Depression and Hypomagnesemia as Independent and Synergistic Predictors of Cognitive Impairment in Older Adults Post-COVID-19: A Prospective Cohort Study
Bibliographic record
Abstract
Background/Objectives: Cognitive impairment in older adults has emerged as a growing public health concern, particularly in relation to COVID-19 infection and its associated neuropsychiatric symptoms. The identification of modifiable risk factors may contribute to the development of targeted preventive strategies. This study aimed to assess predictors of cognitive impairment in older adults with and without recent SARS-CoV-2 infection. Methods: A prospective cohort study was conducted from June 2023 to March 2024 at a tertiary hospital in western Mexico. Adults aged 65 years or older with confirmed SARS-CoV-2 infection within the previous six months, along with uninfected controls, were enrolled. Cognitive function (Mini-Mental State Examination), depression (PHQ-9), anxiety (Geriatric Anxiety Inventory), insomnia (Insomnia Severity Index), functional status (Katz Index and Lawton–Brody Scale), and laboratory markers were evaluated at baseline, three months, and six months. The primary outcome was cognitive impairment at six months. Independent predictors were identified using a multivariable generalized linear mixed-effects model. Results: Among the 111 participants, 20 (18.8%) developed cognitive impairment within six months. Low serum magnesium (adjusted risk ratio [aRR] 2.73; 95% CI 1.04–7.17; p = 0.041) and depression (aRR 5.57; 95% CI 1.88–16.48; p = 0.002) were independently associated with a higher risk. A significant synergistic among COVID-19, depression, and hypomagnesemia was observed (RR 44.30; 95% CI 9.52–206.21; p < 0.001), corresponding to the group with simultaneous presence of all three factors compared to the group with none. Conclusions: Depression and hypomagnesemia appear to be independent predictors of cognitive impairment in older adults with recent COVID-19 infection. These findings suggest potential targets for prevention and support the implementation of routine neuropsychiatric and biochemical assessments in this population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".