Lactate, once regarded as a metabolic byproduct, is now recognized as a critical immunometabolic regulator that shapes immune responses in both physiological and pathological contexts. This review examines how lactate accumulation occurs across diverse disease settings, including cancer, sepsis, and diabetes, through mechanisms such as hypoxia, mitochondrial dysfunction, and pharmacologic intervention. We then explore how lactate modulates immunity via four integrated mechanisms: transporter-mediated flux, receptor signaling (e.g., GPR81), context-dependent metabolic rewiring, and histone/protein lactylation. Particular emphasis is placed on the dichotomous effects of endogenous versus exogenous lactate, with the former supporting glycolytic effector functions and the latter reprogramming immune cells toward regulatory phenotypes via redox shifts and epigenetic remodeling. The review also highlights how the directionality of lactate transport, and the metabolic readiness of the cell determine, whether lactate sustains inflammation or promotes resolution. After analyzing emerging data across immune cell subsets and disease contexts, we propose that lactate serves as a dynamic rheostat that integrates environmental cues with intracellular metabolic and epigenetic programming. Understanding these context-dependent mechanisms is essential for the rational design of lactate-targeted immunotherapies that aim to modulate immune responses without disrupting systemic homeostasis.

Sepsis, a condition characterized by life-threatening organ dysfunction due to a dysregulated host response to infection, significantly impacts global health, with mortality rates varying widely across regions. Traditional therapeutic strategies that target hyperinflammation and immunosuppression have largely failed to improve outcomes, underscoring the need for innovative approaches. This review examines the development of therapeutic agents for sepsis, with a focus on clinical trials addressing hyperinflammation and immunosuppression. It highlights the frequent failures of these trials, explores the underlying reasons, and outlines current research efforts aimed at bridging the gap between theoretical advancements and clinical applications. Although personalized medicine and phenotypic categorization present promising directions, this review emphasizes the importance of understanding the complex pathogenesis of sepsis and developing targeted, effective therapies to enhance patient outcomes. By addressing the multifaceted nature of sepsis, future research can pave the way for more precise and individualized treatment strategies, ultimately improving the management and prognosis of sepsis patients.

Eun Jung Choi, Chang Hyun Jeon, Dong Ho Park, and Tae-Hwan Kwon. Mol. Cells 2020;43:964-73. https://doi.org/10.14348/molcells.2020.0198

Iron plays an important role in macrophage polarization by altering metabolic and redox status. However, the impact of iron on the immune status of macrophages is still controversial. In this study, we report that ferric ammonium citrate (FAC) upregulates PD-L1 expression in macrophages. FAC not only altered the phenotype of macrophages but also led to enriching immune-modulatory T cell subsets. Since iron is known to be a constituent of coenzymes facilitating metabolic processes in mitochondria, we examined the metabolic status of FAC-overloaded macrophages by measuring the oxygen consumption rate (OCR) and the represented coenzyme, aconitase. In addition to enhancement of metabolic processes, FAC accelerated the Fenton reaction in macrophages, which also contributed to the facilitation of oxygen consumption. We reasoned that the enhancement of the OCR leads to the production of reactive oxygen species (ROS), which are directly linked to PD-L1 induction. Using ferrostatin, rotenone, and N-acetyl-L-cysteine, we confirmed that metabolic and redox regulation is responsible for FAC-mediated PD-L1 expression. Furthermore, we suggested that FAC-induced ROS production may explain FAC-mediated pro- and anti-inflammatory responses in macrophages. These findings may extend our understanding of regulating iron concentration during immune checkpoint therapy in cancer patients.

Diabetes significantly impacts the immune system; however, its role in worsening sepsis prognosis remains poorly understood. This study investigated the effect of hyperferritinemia on immune modulation in septic diabetic patients. A cohort study at Kyungpook National University Hospital stratified sepsis patients by diabetes status and followed them for 28 days. Additionally, CD4⁺ T cells from mice were analyzed for proliferation, apoptosis, and metabolic changes under ferritin treatment. Results from the clinical study showed higher ferritin levels in diabetic patients, and those with lower lymphocyte counts had increased mortality. In the mice study, ferritin inhibited T cell activation and proliferation by shifting metabolism from glycolysis to oxidative phosphorylation without increasing cell death. These findings suggest that the suppression of T cell proliferation due to elevated ferritin levels contributes to an immunosuppressive environment, leading to worse outcomes. In conclusion, hyperferritinemia is a biomarker for sepsis severity, particularly in diabetic patients, highlighting potential therapeutic strategies targeting ferritin levels or glycolytic pathways.

Background: Insufficient participant enrollment is a major factor responsible for clinical trial failure. Objective: We formulated a machine learning (ML)–based framework using clinical laboratory parameters to identify participants eligible for enrollment in a bioequivalence study. Methods: We acquired records of 11,592 patients with gastric cancer from the electronic medical records of Kyungpook National University Hospital in Korea. The ML model was developed using 8 clinical laboratory parameters, including complete blood count and liver and kidney function tests, along with the dates of acquisition. Two datasets were collected: (1) a training dataset to design an ML-based candidate selection method and (2) a test dataset to evaluate the performance of the proposed method. The generalization performance of the ML-based method was confirmed using the F₁-score and the area under the curve (AUC). The proposed model was compared with a random selection method to evaluate its efficacy in recruiting participants. Results: The weighted ensemble model achieved strong performance with an F₁-score above 0.8 and an AUC value exceeding 0.8, demonstrating its ability to accurately identify valid clinical trial candidates while minimizing misclassification. Its high sensitivity further enhanced the model’s efficiency in prioritizing patients for screening. In a case study, the proposed ML model reduced the workload by 57%, efficiently identifying 150 valid patients from a pool of 209, compared to the 485 patients required by random selection. Conclusions: The proposed ML-based framework using clinical laboratory parameters can be used to identify patients eligible for a clinical trial, enabling faster participant enrollment.