DNA methylation profiles in sibling pairs discordant for intrauterine exposure to maternal gestational diabetes

Blood methylation pattern reflects epigenetic remodelling in adipose tissue after bariatric surgery

BACKGROUND

Studies on DNA methylation following bariatric surgery have primarily focused on blood cells, while it is unclear to which extend it may reflect DNA methylation profiles in specific metabolically relevant organs such as adipose tissue. Here, we investigated whether adipose tissue depots specific methylation changes after bariatric surgery are mirrored in blood.

METHODS

Using Illumina 850K EPIC technology, we analysed genome-wide DNA methylation in paired blood, subcutaneous and omental visceral AT (SAT/OVAT) samples from nine individuals (N = 6 female) with severe obesity pre- and post-surgery.

FINDINGS

The numbers and effect sizes of differentially methylated regions (DMRs) post-bariatric surgery were more pronounced in AT (SAT: 12,865 DMRs from -11.5 to 10.8%; OVAT: 14,632 DMRs from -13.7 to 12.8%) than in blood (9267 DMRs from -8.8 to 7.7%). Cross-tissue DMRs implicated immune-related genes. Among them, 49 regions could be validated with similar methylation changes in blood from independent individuals. Fourteen DMRs correlated with differentially expressed genes in AT post bariatric surgery, including downregulation of PIK3AP1 in both SAT and OVAT. DNA methylation age acceleration was significantly higher in AT compared to blood, but remained unaffected after surgery.

INTERPRETATION

Concurrent methylation pattern changes in blood and AT, particularly in immune-related genes, suggest blood DNA methylation mirrors AT's inflammatory state post-bariatric surgery.

FUNDING

The funding sources are listed in the Acknowledgments section.

Gestational diabetes mellitus: genetic factors, epigenetic alterations, and microbial composition

Gestational diabetes mellitus (GDM) is a common metabolic disorder, usually diagnosed during the third trimester of pregnancy that usually disappears after delivery. In GDM, the excess of glucose, fatty acids, and amino acids results in foetuses large for gestational age. Hyperglycaemia and insulin resistance accelerate the metabolism, raising the oxygen demand, and creating chronic hypoxia and inflammation. Women who experienced GDM and their offspring are at risk of developing type-2 diabetes, obesity, and other metabolic or cardiovascular conditions later in life. Genetic factors may predispose the development of GDM; however, they do not account for all GDM cases; lifestyle and diet also play important roles in GDM development by modulating epigenetic signatures and the body's microbial composition; therefore, this is a condition with a complex, multifactorial aetiology. In this context, we revised published reports describing GDM-associated single-nucleotide polymorphisms (SNPs), DNA methylation and microRNA expression in different tissues (such as placenta, umbilical cord, adipose tissue, and peripheral blood), and microbial composition in the gut, oral cavity, and vagina from pregnant women with GDM, as well as the bacterial composition of the offspring. Altogether, these reports indicate that a number of SNPs are associated to GDM phenotypes and may predispose the development of the disease. However, extrinsic factors (lifestyle, nutrition) modulate, through epigenetic mechanisms, the risk of developing the disease, and some association exists between the microbial composition with GDM in an organ-specific manner. Genes, epigenetic signatures, and microbiota could be transferred to the offspring, increasing the possibility of developing chronic degenerative conditions through postnatal life.

Maternal obesity and gestational diabetes reprogram the methylome of offspring beyond birth by inducing epigenetic signatures in metabolic and developmental pathways

BACKGROUND

Obesity is a negative chronic metabolic health condition that represents an additional risk for the development of multiple pathologies. Epidemiological studies have shown how maternal obesity or gestational diabetes mellitus during pregnancy constitute serious risk factors in relation to the appearance of cardiometabolic diseases in the offspring. Furthermore, epigenetic remodelling may help explain the molecular mechanisms that underlie these epidemiological findings. Thus, in this study we explored the DNA methylation landscape of children born to mothers with obesity and gestational diabetes during their first year of life.

METHODS

We used Illumina Infinium MethylationEPIC BeadChip arrays to profile more than 770,000 genome-wide CpG sites in blood samples from a paediatric longitudinal cohort consisting of 26 children born to mothers who suffered from obesity or obesity with gestational diabetes mellitus during pregnancy and 13 healthy controls (measurements taken at 0, 6 and 12 month; total N = 90). We carried out cross-sectional and longitudinal analyses to derive DNA methylation alterations associated with developmental and pathology-related epigenomics.

