The Tri-ponderal Mass Index is associated with adiposity in adolescent type 2 diabetes mellitus: a cross-sectional analysis
Bibliographic record
Abstract
Abstract Pediatric type 2 diabetes mellitus (T2DM) patients are often overweight or obese, yet there are no validated clinical measures of adiposity to stratify cardiometabolic risk in this population. The tri-ponderal mass index (TMI, kg/m3) has recently been reported as a measure of adiposity in children, but there has been no validation of the association of TMI with adiposity in pediatric T2DM. We hypothesized that in children with T2DM, the TMI can serve as a more accurate measure of adiposity when compared to BMI z-score, and that it is associated with components of the metabolic syndrome. This is a cross-sectional secondary data analysis from the Improving Renal Complications in Adolescents with Type 2 Diabetes Through REsearch (iCARE) study (n = 116, age 10.20–17.90 years). Spearman’s correlations and multivariable regression were used in the analyses. When compared to DXA, TMI demonstrated significant correlation with total adiposity versus BMI z-score (TMI r = 0.74, p-value < 0.0001; BMI z-score r = − 0.08, p-value 0.403). In regression analyses, TMI was associated with WHtR (B = 35.54, 95% CI 28.81, 42.27, p-value < 0.0001), MAP dipping (B = 1.73, 95% CI 0.12, 3.33, p-value = 0.035), and HDL (B = − 5.83, 95% CI − 10.13, − 1.54, p-value = 0.008). In conclusion, TMI is associated with adiposity and components of the metabolic syndrome in pediatric T2DM patients.
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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 | 0.002 |
| 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) | 0.001 | 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".