Treatable Inborn Errors of Metabolism Causing Intellectual Disability: A Review and Diagnostic Approach
Notice bibliographique
Résumé
Abstract Intellectual disability (ID) is a devastating condition, affecting between 2% and 3% of children and adults globally. Early recognition of underlying conditions associated with ID that are amenable to treatment can dramatically improve health outcomes and decrease burdens to patients, families and society. However, current recommendations to investigate genetic causes of ID are based on frequencies of single conditions and diagnostic yields, rather than availability of causal therapy. Inborn errors of metabolism (IEM) constitute a subgroup of rare genetic conditions for which an increasing number of treatments has become available. Our systematic literature review identified 81 IEMs which present with ID as a prominent feature and are amenable to causal therapy. Therapeutic modalities were also identified and prioritised. The evidence created by our research has been translated into a two‐tiered diagnostic protocol currently implemented in our institution, the BC Children's Hospital. The first tier of this protocol includes easily available biochemical group tests, with the potential to identify 65% of the currently known treatable IDs. This first tier can be applied by community paediatricians and specialists in all patients presenting with global developmental delay/ID without referral to a specialised centre. The second tier includes specific tests, which should be ordered based on the differential diagnosis, established by physicians experienced with rare treatable IDs. A digital tool (an App www.treatable‐id.org ) supports the protocol, and serves as information portal for all users, ranging from students to specialists. Key Concepts: Inborn errors of metabolisms (IEMs) constitute a subgroup of rare genetic conditions for which an increasing number of treatments has become available. Early recognition of IEMs allows for timely initiation of treatment to prevent or minimise brain damage. Early recognition and treatment of IEMs is crucial for improving health outcomes and reducing disease burden for affected individuals, their families and societies. A total of 81 treatable IEM presenting with intellectual disability as a major feature were identified in the systematic review. Sixty‐two percent of IDs can be reliably detected through a panel of readily available metabolic screening tests on blood and urine. The remainder of treatable IDs is diagnosed via disease‐specific tests. A protocol indicating metabolic group tests capturing 65% of treatable IDs enables community‐based paediatricians and other specialists to perform the first tier diagnostic work‐up of treatable Ids. Normal newborn screening results in a patient with ID of unknown origin should not reassure the clinician that treatable metabolic disorders have been ruled out, as the patient might not have been screened for a particular disease or at all. Therapeutic modalities are accessible and most with acceptable side effects. The development of the treatable ID App capitalises on new digital and social media to raise awareness for rare treatable diseases and increase the likelihood of early diagnosis in children with ID of unknown origin.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,015 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».