Metabolomic Relationships Between Lung Cancer and Alzheimer's Disease Using Serum Nuclear Magnetic Resonance Spectroscopy
Bibliographic record
Abstract
Lung cancer (LC) and Alzheimer's disease (AD) are both age-associated diseases with high rates of mortality. Studies have reported a possible inverse relationship between LC and AD incidences; however, possible shared molecular mechanisms have not been well investigated. Better characterizations of both diseases and their potential molecular relationships may advance the development of successful therapies for both LC and AD. Metabolomics, as a holistic study of the entire measurable metabolome, has the potential to probe into their metabolic connections. Herein, we used high-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) spectroscopy to study 36 human serum samples collected from primary lung adenocarcinoma patients with or without AD, or AD and related dementia (ADRD). We identified 88 metabolites with 66 metabolites differentiating LC patients from controls, and 80 metabolites discerning LC patients without ADRD from those with ADRD. Our results demonstrate the capability of metabolomics to reveal inversely dysregulated glycolysis, oxidative phosphorylation, and proline metabolism in LC and ADRD.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".