong\u003e149\u003c/strong\u003e. https://doi.org/10.4103/ijmr.IJMR_1927_17.\u003c/li\u003e\n\u003cli\u003eShastri, M.D., Shukla, S.D., Chong, W.C., Dua, K., Peterson, G.M., Patel, R.P., Hansbro, P.M., Eri, R., O\u0026rsquo;Toole, R.F. and De Miranda, A.S. (2018) Role of Oxidative Stress in the Pathology and Management of Human Tuberculosis. \u003cem\u003eOxidative Medicine and Cellular Longevity\u003c/em\u003e \u003cstrong\u003e2018\u003c/strong\u003e. https://doi.org/10.1155/2018/7695364.\u003c/li\u003e\n\u003cli\u003eShibata, K. and Sakamoto, M. (2016) Urinary branched-chain 2-oxo acids as a biomarker for function of B-group vitamins in humans. \u003cem\u003eJournal of Nutritional Science and Vitaminology\u003c/em\u003e \u003cstrong\u003e62,\u003c/strong\u003e 220-228. https://doi.org/10.3177/jnsv.62.220.\u003c/li\u003e\n\u003cli\u003eShillitoe, E., Weinstock, R., Kim, T., Simon, H., Planer, J., Noonan, S. and Cooney, R. (2012) The oral microflora in obesity and type-2 diabetes. \u003cem\u003eJournal of Oral Microbiology\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e. https://doi.org/10.3402/jom.v4i0.19013.\u003c/li\u003e\n\u003cli\u003eSiddik, M.A.D. and Shin, A.C. (2019) Recent Progress on Branched-Chain Amino Acids in Obesity, Diabetes, and Beyond. \u003cem\u003eEndocrinology and Metabolism\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e. https://doi.org/10.3803/EnM.2019.34.3.234.\u003c/li\u003e\n\u003cli\u003eSinghal, A. and Cheng, C.Y. (2018) Host NAD+ metabolism and infections: therapeutic implications. \u003cem\u003eInternational Immunology\u003c/em\u003e \u003cstrong\u003e31,\u003c/strong\u003e 59-67. https://doi.org/10.1093/intimm/dxy068.\u003c/li\u003e\n\u003cli\u003eStorla, D.G., Yimer, S. and Bjune, G.A. (2008) A systematic review of delay in the diagnosis and treatment of tuberculosis. \u003cem\u003eBMC Public Health\u003c/em\u003e \u003cstrong\u003e8,\u003c/strong\u003e 15. https://doi.org/10.1186/1471-2458-8-15.\u003c/li\u003e\n\u003cli\u003eSultana, M.R., Bagul, P.K., Katare, P.B., Anwar Mohammed, S., Padiya, R. and Banerjee, S.K. (2016) Garlic activates SIRT-3 to prevent cardiac oxidative stress and mitochondrial dysfunction in diabetes. \u003cem\u003eLife Sci\u003c/em\u003e \u003cstrong\u003e164,\u003c/strong\u003e 42-51. https://doi.org/10.1016/j.lfs.2016.08.030.\u003c/li\u003e\n\u003cli\u003eSun, J., Siroy, A., Lokareddy, R.K., Speer, A., Doornbos, K.S., Cingolani, G. and Niederweis, M. (2015) The tuberculosis necrotizing toxin kills macrophages by hydrolyzing NAD. \u003cem\u003eNature Structural \u0026amp; Molecular Biology\u003c/em\u003e \u003cstrong\u003e22,\u003c/strong\u003e 672-678. https://doi.org/10.1038/nsmb.3064.\u003c/li\u003e\n\u003cli\u003eSuzuki, Y., Suda, T., Asada, K., Miwa, S., Suzuki, M., Fujie, M., Furuhashi, K., Nakamura, Y., Inui, N., Shirai, T., Hayakawa, H., Nakamura, H. and Chidaa, K. (2012) Serum indoleamine 2,3-dioxygenase cctivity predicts prognosis of pulmonary tuberculosis. \u003cem\u003eClinical and Vaccine Immunology\u003c/em\u003e \u003cstrong\u003e19,\u003c/strong\u003e 436-442. https://doi.org/10.1128/CVI.05402-11.\u003c/li\u003e\n\u003cli\u003eSzymańska, E., Saccenti, E., Smilde, A.K. and Westerhuis, J.A. (2012) Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies. \u003cem\u003eMetabolomics\u003c/em\u003e \u003cstrong\u003e8,\u003c/strong\u003e 3-16. https://doi.org/10.1007/s11306-011-0330-3.\u003c/li\u003e\n\u003cli\u003eTheron, I.J., Mason, S., van Reenen, M., Stander, Z., Kleynhans, L., Ronacher, K. and Loots, D.T. (2024) Characterizing poorly controlled type 2 diabetes using (1)H-NMR metabolomics. \u003cem\u003eMetabolomics\u003c/em\u003e \u003cstrong\u003e20,\u003c/strong\u003e 54. https://doi.org/10.1007/s11306-024-02127-w.