Ethnicity matters: A Systematic Review and Meta-Analysis of the Non-Linear Relationship Between Alcohol Consumption and Prevalence and Incidence of Hepatic Steatosis
Bibliographic record
Abstract
BACKGROUND: Fatty liver (hepatic steatosis) is one of the most common diseases globally, with increasing prevalence. The role of alcohol consumption in the development of hepatic steatosis has not been systematically examined. METHODS: We searched Medline, Embase, and ProQuest Dissertations & Theses Global for original data on the relationship between alcohol consumption and hepatic steatosis measured by non-invasive imagery, excluding studies conducted in participants <18years, or subgroups related to viral and drug-induced liver disease. We identified 18 articles reporting adjusted data (Japan=11, other high-income countries=7). Random-effect categorical meta-analyses (<20g/day pure alcohol consumption vs non-drinkers) and dose-response meta-analyses for the whole range of alcohol consumption were conducted. RESULTS: In total, 99,370 participants and 25,662 cases of hepatic steatosis were included. In Japan, low alcohol consumption was consistently associated with substantially reduced incidence and prevalence of hepatic steatosis compared to non-drinkers (RR for <20g pure alcohol/day=0.75, 95% CI: 0.71-0.79, I(2)=0%). No overall association was found in other countries (RR=1.05, 95% CI: 0.86-1.30, I(2)=84%). Dose-response analyses in Japan (up to 80g/day) showed an inverse relationship in men and a J-shape in women. CONCLUSIONS: Alcohol consumption showed a complex association with hepatic steatosis with substantial differences by ethnicity and sex. Low alcohol consumption was beneficial in Japan with good epidemiological evidence, whereas there was no association in other countries. However, heterogeneity was large in countries other than Japan. More and higher quality research in diverse ethnic populations is needed to further clarify this relationship.
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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.041 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".