Association between asthma, corticosteroids and allostatic load biomarkers: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Allostatic load, a measure of early ageing or 'wear and tear' from adapting to environmental challenges, has been suggested as a framework with which to understand the stress-related disruption of multiple biological systems which may be linked to asthma. Considering the socioeconomic context is also critical given asthma and allostatic overload are more common in lower socioeconomic groups. AIMS: Estimate the relationship between allostatic load and its constituent biomarkers, asthma and corticosteroid prescribing while controlling for socioeconomic status. METHODS: (a nationally representative survey of UK community-dwelling adults) waves 1-3 (2009-2012) allowed the identification of a sex-specific risk profile across 12 biomarkers used to construct an Allostatic Load Index for a sample of 9816 adults. Regression analyses were used to examine the association of asthma status and corticosteroid prescriptions with allostatic load and its constituent biomarkers while controlling for socioeconomic status (n=9805). RESULTS: Subjects with currently treated asthma and no corticosteroid prescription have an allostatic load 1.21 times higher than those without asthma (p<0.001). Asthmatic subjects in receipt of inhaled corticosteroids had an allostatic load, approximately 1.12 times higher than those without asthma (p<0.001). This association persisted in sensitivity analyses and appeared to be driven by an association with specific biomarkers (dehydroepiandrosterone-sulfate, waist-to-height ratio and C-reactive protein). CONCLUSION: Early ageing, in the form of a higher allostatic load, was present even in the mildest asthma group not receiving inhaled corticosteroids. Allostatic load is helpful in understanding the increased all-cause mortality and multimorbidity observed in asthma.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".