Integrative analysis reveals novel associations between DNA methylation and the serum metabolome of adolescents with type 2 diabetes: A cross-sectional study

Global trends and hotspots of type 2 diabetes in children and adolescents: A bibliometric study and visualization analysis

BACKGROUND

Epidemiological surveys indicate an increasing incidence of type 2 diabetes mellitus (T2DM) among children and adolescents worldwide. Due to rapid disease progression, severe long-term cardiorenal complications, a lack of effective treatment strategies, and substantial socioeconomic burdens, it has become an urgent public health issue that requires management and resolution. Adolescent T2DM differs from adult T2DM. Despite a significant increase in our understanding of youth-onset T2DM over the past two decades, the related review and evidence-based content remain limited.

AIM

To visualize the hotspots and trends in pediatric and adolescent T2DM research and to forecast their future research themes.

METHODS

This study utilized the terms "children", "adolescents", and "type 2 diabetes", retrieving relevant articles published between 1983 and 2023 from three citation databases within the Web of Science Core Collection (SCI, SSCI, ESCI). Utilizing CiteSpace and VoSviewer software, we analyze and visually represent the annual output of literature, countries involved, and participating institutions. This allows us to predict trends in this research field. Our analysis encompasses co-cited authors, journal overlays, citation overlays, time-zone views, keyword analysis, and reference analysis, etc.

RESULTS

A total of 9210 articles were included, and the annual publication volume in this field showed a steady growth trend. The United States had the highest number of publications and the highest H-index. The United States also had the most research institutions and the strongest research capacity. The global hot journals were primarily diabetes professional journals but also included journals related to nutrition, endocrinology, and metabolism. Keyword analysis showed that research related to endothelial dysfunction, exposure risk, cardiac metabolic risk, changes in gut microbiota, the impact on comorbidities and outcomes, etc., were emerging keywords. They have maintained their popularity in this field, suggesting that these areas have garnered significant research interest in recent years.

CONCLUSION

Pediatric and adolescent T2DM is increasingly drawing global attention, with genes, behaviors, environmental factors, and multisystemic interventions potentially emerging as future research hot spots.

Machine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data

Colorectal cancer is one of the leading causes of cancer-related mortality in the world. Incidence and mortality are predicted to rise globally during the next several decades. When detected early, colorectal cancer is treatable with surgery and medications. This leads to the requirement for prognostic and diagnostic biomarker development. Our study integrates machine learning models and protein network analysis to identify protein biomarkers for colorectal cancer. Our methodology leverages an extensive collection of proteome profiles from both healthy and colorectal cancer individuals. To identify a potential biomarker with high predictive ability, we used three machine learning models. To enhance the interpretability of our models, we quantify each protein's contribution to the model's predictions using SHapley Additive exPlanations values. Three classifiers-LASSO, XGBoost, and LightGBM were evaluated for predictive performance along with hyperparameter tuning of each model using grid search, with LASSO achieving the highest AUC of 75% in the UK Biobank dataset and the AUCs for LightGBM and XGBoost are 69.61% and 71.42%, respectively. Using SHapley Additive exPlanations values, TFF3, LCN2, and CEACAM5 were found to be key biomarkers associated with cell adhesion and inflammation. Protein quantitative trait loci analyze studies provided further evidence for the involvement of TFF1, CEACAM5, and SELE in colorectal cancer, with possible connections to the PI3K/Akt and MAPK signaling pathways. By offering insights into colorectal cancer diagnostics and targeted therapeutics, our findings set the stage for further biomarker validation.

The Rise of Type 2 Diabetes in Children and Adolescents: An Emerging Pandemic

AIM

This review explores the increasing prevalence of Type 2 Diabetes Mellitus (T2DM) in children and adolescents, focusing on its etiology, risk factors, complications, and the importance of early detection and management. It also highlights the need for a multidisciplinary, family-centered approach in managing T2DM in pediatric populations, with an emphasis on nutrition, exercise, and lifestyle interventions.

