Impact of elevated body mass index (BMI) on cognitive functioning and inflammation in persons with post-COVID-19 condition: a secondary analysis
Bibliographic record
Abstract
Abstract Background: Individuals who have recovered from the acute stage of SARS-CoV-2 infection may be at risk of developing post-COVID-19 condition (PCC), characterised by a spectrum of persisting, non-specific, and functionally impairing symptoms across multiple organ systems. Obesity has been implicated as a risk factor for PCC, mediated by chronic systemic inflammation. The foregoing has also been separately reported to mediate cognitive dysfunction in PCC. Methods: This is a post-hoc analysis of a randomised, double-blinded, placebo-controlled clinical trial evaluating vortioxetine treatment for cognitive impairments in persons with PCC who received vortioxetine or placebo for eight weeks. This analysis comprises baseline data, examining the impact of BMI on cognitive functioning measured by the Digit Symbol Substitution Test (DSST) and Trails Making Tests (TMT)-A/B, as well as inflammation, via serum c-reactive protein (CRP) and erythrocyte sedimentation rate (ESR). Results: Complete data from 70 participants were statistically analysed and adjusted for age and sex. BMI was negatively correlated with performance on the DSST ( β = −0.003, p = 0.047), TMT-A ( β = −0.006, p = 0.025), and TMT-B ( β = −0.006, p = 0.002). BMI was positively correlated with serum CRP (unstandardized β = 0.193, standardized β = 0.612, p < 0.001) and ESR ( β = 0.039, p < 0.001) levels. Conclusion: We observed a significant negative correlation between BMI and cognitive functioning, and a significant positive correlation between BMI and inflammation in persons with PCC, suggesting a bidirectional interplay between BMI, PCC, and cognitive function; individuals with an elevated BMI may be at a greater risk of developing PCC and/or presenting with greater cognitive deficits mediated by chronic systemic inflammation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".