Diagnostic potential of serum biomarkers including HMGB1 and Klotho in cognitive impairment among type 2 diabetes mellitus patients: a case–control study
Bibliographic record
Abstract
Type 2 Diabetes Mellitus (T2DM) is a rapidly growing global health burden. Beyond vascular and metabolic complications, it is increasingly recognized as a major contributor to cognitive impairment and dementia. Cognitive dysfunction in T2DM often goes undetected until advanced stages, limiting effective intervention. Early detection strategies are therefore urgently needed. This study investigated the diagnostic potential of neuroinflammatory, metabolic/insulin signaling, and neuroprotective biomarkers in identifying cognitive impairment among T2DM patients. In a tertiary care hospital, 200 participants were enrolled in a case–control study (100 participants in each group). Cognitive performance was assessed by the Montreal Cognitive Assessment (MoCA). The concentrations of HMGB1, IL-1β, TLR4, IL-6, ADAM-10, mTOR, PI3K, Akt, TNF-α, and Klotho in the serum were measured by ELISA. Analysis of correlation and receiver operating characteristic (ROC) curves were performed. MoCA assessments were lower for patients with T2DM than for controls. Elevated serum concentrations of HMGB1, IL-1β, TLR4, IL-6, ADAM-10, TNF-α, mTOR, and PI3K with lower Akt and Klotho concentrations were observed in the T2DM patients. These biomarkers negatively correlated with MoCA scores ( p < 0.001), indicating their potential role in cognitive impairment. ROC analysis identified ADAM-10 (AUC = 0.817), IL-1β (AUC = 0.792), and Klotho (AUC = 0.799) as prominent biomarkers of impairment in cognition. This study demonstrates that dysregulation of neuroinflammatory and metabolic pathways is strongly associated with cognitive decline in T2DM. Given the rising global prevalence of diabetes and dementia, incorporating serum biomarkers such as ADAM-10, IL-1β, and Klotho into clinical screening may enable earlier identification, risk stratification, and timely intervention in diabetes-associated cognitive impairment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".