The continuously evolving phenotype of succinic semialdehyde dehydrogenase deficiency
Bibliographic record
Abstract
The objective of the study is to evaluate the evolving phenotype and genetic spectrum of patients with succinic semialdehyde dehydrogenase deficiency (SSADHD) in long-term follow-up. Longitudinal clinical and biochemical data of 22 pediatric and 9 adult individuals with SSADHD from the patient registry of the International Working Group on Neurotransmitter related Disorders (iNTD) were studied with in silico analyses, pathogenicity scores and molecular modeling of ALDH5A1 variants. Leading initial symptoms, with onset in infancy, were developmental delay and hypotonia. Year of birth and specific initial symptoms influenced the diagnostic delay. Clinical phenotype of 26 individuals (median 12 years, range 1.8-33.4 years) showed a diversifying course in follow-up: 77% behavioral problems, 76% coordination problems, 73% speech disorders, 58% epileptic seizures and 40% movement disorders. After ataxia, dystonia (19%), chorea (11%) and hypokinesia (15%) were the most frequent movement disorders. Involvement of the dentate nucleus in brain imaging was observed together with movement disorders or coordination problems. Short attention span (78.6%) and distractibility (71.4%) were the most frequently behavior traits mentioned by parents while impulsiveness, problems communicating wishes or needs and compulsive behavior were addressed as strongly interfering with family life. Treatment was mainly aimed to control epileptic seizures and psychiatric symptoms. Four new pathogenic variants were identified. In silico scoring system, protein activity and pathogenicity score revealed a high correlation. A genotype/phenotype correlation was not observed, even in siblings. This study presents the diversifying characteristics of disease phenotype during the disease course, highlighting movement disorders, widens the knowledge on the genotypic spectrum of SSADHD and emphasizes a reliable application of in silico approaches.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".