Brain Docosahexaenoic Acid [DHA] Incorporation and Blood Flow Are Increased in Chronic Alcoholics: A Positron Emission Tomography Study Corrected for Cerebral Atrophy
Bibliographic record
Abstract
OBJECTIVE: Chronic alcohol dependence has been associated with disturbed behavior, cerebral atrophy and a low plasma concentration of docosahexaenoic acid (DHA, 22∶6n-3), particularly if liver disease is present. In animal models, excessive alcohol consumption is reported to reduce brain DHA concentration, suggesting disturbed brain DHA metabolism. We hypothesized that brain DHA metabolism also is abnormal in chronic alcoholics. METHODS: We compared 15 non-smoking chronic alcoholics, studied within 7 days of their last drink, with 22 non-smoking healthy controls. Using published neuroimaging methods with positron emission tomography (PET), we measured regional coefficients (K*) and rates (J(in)) of DHA incorporation from plasma into the brain of each group using [1-(11)C]DHA, and regional cerebral blood flow (rCBF) using [(15)O]water. Data were partial volume error corrected for brain atrophy. Plasma unesterified DHA concentration also was quantified. RESULTS: Mean K* for DHA was significantly and widely elevated by 10-20%, and rCBF was elevated by 7%-34%, in alcoholics compared with controls. Unesterified plasma DHA did not differ significantly between groups nor did whole brain J(in), the product of K* and unesterified plasma DHA concentration. DISCUSSION: Significantly higher values of K* for DHA in alcoholics indicate increased brain avidity for DHA, thus a brain DHA metabolic deficit vis-à-vis plasma DHA availability. Higher rCBF in alcoholics suggests increased energy consumption. These changes may reflect a hypermetabolic state related to early alcohol withdrawal, or a general brain metabolic change in chronic alcoholics.
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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) | 0.002 | 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".