Recent advancements in metabolomics and microbiomics have identified several microbial and metabolic biomarkers that hold significant promise for the diagnosis and management of tuberculosis in the context of diabetes mellitus.In the realm of microbial biomarkers, specific gut microbiota genera have been identified as potential indicators of latent tuberculosis infection (LTBI) in patients with poorly controlled diabetes. These genera include Prevotella_9, Streptococcus, Actinomyces, Bacteroides, Alistipes, and Blautia. These microbial markers have demonstrated a high degree of accuracy in predicting LTBI status, achieving an area under the receiver operating characteristic (ROC) curve of 0.872, thereby underscoring their potential diagnostic utility (15). Wang et al. (2025) (16) revealed significant disruptions in the gut microbiota composition and metabolomic profiles of pulmonary tuberculosis (PTB) and PTB–DM patients. Key findings included reduced α- and β-diversity of gut microbiota, with specific genera such as g-Roseburia, g-Ruminococcaceae_UCG.013, and g-Lachnospiraceae_unclassified being significantly diminished in PTB–DM patients.

Additionally, untargeted metabolomics identified significant alterations in amino acid metabolism, including elevated levels of serine, proline, and histidine. Metabolomic studies have revealed that lipid, amino acid, and carbohydrate metabolisms are dysregulated in both TB and DM, with potential biomarkers such as kynurenine pathway metabolites and short-chain fatty acids (SCFAs) showing promise for early diagnosis and treatment (17). Studies measuring plasma eicosanoid levels, including LXA4, 15-epi-LXA4, LTB4, and PGE2, have shown that these mediators are significantly altered in patients with TB-DM. Elevated levels of LXA4 and 15-epi-LXA4 correlate with disease severity and bacterial burden, indicating their potential as biomarkers for disease progression and treatment response (18). Studies have also identified a pro-atherogenic lipid profile characterized by increased sphingomyelins and remnant-like lipoprotein particles in TB-DM patients (19). Metabolomic and transcriptomic analyses have underscored the importance of IL-17, PI3K-AKT signaling pathway, and PPAR signaling pathway in the pathogenesis of TB-DM comorbidity (20). This finding suggests that lipid profiling could serve as a valuable tool for assessing cardiovascular risk in patients with comorbid TB and DM (21), emphasizing the need for further research to validate the specific metabolite biomarkers in larger cohorts (22, 23). Therefore, specific gut microbiota genera such as Prevotella_9, Streptococcus, and Bacteroides, as well as lipid mediators like kynurenine pathway metabolites, have been identified as potential biomarkers for both diabetes mellitus and tuberculosis comorbidity, showing promise for early diagnosis and treatment.

Studies have illuminated significant changes in the immune profiles of patients with tuberculosis and diabetes mellitus comorbidity, particularly in the frequencies and functions of key immune cells. Investigations into the immunological landscape of DM-TB co-infection have revealed that CD8⁺ T cells and NK cells exhibit altered cytokine production and cytotoxic potential. Elevated frequencies of CD8⁺ T cells expressing IFN-γ, IL-2, and IL-17F, as well as NK cells expressing TNF-α and IL-17A, have been observed in TB-DM patients. These elevated frequencies suggest that these immune cells could serve as biomarkers for disease severity and treatment monitoring, with high sensitivity and specificity for identifying active disease (24). This finding underscores the potential of these immune cells as indicators of disease progression and treatment response. The influence of type 2 diabetes (T2DM) on CD8⁺ T-cell responses in latent Mycobacterium tuberculosis infection has also been examined. Research indicates that diabetes is associated with reduced frequencies of CD8⁺ T cells expressing cytokines (Th1, Th2, Th17) and increased expression of cytotoxic markers. This advocates that diabetes may impair immune responses, contributing to increased susceptibility to tuberculosis. The findings bring to the fore the potential of CD8⁺ T cell markers as indicators of immune dysfunction in this comorbidity, with implications for early diagnosis and treatment (25).

