Treatment of hypoglycemia due to a rare pathogenic variant in <scp><i>AKT2</i></scp> with waxy maize heat‐modified starch
Bibliographic record
Abstract
Key Clinical Message The gain‐of‐function AKT2 c.49G>A variant causes hypoketotic hypoglycemia with variable associated features. Due to lack of effective medications, treatment is primarily supportive. This report suggests waxy maize heat is a viable treatment option. Abstract The serine–threonine kinase AKT2 is a critical mediator of insulin's anabolic effects, particularly cellular glucose uptake. The gain‐of‐function c.49G>A, p.(Glu17Lys) AKT2 variant results in hypoketotic hypoglycemia with suppressed insulin and free fatty acid levels due to constitutive activation of the insulin signaling cascade. Although biochemical similarities exist among the eight individuals identified to date, the associated phenotype varies considerably. Treatment of these patients remains challenging, consisting primarily of frequent feeds with uncooked cornstarch. We describe a female with hemihypertrophy, developmental delay, and dysmorphic features who presented to our center with hypoglycemic seizures at age 6 months. Critical sample revealed hypoketotic hypoglycemia, undetectable insulin, and suppressed free fatty acids. Molecular testing confirmed a pathogenic c.49G>A, p.(Glu17Lys) AKT2 mutation. Glycemic control was initially difficult to establish, with recurrent hypoglycemia despite high glucose infusion rates. Following in‐hospital administration of waxy maize heat‐modified starch at age 4‐years, she remained euglycemic overnight, despite a previous report showing no benefit compared to uncooked cornstarch in an infant with the same mutation. Our report suggests waxy maize heat‐modified starch is a viable treatment option for patients with activating c.49G>A AKT2 mutations and provides further evidence of a broad phenotypic spectrum.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".