RESULTS

We identified abundant DNA methylation changes during child development from birth to 6 months and, to a lesser extent, up to 12 months of age. Using cross-sectional analyses, we discovered DNA methylation biomarkers maintained across the first year of life that could discriminate children born to mothers who suffered from obesity or obesity with gestational diabetes. Importantly, enrichment analyses suggested that these alterations constitute epigenetic signatures that affect genes and pathways involved in the metabolism of fatty acids, postnatal developmental processes and mitochondrial bioenergetics, such as CPT1B, SLC38A4, SLC35F3 and FN3K. Finally, we observed evidence of an interaction between developmental DNA methylation changes and maternal metabolic condition alterations.

CONCLUSIONS

Our observations highlight the first six months of development as being the most crucial for epigenetic remodelling. Furthermore, our results support the existence of systemic intrauterine foetal programming linked to obesity and gestational diabetes that affects the childhood methylome beyond birth, which involves alterations related to metabolic pathways, and which may interact with ordinary postnatal development programmes.

Gestational Diabetes Mellitus: Diagnostic Approaches and Maternal-Offspring Complications

Gestational diabetes mellitus (GDM) is the most common complication during pregnancy and is defined as any degree of glucose intolerance with onset or first recognition during pregnancy. GDM is associated with adverse pregnancy outcomes and long-term offspring and maternal complications. For GDM screening and diagnosis, a two-step approach (1-hour 50 g glucose challenge test followed by 3-hour 100 g oral glucose tolerance test) has been widely used. After the Hyperglycemia and Adverse Pregnancy Outcome study implemented a 75 g oral glucose tolerance test in all pregnant women, a one-step approach was recommended as an option for the diagnosis of GDM after 2010. The one-step approach has more than doubled the incidence of GDM, but its clinical benefit in reducing adverse pregnancy outcomes remains controversial. Long-term complications of mothers with GDM include type 2 diabetes mellitus and cardiovascular disease, and complications of their offspring include childhood obesity and glucose intolerance. The diagnostic criteria of GDM should properly classify women at risk for adverse pregnancy outcomes and long-term complications. The present review summarizes the strengths and weaknesses of the one-step and two-step approaches for the diagnosis of GDM based on recent randomized controlled trials and observational studies. We also describe the long-term maternal and offspring complications of GDM.

Identification of diagnostic cytosine-phosphate-guanine biomarkers in patients with gestational diabetes mellitus via epigenome-wide association study and machine learning

OBJECTIVE

To explore gestational diabetes mellitus (GDM) diagnostic markers and establish the predictive model of GDM.

METHODS

We downloaded the DNA methylation data of GSE70453 and GSE102177 from the Gene Expression Omnibus database. Epigenome-wide association study (EWAS) was performed to analyze the relationship between cytosine-phosphate-guanine (CpG) methylation and GDM. And then the logistic regression models were constructed, with the β-values of CpG sites as predictor variable and the GDM occurrence as binary outcome variable. Data from GSE70453 served as training sets and data from GSE102177 served as verification sets.

RESULTS

The EWAS and overlap analysis identified nine-shared significant CpGs in the two DNA methylation data sets. Remarkably, these nine CpGs were differently methylated in GDM samples compared to their matched normal specimens, among which five fully methylated CpGs were finally selected. Importantly, we established a binary logistic regression model based on the above five CpGs, in which cg11169102, cg21179618 and cg21620107 were critical. Hence, we further built a logistic regression model by using the three CpGs and found that the area under the curve was 0.8209. The validation of the model by using the verification sets indicated the area under the curve was 0.8519.

CONCLUSIONS

We identified potential CpG biomarkers for the diagnosis of gestational diabetes mellitus patients through using EWAS and Logistic regression models in combination.

Maternal Hyperglycemia during Pregnancy Increases Adiposity of Offspring

BACKGROUND

The effect of intrauterine hyperglycemia on fat mass and regional fat proportion of the offspring of mothers with gestational diabetes mellitus (OGDM) remains to be determined.

