Exploring the Syndemic of Steatotic Liver Disease, Socioeconomic Inequities and Cancer Risk in the <scp>UK</scp> Biobank
Bibliographic record
Abstract
BACKGROUND: Steatotic liver disease (SLD), formerly known as fatty liver disease, is associated with increased cancer risk. However, the impact of socioeconomic inequities remains understudied. This study investigates the relationship between SLD, socioeconomic position (SEP) and cancer risk using a syndemic framework. METHODS: Using UK Biobank data, we defined metabolic dysfunction-associated SLD (MASLD), MASLD with increased alcohol intake (MetALD) and alcoholic liver disease (ALD), based on the Fatty Liver Index, cardiometabolic criteria and alcohol consumption. SEP was derived via latent class analysis using education, household income and employment. We used Cox proportional hazards models to examine the associations between MASLD, MetALD and ALD and the incidence of any, obesity-related and digestive cancers. We then evaluated the combined effect of these SLD subcategories and SEP on cancer outcomes. RESULTS: Among 325 476 individuals, 91 651 had MASLD, 25 649 MetALD and 8005 ALD. Over 11.7 years median follow-up, 35 775 first incident cancers occurred (15 426 obesity-related; 6959 digestive). MASLD, MetALD and ALD were each associated with an increased risk of all cancer outcomes (hazard ratios [HR] ranging from 1.09 to 1.73). The combination of MASLD and low SEP was associated with an increased risk of any (HR: 1.14, 95% CI: 1.08-1.19), obesity-related (HR: 1.25, 95% CI: 1.16-1.33) and digestive cancers (HR: 1.37, 95% CI: 1.23-1.53). Similar trends were observed for individuals with MetALD or ALD and low SEP across all cancer outcomes. CONCLUSION: SLD is independently associated with increased risk of any, obesity-related and digestive cancers. These risks are amplified by socioeconomic inequities, highlighting the need for integrated approaches that consider both clinical and social determinants of health.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".