Epigenetic adaptation of beta cells across lifespan and disease

Manduchi, Elisabetta; Descamps, Hélène C.; Liu, Jinping; Schug, Jonathan; Da, Tong; Lahori, Deeksha; El-Mekkoussi, Hilana; Lee, Michelle; Feleke, Eseye; Bernstein, Diana; Liu, Chengyang; Naji, Ali; Glaser, Benjamin; Kaestner, Klaus H.; Avrahami, Dana

doi:10.1038/s42255-026-01495-y

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Published: 24 April 2026

Epigenetic adaptation of beta cells across lifespan and disease

Nature Metabolism volume 8, pages 941–956 (2026) Cite this article

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Abstract

Although the prevalence of type 2 diabetes (T2D) increases with age, most adults maintain normoglycaemia despite rising insulin resistance owing to the adaptive capacity of pancreatic beta cells to meet increased metabolic demand. However, persistent insulin resistance can lead to beta cell dysfunction and T2D onset. Here we show the mapping of genome-wide DNA methylation (DNAm) patterns and the epigenomic basis of beta cell adaptations by leveraging cell-type-specific methylome data from the Human Pancreas Analysis Program. In healthy donors, we identify progressive age-related demethylation enriched in cis-regulatory elements at beta cell identity and function genes. By contrast, alpha cells show the opposite trajectory, with subtle, age-related hypermethylation. In T2D beta cells, but not alpha cells, we observed further demethylation compared to healthy controls, underscoring a unique capacity of beta cells to respond to changes in metabolic demand. Together, our findings suggest that DNAm remodelling in healthy beta cells reflects a long-term adaptation to metabolic demand, which, in T2D, is accelerated as part of a compensatory response that ultimately fails under sustained insulin resistance.

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Main

Ageing is commonly described as the gradual decline in physiological functions and metabolic regulation, a process observed across the animal kingdom. However, although physiological deterioration predominantly occurs in the later stages of life, recent studies suggest that the ageing process can also include phases of physiological adaptation and even functional improvement. During human adulthood, many physiological functions remain stable for decades, reflecting ongoing adaptation. For instance, older adults maintain the ability to improve cardiovascular fitness and cardiac output in response to aerobic exercise¹, enhance endothelial function to reduce arterial stiffness² and sustain daily energy expenditure³.

Pancreatic beta cells have a crucial role in regulating glucose homoeostasis by secreting insulin in response to elevated blood glucose levels. Failure to adequately compensate for insulin resistance can lead to persistent hyperglycaemia and, eventually, T2D⁴. Although insulin resistance and elevated blood glucose levels tend to worsen with age^4,5,6, most older individuals maintain normoglycaemia. This suggests that beta cells, which are long-lived and rarely proliferate in adulthood^7,8,9,10, must sustain function over decades by continually adapting to fluctuating metabolic demands. Indeed, beta cells can adapt during physiological insulin-resistant states, such as pregnancy and puberty, by expanding their mass and enhancing their capacity to secrete insulin^11,12. Our prior transcriptomic analysis of human beta cell ontogeny supports the notion that beta cells activate genes critical to their function to sustain enhanced activity with age¹³. This finding is further corroborated by a recent report¹⁴ that highlights a beneficial adaptive response in beta cells throughout adulthood, associated with enhanced glucose-stimulated insulin secretion (GSIS), that persists at least until age 60 (ref. ¹⁴).

DNAm, a relatively stable epigenetic mark, exhibits cell-specific patterns in regulatory regions, helping to maintain cell-type-specific expression programmes across multiple cell generations and throughout life¹⁵. Despite its stability, DNAm patterns are altered gradually over the human lifespan, with changes at specific sites serving as an epigenetic clock and providing one of the most accurate predictors of chronological age¹⁶. Although numerous studies have shown DNAm alterations in the context of cancer¹⁷, far fewer have explored how factors related to metabolic diseases impact DNAm and, more specifically, how these methylation changes may contribute to the development of T2D. Previous studies have reported DNAm alterations in the promoters and enhancers of genes associated with insulin secretion in whole islets of deceased organ donors with T2D^18,19,20. However, as the composition of human islets varies dramatically both within and between individuals¹⁰, with alterations in the alpha-to-beta cell ratio in patients with T2D owing to reduced beta cell mass^21,22 or possibly alpha cell expansion²³, it is essential to analyse T2D methylomes in a cell-type-specific manner. This is further underscored by the distinct global methylation patterns between alpha and beta cells^24,25 and by the unique DNAm alterations observed in beta cells across the human lifespan and in T2D, as described below.

In this work, we leveraged the islet molecular phenotyping data generated by the Human Pancreas Analysis Program (HPAP; https://hpap.pmacs.upenn.edu)^26,27. To investigate epigenetic adaptation in beta cells across the human lifespan, we analysed genome-wide DNAm patterns in sorted beta and alpha cell populations from control (CTL) donors of various ages (here and throughout, ‘control’ refers to non-diabetic, autoantibody-negative individuals). This analysis revealed age-associated demethylation in beta cells, particularly in regulatory regions near islet transcription factor (TF) binding sites. Although these regions exist in a hypomethylated state in beta cells, the ongoing process of maintaining that state may require active demethylation, which counteracts maintenance methylation^28,29. This process may help maintain enhancer activity and sustain stable expression of key beta cell function genes to preserve cell identity throughout the long lifespan of beta cells. By contrast, alpha cells exhibited a subtler and opposite age-related trend, with a predominance of hypermethylation even in active regulatory regions.

Interestingly, we noted a beta cell-specific additional demethylation process in T2D compared to CTL, suggestive of an intensified compensatory response. By contrast, alpha cells from diabetic donors exhibited minimal methylation changes compared to healthy donors. Our findings suggest that in healthy individuals, the beta cell epigenome supports lifelong adaptation to metabolic demand. In T2D, however, enhanced demethylation probably reflects mounting stress on beta cells as diabetes progresses, representing an intensified attempt to maintain beta cell function that ultimately becomes unsustainable.

Results

DNA methylation patterns distinguish alpha and beta cell regulatory landscapes

DNAm has a crucial role in gene silencing, which is essential for maintaining cell type identity and cellular expression programmes throughout the lifespan and across cell divisions¹⁵. This stable methylation pattern is established by regulated processes of methylation and demethylation, and it is preserved by the activity of maintenance methyltransferase (DNMT1) as well as de novo methyltransferases such as DNMT3A^30,31. To confirm that our dataset captures robust cell identity differences, quality control clustering of all 154 HPAP methylome samples shows clear separation between alpha, beta and exocrine cells regardless of sex, ancestry, age, diabetes status and assay type, with the outliers in each branch corresponding to samples with a low purity score (Extended Data Fig. 1).