MATERIALS AND METHODS

A literature review was conducted using PubMed, Google Scholar, and Scopus to incorporate studies from 2015 to 2024 on T2DM in youths/adolescents/children, focusing on epidemiology, risk factors, and prevention strategies. Studies on Type 1 Diabetes Mellitus (T1DM) or adult populations were excluded.

RESULTS

T2DM is a complex metabolic disorder with various societal, behavioral, environmental, and genetic risk factors. It accounts for one in three new childhood diabetes cases, with rising incidence among American Indian/Alaska Native, Black, and Hispanic/Latino children. The increase in T2DM incidence correlates with growing childhood obesity rates. Early onset significantly raises the risk of complications like retinopathy, nephropathy, neuropathy, cardiovascular diseases, nonalcoholic fatty liver disease, and obstructive sleep apnea. Early detection, screening, and treatment can prevent or delay these complications. A family-centered, multidisciplinary approach is essential for effective management, including lifestyle and behavioral support.

CONCLUSIONS

T2DM in children is a growing health concern with severe implications. Early detection and management, including nutrition and exercise counseling, are critical in reducing long-term complications. A multidisciplinary approach is vital for improving outcomes and minimizing morbidity and mortality.

Apitherapy for diabetes mellitus: mechanisms and clinical implications

INTRODUCTION

Diabetes mellitus is a complex disease in terms of its causes and pathophysiological processes, it produces a significant impact on health and leads to complications that are difficult to manage.

CONTENT

This review summarizes and analyzes recent advances in the understanding of the mechanisms of diabetes mellitus and how apitherapy affects them. Also present the available clinical evidence on its application.

SUMMARY

Apitherapy (complementary-integral use of beehive products) is a potentially useful therapeutic system with a significant level of evidence. This review shows and analyzes the preclinical and clinical evidence on the use of apitherapy in diabetes mellitus.

OUTLOOK

Apitherapy shows significant effects on epigenetics, chronic inflammation, oxidative stress, metabolic control, dysbiosis, premature cell death and tissue remodeling. Clinical evidence shows an impact on these mechanisms. Apitherapy is a very useful complementary medicine in the treatment of diabetes mellitus.

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

The roles of the gut microbiota, metabolites, and epigenetics in the effects of maternal exercise on offspring metabolism

Metabolic diseases, including obesity, dyslipidemia, and type 2 diabetes, have become severe challenges worldwide. The Developmental Origins of Health and Disease (DOHaD) hypothesis suggests that an adverse intrauterine environment can increase the risk of metabolic disorders in offspring. Studies have demonstrated that maternal exercise is an effective intervention for improving the offspring metabolic health. However, the pathways through which exercise works are unclear. It has been reported that the gut microbiota mediates the effect of maternal exercise on offspring metabolism, and epigenetic modifications have also been proposed to be important molecular mechanisms. Microbial metabolites can influence epigenetics by providing substrates for DNA or histone modifications, binding to G-protein-coupled receptors to affect downstream pathways, or regulating the activity of epigenetic modifying enzymes. This review aims to summarize the intergenerational effect of maternal exercise and proposes that gut microbiota-metabolites-epigenetic regulation is an important mechanism by which maternal exercise improves offspring metabolism, which may yield novel targets for the early prevention and intervention of metabolic diseases.

Metaboepigenetic regulation of gene expression in obesity and insulin resistance

INTRODUCTION

Variation in DNA methylation (DNAm) in adipose tissue is associated with the pathogenesis of obesity and insulin resistance. The activity of enzymes involved in altering DNAm levels is dependent on several metabolite cofactors.

OBJECTIVES

To understand the role of metabolites as mechanistic regulators of epigenetic marks, we tested the association between selected plasma metabolites and DNAm levels in the adipose tissue of African Americans.

METHODS

In the AAGMEx cohort (N = 256), plasma levels of metabolites were measured by untargeted liquid chromatography-mass spectrometry; adipose tissue DNAm and transcript levels were measured by reduced representation bisulfite sequencing, and expression microarray, respectively.

