Polymorphisms in Alcohol Dehydrogenase (ADH): A case study on the effects of ADH and ALDH on alcoholism among Native American population
Bibliographic record
Abstract
Genetic variations in an individual affects the way alcohol is metabolized in the body. Alcohol Dehydrogenase (ADH) and Aldehyde Dehydrogenase (ALDH) are the two known enzymes that participate in alcohol metabolism. Polymorphisms of these enzymes are reported to make one more or less susceptible to alcoholism in some ethnic groups. The current study is a review of various articles highlighting the effects of ADH and ALDH on different populations.\nA study of 26 Native American, 21 Inuit, and 17 caucasian ethnic groups revealed the influence of ADH and ALDH on alcohol dependence. In one of the studies, different ADH allele populations were studied and found that the presence of ADH1B*1 allele led to increase in alcoholism whereas ADH1B*2 and ADH1B*3 alleles led to decrease in alcoholism. In another study, each participant was given alcohol intravenously until their blood alcohol was at approximately 125 mg.%. The rate of metabolism was calculated using body weight, concentration of alcohol, and the time it took for blood alcohol levels to reach a desired amount. The rate of decline of alcohol metabolism among Caucasian was 0.370 mg.% per minute, Native American 0.259 mg.% per minute, and the Inuit population 0.264 mg.% per minute. The study found that the Native American and Inuit rate of decline were similar, and the alcohol metabolism is much slower than the Caucasian counterparts. ADH polymorphism affects the ability to metabolize alcohol at different rates among different ethnicities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".