To characterize the cell-type-specific methylation landscapes of pancreatic alpha and beta cells in our cohort, we compared methylomes from 14 CTL donors with high sample purity in both cell types (average (±s.d.) purity scores of 86 ± 6% and 91 ± 7% for alpha and beta cells, respectively; see Supplementary Table 1). The donors in this comparison represented an age range from 26 to 58 years. Principal component analysis (PCA) of methylation levels revealed that the cell identity of the sample accounted for 44% of the variance (Fig. 1a). Comparison of methylation profiles between alpha and beta cells identified 78,389 differentially methylated regions (DMRs) with a false discovery rate (FDR) of <0.01 and |delta| > 10% (Fig. 1b), whose distribution of average DNAm differences is shown in Fig. 1c.

**Fig. 1: DNAm distinguishes alpha and beta cells in association with their cell-type-specific regulatory landscapes.**

We next focused on the 7,522 DMRs with the strongest effect sizes (|delta| > 40%) and analysed the signals from published islet chromatin immunoprecipitation sequencing (ChIP-seq) and alpha and beta single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) experiments across 10 kb regions centred at these DMRs (see Methods). This analysis revealed that regions hypomethylated in both beta and alpha cells were enriched for active chromatin marks and depleted of repressive marks (Fig. 1d). Consistently, hypomethylated regions in beta and alpha cells overlapped with open chromatin in each respective cell type (Fig. 1e). Finally, analysis of published TF binding data for whole islets³² revealed that binding sites for FOXA2, MAFB and NKX2.2, which are expressed in both alpha and beta cells, are mostly hypomethylated in both cell types, whereas binding sites for NKX6.1 and PDX1, which are specific to beta cells, are predominantly hypomethylated in beta cells (Fig. 1e). These findings further demonstrate that alpha-cell-specific and beta-cell-specific hypomethylated regions are enriched in cell-type-specific regulatory elements, extending prior observations³³.

To test which gene sets are associated with hypomethylation in alpha and beta cells, we applied a hypergeometric test using DMRs selected with thresholds of FDR < 0.01 and |delta| > 40%, considering genes annotated as DMR-distal, as described in Methods. This analysis showed that although common biological pathways important for both cell types, such as regulation of secretion and exocytosis, are associated with hypomethylation in both alpha and beta cells, other gene sets are specifically associated with hypomethylation in either cell type (Fig. 1f). For example, genes critical for beta cell function, such as ADCYAP1, IAPP, INS, ABCC8 and SYT7 (‘BETA_GENES’ in Fig. 1f and Supplementary Table 9), as well as beta-cell-specific TFs including PDX1, NKX6.1, MNX1, PAX6 and SIX3 (‘GO_ENDOCRINE_SYSTEM_DEVELOPMENT’ in Fig. 1f and Supplementary Table 9) are associated with hypomethylation in beta cells. By contrast, hypomethylation in alpha cells is associated with alpha cell function genes such as GCG, DPP4, FAP, FEV and ADRB1 (Beta-1 Adrenergic Receptor), the latter known to stimulate glucagon secretion under adrenergic stress³⁴ (‘ALPHA_GENES’ in Fig. 1f and Supplementary Table 10).

Age-progressive DNA methylation loss in beta cell regulatory regions

Beta cells are long-lived cells that must continually adapt to fluctuating metabolic demands³⁵. These ongoing adaptations may be supported by the accumulation of epigenetic changes over time. Unlike gene expression, which is highly variable across individuals and becomes increasingly heterogeneous with age³⁶, DNAm provides a more stable epigenetic signature that reflects cellular identity and differentiation state. Although age-related DNAm changes have been documented in blood and brain tissues^37,38, they have not been studied in human beta cells. To address this knowledge gap, we analysed DNAm profiles from pancreatic beta cells collected from 21 HPAP CTL donors across a broad age spectrum from 8 to 64 years old. UXM-estimated purity scores had a mean (±s.d.) of 91 ± 6%. Moreover, based on the UXM-estimated cell type composition (see Methods), there was no strong association between age and the proportion of cells of any given type (maximum R² = 0.1), including monocytes and macrophages (R² = 0.048). Supplementary Table 2 provides the characteristics of the samples used in this comparison. PCA of methylation levels indicated that PC2 was significantly correlated with age (R² = 0.62, P < 0.001) (Fig. 2a). In this comparison we analysed age as a numeric covariate, and to measure effect size, we defined delta as the difference in average methylation between the group of older donors aged 39–58 years and that of younger donors aged 18–35 years (see Methods). This comparison identified 8,748 DMRs at the thresholds of FDR < 0.01, |delta| > 5%, with a striking 96% (8,394 DMRs) showing hypomethylation with age (Fig. 2b). Although the magnitude of methylation changes was generally modest, with most DMRs showing |delta| < 30% (Fig. 2c), the differences were consistent and statistically significant. Therefore, despite the small effect size, ageing in beta cells is associated with a robust and widespread hypomethylation signature.

**Fig. 2: Progressive loss of DNAm at regulatory regions in beta cells with age (part I).**

Next, we analysed the signals from islet ChIP-seq and beta snATAC-seq experiments across 10 kb regions centred at these age-related beta cell DMRs (see Methods) (Fig. 2d,e). Our analysis revealed that regions hypomethylated with age are predominantly located in beta-cell-specific active regulatory chromatin domains, as evidenced by significant enrichment for H3K27ac, H3K4me1, beta-ATAC regions and binding sites for islet and beta-cell-specific TFs such as NKX6.1 and PDX1. To validate these findings, we assessed the average methylation levels within previously identified islet active enhancer regions³², which revealed subtle progressive demethylation with age (Fig. 3a). The distribution of slopes from linear fits of methylation versus age in these enhancer regions, representing the rate of change of DNAm per year, is clearly shifted towards negative values (Fig. 3b). Although the absolute values of the slopes are modest, they reflect a consistent directional trend, with 82% of the 28,813 fits exhibiting negative slopes and relatively strong negative associations, as indicated by the corresponding Pearson correlation coefficients (median Pearson correlation coefficient = −0.35; Fig. 3c) and P values (median P = 0.06; Fig. 3d). Notably, enhancer regions can be actively demethylated by TET enzymes, which also help to protect against age-related functional decline in other tissues such as the brain^28,39.

