Metabolomics-based prediction model for diabetes: A comprehensive analysis of biomarkers and machine learning approaches
Bibliographic record
Abstract
AIMS: To develop a prediction model for diabetes using metabolomics data and to evaluate various machine learning approaches and identify the most effective framework for disease prediction. METHODS: A comprehensive analysis was conducted on the Qatar Biobank dataset comprising metabolomics profiles, instrument measurements, and clinical diagnoses from 450 Qatari nationals. Targeted metabolites were selected based on correlation strength with diabetes status. Five machine learning models (Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Neural Network) were evaluated for their predictive performance using metrics including accuracy, precision, recall, F1 score, and ROC AUC. RESULTS: Among 450 individuals, 9.33 % (n = 42) were diagnosed with diabetes. Correlation analysis identified 140 metabolites significantly associated with diabetes status (p < 0.05). The most strongly correlated metabolites included glucose (r = 0.281, p < 0.0001), mannose (r = 0.247, p < 0.0001), and 1,5-anhydroglucitol (r = -0.297, p < 0.0001). Logistic Regression demonstrated superior performance with the highest accuracy (93.3 %), F1 score (0.625), and ROC AUC (0.941) compared to other models. CONCLUSION: Metabolomics data can effectively predict diabetes status, with logistic regression providing the optimal balance of performance and interpretability. The identified metabolites offer potential biomarkers for early diabetes detection and monitoring. This model could serve as a valuable tool for clinical risk assessment and personalized preventive 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".