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Record W2024820843 · doi:10.1016/j.ymgme.2011.11.191

Treatable inborn errors of metabolism causing intellectual disability: A systematic literature review

2011· review· en· W2024820843 on OpenAlexaff
Clara van Karnebeek, Sylvia Stöckler

Bibliographic record

VenueMolecular Genetics and Metabolism · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsBritish Columbia Children's Hospital
Fundersnot available
KeywordsIntellectual disabilityInborn error of metabolismMedicinePopulationHomocysteineNewborn screeningInternal medicinePediatricsPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Intellectual disability ('developmental delay' at age<5 years) affects 2.5% of population worldwide. Recommendations to investigate genetic causes of intellectual disability are based on frequencies of single conditions and on the yield of diagnostic methods, rather than availability of causal therapy. Inborn errors of metabolism constitute a subgroup of rare genetic conditions for which an increasing number of treatments has become available. To identify all currently treatable inborn errors of metabolism presenting with predominantly intellectual disability, we performed a systematic literature review. METHODS: We applied Cochrane Collaboration guidelines in formulation of PICO and definitions, and searched in Pubmed (1960-2011) and relevant (online) textbooks to identify 'all inborn errors of metabolism presenting with intellectual disability as major feature'. We assessed levels of evidence of treatments and characterised the effect of treatments on IQ/development and related outcomes. RESULTS: We identified a total of 81 'treatable inborn errors of metabolism' presenting with intellectual disability as a major feature, including disorders of amino acids (n=12), cholesterol and bile acid (n=2), creatine (n=3), fatty aldehydes (n=1); glucose homeostasis and transport (n=2); hyperhomocysteinemia (n=7); lysosomes (n=12), metals (n=3), mitochondria (n=2), neurotransmission (n=7); organic acids (n=19), peroxisomes (n=1), pyrimidines (n=2), urea cycle (n=7), and vitamins/co-factors (n=8). 62% (n=50) of all disorders are identified by metabolic screening tests in blood (plasma amino acids, homocysteine) and urine (creatine metabolites, glycosaminoglycans, oligosaccharides, organic acids, pyrimidines). For the remaining disorders (n=31) a 'single test per single disease' approach including primary molecular analysis is required. Therapeutic modalities include: sick-day management, diet, co-factor/vitamin supplements, substrate inhibition, stemcell transplant, gene therapy. Therapeutic effects include improvement and/or stabilisation of psychomotor/cognitive development, behaviour/psychiatric disturbances, seizures, neurologic and systemic manifestations. The levels of available evidence for the various treatments range from Level 1b,c (n=5); Level 2a,b,c (n=14); Level 4 (n=45), Level 4-5 (n=27). In clinical practice more than 60% of treatments with evidence level 4-5 is internationally accepted as 'standard of care'. CONCLUSION: This literature review generated the evidence to prioritise treatability in the diagnostic evaluation of intellectual disability. Our results were translated into digital information tools for the clinician (www.treatable-id.org), which are part of a diagnostic protocol, currently implemented for evaluation of effectiveness in our institution. Treatments for these disorders are relatively accessible, affordable and with acceptable side-effects. Evidence for the majority of the therapies is limited however; international collaborations, patient registries, and novel trial methodologies are key in turning the tide for rare diseases such as these.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.007
Bibliometrics0.0140.013
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.021
GPT teacher head0.285
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations211
Published2011
Admission routes1
Has abstractyes

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