Association of renal impairment with cognitive dysfunction in the Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA)
Bibliographic record
Abstract
INTRODUCTION: Chronic kidney disease (CKD) is a recognized risk factor for cognitive impairment. Identification of those at greatest risk of cognitive impairment may facilitate earlier therapeutic intervention. This study evaluated associations between estimated glomerular filtration rate (eGFR) and cognitive function in the Northern Ireland Cohort for the Longitudinal Study of Ageing. METHODS: Data were available for 3412 participants ≥50 years of age living in non-institutionalized settings who attended a health assessment between February 2014 and March 2016. Measures of serum creatinine (SCr) and cystatin C (cys-C) were used for eGFR. Cognitive function was measured using the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE). RESULTS: Following adjustment for potential confounders, a single unit decrease in eGFR was significantly associated with reduced cognitive function defined by an MMSE ≤24/30 {eGFR calculated using serum cys-C [eGFRcys]: β = -0.01 [95% confidence interval (CI) -0.001 to -0.01], P = 0.01} and MoCA <26/30 [β = -0.01 (95% CI -0.002 to -0.02), P = 0.02]. Similarly, CKD Stages 3-5 were also associated with a moderate increase in the odds of cognitive impairment (MMSE ≤24) following adjustment for confounders [eGFRcys: odds ratio 2.73 (95% CI 1.38-5.42), P = 0.004]. CONCLUSIONS: Decreased eGFRcys was associated with a significantly increased risk of cognitive impairment in a population-based cohort of older adults. However, there was no evidence of an association between cognitive impairment and the more commonly used eGFR calculated using SCr. eGFRcys may offer improved sensitivity over eGFRcr in the determination of renal function and associated risk of cognitive impairment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".