Mild cognitive impairment in type 2 diabetes is associated with fibrinogen-to-albumin ratios
Bibliographic record
Abstract
Background Cognitive impairment is the main manifestation of diabetes central neuropathy. Currently, there is no effective dementia treatment; early diagnosis and treatment are particularly crucial. Inflammation index fibrinogen-to-albumin ratio (FAR) has been shown to predict complications of type 2 diabetes (diabetic kidney disease and diabetes-related arteriosclerosis), but its relationship with mild cognitive impairment (MCI) in type 2 diabetes (T2D) is undetermined. In this study, we examined the association between the FAR and mild cognitive impairment in type 2 diabetes. Methods This is a retrospective and cross-sectional study. From January 2022 to December 2022, we have retrieved 328 inpatient medical records for T2D patients hospitalized at the First Hospital of Harbin Medical University from the hospital’s electronic system. Subjects’ cognitive function was assessed and grouped by the MoCA scales. Subjects’ demographic and various laboratory indicators were collected. Using Spearman’s bivariate correlation analysis, the FAR and other clinical variables were analyzed for association strength. A multiple linear regression analysis was conducted to determine the independent relationship between FAR and MoCA scores. Multivariate logistic regression was used to analyze the independent relationship between FAR and MCI. The capacity of the FAR to detect MCI was carried using receiver operating characteristic (ROC) analysis. Results The included participants’ ( n = 328; 61.9% male) mean age was 52.62 ± 10.92 years. MoCA scores and MCI prevalence significantly differed ( p < 0.05) between the four subgroups of FAR quartiles. The FAR and the MoCA score were significantly negatively correlated in the entire population ( p < 0.05). Based on the multiple linear regression analysis, lnFAR and lnMoCA are significantly correlated (β = −0.449, t = −8.21, p < 0.05, R2 = 0.469). In multivariate logistic regression analysis, FAR and MCI were independently correlated after adjusting for covariates (OR 95% CI 34.70 [13.90–86.66]). Finally, the analysis of receptor working characteristics shows that the optimal FAR cut-off value was 0.08 (sensitivity: 95.81%, specificity: 84.47%) for detecting MCI in type 2 diabetes. Conclusion In type 2 diabetes, the FAR was positive associations with MCI and negative associations with MoCA score. The high FAR was associated with an increased risk of MCI. FAR maybe a appropriate indicator of MCI risk for type 2 diabetes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".