Continuous glucose monitoring‐derived time in range is associated with changes in cognitive function test scores in Japanese patients with type 2 diabetes mellitus
Bibliographic record
Abstract
AIMS: Type 2 diabetes mellitus (T2DM) is known to be a risk factor for cognitive dysfunction and dementia. Time in range (TIR), which is derived from continuous glucose monitoring (CGM), has been widely used as an indicator of the quality of glycemic control. While cross-sectional studies have reported an association between CGM-derived TIR and cognitive function scores, few studies have longitudinally investigated the relationship between the two. This study aimed to prospectively investigate the association between CGM-derived TIR and changes in multiple cognitive function scores. MATERIALS AND METHODS: The present study used baseline and 2-year data from an ongoing multicenter cohort study. This study included 197 T2DM patients aged ≥60 years with undiagnosed dementia. Participants were examined with the mini-mental state examination (MMSE), the Japanese version of the Montreal cognitive assessment (MoCA-J) and the digit symbol substitution test (DSST) at both baseline and 2 years. Multiple regression analyses were performed to investigate the association between TIR and changes in cognitive function test scores over 2 years. RESULTS: Multivariate regression analysis showed that there was a significant association between TIR and changes in MMSE (ΔMMSE) over 2 years (standard partial regression coefficient [β] = 0.187, p = 0.005). Similarly, multivariate regression models showed a significant association between TIR and ΔMoCA-J (β = 0.218, p = 0.001) and ΔDSST (β = 0.164, p = 0.036). CONCLUSIONS: In patients with T2DM with undiagnosed dementia, CGM-derived TIR might be associated with overall cognitive decline and reduced processing speed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".