Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis

ZBED3 exacerbates hyperglycemia by promoting hepatic gluconeogenesis through CREB signaling

BACKGROUND

Elevated hepatic glucose production (HGP) is a prominent manifestation of impaired hepatic glucose metabolism in individuals with diabetes. Increased hepatic gluconeogenesis plays a pivotal role in the dysregulation of hepatic glucose metabolism and contributes significantly to fasting hyperglycemia in diabetes. Previous studies have identified zinc-finger BED domain-containing 3 (ZBED3) as a risk gene for type 2 diabetes (T2DM), and its single nucleotide polymorphism (SNPs) is closely associated with the fasting blood glucose level, suggesting a potential correlation between ZBED3 and the onset of diabetes. This study primarily explores the effect of ZBED3 on hepatic gluconeogenesis and analyzes the relevant signaling pathways that regulate hepatic gluconeogenesis.

METHODS

The expression level of ZBED3 was assessed in the liver of insulin-resistant (IR)-related disease. RNA-seq and bioinformatics analyses were employed to examine the ZBED3-related pathway that modulated HGP. To investigate the role of ZBED3 in hepatic gluconeogenesis, the expression of ZBED3 was manipulated by upregulation or silencing using adeno-associated virus (AAV) in mouse primary hepatocytes (MPHs) and HHL-5 cells. In vivo, hepatocyte-specific ZBED3 knockout mice were generated. Moreover, AAV8 was employed to achieve hepatocyte-specific overexpression and knockdown of ZBED3 in C57BL/6 and db/db mice. Immunoprecipitation and mass spectrometry (IP-MS) analyses were employed to identify proteins that interacted with ZBED3. Co-immunoprecipitation (co-IP), glutathione S-transferase (GST) - pulldown, and dual-luciferase reporter assays were conducted to further elucidate the underlying mechanism of ZBED3 in regulating hepatic gluconeogenesis.

RESULTS

The expression of ZBED3 in the liver of IR-related disease models was found to be increased. Under the stimulation of glucagon, ZBED3 promoted the expression of hepatic gluconeogenesis-related genes PGC1A, PCK1, G6PC, thereby increasing HGP. Consistently, the rate of hepatic gluconeogenesis was found to be elevated in mice with hepatocyte-specific overexpression of ZBED3 and decreased in those with ZBED3 knockout. Additionally, the knockdown of ZBED3 in the liver of db/db mice resulted in a reduction in hepatic gluconeogenesis. Moreover, the study revealed that ZBED3 facilitated the nuclear translocation of protein arginine methyltransferases 5 (PRMT5) to influence the regulation of PRMT5-mediated symmetrical dimethylation of arginine (s-DMA) of cyclic adenosine monophosphate (cAMP) response element binding protein (CREB), which in turn affects the phosphorylation of CREB and ultimately promotes HGP.

CONCLUSIONS

This study indicates that ZBED3 promotes hepatic gluconeogenesis and serves as a critical regulator of the progression of diabetes.

Identification of genetic loci enriched in obese or lean T2D cases in the Korean population

BACKGROUND

Obesity causes many complex diseases including type 2 diabetes (T2D). Obesity increases the risk of T2D in Europeans, but there are many non-obese (lean) T2D patients in East Asia.

OBJECTIVE

To discover genetic factors enriched in obese or lean T2D patients, we conducted a genome-wide association (GWA) analysis for T2D stratified by BMI in the Korean population.

METHODS

In the discovery stage, 654 and 247 individuals classified as obese (BMI > 25) and lean (BMI < 23) T2D patients, respectively, were compared with 3,842 control subjects for GWA analysis. Several BMI-stratified T2D variants detected in the discovery stage were further tested in the replication stage, which included 402 obese and 220 lean T2D cases, and 3,615 controls.

RESULTS

Meta-analysis combining the discovery and replication stages detected two variants with genome-wide significance: rs2356138 [P = 2.8 × 10-8, OR = 2.06 (1.59-2.65)] in obese T2D subjects and rs9295478 [P = 2.5 × 10-9, OR = 1.61 (1.38-1.88)] in lean ones. The SNP rs9295478 is located in CDKAL1, a well-known T2D gene previously identified in several GWA studies. Meanwhile, the SNP rs2356138 is a previously unknown variant located in PKP4.

CONCLUSION

We discovered genetic loci enriched in obese or lean T2D patients in the Korean population. Our findings should facilitate more effective control of T2D in Koreans.

Construction of cynomolgus monkey type 2 diabetes models by combining genetic prediction model with high-energy diet

BACKGROUND

Type 2 diabetes mellitus (T2D) is a significant health concern. Research using non-human primates, which develop T2D with similar symptoms and pancreatic changes as humans, is crucial but limited by long timelines and low success rates.

RESULTS

We targeted capture sequenced 61 normal and 81 T2D cynomolgus monkeys using a primer panel that captured 269 potential regulatory regions potentially associated with T2D in the cynomolgus monkey genome. 80 variants were identified to be associated with T2D and were used to construct a genetic prediction model. Among 8 machine learning algorithms tested, we found that the best prediction performance was achieve when the model using support vector machine with polynomial kernel as the machine learning algorithm (AUC = 0.933). Including age and sex in this model did not significantly improve the prediction performance. Using the genetic prediction model, we further screened 22 monkeys and found 13 were high risk while 9 were low risk. After feeding the 22 monkeys with high-energy food for 32 weeks, we found all the 9 low risk monkeys did not develop T2D while 4 out of 13 high risk monkeys (31 %) develop T2D.

CONCLUSIONS

This method greatly increased the success rate of establishing T2D monkey models while decreased the time needed compared to traditional methods. Therefore, we developed a new high-efficiency method to establish T2D monkey models by combining the genetic prediction model and high-energy diet, which will greatly contribute to the research on the clinical characteristics, pathogenesis, complications and potential new treatments.

Multi-omics in exploring the pathophysiology of diabetic retinopathy

Diabetic retinopathy (DR) is a leading global cause of vision impairment, with its prevalence increasing alongside the rising rates of diabetes mellitus (DM). Despite the retina's complex structure, the underlying pathology of DR remains incompletely understood. Single-cell RNA sequencing (scRNA-seq) and recent advancements in multi-omics analyses have revolutionized molecular profiling, enabling high-throughput analysis and comprehensive characterization of complex biological systems. This review highlights the significant contributions of scRNA-seq, in conjunction with other multi-omics technologies, to DR research. Integrated scRNA-seq and transcriptomic analyses have revealed novel insights into DR pathogenesis, including alternative transcription start site events, fluctuations in cell populations, altered gene expression profiles, and critical signaling pathways within retinal cells. Furthermore, by integrating scRNA-seq with genetic association studies and multi-omics analyses, researchers have identified novel biomarkers, susceptibility genes, and potential therapeutic targets for DR, emphasizing the importance of specific retinal cell types in disease progression. The integration of scRNA-seq with metabolomics has also been instrumental in identifying specific metabolites and dysregulated pathways associated with DR. It is highly conceivable that the continued synergy between scRNA-seq and other multi-omics approaches will accelerate the discovery of underlying mechanisms and the development of novel therapeutic interventions for DR.