**Fig. 3: Progressive loss of DNAm at regulatory regions in beta cells with age (part II).**

To explore the mechanisms driving age-associated demethylation in active enhancers, we examined methylation levels at TF binding sites of NKX6.1 and PDX1, which are enriched in beta cell enhancers⁴⁰, focusing on those showing the greatest variation across age. We observed progressive demethylation with increasing donor age at these sites (Fig. 3e,i), and the distribution of slopes from linear fits of methylation versus age was clearly shifted toward negative values, shown for PDX1 in Fig. 3f and for NKX6.1 in Fig. 3j. For regions with negative slopes, the median Pearson correlation was –0.56 with a median P value of 0.004 for PDX1 (Fig. 3g,h), and –0.55 with a median P value of 0.005 for NKX6.1 (Fig. 3k,l).

We next asked whether age-related DMRs in beta cells were enriched near genes associated with specific biological pathways or gene sets. Using DMRs filtered by FDR < 0.01 and |delta| >10% and considering DMR-distal genes (as defined in Methods), we found that hypomethylated regions in older donors were enriched in beta-cell-specific regulatory regions, such as beta cell ATAC peaks, and near genes involved in beta cell function, cell cycle regulation and, notably, DNA damage response (DDR) and T2D-associated genes, whereas the disallowed gene set showed no enrichment (Fig. 3m and Supplementary Table 11). To explore potential links to beta cell heterogeneity, we assessed enrichment for subtype gene signatures^41,42 and found that genes characteristic of the ST8SIA1-positive and βHI subpopulations were enriched among beta cell age-related hypomethylated DMRs (Fig. 3m and Supplementary Table 11). However, this enrichment could be influenced by age-related changes in the proportion of these subpopulations, as our methylomes represent a bulk signal integrating inputs from diverse beta cell populations. Gene set enrichment analysis (GSEA) of HPAP single-cell RNA sequencing (scRNA-seq) data from donors in a similar age range revealed upregulation of the p53 pathway in older individuals, including increased expression of CDKN2A, CDKN1A and CDKN2B (Extended Data Fig. 2a and Supplementary Table 12), supporting progressive activation of stress response and senescence programmes in beta cells with age. In line with previous reports¹⁴, GSEA also showed age-associated downregulation of the HALLMARK beta cell gene set as a whole (Extended Data Fig. 2b and Supplementary Table 13), although most individual genes were not significantly downregulated (Extended Data Fig. 2c). The mild downregulation of this gene set is unlikely to compromise beta cell identity or function, as key functional pathways such as hormone secretion and oxidative phosphorylation remain unchanged with age, along with the expression of major beta cell identity genes such as PDX1 and NEUROD1 (Extended Data Fig. 2c).

To further explore whether the observed demethylation with age was directly correlated with adaptation, we analysed the same 21 beta cell methylomes for correlation with body mass index (BMI) and with GSIS (measured by the ratio of glucose-stimulated [B] versus basal [A] insulin secretion; for details see Methods). The R² for BMI versus age in this dataset was 0.18. The correlations between the first five beta cell methylation-based PCs and BMI were much weaker (R² ranging from 0.009 to 0.21) than the noted strong correlation between PC2 and age (R² = 0.62). A BMI-based DMR analysis with the same thresholds as those used for age identified 599 regions, and only 47 of the 8,394 age-demethylated DMRs were among these. As for GSIS, the R² for the B/A ratio versus age was negligible (0.0018). The correlations between the first five beta cell methylation-based PCs and the B/A ratio were much weaker (R² ranging from 0 to 0.18) than the noted strong correlation between PC2 and age. A GSIS-based DMR analysis with the same thresholds as those used for age identified only 20 regions, none of which were among the 8,394 age-demethylated DMRs. These observations, together with heatmaps of the age-hypomethylated regions in healthy beta cells ordered by age, BMI and GSIS (Extended Data Fig. 3), indicate that BMI and GSIS are not the main drivers of the age-demethylated DMRs identified above.

In contrast to beta cells, alpha cells show progressive DNA hypermethylation with age

For alpha cells, we analysed 14 samples from CTL donors spanning an age range from 18 to 58 years. UXM-estimated purity scores had a mean (±s.d.) of 87 ± 5%. Based on the UXM-estimated cell type composition (see Methods), there was no association between age and the proportion of cells of any given type (maximum R² = 0.22), including monocytes and macrophages (R² = 0.074). Supplementary Table 3 provides the characteristics of the samples used in this comparison. PCA of methylation levels indicated that PC2 was significantly correlated with age (R² = 0.77, P < 0.001) (Fig. 4a). We analysed the CTL alpha cell age comparison in the same manner as described above for beta cells using the same thresholds of FDR < 0.01 and |delta| > 5% and identified 1,466 DMRs, of which, surprisingly, 1,344 (92%) were hypermethylated with age, although the magnitude of methylation changes was modest, with most DMRs showing |delta| < 20% (Fig. 4b–c). A subset of age-dependent hypermethylation occurred in active regulatory domains, but a higher proportion occurred in repressed regions that are not bound by TFs, as illustrated by the enrichment heatmaps of signals from the islet ChIP-seq and alpha snATAC-seq experiments over the 10 kb regions centred at these DMRs (Extended Data Fig. 4; see Methods for details).

**Fig. 4: Alpha cells show modest age-related DNA hypermethylation, opposite to the trend in beta cells.**

We next examined whether age-related DMRs in alpha cells were enriched near specific pathways or gene sets. Using an FDR < 0.01 and |delta| > 10% cutoff, we did not detect enrichment for specific pathways or gene sets in association with either hypomethylated or hypermethylated regions with age in alpha cells. Analysis of pseudo-bulk alpha cell gene expression data across age did not reveal a correlation with gene silencing or reduced expression with age, possibly because of the low effect size and the higher degree of variability of transcriptomic versus CpG methylation data.

To further explore the discrepancy between alpha and beta cells, we compared their methylation levels across ages at binding sites for FOXA2 and NKX2.2, two TFs expressed at similar levels in both cell types^{32,43,44,45,46}. We used the same number of available samples (13) for each cell type (mostly common donors and, when not available, aged-matched donors) and, for each cell type separately, considered the 500 binding sites whose methylation varied most across ages; that is, focusing on sites most affected by age in that cell type. As illustrated in Fig. 4d,e and Extended Data Fig. 5, we observed subtle progressing demethylation of these regions with age in beta cells, whereas the trend in alpha cells was opposite. The distribution of slopes from linear fits of methylation versus age in these regions is shifted towards positive values in alpha cells and negative values in beta cells, with the shift more evident at FOXA2 binding sites than at NKX2.2 binding sites in alpha cells (Fig. 4f and Extended Data Fig. 6a). Moreover, the regions with negative slopes in beta cells correspond to significantly better linear fits than the regions with positive slopes in alpha cells (Fig. 4g,h and Extended Data Fig. 6b,c).