METHODS

The body composition of OGDM (n=25) and offspring of normoglycemic mothers (n=49) was compared using dualenergy X-ray absorptiometry at age 5 years. The relationship between maternal glucose concentration during a 100 g oral glucose tolerance test (OGTT) and regional fat mass or proportion was analyzed after adjusting for maternal prepregnancy body mass index (BMI).

RESULTS

BMI was comparable between OGDM and control (median, 16.0 kg/m2 vs. 16.1 kg/m2 ). Total, truncal, and leg fat mass were higher in OGDM compared with control (3,769 g vs. 2,245 g, P=0.004; 1,289 g vs. 870 g, P=0.017; 1,638 g vs. 961 g, P=0.002, respectively), whereas total lean mass was lower in OGDM (15,688 g vs. 16,941 g, P=0.001). Among OGDM, total and truncal fat mass were correlated with fasting and 3-hour glucose concentrations of maternal 100 g OGTT during pregnancy (total fat mass, r=0.49, P=0.018 [fasting], r=0.473, P=0.023 [3-hour]; truncal fat mass, r=0.571, P=0.004 [fasting], r=0.558, P=0.006 [3-hour]), but there was no correlation between OGDM leg fat mass and maternal OGTT during pregnancy. Regional fat indices were not correlated with concurrent maternal 75 g OGTT values.

CONCLUSION

Intrauterine hyperglycemia is associated with increased fat mass, especially truncal fat, in OGDM aged 5 years.

Identification of Diagnostic CpG Signatures in Patients with Gestational Diabetes Mellitus via Epigenome-Wide Association Study Integrated with Machine Learning

Background

Gestational diabetes mellitus (GDM) is the most prevalent metabolic disease during pregnancy, but the diagnosis is controversial and lagging partly due to the lack of useful biomarkers. CpG methylation is involved in the development of GDM. However, the specific CpG methylation sites serving as diagnostic biomarkers of GDM remain unclear. Here, we aimed to explore CpG signatures and establish the predicting model for the GDM diagnosis.

Methods

DNA methylation data of GSE88929 and GSE102177 were obtained from the GEO database, followed by the epigenome-wide association study (EWAS). GO and KEGG pathway analyses were performed by using the clusterProfiler package of R. The PPI network was constructed in the STRING database and Cytoscape software. The SVM model was established, in which the β-values of selected CpG sites were the predictor variable and the occurrence of GDM was the outcome variable.

Results

We identified 62 significant CpG methylation sites in the GDM samples compared with the control samples. GO and KEGG analyses based on the 62 CpG sites demonstrated that several essential cellular processes and signaling pathways were enriched in the system. A total of 12 hub genes related to the identified CpG sites were found in the PPI network. The SVM model based on the selected CpGs within the promoter region, including cg00922748, cg05216211, cg05376185, cg06617468, cg17097119, and cg22385669, was established, and the AUC values of the training set and testing set in the model were 0.8138 and 0.7576. The AUC value of the independent validation set of GSE102177 was 0.6667.

Conclusion

We identified potential diagnostic CpG signatures by EWAS integrated with the SVM model. The SVM model based on the identified 6 CpG sites reliably predicted the GDM occurrence, contributing to the diagnosis of GDM. Our finding provides new insights into the cross-application of EWAS and machine learning in GDM investigation.

The Placenta as a Target of Epigenetic Alterations in Women with Gestational Diabetes Mellitus and Potential Implications for the Offspring

Gestational diabetes mellitus (GDM) is a pregnancy complication first detected in the second or third trimester in women that did not show evident glucose intolerance or diabetes before gestation. In 2019, the International Diabetes Federation reported that 15.8% of live births were affected by hyperglycemia during pregnancy, of which 83.6% were due to gestational diabetes mellitus, 8.5% were due to diabetes first detected in pregnancy, and 7.9% were due to diabetes detected before pregnancy. GDM increases the susceptibility to developing chronic diseases for both the mother and the baby later in life. Under GDM conditions, the intrauterine environment becomes hyperglycemic, while also showing high concentrations of fatty acids and proinflammatory cytokines, producing morphological, structural, and molecular modifications in the placenta, affecting its function; these alterations may predispose the baby to disease in adult life. Molecular alterations include epigenetic mechanisms such as DNA and RNA methylation, chromatin remodeling, histone modifications, and expression of noncoding RNAs (ncRNAs). The placenta is a unique organ that originates only in pregnancy, and its main function is communication between the mother and the fetus, ensuring healthy development. Thus, this review provides up-to-date information regarding two of the best-documented (epigenetic) mechanisms (DNA methylation and miRNA expression) altered in the human placenta under GDM conditions, as well as potential implications for the offspring.

