Correlation of Blood Glucose Variability with Infarct Burden and Cognitive Impairment in Patients with Type 2 Diabetes Mellitus Complicated with Recent Small Subcortical Infarct
Bibliographic record
Abstract
Background Recent small subcortical infarct (RSSI) is one of the manifestations of lacunar infarction. It is a common brain disease and can lead to the clinical outcome of disability or dementia in many patients. However, the relationship of infarction burden and cognitive impairment with blood glucose fluctuation in type 2 diabetes mellitus (T2DM) patients with RSSI is not very clear. Objective To explore the correlation of blood glucose variability (GV) with infarction burden and cognitive impairment in T2DM patients with RSSI, and based on this, to build a risk prediction model. Methods A total of 140 patients with T2DM and RSSI who were treated in the Second Affiliated Hospital of Zhengzhou University from January 2021 to June 2022 were retrospectively selected. The basic clinical data of the patients were collected. The 72-hour continuous blood glucose monitoring was performed. The infarct burden was evaluated by the magnetic resonance imaging performance (the study subjects were divided into the high infarction burden group including 45 cases and the low infarction burden group including 95 cases according to the imaging performance). The cognitive function was evaluated by the Montreal Cognitive Assessment (MoCA). Spearman correlation analysis was used to explore the correlation between GV and cognitive function (MoCA score). Multivariate Logistic regression analysis was used to explore the influencing factors of infarction burden and cognitive dysfunction in T2DM patients with RSSI. The receiver operating characteristic (ROC) curve was drawn to evaluate the predictive value of GV on cognitive impairment in T2DM patients with RSSI, and the nomogram predictive model was constructed and the predictive value was analyzed. Results In terms of GV-related indicators, the high infarction burden group had higher standard deviation (SD) and percentage of coefficient of variation (%CV) and lower time in range (TIR) than the low infarction burden group, with statistically significant differences (P<0.05). The results of Spearman correlation analysis showed that SD (rs=0.272, P=0.001) and %CV (rs=0.391, P<0.001) were directly proportional to MoCA score, and TIR (rs=-0.325, P<0.001) was inversely proportional to the MoCA score in T2DM patients with RSSI. The results of multivariate Logistic regression analysis showed that elevated SD〔OR=4.201, 95%CI (1.380, 12.788), P=0.011〕 and %CV〔OR=1.218, 95%CI (1.096, 1.354), P<0.001〕were risk factors for high infarction burden in patients with T2DM and RSSI, while increased TIR〔OR=0.866, 95%CI (0.814, 0.921), P<0.001〕 was a protective factor. Elevated SD〔OR=2.947, 95%CI (1.150, 7.548), P=0.024〕 and %CV〔OR=1.174, 95%CI (1.072, 1.287), P=0.001〕were risk factors for cognitive impairment, while elevated TIR〔OR=0.954, 95%CI (0.917, 0.992), P=0.018〕 was a protective factor in T2DM patients with RSSI. The area under the curve (AUC) of %CV for predicting cognitive impairment in patients with T2DM and RSSI was 0.758〔95%CI (0.660, 0.856), P<0.001〕, with an optimal cut-off value of 29.5%, 66.7% sensitivity and 76.0% specificity. The AUC of TIR in predicting cognitive impairment in T2DM patients with RSSI was 0.714〔95%CI (0.624, 0.804), P<0.001〕, with an optimal cut-off value of 60.5%, 97.2% sensitivity and 44.2% specificity. The nomogram prediction model based on SD, %CV, and TIR for the risk of cognitive impairment in T2DM patients with RSSI demonstrated great clinical benefits, and the internal correction suggested that the actual prediction results were similar to the ideal prediction results. Conclusion Elevated GV indicators such as SD and %CV may be independent risk factors, and increased TIR may be a protective factor for high infarct burden and cognitive dysfunction in T2DM patients with RSSI. %CV and TIR had good predictive value for cognitive dysfunction in T2DM patients with RSSI.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".