Together, these findings demonstrate that despite being closely related cell types residing within the same functional unit, beta and alpha cells undergo opposing age-related DNA methylation trajectories. Beta cells exhibit progressive hypomethylation at regulatory regions, particularly at TF binding sites, potentially reflecting their adaptive, metabolically responsive nature, whereas alpha cells do not show this demethylation process but instead display gradual hypermethylation, predominantly in non-active chromatin regions.

T2D is associated with premature hypomethylation in beta cells

A recent study using medium-throughput DNAm and RNA-seq analysis of whole human islets reported DNAm changes associated with impaired insulin secretion and reduced expression of beta-cell-related genes in T2D donors²⁰. However, given that islet cell composition varies between individuals¹⁰, and T2D islets often exhibit altered alpha-to-beta cell ratios owing to beta cell loss^21,22 or possibly alpha cell expansion²³, whole-islet analysis can obscure cell-type-specific epigenetic signals. This is especially relevant given the marked differences in DNAm profiles between alpha and beta cells and the opposing age-related methylation changes between the two cell types reported above. To capture beta-cell-specific changes in T2D, we analysed beta cell samples from 12 CTL and seven donors with T2D, with similar ages ranging from 33 to 64 years. All samples had high UXM-estimated purity, with a mean (±s.d.) of 91 ± 7% for CTL and 89 ± 4% for T2D donors (see Supplementary Table 4 for characteristics). PCA of methylation levels revealed an association between PC1 and disease status (two-sided Wilcoxon P = 0.04; Fig. 5a).

**Fig. 5: Beta cells exhibit accelerated DNA demethylation in T2D (part I).**

Using FDR < 0.05 and |delta| > 5% thresholds, we identified 9,296 DMRs, the majority of which (8,619; 93%) were hypomethylated in T2D, with most showing modest differences (|delta| < 20%) (Fig. 5b,c). About half of the hypomethylated regions in T2D were enriched for H3K4me1, marking primed enhancers, while a subset also carried H3K27ac, suggesting an active regulatory state. The remaining hypomethylated regions were enriched for the repressive mark H3K27me3 (Fig. 5d). Some hypomethylated regions also showed enrichment for TF binding sites, particularly those bound by FOXA2, NKX2.2 and PDX1 (Fig. 5e).

We then examined DMR-distal genes to DMRs with FDR < 0.05 and |delta| > 10% and found enrichment in T2D-associated demethylation for genes involved in beta-cell-related pathways, mirroring the pattern observed in age-associated hypomethylation (Fig. 5f and Supplementary Table 14). In addition, these regions were enriched for the βHI beta cell subpopulation gene set^41,42, whose relative proportion increases in T2D, suggesting that this enrichment may partly reflect over-representation of this subtype. Notably, T2D hypomethylated DMRs showed no enrichment near disallowed genes⁴⁷ (Fig. 5f and Supplementary Tables 8 and 14), which were also not upregulated in our dataset. By contrast, no significant pathway enrichment was observed for hypermethylated regions, consistent with a less specific, stochastic process. To assess the potential functional consequences of these methylation changes, we examined gene expression in beta cells from CTL (n = 13) and T2D (n = 15) donors of similar age using HPAP scRNA-seq data and found that T2D-associated hypomethylated DMRs did not show a direct correlation with upregulation of their associated genes. In fact, only 1.6% (about 20 genes) of the 1,212 genes associated with hypomethylated DMRs in T2D were differentially expressed (mostly upregulated), and none are known beta cell identity or function genes, suggesting a limited direct role of T2D-associated demethylation in regulating gene expression levels. Analysis of additional scRNA-seq datasets comparing T2D and CTL beta cells^48,49 likewise showed no enrichment of genes upregulated in T2D near hypomethylated DMRs. However, although core beta cell identity genes such as PDX1, NKX6.1, MAFA, ABCC8 and KCNJ11 maintain stable expression levels in T2D donors (Extended Data Fig. 7a), we observed reduced expression of broader functional pathways, including oxidative phosphorylation, glycolysis and protein secretion (Supplementary Table 15 and Extended Data Fig. 7b). Genes from these pathways, which are largely not linked to hypomethylation, do not maintain stable expression (Extended Data Fig. 7c), supporting intact core identity program but compromised beta cell function in T2D.

Next, we compared average beta cell methylation levels at PDX1 binding sites using seven CTL and seven T2D donors matched for age, selecting the 500 most variable sites across age within each condition. This analysis revealed that although demethylation at these sites tends to progress with age in CTL donors (Fig. 6a), as was shown above using a larger number of samples (Fig. 3e), this trend is less evident in beta cells from donors with T2D (Fig. 6b). The distribution of slopes from linear fits of methylation versus age at these regions was significantly more negative in CTL donors than those with T2D (Fig. 6c). The Pearson correlation coefficients for regions with negative slopes were also significantly more negative in CTL donors (Fig. 6d), and their associated P values trended lower (Fig. 6e). Although this analysis is based on a relatively small number of age-matched donors and should be interpreted with caution, the consistent differences in slope direction and correlation values across matched CTL and T2D samples support a more robust age-related demethylation process in non-diabetic donors at these regions. The lack of a clear age-related demethylation trajectory in T2D donors probably reflects already reduced methylation levels in beta cells early in life, possibly driven by chronic stress during diabetes progression. To illustrate this observation, we plotted the median methylation levels of the same PDX1 binding sites in T2D and CTL donors matched for age. As shown in Fig. 6f, focusing on the peaks in the fifth quintile of the median methylation levels (~55–65%), T2D donors consistently exhibit lower methylation than age-matched controls across the age range. This pattern supports the idea that demethylation at these sites occurs earlier in life in T2D beta cells. Similar trends were observed at FOXA2 and NKX2.2 binding sites in the fifth quintile (Extended Data Fig. 8), further supporting this observation. We explored the possibility of a genetic link by examining the T2D-associated single nucleotide polymorphisms (SNPs) identified in previous work⁵⁰. GREGOR enrichment analysis did not detect a particularly significant enrichment of these SNPs and their proxies in the beta cell hypomethylated DMRs in T2D from our results (P = 0.11).

**Fig. 6: Beta cells exhibit accelerated DNA demethylation in T2D (part II).**

Finally, we performed an analysis of DNAm changes in alpha cells from nine T2D and 13 control donors matched for age (33–63 years) and with high UXM-estimated purity (mean ± s.d., 87 ± 5% for CTL; 86 ± 8% for T2D; Supplementary Table 5). Despite the larger number of alpha cell samples compared to the beta cell analysis, we identified significantly fewer DMRs (1,574) between T2D and CTL alpha cells using the same thresholds (FDR < 0.05 and |delta| > 5%) (Fig. 7a) and comparable to the numbers of DMRs in shuffled data (Supplementary Table 16). These DMRs showed a roughly equal distribution of hypomethylation and hypermethylation (Fig. 7b). Most were marked by the repressive histone modification H3K27me3 (Fig. 7c), while hypermethylated regions in T2D were also somewhat enriched for NKX2.2 binding (Fig. 7d). Neither hypomethylated nor hypermethylated regions were associated with specific biological pathways.

