3.1 Clinical characteristics of the study participants
Our study population was characterized by 57% (8/14) of male gender. The mean age of HS was 41±7.1, whereas it was 65.3±7.4 and 69.6±11.15 in Pre-Diab and T2D, respectively. In the Pre-Diab state, we observed the following values: BMI=27.5 ± 2.35, HbA1c=6.2±0.42, glucose=103±5.69 (mg/dL). Only 33% (n=2) were smokers. All Pre-Diab patients showed dyslipidemias and were treated with statins, 66.7% (n=4) were treated with acetylcholinesterase inhibitors (ACEi), 50% (n=3) with B-blockers and calcium inhibitors, 33.3% (n=2) with angiotensin II receptor blockers (ARBs). In the T2D state, we registered the following values: the BMI=29.7±3.6, HbA1c=6.6±0.2, glucose=222.0±103.3 (mg/dL). Moreover, DBP was 85.8±6.6, SBP was 140.5±11.4 and 50% (n=3) were smokers. Dyslipidemia was diagnosed in 83.3% (n=5). About drug therapy, all patients were treated with statins and B-blockers; 66.7% (n=4) were treated with ACEi, 33.3% (n=2) with calcium inhibitors and ARBs.
3.2 Differentially methylated regions (DMRs)
3.2.1 DMR global trend
DNA methylation profiles covering >300,000 CpG dinucleotides in both T cell types isolated from each study participant were generated by using Illumina Nextseq 500 platform. Then, multiple t-test was performed. Overall, circulating T cells revealed no statistically significant changes in global DNA methylation trend in HS vs increasing hyperglycemia (Supplementary Table 1).
3.2.2 Unique DMR trend in HS vs Pre-Diab and T2D
In order to characterize the most significant DMRs in distinct Pre-Diab and T2D groups, we firstly showed distribution values for each sample. Annotated DMRs were mapped according to their distance from established CpG islands. Notably, DNA methylation dynamics were rather concentrated among CpG islands located in the promoter regions (about 30-35% of total DMRs), for both T cell types and for each comparison. In order to individuate the overlapping changes during increasing hyperglycemia we calculated the number of annotated DMR-related genes by using Venn Diagram (Supplementary Figure 4). Moreover, we performed heatmaps to show the DMR methylation change (Supplementary Figure 5) (Supplementary Table 2 and 3). The summary of selected significant DMRs are shown in Table 2. Moreover, we identified the genomic locations most impacted by changes in DNA methylation between HS and increasing hyperglycemia. Then, we discerned hypo- and hypermethylated DMRs-related genes (Supplementary Figure 5). In general, we observed that the number of hypermethylated DMRs was greater than those hypomethylated in CD04+ T cells; on the contrary, CD08+ T cells showed a higher number of hypomethylated DMRs than hypermethylated ones.
18.104.22.168 Analysis of CD04+ T cells
We identified 437 DMRs (FDR <0.05) and 418 annotated genes, of which 35% (n=154) were hypo- and 65% (n=283) were hypermethylated in HS vs Pre-Diab (Supplementary Table 4). Moreover, from comparison between HS and T2D, we identified 351 DMRs (FDR <0.05) associated with 335 annotated genes, of which 32% (n=112) were hypo- and 68% (n=239) were hypermethylated (Supplementary Table 5). Finally, from comparison between Pre-Diab and T2D groups, we identified 84 DMRs (FDR <0.05) associated with 83 annotated genes of which 51% (n=43) were hypo- and 49% (n=41) were hypermethylated (Supplementary Figure 3) (Supplementary Figure 5).
22.214.171.124 Analysis of CD08+ T cells
We identified 594 DMRs (FDR < 0.05) of which 74% (n=438) were hypo- and 26% (n=156) were hypermethylated associated with 566 annotated genes by comparing HS vs Pre-Diab group (Supplementary Table 6). From comparison between HS and T2D, we identified 786 DMRs (FDR < 0.05), of which 68% (n=535) were hypo- and 32% (n=251) were hypermethylated and associated with 717 annotated genes (Supplementary Table 7). Finally, from comparison between Pre-Diab and T2D groups, we identified 62 DMRs (FDR < 0.05) of which 37% (n=23) were hypo- and 63% (n=39) were hypermethylated and associated with 59 annotated genes (Supplementary Figure 5) (Supplementary Figure 3).
