New insights into the landscape of <i>ALPL</i> gene variants in patients with hypophosphatasia from the Global <scp>HPP</scp> Registry
Bibliographic record
Abstract
Hypophosphatasia (HPP) is a rare, inherited metabolic disease characterized by low tissue-nonspecific alkaline phosphatase activity due to ALPL gene variants. We describe ALPL variants from the observational, prospective, multinational Global HPP Registry. Inclusion in the analysis required a diagnosis of HPP, low serum ALP activity, and ≥1 ALPL variant. Of 1176 patients enrolled as of September 2022, 814 met inclusion criteria in Europe (48.9%), North America (36.7%), Japan (10.2%), Australia (2.6%), and elsewhere (1.6%). Most patients (74.7%) had 1 ALPL variant; 25.3% had ≥2 variants. Nearly all patients (95.6%) had known disease-causing variants; 4.4% had variants of uncertain significance. Disease-causing variants were predominantly missense (770/1556 alleles). The most common variants were c.571G>A (102/1628 alleles), c.1250A>G (66/1628 alleles), and c.1559del (61/1628 alleles). Variant profiles were generally consistent, except in Japan, where a higher proportion of patients (68.7%) had ≥2 ALPL variants, likely because more had disease onset before age 6 months (53.0% vs. 10.1%-23.1% elsewhere). Frameshift mutations (61/164 alleles) and inframe deletions (7/164 alleles) were more common in Japan. Twenty-three novel variants were discovered, each in a single geographic region, predominantly Europe. Analyses confirmed previously known ALPL variants, identified novel variants, and characterized geographic variation in frequency and type of ALPL variants in a large population.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".