Identification of proteins associated with type 2 diabetes risk in diverse racial and ethnic populations

AIMS/HYPOTHESIS

Several studies have reported associations between specific proteins and type 2 diabetes risk in European populations. To better understand the role played by proteins in type 2 diabetes aetiology across diverse populations, we conducted a large proteome-wide association study using genetic instruments across four racial and ethnic groups: African; Asian; Hispanic/Latino; and European.

METHODS

Genome and plasma proteome data from the Multi-Ethnic Study of Atherosclerosis (MESA) study involving 182 African, 69 Asian, 284 Hispanic/Latino and 409 European individuals residing in the USA were used to establish protein prediction models by using potentially associated cis- and trans-SNPs. The models were applied to genome-wide association study summary statistics of 250,127 type 2 diabetes cases and 1,222,941 controls from different racial and ethnic populations.

RESULTS

We identified three, 44 and one protein associated with type 2 diabetes risk in Asian, European and Hispanic/Latino populations, respectively. Meta-analysis identified 40 proteins associated with type 2 diabetes risk across the populations, including well-established as well as novel proteins not yet implicated in type 2 diabetes development.

CONCLUSIONS/INTERPRETATION

Our study improves our understanding of the aetiology of type 2 diabetes in diverse populations.

DATA AVAILABILITY

The summary statistics of multi-ethnic type 2 diabetes GWAS of MVP, DIAMANTE, Biobank Japan and other studies are available from The database of Genotypes and Phenotypes (dbGaP) under accession number phs001672.v3.p1. MESA genetic, proteome and covariate data can be accessed through dbGaP under phs000209.v13.p3. All code is available on GitHub ( https://github.com/Arthur1021/MESA-1K-PWAS ).

An alkaloid enriched fraction from Murraya koenigii (L.) Spreng. Leaves ameliorate HFD-induced obesity and metabolic complexities in C57BL/6J mice

ETHNOPHARMACOLOGICAL RELEVANCE

Murraya koenigii commonly known as curry leaf, is traditionally used in India to manage various ailments including diabetes mellitus. Curry leaves are well documented in Indian Ayurvedic system of medicine for beneficial effects in skin eruptions, dysentery, emesis, poisonous bites and bruises. The anti-hyperglycemic and anti-hyperlipidemic effects of curry leaf extracts have been demonstrated through several in vitro and in vivo experiments previously.

AIM OF THE STUDY

To prepare an alkaloid enriched fraction (AEF) from M. koenigii and its evaluation on i) in vitro adipogenesis process and ii) in vivo high fat diet-induced obesity in C57BL/6J mice.

MATERIALS AND METHODS

MKME and AEF were prepared from M. koenigii leaves. The four carbazole alkaloids (bioactive markers) isolated from AEF were quantitatively determined in the leaves by RP-HPLC method. MKME and AEF were studied for anti-obesogenic activity in adipocytes in vitro and in HFD-induced C57BL/6J obese mice in vivo. At the termination of the in vivo study, lipid profile, hepatic and renal injury and glucose levels were analyzed in the blood samples. Animal tissues were examined histopathologically to determine any signs of damage. Repeated dose oral toxicity study for 28 days on Sprague-Dawley rats was also performed to determine the safety profile of AEF.

RESULTS

Both MKME and AEF displayed anti-obesogenic activity at 25 μg/ml concentration in vitro and showed 54.06 ± 3.86% and 37.46 ± 3.17% lipid accumulation, respectively compared to control. Further, supplementation of AEF and MKME in HFD-fed C57BL/6J mice helped in controlling weight gain, improved dyslipidemia and glucose intolerance significantly. AEF showed better anti-obesity activity than MKME both in vitro and in vivo study. Repeated administration of AEF up to 1 g/kg dose for 28 days showed no pathological tissue damage. Both MKME and AEF were standardized using a simple and validated RP-HPLC method.

CONCLUSION

Present study was aimed at preparation of a novel alkaloid-enriched fraction from methanolic extract of M. koenigii leaf and its evaluation for anti-diabesity effect. Our results demonstrated AEF to be a promising plant-based therapy for ameliorating obesity and related metabolic complications in HFD-fed C57BL/6J mice.

Novel associations between KCNQ1 rs231840 polymorphism and preeclampsia in Chinese gestational women: A case-control candidate genetic study

Preeclampsia is a complex disorder with genetic and environmental interactions. In this study, we analyzed the associations of KCNQ1gene polymorphisms with preeclampsia in Chinese pregnant women. The 3 candidate single-nucleotide polymorphisms rs231840, rs2237892, and rs2237895 were genotyped in this case-control study; clinical and biochemical data were included and SNPs were gathered from 248 individuals with preeclampsia and 237 controls. The TT genotype rs231840 increased the risk of preeclampsia (OR: 1.633; 95% CI: 1.027-2.597) and was associated with higher blood glucose levels. The haplotype TCA containing the allele of rs231840 (T), rs2237892 (C), and rs2237895 (A) was highly protective against preeclampsia and associated with the levels of blood glucose in preeclamptic patients. A novel function was found for the haplotype CCA in SNPs rs231840 (C), rs2237892 (C), and rs2237895 (A); it might be a protective combination against preeclampsia. The KCNQ1 (TT) genotype seems to be associated with preeclampsia and might affect the regulation of blood glucose in Chinese pregnant women.

Genetic studies of type 2 diabetes, and microvascular complications of diabetes

Genome-wide association studies (GWAS) have significantly advanced the identification of genetic susceptibility variants associated with complex diseases. As of 2023, approximately 800 variants predisposing individuals to the risk of type 2 diabetes (T2D) were identified through GWAS, and the majority of studies were predominantly conducted in European populations. Despite the shared nature of the majority of genetic susceptibility loci across diverse ethnic populations, GWAS in non-European populations, including Japanese and East Asian populations, have revealed population-specific T2D loci. Currently, polygenic risk scores (PRSs), encompassing millions of associated variants, can identify individuals with a higher T2D risk than the general population. However, GWAS focusing on microvascular complications of diabetes have identified a limited number of disease-susceptibility loci. Ongoing efforts are crucial to enhance the applicability of PRS for all ethnic groups and unravel the genetic architecture of microvascular complications of diabetes.

