Relationship of Body Composition With Middle Cerebral Artery Hemodynamic Using Compositional Data Analysis in Middle-Age Adults From Toledo Study for Healthy Aging
Bibliographic record
Abstract
Excess adipose tissue may promote chronic systemic inflammation and oxidative stress, causing endothelial damage. Early evidence indicates that obesity may be associated with poorer cerebral perfusion. The purpose of this study was to examine the relationship between body composition and cerebral hemodynamics. A total of 248 middle-aged adults (50-58 years old; 55% women) underwent a ramp test on a cycle-ergometer until volitional exhaustion. Gas exchange was assessed on a breath-by-breath basis. Mean middle cerebral artery velocity (MCAv) was measured using transcranial Doppler, and pulsatility index (PI) was calculated. Body composition was assessed by dual X-ray absorptiometry. Statistical analyses were performed using a compositional data approach including a 3-compartment model for body composition (trunk fat mass, extremities fat mass, and fat-free mass). The unadjusted models for the whole sample showed that trunk fat mass relative to other compartments was negatively associated with MCAvrest, MCAvmax, and gain, and positively associated with PImax; extremities fat mass relative to other compartments was positively associated with MCAvrest and MCAvmax, and negatively associated with PImax; and fat-free mass relative to other compartments was positively associated with PImax. These associations were sex-dependent, remaining in the women's subgroup. However, after adjusting for confounders, these associations became nonsignificant, except for PImax in the whole sample and women's subgroup. These findings suggest a possible association between cerebral hemodynamics and body composition in middle-aged adults, highlighting sex-specific differences. Moreover, our results indicate that higher trunk fat mass relative to other compartments may negatively affect cerebral hemodynamics, reducing MCAv and increasing PImax.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".