Can Adoption of Regular Exercise Later in Life Prevent Metabolic Risk for Cardiovascular Disease?
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of chronic exercise training initiated later in life upon development of metabolic markers of cardiovascular disease risk. RESEARCH DESIGN AND METHODS: Two inception cohorts of previously sedentary healthy adults aged 55-75 years at baseline (1989), one initiating regular supervised physical exercise training and the other a geographical similar sedentary control, were assessed for anthropometric, biochemical, and clinical markers of the metabolic syndrome and comorbidity over 10 years. RESULTS: At baseline, active individuals aged 68 years compared with sedentary individuals aged 67 years had similar fitness levels (5.7 vs. 5.8 metabolic equivalents). At follow-up, complete data were obtained for 161 active and 136 sedentary subjects. Drop out occurred primarily because of failure to adhere to the exercise regimen and poor physical health for active and sedentary individuals, respectively. More metabolic abnormalities were seen in the sedentary group than in the active group for one or two (64 vs. 36%, P < 0.001) and three or more (35 vs. 22%, P < 0.003) abnormalities, respectively. In those assessed at follow-up, the sedentary group compared with the active group had lower fitness levels (5.0 [13.8% decrease] vs. 5.9 [3.5% increase] metabolic equivalents), had a greater likelihood of a positive exercise electrocardiogram or symptom (32 vs. 10%, P < 0.001), and had more comorbid conditions (Charlson Comorbidity Index score 0.9 vs. 0.4, P < 0.01). CONCLUSIONS: Higher fitness achieved over 10 years of regular exercise training in older adults was associated with reduced development of metabolic risk factors for cardiovascular disease, fewer exercise-induced cardiac abnormalities, and reduced comorbidity.
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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 | 0.006 |
| 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 | 0.001 | 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".