Some researches also have explored the function of innate lymphoid cells (ILCs) in TB-DM co-infection. Studies have shown that ILC subsets and their cytokine production are altered in patients with both conditions. Notably, elevated IL-22 production in ILC3 has been observed in TB-DM patients, suggesting that ILCs could be targeted to modulate immune responses in co-infected individuals. This hints at the potential of ILCs as biomarkers for disease progression, with the ability to identify immune dysregulation in comorbid TB and DM (26). The expression of Th1/Th17 cytokines, cytotoxic markers, and immune markers in gamma-delta (γδ) T cells has been investigated in latent tuberculosis patients with diabetes and pre-diabetes. Investigations have found that diabetes and pre-diabetes are associated with reduced expression of these markers in γδ T cells, indicating compromised immune function. This underscores the role of γδ T cells in the pathogenesis of LTB and diabetes comorbidity, with potential as biomarkers for immune status (27).

The frequency of natural-killer T cells (NKT cells) has been examined in peripheral blood and bronchoalveolar lavage fluid of pulmonary tuberculosis patients with and without type 2 diabetes mellitus. NKT cells have been found to be significantly increased in TB patients with DM, suggesting their potential as diagnostic markers for active TB (28). Monocyte surface markers, such as CCR2, CD11b, and RAGE, have been associated with altered monocyte function and TB susceptibility in DM2 patients (29). Targeting CCR2 may enhance the immune response and improve treatment outcomes in patients with comorbid TB and DM. In the aggregate, the identification of altered immune cell profiles, including CD8⁺ T cells, NK cells, ILCs, γδ T cells, monocytes, and NKT cells, offers valuable insights into the pathogenesis of DM-TB and signifies potential biomarkers for diagnosis, treatment monitoring, and therapeutic intervention.

In addition,Ye et al. (2025) (30) conducted a comprehensive study that mined transcriptome data from the GEO database and utilized weighted gene co-expression network analysis (WGCNA) combined with ten machine learning algorithms to identify immune biomarkers associated with DM–TB. The study identified three key immune-related biomarkers—CETP (cholesteryl ester transfer protein), TYROBP (TYRO protein tyrosine kinase binding protein), and SECTM1 (secreted and transmembrane protein 1)—which were used to construct an early alert model for DM–TB. This model demonstrated significant predictive efficiency with an AUC of 0.86 in the training set and 0.901 in the validation set. The identified biomarkers and the constructed model provide a new strategy for early screening and risk prediction of DM–TB, highlighting the potential of immune-related markers in managing this complex comorbidity. Hence,elevated frequencies of CD8+ T cells and NK cells, which express cytokines such as IFN-γ and IL-17, have been observed in patients with DM-TB comorbidity, indicating their potential as biomarkers for disease severity and treatment monitoring.

The interplay between tuberculosis and diabetes mellitus has been shown to significantly alter the inflammatory response in affected individuals. Elevated levels of inflammatory markers such as C-reactive protein, ferritin, and hepcidin have been consistently observed in patients with DM-TB comorbidity. These markers not only reflect the heightened inflammatory state but also hold potential for monitoring disease severity and guiding treatment strategies. Pre-diabetes has been associated with elevated systemic levels of pro-inflammatory cytokines, which may exacerbate TB pathology (31). Elevated ferritin and hepcidin levels have been identified as indicators of altered iron metabolism in TB patients with diabetes. These changes are associated with increased disease severity and could serve as valuable biomarkers for monitoring the progression of DM-TB comorbidity (32). The modulation of iron status is critical, as it influences both the host’s immune response and the pathogen’s ability to thrive. Intermediate hyperglycemia and diabetes have been shown to significantly alter the host transcriptome in TB patients, characterized by heightened inflammatory responses and reduced type I interferon responses. These alterations suggest that metabolic dysregulation exacerbates immune dysfunction, potentially worsening TB outcomes (33). This alludes to the importance of addressing glycemic control as part of the management strategy for DM-TB comorbidity.