RESULTS

Among the 21 one-carbon metabolism pathway metabolites evaluated, six were associated with gluco-metabolic traits (PFDR < 0.05, for BMI, SI, or Matsuda index) in AAGMEx. Methylation levels of 196, 116, and 180 CpG-sites were associated (P < 0.0001) with S-adenosylhomocysteine (SAH), cystine, and hypotaurine, respectively. Cis-expression quantitative trait methylation (cis eQTM) analyses suggested the role of metabolite-level-associated CpG sites in regulating the expression of adipose tissue transcripts, including genes in G-protein coupled receptor signaling pathway. Plasma SAH level-associated CpG sites chr19:3403712 and chr19:3403735 were also associated with the expression of G-protein subunit alpha 15 (GNA15) in adipose. The expression of GNA15 was significantly correlated with BMI (β = 1.87, P = 1.9 × 10-16) and SI (β = -1.61, P = 2.49 × 10-5).

CONCLUSION

Our study suggests that a subset of metabolites modulates the methylation levels of CpG sites in specific loci and, in turn, regulates the expression of transcripts involved in obesity and insulin resistance.

Cerebrospinal fluid metabolomes of treatment-resistant depression subtypes and ketamine response: a pilot study

Depression is a disorder with variable presentation. Selecting treatments and dose-finding is, therefore, challenging and time-consuming. In addition, novel antidepressants such as ketamine have sparse optimization evidence. Insights obtained from metabolomics may improve the management of patients. The objective of this study was to determine whether compounds in the cerebrospinal fluid (CSF) metabolome correlate with scores on questionnaires and response to medication. We performed a retrospective pilot study to evaluate phenotypic and metabolomic variability in patients with treatment-resistant depression using multivariate data compression algorithms. Twenty-nine patients with treatment-resistant depression provided fasting CSF samples. Over 300 metabolites were analyzed in these samples with liquid chromatography-mass spectrometry. Chart review provided basic demographic information, clinical status with self-reported questionnaires, and response to medication. Of the 300 metabolites analyzed, 151 were present in all CSF samples and used in the analyses. Hypothesis-free multivariate analysis compressed the resultant data set into two dimensions using Principal Component (PC) analysis, accounting for ~ 32% of the variance. PC1 accounted for 16.9% of the variance and strongly correlated with age in one direction and 5-methyltetrahydrofolate, homocarnosine, and depression and anxiety scores in the opposite direction. PC2 accounted for 15.4% of the variance, with one end strongly correlated with autism scores, male gender, and cognitive fatigue scores, and the other end with bipolar diagnosis, lithium use, and ethylmalonate disturbance. This small pilot study suggests that complex treatment-resistant depression can be mapped onto a 2-dimensional pathophysiological domain. The results may have implications for treatment selection for depression subtypes.

Epigenome editing for targeted DNA (de)methylation: a new perspective in modulating gene expression

Traditionally, it has been believed that inheritance is driven as phenotypic variations resulting from changes in DNA sequence. However, this paradigm has been challenged and redefined in the contemporary era of epigenetics. The changes in DNA methylation, histone modification, non-coding RNA biogenesis, and chromatin remodeling play crucial roles in genomic functions and regulation of gene expression. More importantly, some of these changes are inherited to the next generations as a part of epigenetic memory and play significant roles in gene expression. The sum total of all changes in DNA bases, histone proteins, and ncRNA biogenesis constitutes the epigenome. Continuous progress in deciphering epigenetic regulations and the existence of heritable epigenetic/epiallelic variations associated with trait of interest enables to deploy epigenome editing tools to modulate gene expression. DNA methylation marks can be utilized in epigenome editing for the manipulation of gene expression. Initially, genome/epigenome editing technologies relied on zinc-finger protein or transcriptional activator-like effector protein. However, the discovery of clustered regulatory interspaced short palindromic repeats CRISPR)/deadCRISPR-associated protein 9 (dCas9) enabled epigenome editing to be more specific/efficient for targeted DNA (de)methylation. One of the major concerns has been the off-target effects, wherein epigenome editing may unintentionally modify gene/regulatory element which may cause unintended change/harmful effects. Moreover, epigenome editing of germline cell raises several ethical/safety issues. This review focuses on the recent developments in epigenome editing tools/techniques, technological limitations, and future perspectives of this emerging technology in therapeutics for human diseases as well as plant improvement to achieve sustainable developmental goals.

