Low Cardiorespiratory Fitness Levels and Elevated Blood Pressure
Bibliographic record
Abstract
Individuals with poor cardiorespiratory fitness have higher blood pressure than fit individuals. Individuals with low fitness levels also tend to be characterized by higher visceral adiposity compared with physically fit individuals. We tested the hypothesis that the relationship between low fitness and elevated blood pressure could be related, at least in part, to the higher level of visceral adipose tissue often found among unfit individuals. This study included 407 asymptomatic, nondiabetic participants. Visceral adipose tissue was assessed by computed tomography, and fitness was measured by a progressive submaximal physical working capacity test. Participants in the highest visceral adipose tissue tertile showed the highest systolic and diastolic blood pressures, whereas participants in the highest fitness tertile had the lowest blood pressure values (P<0.001). When participants were classified into fitness tertiles and then subdivided on the basis of visceral adipose tissue (high versus low), participants with a high visceral adipose tissue had higher systolic and diastolic blood pressure values (P=0.01), independent of their fitness category. Linear regression analyses showed that age and visceral adipose tissue, but not fitness, predicted systolic blood pressure (r(2)=0.11 [P<0.001], 0.12 [P<0.001], and 0.01 [P value nonsignificant], for age, visceral adipose tissue, and fitness, respectively) and diastolic blood pressure (r(2)=0.17 [P<0.001], 0.14 [P<0.001], and 0.01 [P value nonsignificant], for age, visceral adipose tissue, and fitness, respectively). Individuals with high visceral adipose tissue levels have higher blood pressure, independent of their fitness. Visceral adipose tissue may represent an important clinical target in the management of elevated blood pressure.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".