Prenatal gestational diabetes mellitus exposure and accelerated offspring DNA methylation age in early childhood

Background: We investigated the association between prenatal GDM exposure and offspring DNA methylation (DNAm) age at 3-10 years of age in the Tianjin GDM Observational Study. Methods: This study included 578 GDM and 578 non-GDM mother-child pairs. Children underwent an exam with anthropometric measurements and blood draw for DNAm analysis (Illumina 850 K array) at a median age of 5.9 years (range 3.1-10.2). DNAm age was calculated using two epigenetic clock algorithms (Horvath and Hannum). The residual resulting from regressing DNAm age on chronological age was used as a metric for age acceleration. Results: Chronological age was positively correlated with Horvath DNAm age (r = 0.53, p < 0.0001) and Hannum DNAm age (r = 0.38, p < 0.0001). Offspring age acceleration was higher in the GDM group than non-GDM group after adjustment for potential confounders (Horvath: 4.96 months higher, adjusted for sex, pre-pregnancy BMI, cell-type proportions, and technical bias, p = 0.0002; Hannum: 11.2 months higher, adjusted for cell-type proportions and technical bias, p < 0.0001). Increased offspring DNAm age acceleration was associated with increased offspring weight-for-age Z-score, BMI-for-age-Z-score, waist circumference, body fat percentage, subscapular skinfold, suprailiac skinfold, upper-arm circumference, and blood pressure; findings were stronger in the GDM group. Conclusions: We found that offspring of women with GDM exhibit accelerated epigenetic age compared to control participants, independent of other maternal factors. Epigenetic age in offspring was associated with cardiometabolic risk factors, suggesting that GDM and GDM-associated factors may have long-term effects on offspring epigenetic age and contribute to health outcomes.

Gestational Diabetes Mellitus Affects Offspring's Epigenome. Is There a Way to Reduce the Negative Consequences?

Gestational diabetes mellitus (GDM) is the most common pregnancy complication worldwide and may result in short-term and long-term consequences for offspring. The present review highlights evidence of epigenetic programming, mostly from human studies, which occurs in offspring exposed to maternal GDM during different stages of development, paying special attention to the differences in sensitivity of offspring to maternal hyperglycemia as a result of sex-related factors. We also aim to answer the following question: If these epigenetic changes are constant throughout the lifetime of the offspring, how do they present phenotypically?

mCSEA: detecting subtle differentially methylated regions

MOTIVATION

The identification of differentially methylated regions (DMRs) among phenotypes is one of the main goals of epigenetic analysis. Although there are several methods developed to detect DMRs, most of them are focused on detecting relatively large differences in methylation levels and fail to detect moderate, but consistent, methylation changes that might be associated to complex disorders.

RESULTS

We present mCSEA, an R package that implements a Gene Set Enrichment Analysis method to identify DMRs from Illumina450K and EPIC array data. It is especially useful for detecting subtle, but consistent, methylation differences in complex phenotypes. mCSEA also implements functions to integrate gene expression data and to detect genes with significant correlations among methylation and gene expression patterns. Using simulated datasets we show that mCSEA outperforms other tools in detecting DMRs. In addition, we applied mCSEA to a previously published dataset of sibling pairs discordant for intrauterine hyperglycemia exposure. We found several differentially methylated promoters in genes related to metabolic disorders like obesity and diabetes, demonstrating the potential of mCSEA to identify DMRs not detected by other methods.

AVAILABILITY AND IMPLEMENTATION

mCSEA is freely available from the Bioconductor repository.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integrated metabolome analysis reveals novel connections between maternal fecal metabolome and the neonatal blood metabolome in women with gestational diabetes mellitus