Zinc finger BED-type containing 3 promotes hepatic steatosis by interacting with polypyrimidine tract-binding protein 1

AIMS/HYPOTHESIS

The relationship between metabolic dysfunction-associated steatotic liver disease (MASLD) and type 2 diabetes mellitus, insulin resistance and the metabolic syndrome is well established. While zinc finger BED-type containing 3 (ZBED3) has been linked to type 2 diabetes mellitus and the metabolic syndrome, its role in MASLD remains unclear. In this study, we aimed to investigate the function of ZBED3 in the context of MASLD.

METHODS

Expression levels of ZBED3 were assessed in individuals with MASLD, as well as in cellular and animal models of MASLD. In vitro and in vivo analyses were conducted using a cellular model of MASLD induced by NEFA and an animal model of MASLD induced by a high-fat diet (HFD), respectively, to investigate the role of ZBED3 in MASLD. ZBED3 expression was increased by lentiviral infection or tail-vein injection of adeno-associated virus. RNA-seq and bioinformatics analysis were employed to examine the pathways through which ZBED3 modulates lipid accumulation. Findings from these next-generation transcriptome sequencing studies indicated that ZBED3 controls SREBP1c (also known as SREBF1; a gene involved in fatty acid de novo synthesis); thus, co-immunoprecipitation and LC-MS/MS were utilised to investigate the molecular mechanisms by which ZBED3 regulates the sterol regulatory element binding protein 1c (SREBP1c).

RESULTS

In this study, we found that ZBED3 was significantly upregulated in the liver of individuals with MASLD and in MASLD animal models. ZBED3 overexpression promoted NEFA-induced triglyceride accumulation in hepatocytes in vitro. Furthermore, the hepatocyte-specific overexpression of Zbed3 promoted hepatic steatosis. Conversely, the hepatocyte-specific knockout of Zbed3 resulted in resistance of HFD-induced hepatic steatosis. Mechanistically, ZBED3 interacts directly with polypyrimidine tract-binding protein 1 (PTBP1) and affects its binding to the SREBP1c mRNA precursor to regulate SREBP1c mRNA stability and alternative splicing.

CONCLUSIONS/INTERPRETATION

This study indicates that ZBED3 promotes hepatic steatosis and serves as a critical regulator of the progression of MASLD.

DATA AVAILABILITY

RNA-seq data have been deposited in the NCBI Gene Expression Omnibus ( www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE231875 ). MS proteomics data have been deposited to the ProteomeXchange Consortium via the iProX partner repository ( https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD041743 ).

Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study

In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.

Understanding the Genetic Landscape of Gestational Diabetes: Insights into the Causes and Consequences of Elevated Glucose Levels in Pregnancy

Background/Objectives: During pregnancy, physiological changes in maternal circulating glucose levels and its metabolism are essential to meet maternal and fetal energy demands. Major changes in glucose metabolism occur throughout pregnancy and consist of higher insulin resistance and a compensatory increase in insulin secretion to maintain glucose homeostasis. For some women, this change is insufficient to maintain normoglycemia, leading to gestational diabetes mellitus (GDM), a condition characterized by maternal glucose intolerance and hyperglycaemia first diagnosed during the second or third trimester of pregnancy. GDM is diagnosed in approximately 14.0% of pregnancies globally, and it is often associated with short- and long-term adverse health outcomes in both mothers and offspring. Although recent studies have highlighted the role of genetic determinants in the development of GDM, research in this area is still lacking, hindering the development of prevention and treatment strategies. Methods: In this paper, we review recent advances in the understanding of genetic determinants of GDM and glycaemic traits during pregnancy. Results/Conclusions: Our review highlights the need for further collaborative efforts as well as larger and more diverse genotyped pregnancy cohorts to deepen our understanding of the genetic aetiology of GDM, address research gaps, and further improve diagnostic and treatment strategies.

Semi-supervised Double Deep Learning Temporal Risk Prediction (SeDDLeR) with Electronic Health Records

BACKGROUND

Risk prediction plays a crucial role in planning for prevention, monitoring, and treatment. Electronic Health Records (EHRs) offer an expansive repository of temporal medical data encompassing both risk factors and outcome indicators essential for effective risk prediction. However, challenges emerge due to the lack of readily available gold-standard outcomes and the complex effects of various risk factors. Compounding these challenges are the false positives in diagnosis codes, and formidable task of pinpointing the onset timing in annotations.

OBJECTIVE

We develop a Semi-supervised Double Deep Learning Temporal Risk Prediction (SeDDLeR) algorithm based on extensive unlabeled longitudinal Electronic Health Records (EHR) data augmented by a limited set of gold standard labels on the binary status information indicating whether the clinical event of interest occurred during the follow-up period.

METHODS

The SeDDLeR algorithm calculates an individualized risk of developing future clinical events over time using each patient's baseline EHR features via the following steps: (1) construction of an initial EHR-derived surrogate as a proxy for the onset status; (2) deep learning calibration of the surrogate along gold-standard onset status; and (3) semi-supervised deep learning for risk prediction combining calibrated surrogates and gold-standard onset status. To account for missing onset time and heterogeneous follow-up, we introduce temporal kernel weighting. We devise a Gated Recurrent Units (GRUs) module to capture temporal characteristics. We subsequently assess our proposed SeDDLeR method in simulation studies and apply the method to the Massachusetts General Brigham (MGB) Biobank to predict type 2 diabetes (T2D) risk.

RESULTS

SeDDLeR outperforms benchmark risk prediction methods, including Semi-parametric Transformation Model (STM) and DeepHit, with consistently best accuracy across experiments. SeDDLeR achieved the best C-statistics ( 0.815, SE 0.023; vs STM +.084, SE 0.030, P-value .004; vs DeepHit +.055, SE 0.027, P-value .024) and best average time-specific AUC (0.778, SE 0.022; vs STM + 0.059, SE 0.039, P-value .067; vs DeepHit + 0.168, SE 0.032, P-value <0.001) in the MGB T2D study.

CONCLUSION

SeDDLeR can train robust risk prediction models in both real-world EHR and synthetic datasets with minimal requirements of labeling event times. It holds the potential to be incorporated for future clinical trial recruitment or clinical decision-making.

