The Association Between Diabetes Mellitus and Mild Behavioral Impairment Among Mild Cognitive Impairment: Findings from Singapore
Bibliographic record
Abstract
BACKGROUND: Mild behavioral impairment (MBI) describes persistent behavioral changes in later life as an at-risk state for dementia. While cardiovascular risk factors (CVRFs) are linked to dementia, it is uncertain how CVRFs are associated with MBI. OBJECTIVE: To determine the prevalence of MBI and its association with CVRFs among cognitively normal (CN) and mild cognitive impairment (MCI) individuals in Singapore. METHODS: 172 individuals (79 CN and 93 MCI) completed the MBI-checklist (MBI-C). The prevalence of MBI and MBI-C sub-domain characteristics among CN and MCI were examined. Regression models evaluated the relationships between MBI-C sub-domain scores with CVRFs. RESULTS: The prevalence of MBI and mean MBI-C total score were significantly higher among MCI than CN (34.4%versus 20.3%, p = 0.022 and 7.01 versus 4.12, p = 0.04). The highest and lowest-rated sub-domains among CN and MCI were impulse dyscontrol and abnormal thoughts and perception respectively. Within the MCI cohort, a higher proportion of individuals with diabetes mellitus (DM) had MBI compared to individuals without DM (28.1%versus 10.4%, p = 0.025). The interaction of DM and MCI cohort resulted in significantly higher mean MBI-C total, decreased motivation, emotional dysregulation, impulse dyscontrol, and abnormal thoughts and perception sub-domain scores. CONCLUSION: The prevalence of MBI is higher among a Singapore cohort compared to Caucasian cohorts. The associations of DM with both the presence and severity of MBI among MCI suggest that DM may be a risk factor for MBI. The optimization of DM may be a potential therapeutic approach to improve clinical outcomes among MCI with MBI.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".