Smoking, tobacco dependence, and neurometabolites in the dorsal anterior cingulate cortex
Bibliographic record
Abstract
Cigarette smoking has a major impact on global health and morbidity, and positron emission tomographic research has provided evidence for reduced inflammation in the human brain associated with cigarette smoking. Given the consequences of inflammatory dysfunction for health, the question of whether cigarette smoking affects neuroinflammation warrants further investigation. The goal of this project therefore was to validate and extend evidence of hypoinflammation related to smoking, and to examine the potential contribution of inflammation to clinical features of smoking. Using magnetic resonance spectroscopy, we measured levels of neurometabolites that are putative neuroinflammatory markers. N-acetyl compounds (N-acetylaspartate + N-acetylaspartylglutamate), glutamate, creatine, choline-compounds (phosphocholine + glycerophosphocholine), and myo-inositol, have all been linked to neuroinflammation, but they have not been examined as such with respect to smoking. We tested whether people who smoke cigarettes have brain levels of these metabolites consistent with decreased neuroinflammation, and whether clinical features of smoking are associated with levels of these metabolites. The dorsal anterior cingulate cortex was chosen as the region-of-interest because of previous evidence linking it to smoking and related states. Fifty-four adults who smoked daily maintained overnight smoking abstinence before testing and were compared with 37 nonsmoking participants. Among the smoking participants, we tested for associations of metabolite levels with tobacco dependence, smoking history, craving, and withdrawal. Levels of N-acetyl compounds and glutamate were higher, whereas levels of creatine and choline compounds were lower in the smoking group as compared with the nonsmoking group. In the smoking group, glutamate and creatine levels correlated negatively with tobacco dependence, and creatine correlated negatively with lifetime smoking, but none of the metabolite levels correlated with craving or withdrawal. The findings indicate a link between smoking and a hypoinflammatory state in the brain, specifically in the dorsal anterior cingulate cortex. Smoking may thereby increase vulnerability to infection and brain injury.
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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 | 0.000 |
| 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".