Prediction models for incident Type 2 diabetes mellitus in the older population: KORA S4/F4 cohort study
Bibliographic record
Abstract
BACKGROUND: The aim was to derive Type 2 diabetes prediction models for the older population and to check to what degree addition of 2-h glucose measurements (oral glucose tolerance test) and biomarkers improves the predictive power of risk scores which are based on non-biochemical as well as conventional clinical parameters. METHODS: Oral glucose tolerance tests were carried out in a population-based sample of 1353 subjects, aged 55-74 years (62% response) in Augsburg (Southern Germany) from 1999 to 2001. The cohort was reinvestigated in 2006-2008. Of those individuals without diabetes at baseline, 887 (74%) participated in the follow-up. Ninety-three (10.5%) validated diabetes cases occurred during the follow-up. In logistic regression analyses for model 1, variables were selected from personal characteristics and additional variables were selected from routinely measurable blood parameters (model 2) and from 2-h glucose, adiponectin, insulin and homeostasis model assessment of insulin resistance (HOMA-IR) (model 3). RESULTS: Age, sex, BMI, parental diabetes, smoking and hypertension were selected for model 1. Model 2 additionally included fasting glucose, HbA(1c) and uric acid. The same variables plus 2-h glucose were selected for model 3. The area under the receiver operating characteristic curve significantly increased from 0.763 (model 1) to 0.844 (model 2) and 0.886 (model 3) (P<0.01). Biomarkers such as adiponectin and insulin did not improve the predictive abilities of models 2 and 3. Cross-validation and bootstrap-corrected model performance indicated high internal validity. CONCLUSIONS: This longitudinal study in an older population provides models to predict the future risk of Type 2 diabetes. The OGTT, but not biomarkers, improved discrimination of incident diabetes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".