Epigenetic age in African American adolescents with type 2 diabetes: A cross-sectional case-control study protocol

Background and Aims

Type 2 diabetes (T2D) is a disease caused by a relative insulin deficiency compared to the significant insulin requirement needed by the body to achieve glycemic control. T2D in adolescence appears to be increasing in prevalence over the past several decades, necessitating studies to understand for the onset of the disease to occur early in the lifespan. Given the high burden of disease, specifically in young African American adolescents, our study chose to focus initially on feasibility of recruitment of this population.

Methods

Data was collected at a single study center at Children's of Alabama. The protocol was completed as part of routine care or at a study visit. The study team was able to leverage the Electronic Medical Record to prescreen eligible patients to discuss the study. A variety of times of day were utilized to improve likely of success with reaching potential participants. Inclusion criteria for patients with T2D was focused on the adolescent population (ages 12-18 years), with no history of an obesity syndrome. DNA methylation age will be calculated using the EPIC 850K array. Statistical analysis will be done using linear regression analysis, adjusting for covariates.

Conclusions

This study's aim was to screen and enroll young African American adolescents for a study investigating epigenetic aging and T2D. Our study found that more direct contact (face-to-face- or phone call) improve success of recruitment. Leveraging the electronic medical record also helped improve success with pre-screening participants. Challenges included recruiting participants who might come from long distances to a tertiary care center. Consolidating appointments helped improve the success of reaching these participants. Other challenges included frequent address changes and changed phone numbers. Close attention to the barriers as well as the successes will aid in understanding effective strategies for this important population.

Integrated epigenome, whole genome sequence and metabolome analyses identify novel multi-omics pathways in type 2 diabetes: a Middle Eastern study

BACKGROUND

T2D is of high prevalence in the middle east and thus studying its mechanisms is of a significant importance. Using 1026 Qatar BioBank samples, epigenetics, whole genome sequencing and metabolomics were combined to further elucidate the biological mechanisms of T2D in a population with a high prevalence of T2D.

METHODS

An epigenome-wide association study (EWAS) with T2D was performed using the Infinium 850K EPIC array, followed by whole genome-wide sequencing SNP-CpG association analysis (> 5.5 million SNPs) and a methylome-metabolome (CpG-metabolite) analysis of the identified T2D sites.

RESULTS

A total of 66 T2D-CpG associations were identified, including 63 novel sites in pathways of fructose and mannose metabolism, insulin signaling, galactose, starch and sucrose metabolism, and carbohydrate absorption and digestion. Whole genome SNP associations with the 66 CpGs resulted in 688 significant CpG-SNP associations comprising 22 unique CpGs (33% of the 66 CPGs) and included 181 novel pairs or pairs in novel loci. Fourteen of the loci overlapped published GWAS loci for diabetes related traits and were used to identify causal associations of HK1 and PFKFB2 with HbA1c. Methylome-metabolome analysis identified 66 significant CpG-metabolite pairs among which 61 pairs were novel. Using the identified methylome-metabolome associations, methylation QTLs, and metabolic networks, a multi-omics network was constructed which suggested a number of metabolic mechanisms underlying T2D methylated genes. 1-palmitoyl-2-oleoyl-GPE (16:0/18:1) - a triglyceride-associated metabolite, shared a common network with 13 methylated CpGs, including TXNIP, PFKFB2, OCIAD1, and BLCAP. Mannonate - a food component/plant shared a common network with 6 methylated genes, including TXNIP, BLCAP, THBS4 and PEF1, pointing to a common possible cause of methylation in those genes. A subnetwork with alanine, glutamine, urea cycle (citrulline, arginine), and 1-carboxyethylvaline linked to PFKFB2 and TXNIP revealed associations with kidney function, hypertension and triglyceride metabolism. The pathway containing STYXL1-POR was associated with a sphingosine-ceramides subnetwork associated with HDL-C and LDL-C and point to steroid perturbations in T2D.

CONCLUSIONS

This study revealed several novel methylated genes in T2D, with their genomic variants and associated metabolic pathways with several implications for future clinical use of multi-omics associations in disease and for studying therapeutic targets.