Characterizing Common and Rare Variations in Nontraditional Glycemic Biomarkers Using Multivariate Approaches on Multiancestry ARIC Study

Genetic studies of nontraditional glycemic biomarkers, glycated albumin and fructosamine, can shed light on unknown aspects of type 2 diabetes genetics and biology. We performed a multiphenotype genome-wide association study of glycated albumin and fructosamine from 7,395 White and 2,016 Black participants in the Atherosclerosis Risk in Communities (ARIC) study on common variants from genotyped/imputed data. We discovered two genome-wide significant loci, one mapping to a known type 2 diabetes gene (ARAP1/STARD10) and another mapping to a novel region (UGT1A complex of genes), using multiomics gene-mapping strategies in diabetes-relevant tissues. We identified additional loci that were ancestry- and sex-specific (e.g., PRKCA in African ancestry, FCGRT in European ancestry, TEX29 in males). Further, we implemented multiphenotype gene-burden tests on whole-exome sequence data from 6,590 White and 2,309 Black ARIC participants. Ten variant sets annotated to genes across different variant aggregation strategies were exome-wide significant only in multiancestry analysis, of which CD1D, EGFL7/AGPAT2, and MIR126 had notable enrichment of rare predicted loss of function variants in African ancestry despite smaller sample sizes. Overall, 8 of 14 discovered loci and genes were implicated to influence these biomarkers via glycemic pathways, and most of them were not previously implicated in studies of type 2 diabetes. This study illustrates improved locus discovery and potential effector gene discovery by leveraging joint patterns of related biomarkers across the entire allele frequency spectrum in multiancestry analysis. Future investigation of the loci and genes potentially acting through glycemic pathways may help us better understand the risk of developing type 2 diabetes.

ARTICLE HIGHLIGHTS

Single cell multiome profiling of pancreatic islets reveals physiological changes in cell type-specific regulation associated with diabetes risk

Physiological variability in pancreatic cell type gene regulation and the impact on diabetes risk is poorly understood. In this study we mapped gene regulation in pancreatic cell types using single cell multiomic (joint RNA-seq and ATAC-seq) profiling in 28 non-diabetic donors in combination with single cell data from 35 non-diabetic donors in the Human Pancreas Analysis Program. We identified widespread associations with age, sex, BMI, and HbA1c, where gene regulatory responses were highly cell type- and phenotype-specific. In beta cells, donor age associated with hypoxia, apoptosis, unfolded protein response, and external signal-dependent transcriptional regulators, while HbA1c associated with inflammatory responses and gender with chromatin organization. We identified 10.8K loci where genetic variants were QTLs for cis regulatory element (cRE) accessibility, including 20% with lineage- or cell type-specific effects which disrupted distinct transcription factor motifs. Type 2 diabetes and glycemic trait associated variants were enriched in both phenotype- and QTL-associated beta cell cREs, whereas type 1 diabetes showed limited enrichment. Variants at 226 diabetes and glycemic trait loci were QTLs in beta and other cell types, including 40 that were statistically colocalized, and annotating target genes of colocalized QTLs revealed genes with putatively novel roles in disease. Our findings reveal diverse responses of pancreatic cell types to phenotype and genotype in physiology, and identify pathways, networks, and genes through which physiology impacts diabetes risk.

Healthy Lifestyles Modify the Association of Melatonin Receptor 1B Gene and Ischemic Stroke: A Family-Based Cohort Study in Northern China

Limited research has reported the association between MTNR1B gene polymorphisms and ischemic stroke (IS), and there is insufficient evidence on whether adopting a healthy lifestyle can mitigate genetic risks in this context. This study aimed to investigate the associations between MTNR1B gene variants (rs10830963 and rs1387153) and IS, examining the potential effect of gene-lifestyle interactions on IS risk. Conducted in northern China, this family-based cohort study involved 5116 initially IS-free subjects. Genotype data for rs10830963 and rs1387153 in MTNR1B were collected. Eight modifiable lifestyle factors, including body mass index (BMI), smoking, alcohol consumption, dietary habits, physical activity, sedentary time, sleep duration, and chronotype, were considered in calculating healthy lifestyle scores. Multilevel Cox models were used to examine the associations between MTNR1B variants and IS. Participants carrying the rs10830963-G and rs1387153-T alleles exhibited an elevated IS risk. Each additional rs10830963-G allele and rs1387153-T allele increased the IS risk by 36% (HR = 1.36, 95% CI, 1.12-1.65) and 32% (HR = 1.32, 95% CI, 1.09-1.60), respectively. Participants were stratified into low, medium, and high healthy lifestyle score groups (1537, 2188, and 1391 participants, respectively). Genetic-lifestyle interactions were observed for rs10830963 and rs1387153 (p for interaction < 0.001). Notably, as the healthy lifestyle score increased, the effect of MTNR1B gene variants on IS risk diminished (p for trend < 0.001). This study underscores the association between the MTNR1B gene and IS, emphasizing that adherence to a healthy lifestyle can mitigate the genetic predisposition to IS.

Genome-wide association study of hospitalized patients and acute kidney injury

Acute kidney injury (AKI) is a common and devastating complication of hospitalization. Here, we identified genetic loci associated with AKI in patients hospitalized between 2002-2019 in the Million Veteran Program and data from Vanderbilt University Medical Center's BioVU. AKI was defined as meeting a modified KDIGO Stage 1 or more for two or more consecutive days or kidney replacement therapy. Control individuals were required to have one or more qualifying hospitalizations without AKI and no evidence of AKI during any other observed hospitalizations. Genome-wide association studies (GWAS), stratified by race, adjusting for sex, age, baseline estimated glomerular filtration rate (eGFR), and the top ten principal components of ancestry were conducted. Results were meta-analyzed using fixed effects models. In total, there were 54,488 patients with AKI and 138,051 non-AKI individuals included in the study. Two novel loci reached genome-wide significance in the meta-analysis: rs11642015 near the FTO locus on chromosome 16 (obesity traits) (odds ratio 1.07 (95% confidence interval, 1.05-1.09)) and rs4859682 near the SHROOM3 locus on chromosome 4 (glomerular filtration barrier integrity) (odds ratio 0.95 (95% confidence interval, 0.93-0.96)). These loci colocalized with previous studies of kidney function, and genetic correlation indicated significant shared genetic architecture between AKI and eGFR. Notably, the association at the FTO locus was attenuated after adjustment for BMI and diabetes, suggesting that this association may be partially driven by obesity. Both FTO and the SHROOM3 loci showed nominal evidence of replication from diagnostic-code-based summary statistics from UK Biobank, FinnGen, and Biobank Japan. Thus, our large GWA meta-analysis found two loci significantly associated with AKI suggesting genetics may explain some risk for AKI.

Causal Effects of Oxidative Stress on Diabetes Mellitus and Microvascular Complications: Insights Integrating Genome-Wide Mendelian Randomization, DNA Methylation, and Proteome

BACKGROUND

Oxidative stress (OS) is involved in the development of diabetes, but the genetic mechanisms are not completely understood. We integrated multi-omics data in order to explore the genetic relations between OS-related genes, diabetes mellitus, and microvascular complications using Mendelian randomization and colocalization analysis.

METHODS

Summary-level data related to OS were acquired from respective studies of methylation, expression, and protein abundance quantitative trait loci. Genetic associations concerning diabetes, diabetic nephropathy (DN), and diabetic retinopathy (DR) were derived from the FinnGen study. Summary-data-based Mendelian randomization (SMR) analysis was conducted to evaluate the correlations between molecular features concerned with OS-related genes and diabetes mellitus, along with its microvascular complications. Additionally, we performed colocalization analysis to determine if the detected signal pairs shared a causal genetic variant.

