Distinct developmental trajectories of body mass index and diabetes risk: A 5‐year longitudinal study of Chinese adults
Bibliographic record
Abstract
AIMS/INTRODUCTION: This longitudinal study aimed to explore whether distinct developmental trajectories of body mass index (BMI) would be predictive of diabetes risk in general Chinese adults. MATERIALS AND METHODS: A total of 4,519 participants aged >18 years who were free of diabetes in 2011 (baseline of the current analysis) were enrolled in this study. All participants completed a medical examination every year during 2011-2016, and BMI levels were measured two to six (average 5.6) times. Group-based trajectory modeling was applied to identify BMI trajectories over time. New-onset diabetes was confirmed in 2016. RESULTS: During 2011-2016, four distinct BMI trajectories were identified according to BMI range and changing pattern over time: "low" (19.6%), "moderate" (33.4%), "moderate-high" (33.4%) and "high" (13.6%). A total of 168 (3.7%) new-onset diabetes cases were confirmed in 2016. Compared with the "low" BMI trajectory, participants in the "high" BMI trajectory were at significantly higher risk for new-onset diabetes (adjusted relative risk 3.24, 95% confidence interval 1.27-8.24). Notably, BMI trajectories based on the first four or three annual BMI tests yielded similar results. By contrast, no significant correlation was found between categories of baseline BMI and new-onset diabetes in 2016 after multivariate adjustment. CONCLUSIONS: The present results show that distinct BMI trajectories, even identified using just four or three annual BMI tests, are significantly associated with new-onset diabetes. Monitoring BMI trajectories over time might provide an important approach to identify subpopulations at higher risk for developing diabetes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".