Relationship Between Trajectories of Trunk Fat Development in Emerging Adulthood and Cardiometabolic Risk at 36 Years of Age
Bibliographic record
Abstract
OBJECTIVE: This study examined trajectories of trunk fat mass (FM) accrual during emerging adulthood of individuals categorized, at 36 years of age, as having higher compared with lower scores of (1) metabolic risk and (2) blood pressure risk. METHODS: Fifty-five individuals from the Saskatchewan Pediatric Bone Mineral Accrual Study (1991-2017) were assessed from adolescence (mean [SD], 11.5 [1.8] years) through emerging adulthood (26.2 [2.2] years) and into early adulthood (35.6 [2.2] years) (median 11 visits per individual). Sex-specific median splits of continuous standardized risk scores at 36 years of age were created. Dual-energy x-ray absorptiometry-assessed trunk FM trajectories were analyzed using multilevel random effects models. RESULTS: Higher risk scores of blood pressure risk and metabolic risk had significantly steeper trajectories of fat development (0.45 [0.11] and 0.44 [0.11] log g, respectively) than the lower risk scores. Dietary fat was not related (P > 0.05). Physical activity was negatively related (-0.04 [0.02] physical activity score) to trunk FM development during emerging adulthood. CONCLUSIONS: Young adults with higher metabolic risk at 36 years of age had greater trunk FM development during both adolescence and emerging adulthood, supporting the need for intervention at both these critical periods of fat accrual.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".