Estimation of PEX1-mediated Zellweger spectrum disorder births and population prevalence by population genetics modeling
Bibliographic record
Abstract
Purpose Zellweger Spectrum Disorder (ZSD) is a rare syndromic disorder characterized by impaired peroxisome assembly and function. Many cases are due to pathogenic variants in the PEX1 gene and are inherited in an autosomal recessive manner. As with many rare diseases, understanding the disease burden and scale of unmet need is challenging but required to support diagnosis, disease management, and development of therapies. We present a population-genetics-based model to estimate births and overall disease prevalence for patients in the United States, European countries, and Japan. Methods We utilized large-scale genetic diversity data sets to estimate the mutational burden per region and integrated genotype-phenotype relationships with real-world survival data to provide patient number estimates for severe, intermediate, and mild segments per age and country. Results We observed regional differences in the variant landscapes expected to contribute to PEX1 -mediated ZSD ( PEX1 -ZSD). Conservative prevalence estimates for the United States, United Kingdom, Germany, France, Italy, Spain, and Japan based solely on known pathogenic variants indicates nearly 500 patients in total. Incorporating predicted pathogenic variants into our model suggests an additional 260 patients with intermediate phenotype and 930 patients with mild phenotype, under the age of 30, across these countries. Conclusion Notably, our model indicates that a significant proportion of patients with intermediate/mild phenotype may go unrecognized by current diagnostic practices. This diagnosis independent model of patient number estimates provides additional insights into the broad spectrum of PEX1 -ZSD on a more global scale and can be used to inform health care strategies for these patients.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".