\u003c/li\u003e\n\u003cli\u003eVillela, A.D., S\u0026aacute;nchez-Quitian, Z.A., Ducati, G., Santos, D.S. and Basso, L.A. (2011) Pyrimidine salvage pathway in Mycobacterium tuberculosis. \u003cem\u003eCurrent Medicinal Chemistry\u003c/em\u003e \u003cstrong\u003e18,\u003c/strong\u003e 1286-1298. https://doi.org/10.2174/092986711795029555.\u003c/li\u003e\n\u003cli\u003eVrieling, F., Alisjahbana, B., Sahiratmadja, E., van Crevel, R., Harms, A.C., Hankemeier, T., Ottenhoff, T.H.M. and Joosten, S.A. (2019) Plasma metabolomics in tuberculosis patients with and without concurrent type 2 diabetes at diagnosis and during antibiotic treatment. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e9,\u003c/strong\u003e 18669. https://doi.org/10.1038/s41598-019-54983-5.\u003c/li\u003e\n\u003cli\u003eWang, B., Ma, Y., Kong, X., Ding, X., Gu, H., Chu, T. and Ying, W. (2014) NAD(+) administration decreases doxorubicin-induced liver damage of mice by enhancing antioxidation capacity and decreasing DNA damage. \u003cem\u003eChem Biol Interact\u003c/em\u003e \u003cstrong\u003e212,\u003c/strong\u003e 65-71. https://doi.org/10.1016/j.cbi.2014.01.013.\u003c/li\u003e\n\u003cli\u003eWang, M., Tan, Y., Shi, Y., Wang, X., Liao, Z. and Wei, P. (2020) Diabetes and sarcopenic obesity: pathogenesis, diagnosis, and treatments. \u003cem\u003eFrontiers in Endocrinology\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e. https://doi.org/10.3389/fendo.2020.00568.\u003c/li\u003e\n\u003cli\u003eWilk, A., Hayat, F., Cunningham, R., Li, J., Garavaglia, S., Zamani, L., Ferraris, D.M., Sykora, P., Andrews, J., Clark, J., Davis, A., Chaloin, L., Rizzi, M., Migaud, M. and Sobol, R.W. (2020) Extracellular NAD+ enhances PARP-dependent DNA repair capacity independently of CD73 activity. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e10,\u003c/strong\u003e 651. https://doi.org/10.1038/s41598-020-57506-9.\u003c/li\u003e\n\u003cli\u003eWilliamson, J.R., Chang, K., Frangos, M., Hasan, K.S., Ido, Y., Kawamura, T., Nyengaard, J.R., van den Enden, M., Kilo, C. and Tilton, R.G. (1993) Hyperglycemic pseudohypoxia and diabetic complications. \u003cem\u003eDiabetes\u003c/em\u003e \u003cstrong\u003e42,\u003c/strong\u003e 801-13. https://doi.org/10.2337/diab.42.6.801.\u003c/li\u003e\n\u003cli\u003eWorkneh, M.H., Bjune, G.A. and Yimer, S.A. (2017) Prevalence and associated factors of tuberculosis and diabetes mellitus comorbidity: A systematic review. \u003cem\u003ePLOS ONE\u003c/em\u003e \u003cstrong\u003e12,\u003c/strong\u003e e0175925. https://doi.org/10.1371/journal.pone.0175925.\u003c/li\u003e\n\u003cli\u003eWorld Health Organisation (2024) Global tuberculosis report 2024. https://www.who.int/teams/global-tuberculosis-programme/tb-reports.\u003c/li\u003e\n\u003cli\u003eWu, S.H., D., D. and Wang, L.C. (2008) A Study of Outlier Analysis Techniques for Delay Testing. \u003cem\u003eIEEE International Test Conference\u003c/em\u003e. https://doi.org/10.1109/TEST.2008.4700548.\u003c/li\u003e\n\u003cli\u003eXie, N., Zhang, L., Gao, W., Huang, C., Huber, P.E., Zhou, X., Li, C., Shen, G. and Zou, B. (2020) NAD(+) metabolism: pathophysiologic mechanisms and therapeutic potential. \u003cem\u003eSignal Transduction and Targeted Therapy\u003c/em\u003e \u003cstrong\u003e5,\u003c/strong\u003e 227. https://doi.org/10.1038/s41392-020-00311-7. \u003c/li\u003e\n\u003cli\u003eYen, N.T.H., Anh, N.K., Jayanti, R.P., Phat, N.K., Vu, D.H., Ghim, J.L., Ahn, S., Shin, J.G., Oh, J.Y., Long, N.P. and Kim, D.H. (2023) Multimodal plasma metabolomics and lipidomics in elucidating metabolic perturbations in tuberculosis patients with concurrent type 2 diabetes. \u003cem\u003eBiochimie\u003c/em\u003e \u003cstrong\u003e211,\u003c/strong\u003e 153-163. https://doi.org/10.1016/j.biochi.2023.04.009.