CpG methylation changes associated with hyperglycemia in type 1 diabetes occur at angiogenic glomerular and retinal gene loci
Bibliographic record
Abstract
Chronic hyperglycemia is a major risk factor for glomerular or retinal microangiopathy and cardiovascular complications of type 1 diabetes (T1D). At the interface of genetics and environment, dynamic epigenetic changes associated with hyperglycemia may unravel some of the mechanisms contributing to these T1D complications. In this study, blood samples were collected from 112 young patients at T1D diagnosis and 3 years later in average. Whole genome-wide bisulfite sequencing was used to measure blood DNA methylation changes of about 28 million CpGs at single base resolution over this time. Chronic hyperglycemia was estimated every 3-4 months by HbA1c measurement. Linear regressions with adjustment to age, sex, treatment duration, blood proportions and batch effects were employed to characterize the relationships between the dynamic changes of DNA methylation and average HbA1c levels. We identified that longitudinal DNA methylation changes at 815 CpGs (with suggestive p-value threshold of 1e-4) were associated with average HbA1c. Most of them (> 98%) were located outside of the promoter regions and were enriched in CpG island shores and multiple immune cell type specific accessible chromatin regions. Among the 36 more strongly associated loci (p-value < 5e-6), 16 were harbouring genes or non-coding sequences involved in angiogenesis regulation, glomerular and retinal vascularization or development, or coronary disease. Our findings support the identification of new genomic sites where CpG methylation associated with hyperglycemia may contribute to long-term complications of T1D, shedding light on potential mechanisms for further exploration.
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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 | 0.001 |
| 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".