Macrophage Immunometabolism in Pulmonary Homeostasis and Chronic Lung Diseases
Bibliographic record
Abstract
Macrophages play a central role in maintaining pulmonary immune homeostasis and responding to injury. In the lung, alveolar macrophages modulate their metabolic profiles to support essential functions such as microbial clearance, inflammation resolution, and tissue repair. Recent studies have shown that these metabolic adaptations are not merely byproducts of activation but represent key regulators of macrophage behavior. In chronic lung diseases including asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF), macrophage metabolism is pathologically reprogrammed, contributing to persistent inflammation in asthma and COPD, or to unrestrained fibrotic remodeling in IPF, and ultimately leading to ongoing tissue damage. Specifically, in asthma, type 2 cytokine signaling promotes alternative macrophage activation, accompanied by increased fatty acid oxidation and disrupted lipid mediator profiles. COPD-associated macrophages exhibit mitochondrial dysfunction, enhanced glycolysis, and iron overload, impairing bacterial phagocytosis and amplifying oxidative stress. In IPF, macrophages simultaneously engage glycolytic and oxidative pathways while losing regulatory metabolites such as itaconate, supporting persistent fibrogenic signaling. These disease-specific metabolic features sustain maladaptive macrophage phenotypes and constitute promising targets for therapeutic intervention. This review outlines current knowledge of macrophage immunometabolism in the lung and its contribution to chronic respiratory diseases. It also discusses strategies to restore metabolic balance, including the use of antioxidants, metabolic modulators, and targeted drug delivery. Understanding macrophage metabolism may open new avenues for treating chronic lung diseases at the level of cellular function.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".