Characterization of Severity in Zellweger Spectrum Disorder by Clinical Findings: A Scoping Review, Meta-Analysis and Medical Chart Review
Bibliographic record
Abstract
Zellweger spectrum disorder (ZSD) is a rare, debilitating genetic disorder of peroxisome biogenesis that affects multiple organ systems and presents with broad clinical heterogeneity. Although severe, intermediate, and mild forms of ZSD have been described, these designations are often arbitrary, presenting difficulty in understanding individual prognosis and treatment effectiveness. The purpose of this study is to conduct a scoping review and meta-analysis of existing literature and a medical chart review to determine if characterization of clinical findings can predict severity in ZSD. Our PubMed search for articles describing severity, clinical findings, and survival in ZSD resulted in 107 studies (representing 307 patients) that were included in the review and meta-analysis. We also collected and analyzed these same parameters from medical records of 136 ZSD individuals from our natural history study. Common clinical findings that were significantly different across severity categories included seizures, hypotonia, reduced mobility, feeding difficulties, renal cysts, adrenal insufficiency, hearing and vision loss, and a shortened lifespan. Our primary data analysis also revealed significant differences across severity categories in failure to thrive, gastroesophageal reflux, bone fractures, global developmental delay, verbal communication difficulties, and cardiac abnormalities. Univariable multinomial logistic modeling analysis of clinical findings and very long chain fatty acid (VLCFA) hexacosanoic acid (C26:0) levels showed that the number of clinical findings present among seizures, abnormal EEG, renal cysts, and cardiac abnormalities, as well as plasma C26:0 fatty acid levels could differentiate severity categories. We report the largest characterization of clinical findings in relation to overall disease severity in ZSD. This information will be useful in determining appropriate outcomes for specific subjects in clinical trials for ZSD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".