Magnetic resonance imaging reveals elevated aortic pulse wave velocity in obese and overweight adolescents
Bibliographic record
Abstract
The aortic pulse wave velocity (PWV) measured via cardiac magnetic resonance (CMR) can be used to non-invasively assess changes in arterial stiffness and potential underlying vascular dysfunction. This technique could unmask early arterial dysfunction in overweight and obese youth at risk for cardiovascular disease. We sought to determine the association between vascular stiffness, percentage body fat, body mass index (BMI), and cardiac function in adolescents across the weight spectrum through both CMR and standard applanation tonometry (AT)-based PWV measurements. PWV and left-ventricular cardiac function were assessed using 3.0 T CMR in obese and overweight (OB/OW) participants (n = 12) and controls (n = 7). PWV was also estimated via carotid-femoral AT. OB/OW participants did not differ from healthy-weight controls regarding cardiometabolic risk factors or physical activity levels, but there was a trend towards higher levels of triglycerides in obese/overweight participants (P = 0.07). Mean PWV was higher in obese participants when corrected for age and sex (P = 0.01), and was positively associated with BMI (β = 0.51, P = 0.02). PWV estimated through AT was not significantly different between groups. Cardiac function measured by left-ventricular ejection fraction z-score was inversely associated with mean PWV (β = -0.57, P = 0.026). Increasing arterial stiffness and decreasing cardiac function were evident among our overweight and obese cohort. PWV estimated by CMR could detect early increases in arterial stiffness vs. traditional AT measurements of PWV.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".