Revisiting trajectories of BMI in youth: An in‐depth analysis of differences between BMI and other adiposity measures
Bibliographic record
Abstract
Abstract Objective Body mass index (BMI) is used to identify trajectories of adiposity in youth, but it does not distinguish fat‐ from fat‐free‐mass. There are other inexpensive measures of adiposity which might better capture fat‐mass in youth The objective of this study is to examine differences between sex‐specific trajectories of BMI and other adiposity indicators (subscapular and triceps skinfold thickness, waist circumference, waist‐to‐height ratio) which may better capture fat‐mass in youth. Methods Data come from four cycles of a longitudinal cohort of 1293 students in Montréal, Canada at ages 12, 15, 17 and 24. Group‐based trajectory models identified sex‐specific adiposity trajectories among participants with data in ≥3 cycles (n = 417 males; n = 445 females). Results There were six trajectory groups in males and females for all five indicators, except for waist circumference (seven) in both sexes and triceps skinfold thickness (four) and waist‐to‐height ratio (five) in females. Most trajectories indicated linear increases; only the skinfold thickness indicators identified a decreasing trajectory. While all indicators identified a trajectory with high levels of adiposity, they differed in the number and relative size of trajectories pertaining to individuals in lower half of the adiposity distribution. Conclusion BMI is a satisfactory indicator of adiposity in youth if the aim of the trajectory analysis is to identify youth with excess adiposity, a known risk factor for cardiometabolic outcomes in adulthood.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".