High-Density Lipoprotein Cholesterol and Nuclear Factor I A in Type 2 Diabetes and Mild Cognitive Impairment: biomarkers and mechanistic insights
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Bibliographic record
Abstract
Introduction Type 2 diabetes (T2D) and mild cognitive impairment (MCI) are interrelated conditions that significantly impair quality of life. This study aimed to identify a feasible biomarker for assessing T2D-MCI risk and to evaluate a potential therapeutic strategy. Material and methods We integrated data from the National Health and Nutrition Examination Survey (NHANES) with Mendelian randomization (MR) to investigate genetic causal relationships between T2D, MCI, and their shared biomarkers. Transcriptomic analysis identified T2D-associated genes. Clinical trials evaluated the short-term effects of modified fasting therapy (MFT) on glucose regulation and cognitive function. Cellular assays and patient samples validated key genes’ roles in biochemical markers and downstream pathways. Results Among 6,356 T2D and 1,138 MCI subjects, vitamin D, high-density lipoprotein cholesterol (HDL-C), globulin, and creatinine were associated with both conditions. MR analysis showed that higher HDL-C levels reduced T2D risk (0.9059, 95% CI: 0.8666–0.9470) but increased MCI risk (OR = 1.0482, 95% CI: 1.0216–1.0755). Nuclear Factor I A (NFIA) was identified as a key HDL-C regulator. In a clinical cohort (17 T2D patients and 23 controls), MFT reduced body mass index fasting glucose, and HDL-C, increased NFIA expression, and improved Montreal Cognitive Assessment scores, especially in T2D-MCI patients. HDL-C rebounded at six months. In vitro, NFIA overexpression increased intracellular HDL-C and suppressed NF-κB signaling, while NFIA knockdown reduced APOA1 and APOE. Conclusions HDL-C has divergent genetic effects on T2D and MCI. NFIA modulates HDL-C and NF-κB activity, supporting metabolic and cognitive improvements. Targeting NFIA through MFT may represent a promising strategy for T2D-MCI prevention and treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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 it