Mental Health and Kidneys: The Interplay Between Cognitive Decline, Depression, and Kidney Dysfunction in Hospitalized Older Adults
Bibliographic record
Abstract
Background: As societies rapidly age, the prevalence of mental health disorders and chronic kidney disease (CKD) is simultaneously rising, and data on the link between these conditions remain inconclusive. This study aimed to investigate the associations among cognitive impairment, depression, and kidney involvement in elderly patients. Methods: A cross-sectional analysis was conducted among hospitalized patients aged ≥65 years. Standardized tools such as the geriatric depression scale (GDS) and Montreal Cognitive Assessment (MoCA) were used to assess depression and cognitive impairment, and kidney function was evaluated using eGFR and albuminuria. Bivariate and multivariate logistic regressions were performed to identify associations. Results: The study population consisted of 719 participants with a median age of 80 years. Kidney and mental health issues were highly prevalent: CKD was identified in 59.4%, cognitive impairment in 74%, and depression in 61.9% of patients. Patients with CKD were older and exhibited lower MoCA scores (p = 0.001), higher GDS scores (p = 0.007), reduced albumin (p < 0.001), lower hemoglobin levels (p < 0.001), and elevated C-reactive protein (p < 0.001). Increased albuminuria was associated with poorer cognition (p < 0.001) but showed no correlation with GDS scores. Additionally, worse cognitive scores (p = 0.001) and increased depression symptoms (p < 0.001) were correlated with declining estimated glomerular filtration rate (eGFR). Conclusions: Cognitive impairment and depressive symptoms are highly prevalent among elderly hospitalized patients. Cognitive decline correlates with increased albuminuria and reduced eGFR, while depression worsens with declining kidney function. These findings highlight the complex interplay between renal health and neuropsychiatric conditions in aging populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".