Treatable Inborn Errors of Metabolism Causing Intellectual Disability: A Review and Diagnostic Approach
Bibliographic record
Abstract
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.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".