RESULTS

At the genetic level, we identified ten potential causal associations of oxidative stress genes with diabetes, along with microvascular complications, through SMR and colocalization analysis. After integrating the DNA methylation quantitative trait loci (mQTL) and expression QTL (eQTL) data, our analyses revealed a correlation between the methylation site cg26343298 and reduced expression of TP53INP1, supporting the protective role of cg26343298 methylation on type 2 diabetes (T2D) and diabetic nephropathy. Similarly, an inverse association was observed between gene methylation and expression in CHEK1 (cg07110182), confirming the beneficial effect of modification of CHEK1 by cg07110182 in diabetic retinopathy. In addition, upregulation of SUOX expression by cg22580629 was linked to a reduced risk of diabetic retinopathy. At circulating protein levels, genetically predicted a higher level of ICAM1 (OR 1.05, 95%CI 1.03-1.08) was positively connected with the risk of diabetic retinopathy.

CONCLUSIONS

This SMR study elucidated that the TP53INP1 gene was putatively associated with T2D and DN risk, while the SUOX and CHEK1 genes were associated with DR risk through oxidative stress mechanisms. Additionally, our study showed a positive correlation between the ICAM-1 protein and DR. These findings may enhance our understanding of their pathogenesis and suggest new therapeutic targets for clinical practice.

Adipose-derived miRNAs as potential biomarkers for predicting adulthood obesity and its complications: A systematic review and bioinformatic analysis

Adipose tissue is the first and primary target organ of obesity and the main source of circulating miRNAs in patients with obesity. This systematic review aimed to analyze and summarize the generation and mechanisms of adipose-derived miRNAs and their role as early predictors of various obesity-related complications. Literature searches in the PubMed and Web of Science databases using terms related to miRNAs, obesity, and adipose tissue. Pre-miRNAs from the Human MicroRNA Disease Database, known to regulate obesity-related metabolic disorders, were combined for intersection processing. Validated miRNA targets were sorted through literature review, and enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes via the KOBAS online tool, disease analysis, and miRNA transcription factor prediction using the TransmiR v. 2.0 database were also performed. Thirty miRNAs were identified using both obesity and adipose secretion as criteria. Seventy-nine functionally validated targets associated with 30 comorbidities of these miRNAs were identified, implicating pathways such as autophagy, p53 pathways, and inflammation. The miRNA precursors were analyzed to predict their transcription factors and explore their biosynthesis mechanisms. Our findings offer potential insights into the epigenetic changes related to adipose-driven obesity-related comorbidities.

Integrated analysis of genes shared between type 2 diabetes mellitus and osteoporosis

Background

The relationship between type 2 diabetes mellitus (T2DM) and osteoporosis (OP) has been widely recognized in recent years, but the mechanism of interaction remains unknown. The aim of this study was to investigate the genetic features and signaling pathways that are shared between T2DM and OP.

Methods

We analyzed the GSE76894 and GSE76895 datasets for T2DM and GSE56815 and GSE7429 for OP from the Gene Expression Omnibus (GEO) database to identify shared genes in T2DM and OP, and we constructed coexpression networks based on weighted gene coexpression network analysis (WGCNA). Shared genes were then further analyzed for functional pathway enrichment. We selected the best common biomarkers using the least absolute shrinkage and selection operator (LASSO) algorithm and validated the common biomarkers, followed by RT-PCR, immunofluorescence, Western blotting, and enzyme-linked immunosorbent assay (ELISA) to validate the expression of these hub genes in T2DM and OP mouse models and patients.

Results

We found 8,506 and 2,030 DEGs in T2DM and OP, respectively. Four modules were identified as significant for T2DM and OP using WGCNA. A total of 19 genes overlapped with the strongest positive and negative modules of T2DM and OP. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed these genes may be involved in pantothenate and CoA biosynthesis and the glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulfate and renin-angiotensin system signaling pathway. The LASSO algorithm calculates the six optimal common biomarkers. RT-PCR results show that LTB, TPBG, and VNN1 were upregulated in T2DM and OP. Immunofluorescence and Western blot show that VNN1 is upregulated in the pancreas and bones of T2DM model mice and osteoporosis model mice. Similarly, the level of VNN1 in the sera of patients with T2DM, OP, and T2DM and OP was higher than that in the healthy group.

Conclusion

Based on the WGCNA and LASSO algorithms, we identified genes and pathways that were shared between T2DM and OP. Both pantothenate and CoA biosynthesis and the glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulfate and renin-angiotensin systems may be associated with the pathogenesis of T2DM and OP. Moreover, VNN1 may be a potential diagnostic marker for patients with T2DM complicated by OP. This study provides a new perspective for the systematic study of possible mechanisms of combined OP and T2DM.

Body mass index stratification optimizes polygenic prediction of type 2 diabetes in cross-biobank analyses

Type 2 diabetes (T2D) shows heterogeneous body mass index (BMI) sensitivity. Here, we performed stratification based on BMI to optimize predictions for BMI-related diseases. We obtained BMI-stratified datasets using data from more than 195,000 individuals (nT2D = 55,284) from BioBank Japan (BBJ) and UK Biobank. T2D heritability in the low-BMI group was greater than that in the high-BMI group. Polygenic predictions of T2D toward low-BMI targets had pseudo-R2 values that were more than 22% higher than BMI-unstratified targets. Polygenic risk scores (PRSs) from low-BMI discovery outperformed PRSs from high BMI, while PRSs from BMI-unstratified discovery performed best. Pathway-specific PRSs demonstrated the biological contributions of pathogenic pathways. Low-BMI T2D cases showed higher rates of neuropathy and retinopathy. Combining BMI stratification and a method integrating cross-population effects, T2D predictions showed greater than 37% improvements over unstratified-matched-population prediction. We replicated findings in the Tohoku Medical Megabank (n = 26,000) and the second BBJ cohort (n = 33,096). Our findings suggest that target stratification based on existing traits can improve the polygenic prediction of heterogeneous diseases.

