Association between childhood obesity and subsequent Type 1 diabetes: a systematic review and meta‐analysis
Bibliographic record
Abstract
AIMS: To review and synthesize the published evidence on the possible association between childhood obesity and the subsequent risk of Type 1 diabetes. METHODS: The PubMed database was systematically searched for studies using childhood obesity, BMI or %weight-for-height as the exposure variable and subsequent Type 1 diabetes as the outcome. Studies were only included if assessment of obesity preceded the diagnosis of Type 1 diabetes. RESULTS: Eight case-control studies and one cohort study were included, comprising a total of 2658 cases. Of these nine studies, seven reported a significant association between childhood obesity, BMI or %weight-for-height and increased risk for Type 1 diabetes. Meta-analysis of the four studies that reported childhood obesity as a categorical exposure produced a pooled odds ratio of 2.03 (95% CI 1.46-2.80) for subsequent Type 1 diabetes; however, in those studies, age at obesity assessment varied from age 1 to 12 years. A dose-response relationship was supported by a continuous association between childhood BMI and subsequent Type 1 diabetes in a meta-analysis of five studies (pooled odds ratio 1.25 (95%CI 1.04-1.51) per 1 sd higher BMI). CONCLUSION: There is overall evidence for an association between childhood obesity, or higher BMI, and increased risk of subsequent Type 1 diabetes. Several theories have been proposed for a causal relationship. Reduction in Type 1 diabetes should be considered as a potential additional benefit of preventing childhood obesity.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.024 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".