Weight loss with or without exercise: effect on classic and novel biomarkers of cardiovascular risk (884.24)
Bibliographic record
Abstract
We previously reported that novel metabolomics‐derived biomarkers (dicarboxyl/hydroxyl acylcarnitines (DC/OH‐ACs) and amino acid‐related metabolites (AAs)) are independent predictors of CAD, CV events, and death and add predictive value to classic CV risk factors in cardiac catheterization patients. Here, we evaluated associations between traditional CV risk factors (FRS; Framingham Risk Score) and DC/OH‐ACs and AAs in sedentary, overweight adults (N=46) randomized to 6 months of: AHA weight maintenance diet (CONTROL), 25% calorie restriction (CR), 12.5% CR + 12.5% increase in exercise energy expenditure (CR+EX), or 15% weight loss via low calorie diet (890 kcals/d; LCD). At baseline, serum DC/OH‐ACs and AAs predicted 97% of FRS. At 6 months, weight loss was greater in LCD (‐11±1 kg) than CR or CR+EX (‐8±1 kg) and in all groups relative to CONTROL (‐1±1 kg). Only in CR+EX did FRS change significantly (‐10±4%) or differ from the change in FRS in CONTROL (+7±4%). Across all groups, changes in serum DC/OH‐ACs and AAs predicted 97% of FRS change. Thus, CR+EX appears to be more effective than CR or LCD in reducing CV risk. Further, DC/OH‐ACs and AAs may have strong predictive value in overweight adults. Grant Funding Source : Supported by U01 AG20478 (ER) and F32 HL112575 (CRM). and
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".