Impact of the Phenylalanine Hydroxylase Gene on Maternal Phenylketonuria Outcome
Bibliographic record
Abstract
OBJECTIVE: The aim of the present study was to examine to what extent maternal and offspring phenylalanine hydroxylase (PAH) genotypes in conjunction with maternal IQ and dietary control during pregnancy are related to cognitive development in offspring of women with phenylketonuria (PKU). METHODS: PAH gene mutations were determined in 196 maternal PKU subjects and their offspring. The women were grouped according to PAH genotype, which predicts the metabolic phenotype (severe PKU, mild PKU, and mild hyperphenylalaninemia [MHP]). IQ was determined in both the mothers (Wechsler Adult Intelligence Scale-Revised at >18 years) and their children (Wechsler Intelligence Scale for Children-Revised at > or = 6-7 years of age). RESULTS: According to PAH genotypes, 62% of the women exhibited severe PKU, 19% exhibited mild PKU, and 19% exhibited MHP. Maternal IQ increased, and the assigned phenylalanine (Phe) levels decreased with decreasing severity of PAH genotype. In offspring of mild maternal PKU, multiple regression analysis showed offspring IQ to be significantly related to maternal IQ but not to Phe exposure during pregnancy, which was <750 micromol/L in all cases of mild PKU. In offspring of mothers with severe PKU and average Phe exposure during pregnancy of 360 to 750 micromol/L, multiple regression analysis revealed both maternal IQ and Phe exposure to be significant predictors of offspring IQ. When average Phe exposure was <360 micromol/L, cognitive development was normal (mean IQ: 105), whereas an average Phe exposure of >750 micromol/L severely depressed offspring IQ (mean IQ: 56) in this group regardless of maternal IQ. It could not be documented that the offspring PAH genotype affects cognitive development. CONCLUSION: Female individuals with severe PKU should be offered a diet for a lifetime. If good metabolic control is established, then women with PKU will have children with IQ scores that are not influenced by their disease.
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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 | 0.002 |
| 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".