Metabolomic signature of pediatric diabetic ketoacidosis: key metabolites, pathways, and panels linked to clinical variables
Bibliographic record
Abstract
BACKGROUND: Diabetic ketoacidosis (DKA) is a serious complication of type 1 diabetes (T1D), arising from relative insulin deficiency and leading to hyperglycemia, ketonemia, and metabolic acidosis. Early detection and treatment are essential to prevent severe outcomes. This pediatric case-control study utilized plasma metabolomics to explore metabolic alterations associated with DKA and to identify predictive metabolite patterns. METHODS: We examined 34 T1D participants, including 17 patients admitted with severe DKA and 17 age- and sex-matched individuals in insulin-controlled states. A total of 215 plasma metabolites were analyzed using proton nuclear magnetic resonance and direct-injection liquid chromatography/mass spectrometry. Multivariate statistical methods, machine learning techniques, and bioinformatics were employed for data analysis. RESULTS: After adjusting for multiple comparisons, 65 metabolites were found to differ significantly between the groups (28 increased and 37 decreased). Metabolomics profiling demonstrated 100% accuracy in differentiating severe DKA from insulin-controlled states. Random forest analysis indicated that classification accuracy was primarily influenced by changes in ketone bodies, acylcarnitines, and phosphatidylcholines. Additionally, groups of metabolites (ranging in number from 8 to 18) correlated with key clinical and biochemical variables, including pH, bicarbonate, glucose, HbA1c, and Glasgow Coma Scale scores. CONCLUSIONS: These findings underscore significant metabolic disturbances in severe DKA and their associations with critical clinical indicators. Future investigations should explore if metabolic alterations in severe DKA can identify patients at increased risk of complications and/or guide future therapeutic interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".