High prevalence of diabetes among young First Nations Peoples with metabolic dysfunction-associated steatotic liver disease: a population-based study in Australia
Bibliographic record
Abstract
BACKGROUND: Liver disease is an important contributor to the mortality gap between First Nations Peoples and non-Indigenous Australian adults. Despite a high burden of metabolic comorbidities among First Nations Peoples, data about the epidemiology of metabolic dysfunction-associated steatotic liver disease (MASLD) in this population is scarce. METHODS: A retrospective analysis of all adults hospitalized with MASLD or metabolic dysfunction-associated steatohepatitis (MASH) with/without cirrhosis during 2007-2019 in the state of Queensland was performed. Patients were followed from the first admission with MASLD/MASH (identified based on validated algorithms) to decompensated cirrhosis and overall mortality. We explored differences according to Indigenous status using Multivariable Cox regression. FINDINGS: 439 First Nations Peoples and 7,547 non-Indigenous Australians were followed for a median of 4.6 years (interquartile range 2.7-7.2). Overall, women were overrepresented, but more so in the First Nations cohort (72.7% vs. 57.0%, p < 0.001). First Nations patients were younger, a higher proportion lived in remote and socioeconomic disadvantaged areas, and had higher comorbidity compared to non-Indigenous Australians (all p < 0.001). Diabetes, the most common comorbidity affecting both groups, was overrepresented in First Nations Peoples versus non-Indigenous Australians (43.5% vs. 30.8%, p < 0.001, respectively). Nineteen (4.3%) First Nations Peoples and 332 (4.4%) of non-Indigenous patients progressed to cirrhosis decompensation (9.0% [95%CI 4.5-17.7] vs. 7.7% [95%CI 6.6-8.9; p = 0.956] respectively within 10 years). In multivariable analysis, there was no association between Indigenous status and progression to decompensated cirrhosis (p = 0.759) and survival (p = 0.437). CONCLUSIONS: This study provides the first population-based epidemiological data on MASLD in First Nations Australians. The high prevalence of diabetes (that is associated with advanced fibrosis and liver disease mortality) among young First Nations Peoples with MASLD raises concern about future risk of progressive liver disease in this patient population. These data highlight the importance of early identification of MASLD, and providing culturally appropriate intervention to reduce disease progression in parallel with the management of cardiometabolic comorbidities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".