Evaluating the Role of Epigenetic Histone Modifications in the Metabolic Memory of Type 1 Diabetes
Bibliographic record
Abstract
We assessed whether epigenetic histone posttranslational modifications are associated with the prolonged beneficial effects (metabolic memory) of intensive versus conventional therapy during the Diabetes Control and Complications Trial (DCCT) on the progression of microvascular outcomes in the long-term Epidemiology of Diabetes Interventions and Complications (EDIC) study. We performed chromatin immunoprecipitation linked to promoter tiling arrays to profile H3 lysine-9 acetylation (H3K9Ac), H3 lysine-4 trimethylation (H3K4Me3), and H3K9Me2 in blood monocytes and lymphocytes obtained from 30 DCCT conventional treatment group subjects (case subjects: mean DCCT HbA1c level >9.1% [76 mmol/mol] and progression of retinopathy or nephropathy by EDIC year 10 of follow-up) versus 30 DCCT intensive treatment subjects (control subjects: mean DCCT HbA1c level <7.3% [56 mmol/mol] and without progression of retinopathy or nephropathy). Monocytes from case subjects had statistically greater numbers of promoter regions with enrichment in H3K9Ac (active chromatin mark) compared with control subjects (P = 0.0096). Among the patients in the two groups combined, monocyte H3K9Ac was significantly associated with the mean HbA1c level during the DCCT and EDIC (each P < 2.2E-16). Of note, the top 38 case hyperacetylated promoters (P < 0.05) included >15 genes related to the nuclear factor-κB inflammatory pathway and were enriched in genes related to diabetes complications. These results suggest an association between HbA1c level and H3K9Ac, and a possible epigenetic explanation for metabolic memory in humans.
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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 | 0.001 | 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 | 0.000 |
| 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".