**Fig. 7: Alpha cells show minimal DNA methylation differences in T2D.**

Altogether, our data demonstrate that beta cells in T2D exhibit an accelerated demethylation pattern resembling age-related changes, predominantly in TF binding sites, a phenomenon not observed in alpha cells, and whose functional consequences remain to be investigated.

Discussion

This study leverages comprehensive human islet cell methylome datasets generated by HPAP, a unique resource that provides high-resolution, cell-type-specific epigenomic profiles from human donors across a range of ages and diabetes status^26,27. Although previous studies have examined DNAm in whole islets, this dataset enables analysis of epigenetic changes in purified alpha cell and beta cell populations. In an accompanying paper, our colleagues⁵¹ analysed DNAm from sorted human alpha and beta cells and, similar to our findings, identified thousands of DMRs between the two cell types. Remarkably, 66% of the genes associated with DMRs in our study were also associated with DMRs in theirs, despite the differences in experimental and bioinformatics methodologies used, underscoring the robustness of this epigenetic mark.

Using a subset of HPAP donors with alpha and beta cell samples of the highest purity, we traced DNAm changes associated with age and T2D. Our analysis revealed that beta cells, but not alpha cells, from healthy donors undergo progressive demethylation with age at active regulatory regions, particularly at TF-bound cis-regulatory elements. This finding is consistent with our previous study in mice⁴⁰, which suggested a conserved adaptive process that supports beta cell function with age. Owing to the limited age range and smaller number of T2D donors, we could not perform an age-related DNAm analysis in T2D. However, a similar demethylation pattern was also noted in T2D compared to CTL donors, suggesting that T2D beta cells may acquire a hypomethylated state earlier in life, consistent with a previous study showing that premature DNA demethylation in peripheral blood leucocytes is associated with increased risk of T2D⁵². Although the changes are modest in magnitude, they are statistically significant and consistently enriched in functionally relevant regions. The enrichment of hypomethylated regions in regulatory elements suggests a regulated and potentially adaptive process, rather than stochastic drift, targeting loci essential for beta cell identity and function.

DNAm patterns in differentiated cells are generally stable and maintained by the coordinated activity of the maintenance methyltransferase DNMT1 together with DNMT3A, which, although traditionally described as a de novo methyltransferase, has been recently shown to also contribute to maintenance methylation^{30,31,53,54,55}. However, these patterns can be actively remodelled by TET enzymes, which oxidize 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC). This intermediate can either initiate active or passive demethylation or persist as a stable epigenetic mark, especially in the context of ageing and disease⁵⁶. Importantly, TET-mediated demethylation accumulates at active regulatory regions and TF binding sites^28,57, where it may counteract DNMT activity and protect enhancers from repressive methylation^28,29,58. In beta cells, such mechanisms could help sustain enhancer activity and support functionality throughout life. Our findings support this idea, as we show that beta-cell-specific gene sets are preferentially hypomethylated, and that core identity genes such as PDX1, MAFA, FOXA2 and ABCC8, which are associated with hypomethylation, maintain stable expression levels throughout life and in T2D. By contrast, other pathways not enriched near hypomethylated regions, such as those involved in mitochondrial metabolism, oxidative stress defence and secretory processes, tend to show reduced expression in T2D, suggesting that in beta cells, demethylation in regulatory regions primarily serves to preserve stable expression of the identity gene program rather than drive broad transcriptional activation. Alternatively, or in addition, the observed demethylation may result from rare replication events triggered by increased metabolic demand throughout life and under diabetic conditions, during which 5hmC-modified sites are not recognized by DNMT1, leading to a loss of CpG methylation in the daughter cells. However, alpha cells, which have been reported to proliferate at a higher rate in T2D²³, do not show substantial hypomethylation, suggesting that replication-dependent passive demethylation alone is insufficient to explain the beta-cell-specific pattern. Clarifying the relative contributions of these mechanisms will require profiling steady-state 5hmC levels in alpha and beta cells from non-diabetic and diabetic donors. Finally, the expression levels of DNMT1, DNMT3A, DNMT3B and the TET enzymes (TET1–3), which collectively regulate DNA methylation state, remain unchanged with age or in T2D, but are lower in alpha cells than in beta cells.

Beta cell gene expression is influenced by conditions such as endoplasmic reticulum stress, oxidative stress and metabolic workload, and gene activation and repression are often reversible, supporting the adaptive capacity of beta cells until chronic stress overwhelms this capacity, leading to failure and progression to T2D⁵⁹. By contrast, DNAm, being a relatively stable epigenetic mark, may not respond to transient metabolic cues or drive rapid changes in gene expression. Indeed, in our study, most age-related and disease-associated demethylated regions did not strongly correlate with increased expression of nearby genes. Nonetheless, the specific demethylation process observed in beta cells but not alpha cells supports the idea that this represents a cell-type-specific epigenetic adaptation. In beta cells, demethylation may serve to preserve enhancer activity and sustain cellular responsiveness over time, rather than directly modulate gene expression acutely. Based on the fact that we did not detect significant overlap between BMI-demethylated and age-demethylated DMRs, this adaptation is mainly associated with ageing, rather than metabolic stress. Although the functional significance of this age-demethylation remains to be fully elucidated, its cell-type specificity highlights a unique epigenetic adaptive response in this long-lived, metabolically responsive cell type. However, in the context of T2D, this mechanism may ultimately fail, leading to impaired function despite preserved expression of core identity genes. Future studies should explore whether targeted modulation of the DNAm machinery, particularly TET activity, can enhance beta cell resilience during ageing and diabetes.

Methods

Human pancreatic islets

Pancreatic islets were procured by the HPAP consortium under the Human Islet Research Network (https://hirnetwork.org) with approval from the University of Florida Institutional Review Board (201600029) and the United Network for Organ Sharing. A legal representative for each donor provided informed written consent before organ retrieval, and the data have been deidentified. The protocols used for the human islet sorting, whole genome bisulfite sequencing (WGBS), scRNA-seq and snATAC-seq assays used in this work are available on the PancDB website at https://hpap.pmacs.upenn.edu/explore/workflow/islet-molecular-phenotyping-studies (see Kaestner lab protocols). In addition to libraries generated with WGBS, the methylome data used in this work also comprises libraries generated using enzymatic methyl sequencing (EM-seq) following these steps: 100 ng DNA input was sheared to 300 bp and cleaned with the Zymo research kit (D4014). Quality control was performed on a BioAnalyzer (Agilent), and EM-seq libraries were generated (New England Biolabs, E7120/E7140). All kits were used following the manufacturer’s instructions.

