Bezisterim-associated anti-inflammatory epigenetic modulation of age acceleration and Alzheimer’s disease genes
Bibliographic record
Abstract
Abstract Treatments with the ability to slow or reduce biological age have therapeutic potential in diseases of aging, including late-onset Alzheimer’s disease (AD). We previously reported that bezisterim, a novel anti-inflammatory insulin sensitizer, modulated epigenetic age acceleration (EAA) in a randomized, placebo-controlled, 30-week AD trial. Here, we expand on those findings through integrative mechanistic analyses linking bezisterim-induced EAA changes with clinical outcomes. Thirty weeks of bezisterim treatment in patients with mild-to-moderate AD showed favorable trends for reduced EAA across 13 independent biological clocks versus placebo. The reduced EAA was predominantly associated with inflammation, cognition, and transcription factor genes that orchestrate broader gene networks. Genome-wide methylation profiling revealed 2581 genes with significant differential promoter methylation (DPM) between the bezisterim and placebo groups. We identified 447 of these as having potentially beneficial DPM based on expected expression related to published aging and AD activities; 179 were AD hub genes. In addition, more than 1000 bezisterim treatment–related, potentially beneficial differential promoter methylation (PBDPM) genes associated with microglial neuroinflammation, pro-inflammatory kinase activity, cognitive decline, lipid metabolism, and transcriptional regulation were correlated with directional improvement in individual neurologic and metabolic clinical measures. The observed changes in PBDPM genes might contribute to the previously reported clinical effects of bezisterim in AD. Bezisterim appears to exert pleiotropic effects through coordinated modulation of aging-related epigenetic programs, potentially counteracting epigenetic-driven neurodegenerative processes at the intersection of inflammation, metabolism, and transcriptional control.
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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 | 0.000 |
| 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 | 0.000 |
| 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".