POS0389 PREVALENCE OF HYPOPHOSPHATASIA IN RHEUMATOLOGY AND OSTEOPOROSIS CARE CLINICS: A SYSTEMATIC LITERATURE REVIEW
Notice bibliographique
Résumé
Background: Hypophosphatasia (HPP) is a rare, inherited metabolic disease caused by deficient tissue-nonspecific alkaline phosphatase (ALP) activity, usually associated with ALPL variants and characterized by compromised bone mineralization, fractures/pseudofractures, muscle weakness, musculoskeletal pain, impaired mobility, early dental loss, and reduced quality of life. Because of its heterogeneous clinical presentation, HPP can be misdiagnosed as osteoporosis, fibromyalgia, or a form of arthritis, among other disorders. In addition, patients presenting with symptoms of both HPP and another of these diseases are often diagnosed with the more common condition. Findings from an increasing number of studies using screening algorithms to identify patients with HPP suggest that the disease may be more prevalent than previously recognized. Objectives: As part of a broader systematic review of the prevalence of HPP identified using screening algorithms in various clinical settings, we sought to describe the use of HPP screening algorithms, specifically in rheumatology and osteoporosis clinics, and to summarize the prevalence of HPP in these settings. Methods: A systematic review of full-length studies published between 2015 and 2024 was conducted using PubMed and search terms related to HPP, prevalence, ALP, and conditions, including rheumatology and osteoporosis. The search was restricted to articles in English. Data were extracted on study characteristics, number and percentage of individuals with persistently low ALP activity (generally ≥2–3 measures below normal), and number of patients identified with HPP. The prevalence of HPP in the study population, including individuals with persistently low ALP activity, is reported. Results: Of 331 publications identified, 59 met criteria for full-text review and 28 were analyzed for data extraction. Of the 28 publications, 3 reported studies in patients treated in a rheumatology setting and 2 in patients in an osteoporosis setting (Table 1). The remaining 23 studies were within settings other than rheumatology and osteoporosis and are not presented here. One additional study with full text in German and abstract in English was identified outside of the PubMed search and included given its relevance and study setting within a rheumatology department. Overall, the percentage of outpatients with persistently low ALP activity ranged from 0.38% (7/1839) to 1.22% (28/2289). In 2 studies in rheumatology outpatients, the prevalence of genetically confirmed HPP among those with persistently low ALP was 46.4% (13/28) and 56.5% (13/23). One study of patients with fibromyalgia and ALP activity below the upper limit of normal reported that 9.3% (57/611) had persistently low ALP, but this did not exclude known secondary causes of low ALP and there was no further evaluation to confirm HPP. Among rheumatology and internal medicine inpatients with persistently low ALP activity, 6.0% (11/182) were genetically confirmed to have HPP. Among patients seen at an osteoporosis clinic who had persistently low ALP, prevalence of genetically confirmed HPP was 57.1% (4/7). In another study in patients seen at a UK osteoporosis clinic, 87.5% (14/16) of those with persistently low ALP had potentially pathogenic ALPL variants, although specific signs and symptoms of HPP were not stated. Conclusion: The prevalence of HPP appears to be high in rheumatology and osteoporosis clinics among adults with persistently low ALP activity. Among adults with persistently low ALP in these settings, at least 46.4% to 57.1% had genetically confirmed HPP. These findings should be a call to action for practitioners in rheumatology and osteoporosis clinics to test for ALP and emphasize the importance of investigating low ALP activity, as HPP is highly prevalent among patients with persistently low ALP. REFERENCES: [1] Alonso N, et al. J Bone Miner Res 2020;35:657-661. https://doi.org/10.1002/jbmr.3928. [2] Feurstein J, et al. Orphanet J Rare Dis 2022;17:435. https://doi.org/10.1186/s13023-022-02572-7. [3] García-Fontana C, et al. Sci Rep 2019;9:9569. https://doi.org/10.1038/s41598-019-46004-2. [4] Injean P, et al. ACR Open Rheumatol 2023;5:524-8. https://doi.org/10.1002/acr2.11591. [5] Karakostas P, et al. Z Rheumatol 2022;81:513-9. https://doi.org/10.1007/s00393-021-00994-5. [6] Kishnani PS, et al. Mol Genet Metab 2017;122:4-17. https://doi.org/10.1016/j.ymgme.2017.07.010. [7] Larid G, et al. RMD Open 2024;10:e004316. https://doi.org/10.1136/rmdopen-2024-004316. [8] Ng E, et al. Osteoporosis Int 2023;34:327-37. https://doi.org/10.1007/s00198-022-06597-3. Table 1 . Acknowledgements: This study was sponsored by Alexion, AstraZeneca Rare Disease, Boston, MA, USA. Editorial support was provided by Peloton Advantage, LLC, an OPEN Health company, and funded by Alexion, AstraZeneca Rare Disease. Disclosure of Interests: Valentin S. Schäfer Valentin S. Schäfer has received lecture honoraria from AbbVie, Novartis, BMS, Chugai, Celgene, Medac, Sanofi, Lilly, Hexal, Pfizer, Janssen, Roche, Shire, Onkowissen, Royal College London, Boehringer Ingelheim, UCB Fresenius, Alexion; consulting fees from Novartis, Chugai, AbbVie, Celgene, Sanofi, Lilly, Hexal, Pfizer, Amgen, BMS, Roche, Gilead, Medac, Boehringer Ingelheim, Alexion, Valentin S. Schäfer has received research support from Novartis, Hexal, Lilly, Roche, Celgene, University of Bonn, Boehringer Ingelheim, Butterfly IQ, MEDAC, Alexion, DGRH, and BMBF, Katie Moss Katie Moss has had advisory board participation/presentations from Alexion, AstraZeneca Rare Disease, Katie Moss has received educational grants from AbbVie, Amgen, Alexion, AstraZeneca Rare Disease, Novartis, and UCB, Carolina Tornero Carolina Tornero has received consulting fees from Amgen and Alexion and honoraria from AbbVie, Theramex, UCB, Amgen, and Gedeon Richter, Carolina Tornero has received grant support from Alexion, Julia F. Charles Julia F. Charles has received honoraria from AbbVie, Alexion, Ultragenyx, Kyowa Kirin, and AbbVie, Julia F. Charles has received grant support from Novartis, Jonathan Adachi Jonathan D. Adachi has received payment/honoraria and consulting fees from Alexion, Amgen, and Sandoz, Jonathan D. Adachi has received grants from Amgen, Andrea J. Singer Andrea J. Singer has received honoraria Agnovos, Amgen, Astellas, Pfizer, Radius Health, and UCB, Andrea J. Singer has received consulting fees from Agnovos, Amgen, Astellas, Pfizer, Radius Health, and UCB, Jessica Hinman Jessica Hinman has received consulting fees from Alexion, Georgiana Wegmann Liliana-Georgiana Wegmann is an employee of and owns stock/options in Alexion, AstraZeneca Rare Disease, Shona Fang Shona Fang is an employee of and owns stock/options in Alexion, AstraZeneca Rare Disease. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».