Editorial: Genetic mechanisms in diabetes pathogenesis

and metabolic dysfunction. Experimental work in diabetic mouse models suggested that electroacupuncture may partially restore intestinal physiology and alter expression of these pathways, though these findings remain preliminary and require further validation in humans. At the post-transcriptional level, Yin et al. reviewed the expanding role of N6-methyladenosine (m6A) RNA modification in diabetes and its complications, describing m6A as a dynamic and reversible regulatory mechanism affecting β-cell function, insulin secretion, inflammation, and vascular injury across multiple tissues. Regulators such as METTL3, METTL14, FTO, WTAP, and YTH domain proteins were shown to exert tissue-specific and context-dependent effects, sometimes opposing each other within the same disease context, underscoring the complexity of post-transcriptional regulation in metabolic disease. Ling et al. provided a focused mini-review on β-cell vulnerability from a different angle, reviewing how oxidative stress-induced protein carbonylation impairs β-cell survival and insulin secretion, and positioning NRF2-mediated antioxidant pathways as candidate therapeutic targets. These mechanistic layers illustrate that diabetes progression cannot be reduced to inherited sequence variation alone. Waqar et al. reported significant associations between TNF-α promoter polymorphisms rs1800629 and rs361525 and T2D susceptibility in a Punjabi population from Pakistan, supporting the role of chronic low-grade inflammation in disease risk, consistent with prior work linking TNF-α genetic variants to dyslipidemia and T2D susceptibility in South Asian populations 1 . Similarly, Parmar et al. showed that the LEPR rs1137101 polymorphism, alongside altered leptin and soluble leptin receptor levels, was associated with T2D susceptibility in the Gujarat population, reinforcing the importance of adipokine signaling and leptin resistance as heritable components of metabolic dysfunction.The overlap between metabolic and infectious disease susceptibility has gained renewed scientific attention, and Chávez-Domínguez et al. offer a compelling illustration of this. Their systematic review identified shared microRNA signatures, including miR-21, miR-155, and miR-223, that have been implicated in both T2D and tuberculosis susceptibility through dysregulated inflammatory, apoptotic, and glucose metabolic pathways. This work highlights the immunometabolic cross-talk that may help explain the well-established but incompletely understood heightened susceptibility to tuberculosis in people with diabetes, a comorbidity that has been quantified as approximately a three-fold increase in TB risk among diabetic individuals 2 . Palihaderu et al., working in a Sri Lankan T2D cohort, identified circulating miR-29a-3p and miR-375-3p as significantly dysregulated in diabetic compared with normoglycemic individuals, linking these miRNAs to β-cell dysfunction and insulin signaling pathways and supporting their potential as minimally invasive biomarkers, while appropriately acknowledging the need for larger-scale validation. Extending these transcriptomic investigations into end-organ complications, Zhang and Yang identified RPL11 as a potential biomarker associated with diabetic retinopathy progression and proposed interactions with bisphenol A exposure, raising questions about how environmental factors may intersect with molecular disease pathways in diabetic tissues. In diabetic nephropathy, Zhang et al. demonstrated that miR-216a-5p protects against high glucose-induced mesangial cell injury by suppressing HMGB1/RAGE signaling and thereby reducing inflammation, fibrosis, and apoptosis. These studies demonstrate how transcriptomic and microRNA-based approaches are contributing both to biomarker discovery and to mechanistic understanding of diabetic complications, though a consistent gap remains the transition from discovery-stage findings in small cohorts to validated clinical tools.The field has moved well beyond a pancreas-centric view of diabetes pathogenesis, and several contributions in this issue make that case forcefully. Saxena et al. demonstrated that altered expression of lipodystrophy-associated genes across visceral and subcutaneous adipose depots strongly correlated with insulin resistance, glycemia, and metabolic syndrome severity in Asian Indians. Their findings support the concept of "adiposopathy," in which dysfunctional adipose tissue acts as a primary driver of metabolic disease, and are particularly relevant to the South Asian "thin-fat" phenotype, where insulin resistance and complications emerge at BMI thresholds lower than those typical of European populations. Notably, a relatively small oligogenic gene subset was sufficient to stratify metabolic syndrome severity, pointing to a tractable entry point for early risk prediction. This is consistent with evidence from data-driven clustering in Asian Indians showing that distinct T2D subgroups carry different risks of microvascular complications, further underscoring the clinical relevance of phenotypic heterogeneity within South Asian populations One of the most consistent messages across this issue is that T2D is not one disease even at the population level. Markelova et al. make this point with particular clarity, applying partitioned polygenic score analyses