The association of hyperglycemia and kidney dysfunction with cognitive impairment in community‐dwelling older adults in Taiwan
Bibliographic record
Abstract
Abstract Background Diabetes was considered one of the risk factors for dementia. However, one‐third of diabetic patients also suffer from kidney disease. It remains unclear the intercorrelation between blood sugar and kidney function that affects the occurrence of mild cognitive impairment for people with normal blood sugar levels. This study aimed to explore the relationship between hyperglycemia and kidney dysfunction (KD) on cognitive impairment in older adults. Method This is an eight‐year cohort study (2011‐2019). A total of 509 non‐demented older adults were recruited from the elderly health checkup program at baseline (2011‐2013) with three biannual follow‐ups. Global and domain‐specific (memory, attention, executive function, and language) cognition were assessed by the Taiwanese version of Montreal Cognitive Assessment (MoCA‐T) and a battery of neuropsychological tests, respectively. MCI was defined as a MoCA‐T score of 22 or 23. Hyperglycemia was defined as fasting blood glucose greater than 100 mg/dL. KD was defined as having either glomerular filtration rate < 60 ml/min/1.73 m2 or proteinuria. The generalized linear mixed model was utilized to examine the effects of hyperglycemia and KD on cognitive performance. Result The prevalence of MCI was 9.4% at baseline. Hyperglycemia [adjusted odds ratio (aOR) = 1.91] and KD (aOR = 1.98) were significantly associated with the risk of MCI over eight years after adjusting for covariates, respectively. In addition, hyperglycemia and KD were significantly associated with poor performance of logical memory (β:‐0.51 to ‐0.01), executive function (β:‐0.25 to ‐0.003), attention (β:‐0.07 to ‐0.03), and verbal fluency (β:‐0.08 to ‐0.005). Hyperglycemia and KD was associated with an increased risk of MCI participants aged less than 75 years (aOR = 2.19–2.21) and in APOE ε4 non‐carriers (aOR = 2.02–2.15). Conclusion Our finding indicated that poor glycemic control and kidney function were associated with an increased risk of MCI or poor performance of cognitive domains in older adults. Cognitive impairment may be reversible through optimal management of glycemia and kidney disease; interventions (e.g., self‐management education, dietary restriction, and sufficient physical activity, etc.) at the early stage will be helpful to prevent dementia.
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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 | 0.000 | 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".