Steatotic liver disease diagnosed in a 24-year-old woman with Rett syndrome: a case report
Bibliographic record
Abstract
Rett syndrome (RTT) is a neurodevelopmental disorder caused by mutations in the MECP2 gene, potentially disrupting lipid metabolism and leading to dyslipidemia (DLD) and steatotic liver disease (SLD). Although SLD has been described in RTT mouse models, it remains undocumented in humans. We herein describe a 24-year-old woman with RTT who was evaluated for abnormal liver enzymes. Imaging revealed hepatic steatosis, and transient elastography showed a controlled attenuation parameter of 342 dB/m and stiffness of 7.1 kPa. Laboratory investigations excluded secondary causes, including insulin resistance, metabolic syndrome, alcohol use, and new medications. Her Homeostatic Model Assessment for Insulin Resistance score was 1.8, her hemoglobin A1c concentration was 4.8%, and her lipid profile showed elevated triglycerides and low-density lipoprotein, consistent with DLD. Liver biopsy confirmed SLD. This case supports the hypothesis that MECP2 mutations in RTT disrupt lipid metabolism through a unique pathophysiologic mechanism, increasing the risk of DLD and SLD independently of traditional metabolic syndrome factors. It highlights the importance of early screening for liver disease in patients with RTT, despite their young age, to prevent complications. Additionally, it validates MECP2-null mouse models as reliable tools for investigating future therapeutic strategies in RTT.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".