High muscular fitness has a powerful protective cardiometabolic effect in adults: influence of weight status
Bibliographic record
Abstract
BACKGROUND: Low levels of muscular fitness (MF) are recognized as an important marker of nutritional status and a predictor of metabolic complications, cardiovascular disease and death, however, the relationship between MF, body mass index (BMI) and the subsequent cardiometabolic protective effects has been less studied among Latin American populations. This study identified an association between MF and the cardiometabolic risk score index (CMRSI) and the lipid-metabolic cardiovascular risk index (LMCRI) in a wide sample of university students grouped according to their BMI. METHODS: Six thousand ninety five healthy males (29.6 ± 11.7 year-old) participated in the study. Absolute strength was measured using a T.K.K. analogue dynamometer (handgrip), and the participant's strength was then calculated relative to their body mass (MF/BM). The LMCRI was derived from the levels of triglycerides, low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), and glucose levels in a blood sample. The CMRSI was calculated by summing the standardized residuals (z-score) for waist circumference, total cholesterol, LDL-c, triglycerides, HDL-c, and median blood pressure. Subjects were divided into six subgroups according to BMI (normal vs. overweight/obese) and MF/BM tertiles (unfit, average, fit). RESULTS: The group of participants with low and moderate levels of MF/BM showed higher CMRSI values independent of BMI (P < 0.001). The group with normal BMI and high MF/BM had the highest levels of cardiometabolic protection. All overweight/obese BMI groups had significantly higher LMCRI values independent of the level of MF/BM (P < 0.001). CONCLUSIONS: Participants with high MF/BM showed reduced cardiometabolic risk, which increased significantly when they were within normal parameters.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".