Comparison of β-Cell Function and Insulin Sensitivity Between Normal-Weight and Obese Chinese With Young-Onset Type 2 Diabetes

Normal-weight individuals with usual-onset type 2 diabetes have reduced β-cell function and greater insulin sensitivity compared with their obese counterparts. The relative contribution of β-cell dysfunction and insulin resistance to young-onset type 2 diabetes (YOD) among normal-weight individuals is not well established. In 44 individuals with YOD (24 with normal weight and 20 with obesity) and 24 healthy control individuals with normoglycemia (12 with normal weight and 12 with obesity), we conducted 2-h 12 mmol/L hyperglycemic clamps to measure acute (0-10 min) and steady-state (100-120 min) insulin and C-peptide responses, as well as insulin sensitivity index. Normal-weight individuals with YOD had lower acute insulin response, steady-state insulin and C-peptide responses, and a higher insulin sensitivity index compared with their obese counterparts with YOD. Compared with BMI-matched healthy control individuals, normal-weight individuals with YOD had lower acute and steady-state insulin and C-peptide responses but a similar insulin sensitivity index. The impairment of steady-state β-cell response relative to healthy control individuals was more pronounced in normal-weight versus obese individuals with YOD. In conclusion, normal-weight Chinese with YOD exhibited worse β-cell function but preserved insulin sensitivity relative to obese individuals with YOD and BMI-matched healthy individuals with normoglycemia. The selection of glucose-lowering therapy should account for pathophysiological differences underlying YOD between normal-weight and obese individuals.

ARTICLE HIGHLIGHTS

The pivotal role of the X-chromosome in the genetic architecture of the human brain

Genes on the X-chromosome are extensively expressed in the human brain. However, little is known for the X-chromosome's impact on the brain anatomy, microstructure, and functional network. We examined 1,045 complex brain imaging traits from 38,529 participants in the UK Biobank. We unveiled potential autosome-X-chromosome interactions, while proposing an atlas outlining dosage compensation (DC) for brain imaging traits. Through extensive association studies, we identified 72 genome-wide significant trait-locus pairs (including 29 new associations) that share genetic architectures with brain-related disorders, notably schizophrenia. Furthermore, we discovered unique sex-specific associations and assessed variations in genetic effects between sexes. Our research offers critical insights into the X-chromosome's role in the human brain, underscoring its contribution to the differences observed in brain structure and functionality between sexes.

DNA methylation signatures of youth-onset type 2 diabetes and exposure to maternal diabetes

OBJECTIVE

Youth-onset type 2 diabetes (T2D) is physiologically distinct from adult-onset, but it is not clear how the two diseases differ at a molecular level. In utero exposure to maternal type 2 diabetes (T2D) is known to be a specific risk factor for youth-onset T2D. DNA methylation (DNAm) changes associated with T2D but which differ between youth- and adult-onset might delineate the impacts of T2D development at different ages and could also determine the contribution of exposure to in utero diabetes.

METHODS

We performed an epigenome-wide analysis of DNAm on whole blood from 218 youth with T2D and 77 normoglycemic controls from the iCARE (improving renal Complications in Adolescents with type 2 diabetes through REsearch) cohort. Associations were tested using multiple linear regression models while adjusting for maternal diabetes, sex, age, BMI, smoking status, second-hand smoking exposure, cell-type proportions and genetic ancestry.

RESULTS

We identified 3830 differentially methylated sites associated with youth T2D onset, of which 3794 were moderately (adjusted p-value < 0.05 and effect size estimate > 0.01) associated and 36 were strongly (adjusted p-value < 0.05 and effect size estimate > 0.05) associated. A total of 3725 of these sites were not previously reported in the EWAS Atlas as associated with T2D, adult obesity or youth obesity. Moreover, three CpGs associated with youth-onset T2D in the PFKFB3 gene were also associated with maternal T2D exposure (FDR < 0.05 and effect size > 0.01). This is the first study to link PFKFB3 and T2D in youth.

CONCLUSION

Our findings support that T2D in youth has different impacts on DNAm than adult-onset, and suggests that changes in DNAm could provide an important link between in utero exposure to maternal diabetes and the onset of T2D.

Impact of the trans-ancestry polygenic risk score on type 2 diabetes risk, onset age and progression among population in Taiwan

Type 2 diabetes (T2D) prevalence in adults at a younger age has increased but the disease status may go unnoticed. This study aimed to determine whether the onset age and subsequent diabetic complications can be attributed to the polygenic architecture of T2D in the Taiwan Han population. A total of 9,627 cases with T2D and 85,606 controls from the Taiwan Biobank were enrolled. Three diabetic polygenic risk scores (PRSs), PRS_EAS and PRS_EUR, and a trans-ancestry PRS (PRS_META), calculated using summary statistic from East Asian and European populations. The onset age was identified by linking to the National Taiwan Insurance Research Database, and the incidence of different diabetic complications during follow-up was recorded. PRS_META (7.4%) explained a higher variation for T2D status. And the higher percentile of PRS is also correlated with higher percentage of T2D family history and prediabetes status. More, the PRS was negatively associated with onset age (β = -0.91 yr), and this was more evident among males (β = -1.11 vs. -0.76 for males and females, respectively). The hazard ratio of diabetic retinopathy (DR) and diabetic foot were significantly associated with PRS_EAS and PRS_META, respectively. However, the PRS was not associated with other diabetic complications, including diabetic nephropathy, cardiovascular disease, and hypertension. Our findings indicated that diabetic PRS which combined susceptibility variants from cross-population could be used as a tool for early screening of T2D, especially for high-risk populations, such as individuals with high genetic risk, and may be associated with the risk of complications in subjects with T2D. NEW & NOTEWORTHY Our findings indicated that diabetic polygenic risk score (PRS) which combined susceptibility variants from Asian and European population affect the onset age of type 2 diabetes (T2D) and could be used as a tool for early screening of T2D, especially for individuals with high genetic risk, and may be associated with the risk of diabetic complications among people in Taiwan.

Identifying latent genetic interactions in genome-wide association studies using multiple traits

The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit ).

Perspectives on genetic studies of type 2 diabetes from the genome-wide association studies era to precision medicine

Genome-wide association studies (GWAS) have facilitated a substantial and rapid increase in the number of confirmed genetic susceptibility variants for complex diseases. Approximately 700 variants predisposing individuals to the risk for type 2 diabetes have been identified through GWAS until 2023. From 2018 to 2022, hundreds of type 2 diabetes susceptibility loci with smaller effect sizes were identified through large-scale GWAS with sample sizes of 200,000 to >1 million. The clinical translation of genetic information for type 2 diabetes includes the development of novel therapeutics and risk predictions. Although drug discovery based on loci identified in GWAS remains challenging owing to the difficulty of functional annotation, global efforts have been made to identify novel biological mechanisms and therapeutic targets by applying multi-omics approaches or searching for disease-associated coding variants in isolated founder populations. Polygenic risk scores (PRSs), comprising up to millions of associated variants, can identify individuals with higher disease risk than those in the general population. In populations of European descent, PRSs constructed from base GWAS data with a sample size of approximately 450,000 have predicted the onset of diseases well. However, European GWAS-derived PRSs have limited predictive performance in non-European populations. The predictive accuracy of a PRS largely depends on the sample size of the base GWAS data. The results of GWAS meta-analyses for multi-ethnic groups as base GWAS data and cross-population polygenic prediction methodology have been applied to establish a universal PRS applicable to small isolated ethnic populations.

BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases

Genome-wide Association Studies (GWAS) methods have identified individual single-nucleotide polymorphisms (SNPs) significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT (https://github.com/wangjr03/BayesKAT), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases.

Assessing the genetic contribution of cumulative behavioral factors associated with longitudinal type 2 diabetes risk highlights adiposity and the brain-metabolic axis

While genetic factors, behavior, and environmental exposures form a complex web of interrelated associations in type 2 diabetes (T2D), their interaction is poorly understood. Here, using data from ~500K participants of the UK Biobank, we identify the genetic determinants of a "polyexposure risk score" (PXS) a new risk factor that consists of an accumulation of 25 associated individual-level behaviors and environmental risk factors that predict longitudinal T2D incidence. PXS-T2D had a non-zero heritability (h2 = 0.18) extensive shared genetic architecture with established clinical and biological determinants of T2D, most prominently with body mass index (genetic correlation [rg] = 0.57) and Homeostatic Model Assessment for Insulin Resistance (rg = 0.51). Genetic loci associated with PXS-T2D were enriched for expression in the brain. Biobank scale data with genetic information illuminates how complex and cumulative exposures and behaviors as a whole impact T2D risk but whose biology have been elusive in genome-wide studies of T2D.

Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning

Diabetes affects >10% of adults worldwide and is caused by impaired production or response to insulin, resulting in chronic hyperglycemia. Pancreatic islet β-cells are the sole source of endogenous insulin and our understanding of β-cell dysfunction and death in type 2 diabetes (T2D) is incomplete. Single-cell RNA-seq data supports heterogeneity as an important factor in β-cell function and survival. However, it is difficult to identify which β-cell phenotypes are critical for T2D etiology and progression. Our goal was to prioritize specific disease-related β-cell subpopulations to better understand T2D pathogenesis and identify relevant genes for targeted therapeutics. To address this, we applied a deep transfer learning tool, DEGAS, which maps disease associations onto single-cell RNA-seq data from bulk expression data. Independent runs of DEGAS using T2D or obesity status identified distinct β-cell subpopulations. A singular cluster of T2D-associated β-cells was identified; however, β-cells with high obese-DEGAS scores contained two subpopulations derived largely from either non-diabetic or T2D donors. The obesity-associated non-diabetic cells were enriched for translation and unfolded protein response genes compared to T2D cells. We selected DLK1 for validation by immunostaining in human pancreas sections from healthy and T2D donors. DLK1 was heterogeneously expressed among β-cells and appeared depleted from T2D islets. In conclusion, DEGAS has the potential to advance our holistic understanding of the β-cell transcriptomic phenotypes, including features that distinguish β-cells in obese non-diabetic or lean T2D states. Future work will expand this approach to additional human islet omics datasets to reveal the complex multicellular interactions driving T2D.

Potential Association of The Pathogenic Kruppel-like Factor 14 (KLF14) and Adiponectin (ADIPOQ) SNVs with Susceptibility to T2DM

AIM

To evaluate the associations of the pathogenic variants in Kruppel-like Factor 14 (KLF 14) and Adiponectin (ADIPOQ) with susceptibility to type 2 diabetes mellitus (T2DM).

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a pandemic metabolic disease characterized by increased blood sugar and caused by resistance to insulin in peripheral tissues and damage to pancreatic beta cells. Kruppel-like Factor 14 (KLF-14) is proposed to be a regulator of metabolic diseases, such as diabetes mellitus (DM) and obesity. Adiponectin (ADIPOQ) is an adipocytokine produced by the adipocytes and other tissues and was reported to be involved in T2DM.

OBJECTIVES

To study the possible association of the KLF-14 rs972283 and ADIPOQ-rs266729 with the risk of T2DM in the Saudi population.

METHODS

We have evaluated the association of KLF-14 rs972283 C>T and ADIPOQ-rs266729 C>G SNV with the risk to T2D in the Saudi population using the Amplification Refractory Mutation System PCR (ARMS-PCR), and blood biochemistry analysis. For the KLF-14 rs972283 C>T SNV we included 115 cases and 116 healthy controls, and ADIPOQ-rs266729 C>G SNV, 103 cases and 104 healthy controls were included.

RESULTS

Results indicated that the KLF-14 rs972283 GA genotype and A allele were associated with T2D risk with OR=2.14, p-value= 0.014 and OR=1.99, p-value=0.0003, respectively. Results also ADIPOQ-rs266729 CG genotype and C allele were associated with an elevated T2D risk with an OR=2.53, p=0.003 and OR=1.66, p-value =0.012, respectively.

CONCLUSION

We conclude that SNVs in KLF-14 and ADIPOQ are potential loci for T2D risk. Future large-scale studies to verify these findings are recommended. These results need further verifications in protein functional and large-scale case control studies before being introduced for genetic testing.

N6-methyladenosine (m6A) methylation in kidney diseases: Mechanisms and therapeutic potential

The N6-methyladenosine (m6A) modification is regulated by methylases, commonly referred to as "writers," and demethylases, known as "erasers," leading to a dynamic and reversible process. Changes in m6A levels have been implicated in a wide range of cellular processes, including nuclear RNA export, mRNA metabolism, protein translation, and RNA splicing, establishing a strong correlation with various diseases. Both physiologically and pathologically, m6A methylation plays a critical role in the initiation and progression of kidney disease. The methylation of m6A may also facilitate the early diagnosis and treatment of kidney diseases, according to accumulating research. This review aims to provide a comprehensive overview of the potential role and mechanism of m6A methylation in kidney diseases, as well as its potential application in the treatment of such diseases. There will be a thorough examination of m6A methylation mechanisms, paying particular attention to the interplay between m6A writers, m6A erasers, and m6A readers. Furthermore, this paper will elucidate the interplay between various kidney diseases and m6A methylation, summarize the expression patterns of m6A in pathological kidney tissues, and discuss the potential therapeutic benefits of targeting m6A in the context of kidney diseases.

Metabolic Syndrome: An Overview on Its Genetic Associations and Gene-Diet Interactions

Metabolic syndrome (MetS) is a cluster of cardiometabolic risk factors that includes central obesity, hyperglycemia, hypertension, and dyslipidemias and whose inter-related occurrence may increase the odds of developing type 2 diabetes and cardiovascular diseases. MetS has become one of the most studied conditions, nevertheless, due to its complex etiology, this has not been fully elucidated. Recent evidence describes that both genetic and environmental factors play an important role on its development. With the advent of genomic-wide association studies, single nucleotide polymorphisms (SNPs) have gained special importance. In this review, we present an update of the genetics surrounding MetS as a single entity as well as its corresponding risk factors, considering SNPs and gene-diet interactions related to cardiometabolic markers. In this study, we focus on the conceptual aspects, diagnostic criteria, as well as the role of genetics, particularly on SNPs and polygenic risk scores (PRS) for interindividual analysis. In addition, this review highlights future perspectives of personalized nutrition with regard to the approach of MetS and how individualized multiomics approaches could improve the current outlook.

