To evaluate the prevalence, subtypes and risk associations of mild cognitive impairment in elderly Indian individuals with type 2 diabetes
Bibliographic record
Abstract
Abstract Background Diabetes is an important modifiable risk factor for dementia. The prevalence of mild cognitive impairment (MCI) in type 2 diabetes (T2D) is high (You et al, 2021). This data is largely derived using screening tools (MMSE or MoCA), with little information on MCI subtypes. Evidence from the South Asian region is particularly scarce, where prevalence of diabetes is high, and is seen a decade earlier compared to Caucasians. Method We have established a cohort of elders (³ 60 years of age), with and without diabetes, visiting a tertiary care hospital in North India (from 2019, ongoing). The participants underwent an interview to collect information on their demographic profile, risk associations, vascular health, and other comorbidities. Culturally validated neuropsychological battery was used for cognitive evaluation. MoCA was done in a subset of patients. Result We recruited 840 participants in the cohort, 683 with T2DM and 157 without T2DM (controls). The mean age of the cohort was 65.3±4.5 years, 65.2% were males. Risk profile is described in Table 1. The cognitive z scores of participants with T2DM ranged from ‐0.25 to +0.44. The worst affected domain was attention, working memory and executive functions (Table 2). Using an actuarial definition of MCI (Bondi MW et al, 2014), the prevalence in participants with T2D was 29.7%. 17.3% had amnestic impairment, 14.8% dysexecutive, 14.2% language, 2.4% visuo‐perceptual and 9.1% multidomain impairment. The cognitive raw scores (by dementia risk stratification) are provided in Table 3. On logistic regression, lower education 3.45(2.09,5.68), depression 2.04(1.26,3.29), poor sleep quality 1.77(1.15,2.71), family income 1.70(1.22,2.38), followed by age 1.05(1.00,1.10) had a significant association with global low cognition. Conclusion Roughly one‐third of elders with T2D visiting tertiary care services in North India have mild cognitive impairment. Amnestic subtype is the most frequent. Attention, working memory and executive functions are worst affected. Enhancing the cognitive reserve, mood and sleep quality may be important targets in prevention of cognitive decline in the elderly population with T2D.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".