Histamine signaling and metabolism identify potential biomarkers and therapies for lymphangioleiomyomatosis
Bibliographic record
Abstract
Inhibition of mTOR is the standard of care for lymphangioleiomyomatosis (LAM). However, this therapy has variable tolerability and some patients show progressive decline of lung function despite treatment. LAM diagnosis and monitoring can also be challenging due to the heterogeneity of symptoms and insufficiency of non-invasive tests. Here, we propose monoamine-derived biomarkers that provide preclinical evidence for novel therapeutic approaches. The major histamine-derived metabolite methylimidazoleacetic acid (MIAA) is relatively more abundant in LAM plasma, and MIAA values are independent of VEGF-D. Higher levels of histamine are associated with poorer lung function and greater disease burden. Molecular and cellular analyses, and metabolic profiling confirmed active histamine signaling and metabolism. LAM tumorigenesis is reduced using approved drugs targeting monoamine oxidases A/B (clorgyline and rasagiline) or histamine H1 receptor (loratadine), and loratadine synergizes with rapamycin. Depletion of Maoa or Hrh1 expression, and administration of an L-histidine analog, or a low L-histidine diet, also reduce LAM tumorigenesis. These findings extend our knowledge of LAM biology and suggest possible ways of improving disease management.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".