3.2.3 Common DMR trend in CD04+ T in HS vs Pre-Diab and T2D
We identified annotated genes common to Pre-Diab and T2D patients in a total number of 53, of which 11 were hypo- and 42 hypermethylated (Supplementary Figure 6). Interestingly, from heatmap we noticed that there were some clusters of DMR-related genes, which retained the same hyper- or hypomethylation level in increasing hyperglycemia, as depicted in red boxes (Supplementary Figure 6). We focused on the top 18 highly significant DMR-related genes, which were shared in HS vs increasing hyperglycemia, of which 13 were hyper- and 5 were hypomethylated (Figure 2) (Table 3). Moreover, we reported two opposite trends for DNA methylation in HS vs increasing hyperglycemia. The first trend demonstrated an increasing grade of DNA methylation from normoglycemia to increasing hyperglycemia. GIGYF2 gene resulted the highest hypermethylated gene (q=2.85E-19 and q=1.78E-21 in Pre-Diab and T2D, respectively). However, ZNF564 gene showed the highest fold change (FC=15) of differential methylation between Pre-Diab and T2D (Figure 2A). The second trend demonstrated an increased level of hypomethylation from normoglycemia to increasing hyperglycemia. In particular, MT1Xgeneresulted the highest hypomethylated gene (q=3.01E-09 and q=6.21E-10 in Pre-Diab and T2D, respectively) (Figure 2B). However, KLK10 and PSMB10 genes showed the highest FC=6 of differential methylation both in Pre-Diab and T2D. Interestingly, MT1X presented an inverse correlation with SBP (-0.70), DBP (-0.82) and AODd (-0.64) in Pre-Diab group and with SBP (-0.55), Glucose (-0.54), Cholesterol (-0.64), LDL (-0.71), LVSD (-0.65) in T2D subjects, as well as a positive correlation with Triglycerides (+0.66).
3.2.4 Common DMR trend in CD08+ T of HS vs Pre-Diab and T2D
We identified a total of 60 annotated genes, of which 40 were hypo- and 19 hypermethylated in HS vs hyperglycemia (Supplementary Figure 6). We focused on the top 20 highly significant DMR-related genes shared in HS vs increasing hyperglycemia, of which 7 were hyper- and 13 were hypomethylated (Table 4). Also, in this case we reported two opposite trends for DNA methylation in HS vs increasing hyperglycemia. The first trend demonstrated an increased level of DNA methylation from normoglycemia to increasing hyperglycemia (Figure 2C). NLRP7 gene resulted the highest hypermethylated (q=4.50E-42 and q=3.49 E-60 in Pre-Diab and T2D, respectively). On the other hand, the PYCR1 gene showed the highest FC=18 of differential methylation in HS vs increasing hyperglycemia. The second trend demonstrated a decreasing grade of methylation from normoglycemia to increasing hyperglycemia (Figure 2D). In particular, SPARC resulted the highest hypomethylated gene (q=3.77E-18 and q=1.20E-14 in Pre-Diab and T2D, respectively). TAB2 gene showed the most FC=6 (q=1.54E-15 and q=1.51E-07 in Pre-Diab and T2D, respectively). We noticed that SPARC characterized Pre-Diab condition, showing a positive correlation with DBP (+0.76), HDL (+0.54), Creatinine (+0.83), LVDd (+0.98), LVSD (+0.98), LAD (+0.98), LVPWd (+0.84), AODd (+0.81), HR (+0.72), Triglycerides (+0.83), LAD (+0.69) and AODd (+0.52) whereas a negative correlation with Cholesterol (-0.52) and LDL (-0.71) in T2D.
3.3 Functional analysis of unique DMRs-related genes in HS vs Pre-Diab and T2D
3.3.1 Analysis of CD04+ T cells
The top biological functions identified by g:Profiler and the number of genes involved are summarized in Supplementary Table 8. DMR-related genes of CD04+ T cells in Pre-Diab patients were mainly involved in the early damages to the micro-domains leading to abnormalities of eye morphology, physiology and movement as well as neurogenesis. Moreover, a major involvement of nervous system differentiation was reported.
126.96.36.199 Analysis of CD08+ T cells
The top biological functions identified by g:Profiler and the number of genes involved are summarized in Supplementary Table 9. In Pre-Diab patients, DMRs induced abnormalities of central nervous system mainly involving high mental function and brain morphology as well as eye abnormalities. Moreover, it raised early damages in cardiac muscle tissue, development of blood vessels, as well as liver, limb and muscle physiology.