Furthermore, higher CD4 and CD8 cell counts have been observed in TB patients with diabetes compared to those with non-tuberculous pneumonia and healthy controls (34). This points to that diabetes alters the immune response to TB, potentially increasing susceptibility and complicating treatment. Serum levels of chemokines IP-10, IL-8, and SDF-1 have been identified as potential biomarkers for the diabetes-TB nexus (35). Altered levels of these chemokines can impact anti-TB immunity, indicating their potential for monitoring disease progression and treatment response in TB-DM comorbidity. Reduced capacity to inhibit the growth of Mycobacterium tuberculosis has been observed in patients with Type 2 Diabetes Mellitus, particularly those with poor glycemic control (36). This reduced capacity is linked to altered cytokine production, which may contribute to increased susceptibility to TB. The Mycobacterial Growth Inhibition Assay (MGIA) has emerged as a potential in vitro marker for assessing immunological control of M. tuberculosis in DM2 patients, offering insights into the development of targeted therapies.

In patients with active pulmonary tuberculosis (APTB), elevated levels of the monocyte to high-density lipoprotein cholesterol ratio (MHR), neutrophils to high-density lipoprotein cholesterol ratio (NHR), C-reactive protein-to-lymphocyte ratio (CLR), and C-reactive protein-to-albumin ratio (CAR) have been identified as potential indicators of type 2 diabetes mellitus risk (37). These biomarkers could facilitate early detection and management of DM2 in APTB patients, enhancing overall treatment efficacy. Moreover, Zhang et al. (2025) (38) assessed a range of inflammatory markers in 276 diabetic patients and 276 patients with diabetes mellitus combined with active tuberculosis (DM-PTB) from Kunming, China. The study identified several inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-neutrophil ratio (PNR), platelet-to-monocyte ratio (PMR), monocyte to high-density lipoprotein ratio (MHR), and monocyte-to-lymphocyte ratio (MLR), as significant predictors of PTB in diabetic patients. A predictive model combining these markers demonstrated high sensitivity (75.0%) and specificity (81.9%) for identifying diabetic patients susceptible to Mycobacterium tuberculosis infection. Thus, elevated C-reactive protein and ferritin levels are consistently observed in patients with DM-TB comorbidity, reflecting heightened inflammation and serving as potential biomarkers for monitoring disease progression.

Optimal drug exposure thresholds have been identified as crucial for improving treatment outcomes in multidrug-resistant TB patients with diabetes. In the study of multidrug-resistant tuberculosis (MDR-TB) and diabetes mellitus comorbidity, significant advancements have been made in understanding the pharmacokinetic (PK) and pharmacodynamic (PD) parameters that influence treatment outcomes (39). Specifically, the AUC/MIC (Area Under the Curve/Minimum Inhibitory Concentration) ratios of various anti-TB drugs have emerged as crucial biomarkers for predicting treatment success and guiding therapeutic strategies. Poor glycemic control (defined as HbA1c ≥7%) was found to significantly reduce the drug exposure (AUC) of several key anti-TB medications, including moxifloxacin, linezolid, bedaquiline, and cycloserine. This reduction in drug exposure can lead to suboptimal treatment efficacy and increased risk of treatment failure. The study identified specific AUC/MIC thresholds that are predictive of favorable treatment outcomes: an AUC/MIC ratio of ≥67 for moxifloxacin was associated with a high sensitivity (97.3%) and specificity (90.0%) in predicting successful treatment outcomes, while an AUC/MIC ratio of ≥245 for bedaquiline was predictive of 6-month sputum culture conversion with a sensitivity of 96.8% and specificity of 100%. These ratios serve as important biomarkers for optimizing treatment regimens in patients with MDR-TB and DM. By monitoring these ratios, clinicians can adjust drug dosages to ensure adequate drug exposure, thereby improving treatment outcomes and reducing the risk of drug resistance. The findings suggest that therapeutic drug monitoring (TDM) should be considered for patients with MDR-TB and DM to ensure that AUC/MIC ratios reach the identified thresholds, helping to individualize treatment plans and improve the management of this complex comorbidity.

