The Association of Asthma with Anxiety, Depression, and Mild Cognitive Impairment among Middle-Aged and Elderly Individuals in Saudi Arabia
Bibliographic record
Abstract
Asthma is a common chronic inflammatory condition with increasing global prevalence. There is some evidence highlighting the effect of asthma on brain functioning. In Saudi Arabia, limited studies have examined the relationship between asthma and mental health, including cognition and mood disorders in older adults in particular. In this study, we examine the association between asthma and mental health outcomes in middle-aged and elderly individuals in Riyadh, Saudi Arabia. In a cross-sectional study, 243 subjects were recruited from outpatient clinics between 2020-2021 (non-asthmatic: n = 159, asthmatic: n = 84). The Montreal Cognitive Assessment test, the Hospital Anxiety and Depression Scale, and the Asthma Control Test were used to assess cognition, anxiety and depression, and asthma control, respectively. Logistic regression analysis while controlling for covariates revealed an association between asthma and symptoms of anxiety and depression (OR = 2.40 [95% CI: 1.07-5.35]) and mild cognitive impairment (MCI) (OR = 1.80 [95% CI: 1.00-3.24]). Poorly controlled asthma increased the odds of anxiety cases (OR = 4.88 [95% CI: 1.09-17.2]). Stratifying analysis by age intervals revealed that asthma was associated with symptoms of anxiety and depression (OR = 2.5 [95% CI: 1.00-6.08]) in middle-aged patients only, while elderly asthmatics had increased odds of having MCI (OR = 7.4 [95% CI: 2.34-23.31]). These findings highlight the possible effects of asthma and its control on mental health among middle-aged and elderly individuals in Saudi Arabia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".