Gestational Diabetes Mellitus (GDM), which is correlated with changes in the gut microbiota, is a risk factor for neonatal inborn errors of metabolism (IEMs). Maternal hyperglycemia exerts epigenetic effects on genes that encode IEM-associated enzymes, resulting in changes in the neonatal blood metabolome. However, the relationship between maternal gut microbiota and the neonatal blood metabolome remains poorly understood. This study aimed at understanding the connections between maternal gut microbiota and the neonatal blood metabolome in GDM. 1H-NMR-based untargeted metabolomics was performed on maternal fecal samples and targeted metabolomics on the matched neonatal dry blood spots from a cohort of 40 pregnant women, including 22 with GDM and 18 controls. Multi-omic association methods (including Co-Inertia Analysis and Procrustes Analysis) were applied to investigate the relationship between maternal fecal metabolome and the neonatal blood metabolome. Both maternal fecal metabolome and the matched neonatal blood metabolome could be separated along the vector of maternal hyperglycemia. A close relationship between the maternal and neonatal metabolomes was observed by multi-omic association approaches. Twelve out of thirty-two maternal fecal metabolites with altered abundances from 872 1H- NMR features (Bonferroni-adjusted P < 0.05) in women with GDM and the controls were identified, among which 8 metabolites contribute (P < 0.05 in a 999-step permutation test) to the close connection between maternal and the neonatal metabolomes in GDM. Four of these eight maternal fecal metabolites, including lysine, putrescine, guanidinoacetate, and hexadecanedioate, were negatively associated (Spearman rank correlation, coefficient value < −0.6, P < 0.05) with maternal hyperglycemia. Biotin metabolism was enriched (Bonferroni-adjusted P < 0.05 in the hypergeometric test) with the four-hyperglycemia associated fecal metabolites. The results of this study suggested that maternal fecal metabolites contribute to the connections between maternal fecal metabolome and the neonatal blood metabolome and may further affect the risk of IEMs.

Transcription Factor RREB1: from Target Genes towards Biological Functions

The Ras-responsive element binding protein 1(RREB1) is a member of zinc finger transcription factors, which is widely involved in biological processes including cell proliferation, transcriptional regulation and DNA damage repair. New findings reveal RREB1 functions as both transcriptional repressors and transcriptional activators for transcriptional regulation of target genes. The activation of RREB1 is regulated by MAPK pathway. We have summarized the target genes of RREB1 and discussed RREB1 roles in the cancer development. In addition, increasing evidences suggest that RREB1 is a potential risk gene for type 2 diabetes and obesity. We also review the current clinical application of RREB1 as a biomarker for melanoma detection. In conclusion, RREB1 is a promising diagnostic biomarker or new drug target for cancers and metabolic diseases.

Insights into the Role of DNA Methylation and Protein Misfolding in Diabetes Mellitus

Background:

Diabetes mellitus is a metabolic disorder that is characterized by impaired glucose tolerance resulting from defects in insulin secretion, insulin action, or both. Epigenetic modifications, which are defined as inherited changes in gene expression that occur without changes in gene sequence, are involved in the etiology of diabetes.

Methods:

In this review, we focused on the role of DNA methylation and protein misfolding and their contribution to the development of both type 1 and type 2 diabetes mellitus.

Results:

Changes in DNA methylation in particular are highly associated with the development of diabetes. Protein function is dependent on their proper folding in the endoplasmic reticulum. Defective protein folding and consequently their functions have also been reported to play a role. Early treatment of diabetes has proven to be of great benefit, as even transient hyperglycemia may lead to pathological effects and complications later on. This has been explained by the theory of the development of a metabolic memory in diabetes. The basis for this metabolic memory was attributed to oxidative stress, chronic inflammation, non-enzymatic glycation of proteins and importantly, epigenetic changes. This highlights the importance of linking new therapeutics targeting epigenetic mechanisms with traditional antidiabetic drugs.

Conclusion:

Although new data is evolving on the relation between DNA methylation, protein misfolding, and the etiology of diabetes, more studies are required for developing new relevant diagnostics and therapeutics.

omicsPrint: detection of data linkage errors in multiple omics studies

Summary

OmicsPrint is a versatile method for the detection of data linkage errors in multiple omics studies encompassing genetic, transcriptome and/or methylome data. OmicsPrint evaluates data linkage within and between omics data types using genotype calls from SNP arrays, DNA- or RNA-sequencing data and includes an algorithm to infer genotypes from Illumina DNA methylation array data. The method uses classification to verify assumed relationships and detect any data linkage errors, e.g. arising from sample mix-ups and mislabeling. Graphical and text output is provided to inspect and resolve putative data linkage errors. If sufficient genotype calls are available, first degree family relations also are revealed which can be used to check parent-offspring relations or zygosity in twin studies.

