Tuberculosis (TB) progression reflects not just bacterial persistence but a dynamic collapse of immune system order — a phenomenon conceptualized as rising biological entropy. Current treatment monitoring, reliant on sputum culture conversion, poorly captures the host immune state and overlooks the systemic destabilization underpinning poor outcomes. This systematic review integrates evidence from immunology, systems biology, and complexity theory to propose immune entropy as a unifying predictive framework for TB treatment monitoring and precision intervention.Methods: We systematically reviewed studies from 2000 until 2025 measuring immunologic, transcriptomic, and metabolomic biomarkers across TB infection states and treatment courses, via searches of PubMed, Embase, and Web of Science. Eligibility required linkage of immune markers to clinical trajectories: progression, treatment response, relapse, or mortality. Data were synthesized to map trajectories of immune order versus disorder.Results: Progression from latent TB to active disease consistently aligned with surges in inflammatory signals (IP-10, IL-6), elevated T-cell activation (CD38+, HLA-DR+, Ki-67+), dysregulated monocyte/lymphocyte ratios, and IFN-driven transcriptomic shifts. Successful treatment reversed these signatures, restoring immune equilibrium. Persisting dysregulation despite therapy predicted relapse and death. Integrating multiscale biomarkers suggests the feasibility of constructing "entropy signatures" — composite immune state maps that anticipate individual treatment trajectories earlier and more precisely than sputum-based measures.Conclusions: Reframing TB treatment response through an entropy lens reveals actionable opportunities: to design dynamic, individualized treatment strategies based on immune system stability, not bacterial detection alone. Developing and validating entropy-driven models — potentially enhanced by AI and systems immunology — could revolutionize TB therapeutic monitoring, shortening trials, accelerating cure prediction, and preventing drug resistance emergence. This visionary paradigm urges a shift from static microbiological endpoints toward dynamic, host-centric monitoring frameworks.

Iron deficiency anemia (IDA, ~53%) and vitamin D deficiency (VDD, >70%) commonly co-occur in tuberculosis (TB), worsening treatment outcomes and mortality. Inflammatory hepcidin elevation restricts iron from Mycobacterium tuberculosis (Mtb), yet induces anemia of inflammation (AI). Vitamin D transcriptionally suppresses hepcidin (HAMP), offering a dual-acting immunometabolic intervention point.Methods: We conducted a structured analysis of 72 studies including:• Preclinical models (β2m-KO mice, C3HeB/FeJ granulomas)• 12 clinical RCTs (vitamin D and iron supplementation)• Spatial transcriptomic datasets from granulomatous lesions• AI-driven cytokine network meta-analysis• Machine learning (clustering, regression) to model micronutrient thresholds predictive of TB outcomes.Key Findings:Vitamin D suppressed hepcidin expression (↓32%, p<0.01) and upregulated ferroportin, enhancing macrophage iron efflux (↑1.8-fold, *p*=0.003).Combined lactoferrin + vitamin D reduced Mtb burden 1.5-log (p<0.001) in iron-overloaded macrophages.ML identified serum 25D >50 nmol/L + hepcidin <13.2 nmol/L as predictive of reduced TB progression (aHR=0.38; 95% CI 0.22–0.66) in high-anemia cohorts.VDR TaqI ‘tt’ genotype carriers showed accelerated sputum conversion with vitamin D (aHR=1.7; 95% CI 1.2–2.4).Conclusions & Vision: Modulating the vitamin D-hepcidin-ferroportin axis concurrently restricts Mtb iron access and ameliorates AI. We propose:Precision trials: Genetically stratified vitamin D supplementation (VDR/DBP screening)HDT development: Clinical evaluation of lactoferrin + vitamin D analogsPolicy integration: Adoption of 25D/hepcidin thresholds in high-anemia settings.