Early blood DNA methylation patterns associated with glycemic progression in a prospective Indian cohort

The prevalence of Type 2 diabetes mellitus (T2DM) is rapidly increasing in India, yet molecular markers that reflect early disease susceptibility remain limited. Epigenetic modifications such as DNA methylation may reflect early metabolic vulnerability preceding overt dysglycemia. In this study, we examined genome-wide DNA methylation patterns in a pilot subset nested within a prospective Indian cohort using Nanopore sequencing and assessed their associations with previously identified metabolite predictors from the same cohort.

Genome-wide DNA methylation profiling was performed on buffy-coat DNA from 12 participants who were normoglycemic at baseline and later classified into normoglycemia, prediabetes, or T2DM based on their glycemic status at 6-year follow-up. At baseline, gene-level aggregation of CpG methylation revealed directionally consistent hypermethylation of seven genes (ABCG1, ADARB2, BCL2, DLC1, EGFLAM, SYK, ZNF516) in individuals who later developed T2DM, while those progressing to prediabetes exhibited six hypermethylated (ABCG1, FLT3, LCP1, MBP, NCOA2, TCF7L2) and five hypomethylated genes (ZFHX3, PAX6, PTPRN2, ERC1, HIPK1). ABCG1 showed consistent hypermethylation across both groups. Longitudinal within-individual comparisons identified additional gene-associated methylation changes, including ANK1, IQSEC1, and RUNX1, and shared alterations in CACNA1C, KANSL1, PTPRN2, and TTC34, while six genes showed stage-dependent directional shifts in methylation (ASB3, EFR3A, PCSK5, KLHL14, PDE4C, UNC5C). Correlation analyses at baseline suggested associations between ABCG1 and EGFLAM methylation, fasting glucose, phosphatidylethanolamine [PE (20:3_18:0)] and insulin sensitivity indices.

This pilot longitudinal study suggests that gene-associated DNA methylation changes in blood may be detectable prior to the onset of dysglycemia. These findings are exploratory and hypothesis-generating, highlighting candidate genes and epigenetic-metabolic associations for targeted validation in larger, independent cohorts using alternative analytical approaches.