Different Predicting Values Of VO2max Metrics On Cognitive Performances In Older Adults With Cardiovascular Risk Factors
Bibliographic record
Abstract
The use of better normalized cardiorespiratory fitness (VO2max) metrics (predicted value (VO2/pred) or lean mass (VO2/LM)) could improve our understanding of the relationship between VO2max and cognition in older adults with cardiovascular risk factors (CVRFs). PURPOSE: Determine the associations between two normalized VO2max metrics (VO2/pred and VO2/LM) and cognitive functions in older individuals with CVRFs. We hypothesized that the VO2/pred will correlate better with cognitive functions than VO2/LM. METHODS: 141 participants (70 ± 6 years) with CVRFs underwent a cardiopulmonary exercise test. Measured VO2max were expressed in percentage of predicted values (VO2/pred) and normalized by lean mass (VO2/LM) measured by bioimpedance. Participants completed a neuropsychological test battery assessing the MoCA, working memory (WM), processing speed (PS), executive function (EF), and verbal memory (VM) for which composite z-score were computed. Participants were classified in terciles for both metrics (VO2/pred : low fit (Lp), medium fit (Mp), high fit (Hp); VO2/LM: low fit (Lm), medium fit (Mm), high fit (Hm)). RESULTS: There was no age difference among the VO2/pred groups, but the Hp group had a higher proportion of women (p < 0.001) and more years of education (p = 0.032). Significant differences between VO2/pred groups were found in the MoCA (p = 0.004), WM (p = 0.039) and EF (p = 0.008) after adjusting for sex and education. VO2/pred was correlated with the MoCA (r = 0.348, p = 0.017), WM (r = 0.349, p = 0.007), PS (r = -0.303, p = 0.026) EF (r = -0.534, p < 0.001) and VM (r = 0.551, p = 0.001) after adjusting for education, sex, and age. For VO2/LM, there was no sex or education difference, but the Hm group was younger (p < 0.001). Significant differences between VO2/LM groups were observed in the MoCA (p = 0.002), WM (p = 0.032) and EF (p < 0.001) after adjusting for age. VO2/LM was correlated with the MoCA (r = 0.346, p = 0.014), WM (r = 0.349, p = 0.008), PS (r = -0.344, p = 0.004) and EF (r = -0.545, p < 0.001) when adjusting for education, sex, and age. CONCLUSIONS: Both higher aerobic fitness metrics are linked to higher MoCA scores and better cognitive functions. However, predicted value normalization appears to be a better predictor of VM while lean mass normalization seems to better predict EF in older adults with CVRFs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".