Low Birth Weight, Cumulative Obesity Dose, and the Risk of Incident Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: Obesity history may provide a better understanding of the contribution of obesity to T2DM risk. METHODS: 17,634 participants from the 1958 National Child Development Study were followed from birth to 50 years. Cumulative obesity dose, a measure of obesity history, was calculated by subtracting the upper cut-off of the normal BMI from the actual BMI at each follow-up and summing the areas under the obesity dose curve. Hazard ratios (HRs) for diabetes were calculated using Cox regression analysis. Three separate models compared the predictive ability of cumulative obesity dose on diabetes risk with the time-varying BMI and last BMI. RESULTS: In final models, 341 of 15,043 (2.27%) participants developed diabetes; male sex and low birth weight were significant confounding variables. Adjusted HRs were 1.080 (95% CI: 1.071, 1.088) per 10-unit cumulative obesity dose, 1.098 (95% CI: 1.080, 1.117) per unit of the time-varying BMI, and 1.146 (95% CI: 1.084, 1.212) per unit of the last BMI. Cumulative obesity dose provided the best predictive ability for diabetes. CONCLUSIONS: Cumulative obesity dose is an improved method for evaluating the impact of obesity history on diabetes risk. The link between low birth weight and T2DM is strengthened by adjusting for cumulative obesity dose.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".