Irisin and betatrophin responses to 9 h of passive heat exposure: Influence of age, hypertension, and type 2 diabetes
Bibliographic record
Abstract
Age- and disease-related metabolic responses to prolonged passive heat exposure are poorly understood. We evaluated serum irisin and betatrophin responses to 9 h of passive heat exposure (40°C, 9% relative humidity) in 19 young adults (19-31 years) and 37 older adults (61-78 years), including those with hypertension (HTN) and type 2 diabetes (T2D). Serum concentrations of irisin and betatrophin were assessed at baseline and at the end of the passive heat exposure using enzyme-linked immunosorbent assays. Generalized linear models were employed to examine changes over time and across subgroups, with fold-change computed as exponentiated coefficients. Younger adults exhibited significantly higher baseline irisin (2.81-fold, p = 0.003) and betatrophin (8.15-fold, p < 0.001) levels compared to older adults. Betatrophin concentrations were further reduced in older adults with HTN (0.55-fold, p = 0.02) and T2D (0.55-fold, p = 0.047). Despite inducing physiological strain, 9 h of passive heat exposure did not alter circulating irisin or betatrophin concentrations in any group (p > 0.66). While passive heat exposure alone does not trigger metabolic hormone responses, lower baseline concentrations may indicate reduced cellular heat tolerance with aging. These findings highlight potential metabolic vulnerabilities in older and heat-sensitive populations and build on previous findings in this series. ClinicalTrials.gov identifier: NCT04353076.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".