Data analysis

Methylome

All available (154) HPAP methylome samples, regardless of cell type or donor disease state, were pre-processed together (see Extended Data Fig. 9 for workflow outline). Adaptors were trimmed from paired-end reads with cutadapt (v.4.4)⁶⁰. Trimmed reads were aligned to the human hg38, Lambda and pUC19 genomes using bwa-meth (v.0.2.7)⁶¹. The resulting sam files were converted to bam files using samtools (v.1.14)⁶², then duplicated reads were marked by sambamba (v.0.8.1)⁶³ and the resulting bam files were sorted and indexed with samtools. We used wgbstools (v.0.2.0)²⁴ to generate preliminary pat, beta and tab files from the hg38 alignments while removing reads with low mapping quality, duplicated or not mapped in a proper pair (using -F 1796 -q 10). We then leveraged the HPAP whole genome sequencing data to identify the C > T and G > A variants at CpG positions for each donor and subtracted them from the tab files, also using bedtools (v.2.26.0)⁶⁴. Using wgbstools again, we generated beta files from the filtered tab files, then computed average coverage, generated bigwig tracks and segmented the genome using these filtered beta files. In general, the segmentation from wgbstools identifies non-overlapping continuous regions of highly correlated CpG sites similarly methylated in each sample but possibly covarying across conditions. We derived the segmentation from all samples with an average coverage above the median (22×) and required a minimum of four CpGs per region and a max region size of 2,000 bp. This segmentation (2,061,859 regions; median size, 607 bp; median number of CpGs, 8) was then applied to all samples and used in subsequent analyses.

For each sample, we estimated purity using the UXM fragment-level deconvolution algorithm²⁴ (v.0.1.0) with reference Atlas.U25.l4.hg38.full.tsv (from https://github.com/nloyfer/UXM_deconv) restricted to the markers for pancreatic cell types, endothelial cells and monocytes and macrophages. Before applying UXM, we refined the atlas for each donor by filtering out CpG positions where that donor had C > T or G > A variants.

We leveraged methylSig (v.1.11.0)⁶⁵ for differential methylation analyses. We performed several comparisons, including (1) alpha versus beta cells in CTL donors; (2) age (as a numeric covariate) separately in alpha and (3) in beta cells of CTL donors; and (4) T2D versus CTL donors separately for alpha and (5) beta cells. For each comparison, we selected relevant samples with sufficiently high average coverage (~20× or more) and estimated purity (~65% or more). For comparisons between T2D and CTL donors, we selected donors in similar age ranges in the two groups. The characteristics of the samples used in each comparison are summarized in Supplementary Tables 1–5. For each comparison, we removed CpGs covered by fewer than ten reads in any of the samples and then aggregated methylation fractions by segmentation regions and removed any region covered by fewer than ten reads in any of the samples. We focused on regions that were not on the X and Y chromosomes, with an average methylation difference of at least 5% between the groups being compared. In the case of the age comparisons (where age was treated as a numeric covariate), the two groups used for this filtering were those of donors aged 39–58 years and 18–35 years. To find DMRs, we used the general model test with DSS and a design dependent on the comparison of interest. In comparison (1), we paired by donor; in comparisons (3) to (5), we included ancestry and assay type (WGBS or EM-seq) in the model. In comparison (2), we included assay type in the model but not ancestry, given that the latter was confounded with age. For each comparison, DMRs for further examination were defined based on thresholds for the FDR and effect size; note that effect size here refers to the absolute value of delta, the difference of methylation levels between the two groups as estimated by methylSig (meth_diff). We set basic thresholds of 0.05 for the FDR and 5% for the effect size. But we made either or both of these thresholds more stringent for those comparisons where we expected less subtle differences (also based on inspection of PC and effect size plots), to focus on the top DMRs. The thresholds chosen are listed in Supplementary Table 6.

To functionally annotate DMRs, we defined the following sets of features based on the Gencode Human v43 transcript models:

1,500 bp upstream of the transcription start site (TSS);
1,501–5,000 bp upstream of TSS;
5,001–50,000 bp upstream of TSS;
1,000 bp downstream of TSS; and
1,001–50,000 bp downstream of TSS and downstream of the transcription end site.

We used bedtools (v.2.26.0)⁶⁴ to mark the features from each of the above sets overlapping with each DMR.

To explore enrichment of signals from alpha, beta and islet-specific snATAC-seq and ChIP-seq experiments, we leveraged EnrichedHeatmap (v.1.32.0)⁶⁶. We downloaded the bigwig files with accessions listed in Supplementary Table 7 from the Common Metabolic Disease Genome Atlas (https://cmdga.org). For H3K27me3, we used data from a previous publication⁶⁷, lifted over to hg38 with LiftOver⁶⁸. In addition, we generated bigwig files for chromatin accessibility in alpha and beta cells from HPAP snATAC-seq experiments in CTL and T2D donors as described below. Our plots also leveraged pheatmap (v.1.0.12) (https://github.com/raivokolde/pheatmap) and ggplot2 (v.3.5.1)⁶⁹. The principal component analyses leveraged PCATools (v.2.14.0) (https://bioconductor.org/packages/PCAtools) with removeVar = 0.1.

The analyses of beta cell methylomes from healthy donors with respect to BMI and GSIS were done in the same manner as the age analyses, using methylSig and with assay type and ancestry in the model. BMI (resp. GSIS) was used as a numeric covariate, and the effect size was based on its top and bottom quartiles. GSIS was measured by the insulin ratio B/A derived from the individual perifusion data obtained from PancDB as follows. The numerator, B, was the maximum insulin secretion value after stimulation with G16.7. The denominator, A, was the average insulin secretion over the time points in the ‘plateau’ amino acid stimulation phase (AAM, before G3 stimulation). For the latter, outlier time points at the beginning of the AAM phase were manually filtered out donor by donor.

As a quality control check, for each of the alpha and beta cell age or T2D versus CTL comparison, we randomly shuffled the sample labels 100 times and ran the same analysis on the shuffled data (summary counts are provided in Supplementary Table 16). For the age comparisons in both cell types and the beta cell T2D comparison, the number of DMRs identified in shuffled data was much reduced (almost null in the age comparisons) and did not follow the imbalance in hypomethylation versus hypermethylation observed in the unshuffled data.

scRNA-seq

For each donor islet sample, we obtained counts with CellRanger (v.7.1.0)⁷⁰ and used Seurat (v.4.9.9.9041)⁷¹ (complemented by SoupX (v.1.6.2)⁷² and scDblFinder (v.1.12.0)⁷³) for clean-up, normalization, pre-processing and data integration. Cells were annotated with two approaches: scSorter (v.0.0.2)⁷⁴ and manual cluster annotation based on selected pancreatic cell markers. Only cells for which the two approaches agreed were assigned to a final cell type.