Availability and implementation

omicsPrint is available from BioConductor; http://bioconductor.org/packages/omicsPrint.

Supplementary information

Supplementary data are available at Bioinformatics online.

Environmental factors influence the epigenetic signature of newborns from mothers with gestational diabetes

Aim: To investigate the degree by which epigenetic signatures in children from mothers with gestational diabetes mellitus (GDM) are influenced by environmental factors. Methods: We profiled the DNA methylation signature of blood from lean, obese and GDM mothers and their respective newborns. Results: DNA methylation profiles of mothers showed high similarity across groups, while newborns from GDM mothers showed a marked distinct epigenetic profile compared with newborns of both lean and obese mothers. Analysis of variance in DNA methylation levels between newborns showed higher variance in the GDM group. Conclusion: Our results suggest that environmental factors, rather than direct transmission of epigenetic marks from the mother, are involved in establishing the epigenetic signature associated with GDM.

Intrauterine exposure to hyperglycemia retards the development of brown adipose tissue

Brown adipose tissue (BAT) is an exclusive tissue of nonshivering thermogenesis. It is fueled by lipids and glucose and involved in energy and metabolic homeostasis. Intrauterine exposure to hyperglycemia during gestational diabetes mellitus may result in abnormal fetal development and metabolic phenotypes in adulthood. However, whether intrauterine hyperglycemia influences the development of BAT is unknown. In this study, mouse embryos were exposed to the intrauterine hyperglycemia environment by injecting streptozocin into pregnant mice at 1 d post coitum (dpc). The structure of BAT was examined by hematoxylin and eosin staining and immunohistochemical analysis. The glucose uptake in BAT was measured in vivo by [¹⁸F]-fluoro-2-deoxyglucose-micro-positron emission tomography. The gene expression in BAT was determined by real-time PCR, and the 5'-C-phosphate-G-3' site-specific methylation was quantitatively analyzed. Intrauterine hyperglycemia exposure resulted in the impaired structure of BAT and decreased glucose uptake function in BAT in adulthood. The expressions of the genes involved in thermogenesis and mitochondrial respiratory chain in BAT, such as Ucp1, Cox5b, and Elovl3, were down-regulated by intrauterine hyperglycemia exposure at 18.5 dpc and at 16 wk of age. Furthermore, higher methylation levels of Ucp1, Cox5b, and Elovl3 were found in offspring of mothers with streptozotocin-induced diabetes. Our results provide the evidence for enduring inhibitory effects of intrauterine hyperglycemia on BAT development in offspring. Intrauterine hyperglycemia is associated with increased DNA methylation of the BAT specific genes in offspring, which support an epigenetic involvement.-Yu, D.-Q., Lv, P.-P., Yan, Y.-S., Xu, G.-X., Sadhukhan, A., Dong, S., Shen, Y., Ren, J., Zhang, X.-Y., Feng, C., Huang, Y.-T., Tian, S., Zhou, Y., Cai, Y.-T., Ming, Z.-H., Ding, G.-L., Zhu, H., Sheng, J.-Z., Jin, M., Huang, H.-F. Intrauterine exposure to hyperglycemia retards the development of brown adipose tissue.

Pathophysiology of Type 2 Diabetes in Koreans

The pathophysiology of type 2 diabetes is characterized by variable degrees of insulin resistance and impaired insulin secretion. Both genetic and environmental factors serve as etiologic factors. Recent genetic studies have identified at least 83 variants associated with diabetes. A significant number of these loci are thought to be involved in insulin secretion, either through β-cell development or β-cell dysfunction. Environmental factors have changed rapidly during the past half century, and the increased prevalence of obesity and diabetes can be attributed to these changes. Environmental factors may affect epigenetic changes and alter susceptibility to diabetes. A recent epidemiologic study revealed that Korean patients with type 2 diabetes already had impaired insulin secretion and insulin resistance 10 years before the onset of diabetes. Those who developed diabetes showed impaired β-cell compensation with an abrupt decrease in insulin secretion during the last 2 years before diabetes developed. The retrograde trajectory of the disposition index differed according to the baseline subgroups of insulin secretion and insulin sensitivity. We hope that obtaining a more detailed understanding of the perturbations in the major pathophysiologic process of diabetes on the individual level will eventually lead to the implementation of precision medicine and improved patient outcomes.