Prevalence and Risk Factors of Metabolic-Associated Fatty Liver Disease in Children with Down Syndrome at a Tertiary Care Center
Bibliographic record
Abstract
Background: The global rise of metabolic-associated fatty liver disease (MAFLD) in children is particularly concerning in high-risk groups such as those with Down Syndrome (DS), who have an elevated risk of obesity and insulin resistance. Despite increasing recognition of MAFLD in pediatric populations, data on its prevalence and risk factors among children with DS in Canada remain limited. Method: This retrospective study reviewed medical records of children with DS at the CHEO Down Syndrome Clinic (2013–2023). A diagnosis of MAFLD required evidence of hepatic steatosis on imaging, lab markers, or biopsy, along with the presence of metabolic risk features. Demographic, laboratory, and diagnostic data were analyzed. Results: Among 503 children with DS (231 females, 271 males; median age: 172 months), 54 (10.7%) had MAFLD. The MAFLD group was older (median age: 205 vs. 163 months, p = 0.0002) and had higher BMI (31.39 vs. 20.5, p < 0.0001). Most cases (47/54) were diagnosed via ultrasound, and 49/54 met MAFLD criteria due to excessive adiposity. Lab results showed a median ALT of 35 U/L, triglycerides of 4.4 mmol/L, and LDL cholesterol of 2.59 mmol/L. FibroScan in 13 children revealed a median transient elastography of 5.3 kPa. BMI was the strongest predictor of MAFLD (OR: 1.2, 95% CI: 1.1–1.2). Conclusions: The DS clinic-based prevalence of MAFLD underscores the need for proactive screening and early intervention. BMI was the strongest predictor, emphasizing targeted management strategies. Further research is needed to refine diagnostic approaches and improve outcomes.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".