For each cell type, we used all available relevant donors for a comparison of interest, with at least 100 cells of that type and processed with SC3Pv3 chemistry. We generated pseudo-bulk counts and used DESeq2 (v.1.38.1) for differential expression analyses⁷⁵. For the age comparison, we did not treat age as a numeric covariate as we did for DMRs, but we compared age group categories (18–35 vs 39–58 years old) based on DESeq2 recommended guidelines.

snATAC-Seq profiles in alpha and beta cells

Chromatin accessibility profiles for alpha and beta cells were generated using HPAP snATAC-seq datasets. Each dataset was first analysed using Signac (v.1.7.0)⁷⁶ and Seurat (v.4.4.0)⁷¹. A standard processing pipeline was applied (https://stuartlab.org/signac/articles/pbmc_vignette). Nuclei with fewer than 1,000 or more than 100,000 reads in peaks, a TSS enrichment score of <1 or a nucleosome signal of >2 were filtered out. Each nucleus was annotated by mapping to an internal multiome reference dataset, and nuclei with a mapping score of >0.8 were kept.

Then, to generate cell-type-specific tracks in each disease group, we used samples from CTL donors with age ≥30 years and samples from T2D donors. With cell types annotated from the previous steps, fragments associated with cells from each donor and each cell type (alpha and beta) were merged to generate pseudo-bulk tracks; tracks with fewer than 30 cells were removed. A bed file including the start, end and chromosome information of each read was created for each donor and each cell type. Bam files were constructed using bedToBam (v.2.30.0)⁶⁴ with a genome size file provided as part of the GRCh38 reference – 2020-A-2.0.0 available on the 10× Genomics website (https://cf.10xgenomics.com/supp/cell-arc/refdata-cellranger-arc-GRCh38-2020-A-2.0.0.tar.gz). Bam files were indexed using samtools (v.1.11 index)⁶². BAMscale (v.0.0.5)⁷⁷ was used to generate scaled coverage tracks, with parameter -t 4. Lastly, to generate cell-type-specific tracks per disease group, cell-type-specific tracks were merged across donors of the same condition using bigWigMerge (v.2) and the output was sorted and converted to a bigwig file using bedGraphToBigWig (v.2.8)⁷⁸.

Gene set analyses

To analyse enrichment of gene sets associated with DNAm changes, we focused on a gene set collection comprising gene sets obtained from MSigDB⁷⁹ and custom gene sets defined in a previous publication¹³. The hypothesis-driven gene set collection is described in Supplementary Table 8. For these analyses, we used the top DMRs (in terms of effect size) to reduce noise and focus on the most robust enrichment. For the comparison between alpha and beta cells, we used a |delta| > 40% cutoff owing to the large inherent differences between these two cell types, whereas for all other comparisons (age and T2D), we used a consistent |delta| > 10% cutoff. We used a hypergeometric test implemented in the Genomica (v.3.0.0) software⁸⁰, considering gene sets with P < 0.01 and FDR < 0.05 to be significant. For the association of genes to DMRs in likely enhancers, we used protein-coding genes in the unions of the following DMR annotation files defined above: 1,501–50,000 bp upstream of TSS or 1,001–50,000 bp downstream of TSS and downstream of the transcription end site. We refer to these genes as ‘DMR-distal’.

To analyse enrichment of gene sets associated with mRNA expression, we used GSEA^79,81 (v.4.3.3) on the DESeq2 normalized scRNA-seq pseudo-bulk counts. The gene sets input in these analyses were the Hallmark gene sets from MSigDB, to cover the main biological programmes.

T2D GWAS SNP enrichment analyses

To examine the 558 T2D-associated SNPs identified in a previous publication⁵⁰ versus the 8,619 beta cell demethylated DMRs in T2D, we used GREGOR (v.1.4.0)⁸² after lifting these regions over to hg19. We used default parameter settings, a threshold of r² = 0.7 for proxies and EUR as the reference population (given that most of the donors in this comparison were from this ancestry).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Raw data and metadata related to this work are available on the PancDB website https://hpap.pmacs.upenn.edu and on dbGaP (https://www.ncbi.nlm.nih.gov/gap) with accession no. phs002465 (v.2). With the PancDB website interactive analysis link, it is also possible to explore Genome Browser tracks for our methylation data, as well as the results of islet perifusion experiments used in our insulin secretion ratio calculations. Finally, the result files for the methylome comparisons in this work and the scRNA-seq DESeq2 results are available in the Supplementary Data.

Code availability

The study was conducted using only publicly available software as outlined above; no new code was generated. The list of software packages and versions used is also available in the Nature Portfolio Reporting Summary linked to this article.

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Acknowledgements

We thank the families of the organ donors and the entire HPAP team for making this study possible. We used data acquired from the HPAP (HPAP-RRID:SCR_016202) database (https://hpap.pmacs.upenn.edu), a Human Islet Research Network (RRID:SCR_014393) consortium supported by NIDDK grants UC4-DK-112217, U01-DK-123594, UC4-DK-112232 and U01-DK-123716. Further support was provided by NIDDK grant U01-DK-134995 to D.A., B.G. and K.H.K. B.G. and D.A. are supported by grants from the Israel Science Foundation and JDRF (1782/18 and 2982/20), the US–Israel Binational Science Foundation (2019314) and the EU’s Horizon 2020 research and innovation program no. 874710. D.A. is supported by the Israel Science Foundation (2166/24). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Finally, we thank the editor and reviewers for their constructive comments and suggestions.