Elevated serum tumor markers, such as CA-125, CA19-9, and CEA, have been proposed as diagnostic indicators for pulmonary tuberculosis (PTB), particularly in patients with diabetes. CA-125 showed high diagnostic performance in PTB patients with diabetes. Specifically, for PTB patients with type 2 diabetes (TB-DM-IT group), CA-125 had a sensitivity of 88.14% (95%CI: 75.0%–91.6%) and a specificity of 95.83% (95%CI: 85.7%–99.9%), with an AUC of 0.963 (95%CI: 0.907–0.990) (40). CA19–9 and CEA showed relatively lower diagnostic performance compared to CA-125, with CA19–9 having a sensitivity of 77.59% and specificity of 60.47% in TB-DM-IT patients, and CEA having a sensitivity of 83.05% and specificity of 60.98% in the same group. In a study involving 246 cases (113 TB, 59 DM, 74 TB+DM) and 133 controls, MPV and PCT levels were significantly altered in TB+DM patients compared to those with TB or DM alone. MPV showed a specificity of 66.1% (DM vs TB+DM) and a sensitivity of 64.9% (DM vs TB+DM), while PCT demonstrated a specificity of 59.4% (DM vs TB+DM) and a sensitivity of 69.5% (DM vs TB+DM) (41). The study indicated that MPV has a specificity of 66.1% and a sensitivity of 64.9% in distinguishing DM from TB-DM when combined with other clinical parameters.

Furthermore, a two-step screening method, involving random plasma glucose testing followed by point-of-care HbA1c, demonstrated a specificity of 70.4% and a sensitivity of 91.7% for identifying DM in newly diagnosed pulmonary TB patients, imply its effectiveness in resource-limited settings (42). It reported a specificity of 74.5% and a sensitivity of 50.0% for detecting DM using the A1CNow+ system with an HbA1c cutoff of ≥6.5% among TB patients, indicating its potential utility in screening for DM in this population. In tandem, the integration of these biomarkers into clinical practice, as shown in Figure 2 ; Table 1 , could significantly enhance the diagnosis and treatment of TB, particularly in patients with comorbid diabetes. Accordingly, serum CA-125, with a sensitivity of 88.14% and specificity of 95.83%, and AUC/MIC ratios of anti-TB drugs like moxifloxacin (≥67) have been identified as highly accurate biomarkers for diagnosing and managing DM-TB comorbidity.

Progress in bioinformatics and proteomics have illuminated the genetic and molecular underpinnings of the comorbidity between DM and TB, offering crucial insights into the shared pathophysiological mechanisms of these diseases. Specifically, bioinformatics analyses have identified key hub genes associated with both DM and TB, which hold promise as potential biomarkers for disease status and therapeutic targets. These hub genes, including STAT1, IFIT3, RSAD2, IFI44L, and XAF1, were identified through comprehensive network analysis and validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) (43). Moreover, further investigation into the regulatory mechanisms involving these hub genes revealed the potential role of specific miRNAs. Seven miRNAs—miR-3680-3p, miR-3059-5p, miR-629-3p, miR-29b-2-5p, miR-514b-5p, miR-4755-5p, and miR-4691-3p were identified as having significant interactions with the hub genes. These miRNAs may play a crucial role in regulating the expression of the hub genes, thereby influencing the pathogenesis of DM and TB comorbidity.

In a complementary approach, proteomics has been employed to analyze plasma proteins in adult patients with active pulmonary tuberculosis and diabetes mellitus. This research has identified several differentially expressed proteins, including haptoglobin and clusterin, which may serve as potential biomarkers linking PTB and diabetes.These findings showcase the importance of understanding the molecular basis of PTB and diabetes comorbidity, paving the way for targeted interventions and improved treatment outcomes (44). Consequently, hub genes such as STAT1 and IFIT3, along with specific miRNAs like miR-3680-3p and miR-3059-5p, have been identified as potential genetic biomarkers associated with both DM and TB, highlighting their role in the pathogenesis of the comorbidity.