FACTORS ASSOCIATED WITH COGNITIVE IMPAIRMENT IN ELDERLY PATIENTS WITH DIABETES MELLITUS
Bibliographic record
Abstract
To the Editor: Cognitive function in elderly subjects has recently attracted considerable attention as a complication related to diabetes mellitus (DM). Many reports indicate that several aspects of brain functions are impaired in older subjects with DM.1, 2 It has been hypothesized that inflammatory mechanisms play a role in the pathogenesis of several age-associated diseases. In addition, high plasma levels of inflammatory proteins reportedly increase the risk of cognitive decline in people without DM,3, 4 although the involvement of inflammation in DM-related cognitive impairment has not been investigated. In the present study, the association between clinical markers such as inflammatory proteins and cognitive function was investigated in nondemented elderly DM subjects. Forty-five outpatients (25 men and 20 women) ranging in age from 65 to 85 (mean age±standard deviation 72.6±5.7) were recruited at Chiaki Hospital (Aichi, Japan). Average hemoglobin A1c and body mass index were 7.0% and 24.6, respectively. Subjects with a diagnosis of dementia or whose score on the Mini-Mental State Examination (MMSE)1 was 23 or lower were excluded, as were those who had a clinical history or neurological symptoms of stroke. The cognitive assessment included MMSE; Word List Recall (immediate and delayed) from a subtest of the Alzheimer's Disease Assessment Scale;5 Digit Symbol Test, a subtest of the Wechsler Adult Intelligence Scale-Revised (WAIS-R);1 and the Stroop Color-Word Test.1 The clinical variables assessed were age, sex, years of education, DM duration, hemoglobin A1c, fasting serum glucose, immunoreactive insulin, body mass index, total cholesterol, high-density lipoprotein cholesterol, triglyceride, systolic blood pressure, diastolic blood pressure, statin use, antihypertensive medication, and smoking and the existence of diabetic microangiopathic complications (neuropathy, nephropathy, retinopathy). The levels of serum tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) were determined using commercially available enzyme-linked immunosorbent assays (Quantikine HS TNFα and Quantikine HS IL-6, R & D Systems, Minneapolis, MN). High-sensitivity C-reactive protein was measured using latex-enhanced assay.6 Comparisons between two groups were made using the Student t test and chi-square analysis. Logistic regression analysis was performed to determine whether the significant variables identified using the Student t test and the chi-square analysis were significant factors that would predict that the scores of the cognitive tests were in the lowest quartiles. Total cholesterol and TNF-α showed a significant difference between the lowest quartile and those in the other three quartiles on the WAIS-R Digit Symbol Test score, as did the distribution of the existence of diabetic neuropathy and the values of diastolic blood pressure for the verbal memory (delayed recall) test. Logistic regression analysis revealed that TNF-α (odds ratio (OR)=44.49, 95% confidence interval (CI)=1.38–1426.30) and the existence of neuropathy (OR=0.09, 95% CI=0.01–0.84) were significant predictors for decline on their respective tests (Table 1). The substitution of nephropathy for neuropathy or of systolic blood pressure for diastolic blood pressure did not change the results in the analysis of verbal memory (delayed recall). The highest quartile of serum TNF-α levels had significantly lower scores on the WAIS-R Digit Symbol Test and MMSE (31.9±9.1 vs 38.9±9.0 and 26.5±1.1 vs 27.9±1.4, respectively). It was not possible to devise significant models for other cognitive tests. Several studies on subjects without DM have demonstrated that inflammatory markers are associated with cognitive impairment.3, 4 Because overexpression of IL-6 led to progressive neuronal loss and decreased learning7 in an animal model, it is possible that inflammation itself could affect cognitive ability. Another potential mechanism could be through atherosclerosis. It has been suggested that inflammatory markers, including TNF-α, are involved in the process of atherogenesis.8 Serum inflammatory markers, including TNF-α, were found to be high in patients with brain infarction, and one study has demonstrated a relationship between inflammatory proteins and silent brain infarctions.9 In the present study, all potential subjects with a clinical history of strokes or focal neurological signs were excluded, although subjects with high serum TNF-α might have silent brain infarctions, which could affect cognitive functions. The factor associated with lower verbal memory scores measured using the Alzheimer's Disease Assessment Scale Word List Recall in the current study was the presence of neuropathy or nephropathy. Several mechanisms are involved in the pathogenesis of diabetic neuropathy, including vascular dysfunction, polyol pathway, and advanced glycation end-product accumulations.10 A common mechanism may be involved in DM-related central nervous system dysfunction and peripheral neuropathy. The current analysis demonstrates that the specific factors associated with decline were different in two different tests, suggesting that multiple factors may cause diabetes-related cognitive decline. Financial Disclosure: This work was supported by a Grant-in-Aid for Longevity Scientific Research H17-Cyouju-013 from the Ministry of Health, Labour and Welfare, Japan. Author Contributions: All authors had active roles in study concept and design, acquisition of data, analysis and interpretation of data, and preparation of manuscript. Sponsor's Role: None.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".