Author information

Authors and Affiliations

Institute of Diabetes, Obesity, and Metabolism, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA
Elisabetta Manduchi, Hélène C. Descamps, Jinping Liu, Jonathan Schug, Tong Da, Deeksha Lahori, Hilana El-Mekkoussi, Michelle Lee, Diana Bernstein & Klaus H. Kaestner
Department of Genetics and Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
Elisabetta Manduchi, Hélène C. Descamps, Jinping Liu, Jonathan Schug, Tong Da, Deeksha Lahori, Hilana El-Mekkoussi, Michelle Lee, Diana Bernstein & Klaus H. Kaestner
The Human Pancreas Analysis Program (RRID:SCR_016202), Philadelphia, PA, USA
Elisabetta Manduchi, Hélène C. Descamps, Jinping Liu, Jonathan Schug, Tong Da, Deeksha Lahori, Hilana El-Mekkoussi, Michelle Lee, Chengyang Liu, Ali Naji & Klaus H. Kaestner
Department of Developmental Biology and Cancer Research, The Hebrew University Hadassah Medical School, Jerusalem, Israel
Eseye Feleke & Dana Avrahami
Department of Endocrinology and Metabolism, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
Eseye Feleke, Benjamin Glaser & Dana Avrahami
Department of Surgery and Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Chengyang Liu & Ali Naji

Authors

Elisabetta Manduchi
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Hélène C. Descamps
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Jinping Liu
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Jonathan Schug
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Tong Da
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Deeksha Lahori
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Hilana El-Mekkoussi
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Michelle Lee
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Eseye Feleke
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Diana Bernstein
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Chengyang Liu
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Ali Naji
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Benjamin Glaser
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Klaus H. Kaestner
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Dana Avrahami
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Contributions

D.A. and K.H.K. conceived and supervised this work. B.G. provided conceptual input. E.M., D.A. and K.H.K. wrote the manuscript. E.M. pre-processed the data, performed most computational analyses and uploaded the data to PancDB and dbGaP. H.C.D. performed human islet sorting, prepared and sequenced the EM-seq, scRNA-seq and snATAC-seq libraries, performed PC analyses and contributed to data interpretation and manuscript outline. D.A. performed gene set analyses and GSEA. J.L. prepared the WGBS libraries. J.S. oversaw the sequencing, provided feedback and helped with data uploads to PancDB and dbGaP. T.D., D.L., D.B. and H.E.-M. contributed to human islet sorting and generated scRNA-seq and snATAC-seq libraries. M.L. performed the snATAC-seq analyses. C.L. and A.N. performed islet isolation from donor tissues. E.F performed additional biological analyses. All authors reviewed and approved the manuscript.

Corresponding authors

Correspondence to Klaus H. Kaestner or Dana Avrahami.

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The authors declare no competing interests.

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Peer review information

Nature Metabolism thanks Guoqiang Gu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Revati Dewal, in collaboration with the Nature Metabolism team.

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Extended data

Extended Data Fig. 1 QC clustering.

Quality control clustering of all 154 HPAP methylome samples. Rows represent the top 1% most variable segmentation regions (18,972) on chromosomes other than X and Y. Columns correspond to samples. Rows and columns are clustered using Manhattan distance as similarity metrics. Horizontal bars at the top indicate values of several covariates for each sample. uxm=UXM estimated purity score, ND.AAB= non-diabetic auto-antibody positive.

Extended Data Fig. 2 Beta cell scRNA-Seq data across age.

MSigDB Hallmark gene sets identified by GSEA as (a) up-regulated with age and (b) down-regulated with age in beta cells from CTL donors. (c) DESeq2 normalized expression (from pseudo-bulk counts) of selected genes across age. Unadjusted two-sided Pearson correlation p-values: GCK (p = 0.05), NEUROD1 (p = 0.36), MAFB (p = 0.01), FOXA2 (p = 0.40), PDX1 (p = 0.19), ISL1 (p = 0.02), ABCC8 (0.11), IAPP (p = 0.06), and SLC2S2 (p = 0.38).

Extended Data Fig. 3 Heatmaps of beta cell age DMRs.

Heatmaps of average methylation of the age-hypomethylated DMRs in beta cells of CTL donors, ordered by (a) age, (b) BMI, and (c) GSIS respectively. GSIS was measured by the insulin secretion ratio (insSecrB2A) described in Methods. The top bars indicate the age, BMI, and insSecrB2A values for each donor.

Extended Data Fig. 4 Enrichment heatmaps for alpha cells DMRs.

Enrichment plot of signals from (a) islet histone modifications ChIP-Seq and from (b) snATAC-Seq in alpha cells and islet TF ChIP-Seq experiments for the 1,466 age-associated DMRs with FDR < 0.01 and |delta | >5% in alpha cells from CTL donors. Each row shows the signal plotted over the 10 kb region centered at one DMR. Top: regions hypermethylated in younger alpha cells; bottom: regions hypermethylated in older alpha cells.

Extended Data Fig. 5 NKX2.2 binding sites methylation heatmaps.

Heatmaps of average methylation for NKX2.2 peaks³² for (a) alpha and (b) beta cells. Shown are the 500 most variable peaks (separately, for each cell type) across 13 age-matched donors.

Extended Data Fig. 6 Linear fit slopes and Pearson correlations for NKX2.2 binding sites.

(a) Violin plots of the slopes of linear fits to methylation versus age corresponding to the regions from Extended Data Fig. 5 in the two cell types. Boxplots of (b) absolute Pearson correlation coefficients and (c) Pearson correlation p-values (for positive association in alpha cells and negative association in beta cells) of methylation versus age at the 306 regions with slope > 0 in alpha cells and the 408 regions with slope<0 in beta cells from Extended Data Fig. 5. Boxplots description: middle line=median; box upper (resp. lower) bound=3rd (resp. 1st) quartile; upper/lower whisker extends to the largest/smallest value within 1.5×IQR; individual points=outliers. The asterisks indicate a two-sided Wilcoxon p-value < 0.001 (p < 2.22e-16 for (a), p = 2.6e-08 for (b) and (c)).

Extended Data Fig. 7 Beta cell scRNA-Seq data in T2D and CTL.

(a) DESeq2 normalized expression (from pseudo-bulk counts) of key beta cell identity genes in CTL (n = 13) and T2D (n = 15) donors of similar age from HPAP scRNA-Seq data, showing stable expression in T2D beta cells. Data are presented as mean values +/− SD. (b) MSigDB Hallmark gene sets identified by GSEA as down-regulated in beta cells from T2D donors compared with CTLs. (c) DESeq2 normalized expression (from pseudo-bulk counts) of selected genes from broader functional pathways for the same donors as (a). Data are presented as mean values +/− SD.

Extended Data Fig. 8 Beta cell methylation versus age in binding sites.

Median methylation values across the (a) FOXA2 and (b) NKX2.2 binding sites in the fifth quintile of the median methylation levels for 7 CTL (blue) and 7 T2D (red) age-matched donors.

Extended Data Fig. 9 Pre-processing workflow.

Workflow of the HPAP methylation data pre-processing.

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Manduchi, E., Descamps, H.C., Liu, J. et al. Epigenetic adaptation of beta cells across lifespan and disease. Nat Metab 8, 941–956 (2026). https://doi.org/10.1038/s42255-026-01495-y

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Received: 01 July 2025
Accepted: 02 March 2026
Published: 24 April 2026
Version of record: 24 April 2026
Issue date: April 2026
DOI: https://doi.org/10.1038/s42255-026-01495-y