\u003c/li\u003e\n\u003cli\u003eYorke, E., Atiase, Y., Akpalu, J., Sarfo-Kantanka, O., Boima, V. and Dey, I.D. (2017) The Bidirectional Relationship between Tuberculosis and Diabetes. \u003cem\u003eTuberc Res Treat\u003c/em\u003e \u003cstrong\u003e2017,\u003c/strong\u003e 1702578. https://doi.org/10.1155/2017/1702578.\u003c/li\u003e\n\u003cli\u003eZhang, A., Qiu, S., Xu, H., Sun, H. and Wang, X. (2014) Metabolomics in diabetes. \u003cem\u003eClinica Chimica Acta\u003c/em\u003e \u003cstrong\u003e429,\u003c/strong\u003e 106-110. http://dx.doi.org/10.1016/j.cca.2013.11.037.\u003c/li\u003e\n\u003cli\u003eZhang, A., Sun, H., Wu, X. and Wang, X. (2012) Urine metabolomics. \u003cem\u003eClinica Chimica Acta\u003c/em\u003e \u003cstrong\u003e414,\u003c/strong\u003e 65-69. http://dx.doi.org/10.1016/j.cca.2012.08.016.\u003c/li\u003e\n\u003cli\u003eZhang, L.Q., Heruth, D.P. and Ye, S.Q. (2011) Nicotinamide Phosphoribosyltransferase in Human Diseases. \u003cem\u003eJournal of Bioanalysis and Biomedicine\u003c/em\u003e \u003cstrong\u003e3,\u003c/strong\u003e 13-25. https://doi.org/10.4172/1948-593X.1000038.\u003c/li\u003e\n\u003cli\u003eZhang, M. and He, J.Q. (2020) Impacts of metformin on tuberculosis incidence and clinical outcomes in patients with diabetes: a systematic review and meta-analysis. \u003cem\u003eEuropean Journal of Clinical Pharmacololgy\u003c/em\u003e \u003cstrong\u003e76,\u003c/strong\u003e 149-159. https://doi.org/10.1007/s00228-019-02786-y.\u003c/li\u003e\n\u003cli\u003eZhao, M., Wang, Y., Li, L., Liu, S., Wang, C., Yuan, Y., Yang, G., Chen, Y., Cheng, J., Lu, Y. and Liu, J. (2021) Mitochondrial ROS promote mitochondrial dysfunction and inflammation in ischemic acute kidney injury by disrupting TFAM-mediated mtDNA maintenance. \u003cem\u003eTheranostics\u003c/em\u003e \u003cstrong\u003e11,\u003c/strong\u003e 1845-1863. https://doi.org/10.7150/thno.50905.\u003c/li\u003e\n\u003cli\u003eZhong, H.J., Yuan, Y., Xie, W.R., Chen, M.H. and He, X.X. (2016) Type 2 Diabetes Mellitus Is Associated with More Serious Small Intestinal Mucosal Injuries. \u003cem\u003ePlos One\u003c/em\u003e \u003cstrong\u003e11,\u003c/strong\u003e e0162354. https://doi.org/10.1371/journal.pone.0162354.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"metabolomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mebo","sideBox":"Learn more about [Metabolomics](http://link.springer.com/journal/11306)","snPcode":"11306","submissionUrl":"https://submission.nature.com/new-submission/11306/3","title":"Metabolomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Tuberculosis, Diabetes, Comorbidity, Metabolomics, Urine","lastPublishedDoi":"10.21203/rs.3.rs-7739707/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7739707/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e\u003cp\u003eTuberculosis (TB) and type 2 diabetes mellitus (T2DM) are highly prevalent diseases resulting in high mortality rates globally. Furthermore, T2DM increases susceptibility to TB and vice versa, worsening disease outcomes. This comorbidity is, however, not well described or understood, despite its rising prevalence globally.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eThis investigation aimed to better characterize the urinary metabolic profiles of patients with the TB and T2DM comorbidity in a South African cohort, to better understand its metabolic basis and associated clinical implications.