across three Russian ancestry groups to show that the dominant biological drivers of T2D differ substantially by genetic background. Yakut populations exhibited stronger genetic signatures for β-cell dysfunction and hyperinsulinemia with lower obesity-related burden, mirroring East Asian metabolic patterns, while Chechen and Tatar groups showed more obesitydriven mechanisms accompanied by distinct anthropometric and lipid profiles. Strikingly, the βcell dysfunction and hyperinsulinemia signals in Yakuts appeared genetically independent of each other, suggesting these are not simply two aspects of the same process but mechanistically separable contributions that ancestry helps unmask. These findings also carry practical implications, as polygenic risk tools calibrated primarily on European cohorts may misrepresent disease risk in populations with distinct underlying pathogenic pathways, a well-documented limitation of current PRS methodology that underscores the urgent need for more ancestrally diverse genomic studies 4 . Evidence from South Asian and European population comparisons further confirms that even the genetic determinants of age at diabetes diagnosis differ between ancestries, reinforcing the need for ancestry-specific genomic investigations 5 .the hemoglobin glycation index (HGI) in the ACCORD trial that are largely independent of known HbA1c and glycemic loci. Sex-specific signals, including GALNT11 in women and HECW2 in men, alongside ancestry-specific associations such as WHRN in Black participants and USF1/NXNL2 in Hispanic individuals, reinforce that even well-established glycemic biomarkers harbor biological complexity that standard population-level analyses can obscure.Gestational diabetes mellitus (GDM) studies in this issue further highlight the value of populationspecific and cross-ancestry investigation. Zhang et al. identified associations between MIF rs1007888, ARAP1 rs1552224, and GDM susceptibility in Chinese women, with MIF increasing risk and ARAP1 showing a protective association in specific subgroups (for e.g. younger women and those with higher BMI), while Zhao et al. conducted a cross-ancestry genome-wide association meta-analysis across British, Finnish, and Chinese populations, identifying novel susceptibility genes ELL2 and ATRAID alongside pathways related to regulation of insulin secretion, regulation of protein secretion, and negative regulation of hexokinase activity. GDM genetic studies have historically been constrained to single-population designs; the cross-ancestry approach adopted by Zhao et al. improves both discovery power and generalizability of findings, an important methodological step for a condition that disproportionately affects women from diverse ethnic backgrounds.Monogenic diabetes is frequently misclassified as type 1 or type 2, and that misclassification has real treatment consequences. Several studies in this issue demonstrate how comprehensive genetic diagnosis corrects that error and directly improves patient outcomes. This is particularly relevant in South Asian contexts, where the profile and frequency of monogenic diabetes subtypes differ substantially from European populations, as demonstrated by a recent pan-India registry study documenting significant clinical and genetic heterogeneity across multiple MODY subtypes 6 . That diagnostic precision depends, however, on testing strategies that go beyond standard sequencing. Lei et al. identified a heterozygous deletion spanning HNF1A exons 1-10, functionally amounting to a whole-gene-loss-of-function event, in a Chinese MODY family that was invisible to next-generation sequencing but confirmed by copy number variant analysis, and which enabled the same insulin-to-sulfonylurea transition seen in the HNF4A cohort. Gong et al. showed that a non-canonical HNF1B splice-site variant caused abnormal mRNA splicing and protein truncation, with pathogenicity established only through minigene functional assays. These two studies together argue that copy number analysis, splice-site assessment, and functional validation are no longer optional additions to monogenic diabetes diagnostics but integral to accurate genetic diagnosis.Several reports in this section also broaden the recognized phenotypic boundaries of known monogenic conditions. Shao and Xu described a ketosis-prone presentation of MODY6 caused by a NEUROD1 5' UTR mutation, a phenotype traditionally considered inconsistent with this subtype, in a single-family patient who achieved stable glycemic control after insulin discontinuation with dipeptidyl peptidase-4 (DPP-4) inhibitor therapy, suggesting preserved incretin responsiveness. As diabetes prevalence continues to rise globally, building diverse, well-phenotyped cohorts that reflect the full range of populations affected by the disease will only become more important.Many of the molecular insights described in this issue, particularly in non-coding RNA biology and epitranscriptomics, still await validation in large human datasets. Closing that gap will require sustained international collaboration and investment in genomic infrastructure in regions historically underrepresented in diabetes research. We hope this collection encourages precisely those efforts, and that the findings presented here contribute to more individualized, mechanisminformed approaches to diabetes prevention, diagnosis, and treatment.