3.3.2 Common DMRs-related genes in HS vs Pre-Diab and T2D
188.8.131.52 Analysis of CD04+ T cells
We evaluated the functional characteristics and signaling pathways associated to DRM-related genes, which are shared by patients with increasing hyperglycemia (Supplementary Table 10). From results, the binding protein was the most prominent in the molecular process group, followed by catalytic activity (mainly hydrolase and transferase enzymes), nucleic acid binding, and transcription process. Interestingly, the human phenotype group highlighted abnormalities of vasculature, mainly aortic morphology and cardiac system. Moreover, abnormalities in eye, digestive system, muscle skeletal system (mainly hypotonia), immune system and liver were predicted.
184.108.40.206 Analysis of CD08+ T cells
A detailed GO analysis of significantly DRM-related genes in increasing hyperglycemia was summarized (Supplementary Table 11). At biological process level, a major regulation of immune system and neuron development was observed followed by hematopoiesis and response to lipid. KEEG highlighted the involvement of the insulin signaling pathway as well as REACTOME pointed to signal transduction (cytokines, interleukins, receptor tyrosine kinases), immune system, metabolism, transcription regulation and neural system, mainly PPIs at synapses.
3.3.3. Chromatin state discovery
We applied ChromHMM, a machine learning approach evaluating epigenomic information (called marks) across multiple cell types (CD04+ and CD08+ T cells) and multiple conditions (HS, Pre-Diab, T2D). As reported, this method allows to recognize chromatin states, by identifying their genomic occurrences . The combination of multiple marks and the relative genomic annotation can be highly informative of distinct biological functions. Starting from 18 emission states (Supplementary Figure 7) (Supplementary Table 12), we selected only genomic regions associated to a decreased (state 1) or increased (states 7, 9) methylation level vs HS. Then, we found 600 genomic regions associated to hypo-state 1 and 13947 associated to hyper-states 7 and 9 from Pre-Diab to T2D. Interestingly, we observed that the most states were distinctly associated with hypermethylation status during increasing hyperglycemia. As a consequence, we supported that a continuous glucose intake could induce a progressive DNA hypermethylation in both T cells.
Since hypermethylation of CpG islands clustered around transcription start sites (TSS) usually modulate gene expression , we considered only methylated regions annotated to promoters (<=1Kb). Then, we performed pathway analysis for these two groups of genes, by using web tool g:Profiler. We founded a total of 61 genes and 1198 genes annotated to hypo-state and hyper-states, respectively (Figure 3). We noted that genes associated to hypo-state enriched “phenotypic abnormalities”. In particular, several genes, such as LRP5, ANGPTL6, SDHC and CCM2, characterized abnormalities of “CV system” and “blood circulation”. Instead, for genes associated to hyper-states, we found an enrichment in many KEGG and REACTOME pathways and numerous “human phenotypes”. In particular, KEGG analysis revealed three crucial signaling pathway, including MAPK, which plays an important role in the “regulation of CV diseases” and “cerebrovascular diseases” , PPAR, which is involved in “myocardial metabolism”,  and ERBB, involved in “diabetes-induced cardiac dysfunction” . From REACTOME, we found 54 genes, such as CD36, PPP2R5C and SOS1 involved in the pathogenesis of “insulin resistance”, “glycemic control of T2D” and “CV diseases”. Moreover, we noticed a lot of genes involved in “innate and adaptive immune system pathways”. Finally, also through GO analysis, we found alterations in numerous “human phenotypes”. A total of 51 genes characterized the increased “inflammatory response”. In particular, some cytokine genes, such as interleukin 1 (IL-1), several complement cascade members, such as C5 and C4B showed a pathogenic role in “arterial hypertension”, whereas SMAD3 mediated “diabetic cardiac hypertrophy”. A large common set of genes was involved in “abnormality” of vascular (n=71), heart (n=45) and systemic arterial (n=28) morphology. Instead, a small group of these common genes, including SMAD3, NDE1, RAD51C, ZMPSTE24, FANCD2, PPARG, MYLK, GTF2I and FANCG, characterized also “abnormal carotid artery morphology”. Finally, another smaller group of genes, containing ABCC8, SAG and UCP2, was associated to “abnormal insulin level”.