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUsing untargeted GCxGC-TOFMS metabolomics, urine samples from 17 patients with TB and T2DM and 34 healthy controls were analyzed and statistically compared to identify significantly altered urinary metabolites.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eTB-T2DM comorbid patients were characterized by altered metabolism of: 1) tryptophan and kynurenine (reduced kynurenic acid, anthranilic acid, picolinic acid) associated with changes to NAD\u003csup\u003e+\u003c/sup\u003e synthesis and a redox imbalance, 2) nucleotides (reduced 3-aminoisobutyric acid, orotic acid, thymine, β-alanine, adenine, hypoxanthine), 3) tyrosine (reduced 3,4-dihydroxyphenylglycol, 4-hydroxy-3-methoxyphenylglycol, hydroxyphenylpyruvate), 4) lipids (reduced dec-2-enedioate, adipic acid, methylmalonic acid), 5) reduced concentrations of various glycine conjugates associated with glycine depletion, and 6) reduced urinary concentrations of various gut microbial metabolites indicative of microbial dysbiosis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThese results indicate several metabolic disruptions to amino acids, nucleotides, lipids, NAD⁺ homeostasis and the host microbiome, in TB-T2DM patients, mainly driven by inflammation and oxidative stress. Overall, the findings indicate synergistic amplification of metabolic stress, associated with immune suppression and TB-T2DM disease progression, and subsequently suggests how TB increases T2DM susceptibility and vice versa, as foundation for further investigations.\u003c/p\u003e","manuscriptTitle":"Characterizing the tuberculosis and type 2 diabetes mellitus comorbidity in a South African cohort using untargeted GCxGC-TOFMS metabolomics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 02:36:55","doi":"10.21203/rs.3.rs-7739707/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-24T09:39:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-31T14:05:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63509504338046730135791129949005143884","date":"2025-10-06T08:16:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103542962539334278497234660296102738191","date":"2025-10-04T08:31:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-30T18:30:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-30T14:27:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-30T14:27:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Metabolomics","date":"2025-09-29T08:27:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"metabolomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mebo","sideBox":"Learn more about [Metabolomics](http://link.springer.com/journal/11306)","snPcode":"11306","submissionUrl":"https://submission.nature.com/new-submission/11306/3","title":"Metabolomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5f9699b5-2261-4d12-9341-baaa93ec7372","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:07:20+00:00","versionOfRecord":{"articleIdentity":"rs-7739707","link":"https://doi.org/10.1007/s11306-025-02389-y","journal":{"identity":"metabolomics","isVorOnly":false,"title":"Metabolomics"},"publishedOn":"2026-01-19 15:58:37","publishedOnDateReadable":"January 19th, 2026"},"versionCreatedAt":"2025-10-15 02:36:55","video":"","vorDoi":"10.1007/s11306-025-02389-y","vorDoiUrl":"https://doi.org/10.1007/s11306-025-02389-y","workflowStages":[]},"version":"v1","identity":"rs-7739707","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7739707","identity":"rs-7739707","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}