Carbohydrate Craving by Alcohol‐Dependent Men During Sobriety: Relationship to Nutrition and Serotonergic Function
Bibliographic record
Abstract
BACKGROUND: Several studies report reduced serotonin (5HT) in alcohol-dependent subjects. Furthermore, alcohol increases 5HT in animals. Thus, alcohol dependence may be an attempt to self-medicate reduced 5HT. Relevant to this, reducing 5HT increases carbohydrate intake, and several studies report increased carbohydrate intake in alcohol-dependent subjects. Like alcohol, carbohydrate increases 5HT. We hypothesized that a subgroup of the alcohol-dependent population self-medicates reduced 5HT with alcohol and alternatively with carbohydrate when not drinking. METHODS: Three groups were recruited: a high carbohydrate craving alcohol-dependent group (n = 10), a low carbohydrate craving alcohol-dependent group (n = 11), and a nonaddicted control group (n = 12). All groups were placed on a high-carbohydrate, low-protein diet for 2 days and then a high-protein, low-carbohydrate diet for 2 days. The effects of diet on mood, alcohol craving, stress, and 5HT were measured. RESULTS: Although both alcohol-dependent groups had similar alcohol cravings at baseline, only the carbohydrate-craving alcohol-dependent group craved alcohol significantly more when under the stress of the research protocol. The carbohydrate-craving alcohol-dependent subjects presented with distinct personality disorders and were uniquely sensitive to the adverse effects of carbohydrate on mood. Diet had a unique effect on 5HT in the high carbohydrate craving alcohol-dependent group. The results of platelet 5HT uptake demonstrated that the high-protein, low-carbohydrate diet significantly increased Km values of high carbohydrate craving alcohol-dependent subjects, whereas it reduced the Km values of both non-carbohydrate-craving alcohol-dependent subjects and nonaddicted controls. CONCLUSION: Carbohydrate-craving alcohol-dependent subjects are a distinct subgroup of the alcohol-dependent population.
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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 | 0.001 |
| 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".