A PPARγ/long noncoding RNA axis regulates adipose thermoneutral remodeling in mice

Interplay between energy-storing white adipose cells and thermogenic beige adipocytes contributes to obesity and insulin resistance. Irrespective of specialized niche, adipocytes require the activity of the nuclear receptor PPARγ for proper function. Exposure to cold or adrenergic signaling enriches thermogenic cells though multiple pathways that act synergistically with PPARγ; however, the molecular mechanisms by which PPARγ licenses white adipose tissue to preferentially adopt a thermogenic or white adipose fate in response to dietary cues or thermoneutral conditions are not fully elucidated. Here, we show that a PPARγ/long noncoding RNA (lncRNA) axis integrates canonical and noncanonical thermogenesis to restrain white adipose tissue heat dissipation during thermoneutrality and diet-induced obesity. Pharmacologic inhibition or genetic deletion of the lncRNA Lexis enhances uncoupling protein 1-dependent (UCP1-dependent) and -independent thermogenesis. Adipose-specific deletion of Lexis counteracted diet-induced obesity, improved insulin sensitivity, and enhanced energy expenditure. Single-nuclei transcriptomics revealed that Lexis regulates a distinct population of thermogenic adipocytes. We systematically map Lexis motif preferences and show that it regulates the thermogenic program through the activity of the metabolic GWAS gene and WNT modulator TCF7L2. Collectively, our studies uncover a new mode of crosstalk between PPARγ and WNT that preserves white adipose tissue plasticity.

Comparison of beta-cell function between Hong Kong Chinese with young-onset type 2 diabetes and late-onset type 2 diabetes

AIMS

We compared beta-cell function in Chinese with type 2 diabetes diagnosed at age < 40 years (young-onset diabetes, YOD) and ≥ 40 years (late-onset diabetes, LOD).

METHODS

In this cross-sectional study, we selected participants from two cohorts of people with type 2 diabetes recruited in 1996-2012 (n = 4,376) and 2020-2021 (n = 794). Multivariable linear regression models were applied to compare homeostasis model assessment of beta-cell function (HOMA2-%B) and fasting plasma C-peptide across diabetes duration at enrolment between YOD and LOD.

RESULTS

The YOD group (n = 1,876, mean [SD] age: 39.9 [7.5] years, median [IQR] diabetes duration: 6 [2-12] years) was more likely to have family history of diabetes (61.6 % vs 43.6 %), obesity (41.9 % vs 26.8 %), dyslipidaemia (61.7 % vs 54.4 %), and worse glycaemic control (mean HbA1c 7.7 % vs 7.4 %) than those with LOD (n = 3,294, age: 60.8 [10.6] years, diabetes duration: 5 [1-10] years). When compared to people with LOD, HOMA2-%B and fasting plasma C-peptide were lower in the YOD group, consistently among those with BMI < 27.5 kg/m2 and HOMA2-IR ≤ 1.6 (median value), adjusted for year at enrolment, sex, diabetes duration, family history of diabetes, HbA1c, weight and lipid indices (p < 0.01). Cross-sectionally, the slopes of decline in HOMA2-%B by diabetes duration were greater in YOD than LOD among individuals with BMI < 27.5 kg/m2 (p-interaction = 0.015).

CONCLUSIONS

Chinese with YOD had accelerated loss of beta-cell function than those with LOD especially in non-obese individuals.

BayesKAT: Bayesian Optimal Kernel-based Test for genetic association studies reveals joint genetic effects in complex diseases

GWAS methods have identified individual SNPs significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power, or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT( https://github.com/wangjr03/BayesKAT ), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules, and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases.

Potential Benefits of Antioxidant Phytochemicals in Type 2 Diabetes

The clinical relationship between diabetes and inflammation is well established. Evidence clearly indicates that disrupting oxidant-antioxidant equilibrium and elevated lipid peroxidation could be a potential mechanism for chronic kidney disease associated with type 2 diabetes mellitus (T2DM). Under diabetic conditions, hyperglycemia, especially inflammation, and increased reactive oxygen species generation are bidirectionally associated. Inflammation, oxidative stress, and tissue damage are believed to play a role in the development of diabetes. Although the exact mechanism underlying oxidative stress and its impact on diabetes progression remains uncertain, the hyperglycemia-inflammation-oxidative stress interaction clearly plays a significant role in the onset and progression of vascular disease, kidney disease, hepatic injury, and pancreas damage and, therefore, holds promise as a therapeutic target. Evidence strongly indicates that the use of multiple antidiabetic medications fails to achieve the normal range for glycated hemoglobin targets, signifying treatment-resistant diabetes. Antioxidants with polyphenols are considered useful as adjuvant therapy for their potential anti-inflammatory effect and antioxidant activity. We aimed to analyze the current major points reported in preclinical, in vivo, and clinical studies of antioxidants in the prevention or treatment of inflammation in T2DM. Then, we will share our speculative vision for future diabetes clinical trials.

Multiple genetic variants at the SLC30A8 locus affect local super-enhancer activity and influence pancreatic β-cell survival and function

Variants at the SLC30A8 locus are associated with type 2 diabetes (T2D) risk. The lead variant, rs13266634, encodes an amino acid change, Arg325Trp (R325W), at the C-terminus of the secretory granule-enriched zinc transporter, ZnT8. Although this protein-coding variant was previously thought to be the sole driver of T2D risk at this locus, recent studies have provided evidence for lowered expression of SLC30A8 mRNA in protective allele carriers. In the present study, combined allele-specific expression (cASE) analysis in human islets revealed multiple variants that influence SLC30A8 expression. Epigenomic mapping identified an islet-selective enhancer cluster at the SLC30A8 locus, hosting multiple T2D risk and cASE associations, which is spatially associated with the SLC30A8 promoter and additional neighbouring genes. Deletions of variant-bearing enhancer regions using CRISPR-Cas9 in human-derived EndoC-βH3 cells lowered the expression of SLC30A8 and several neighbouring genes, and improved insulin secretion. Whilst down-regulation of SLC30A8 had no effect on beta cell survival, loss of UTP23, RAD21 or MED30 markedly reduced cell viability. Although eQTL or cASE analyses in human islets did not support the association between these additional genes and diabetes risk, the transcriptional regulator JQ1 lowered the expression of multiple genes at the SLC30A8 locus and enhanced stimulated insulin secretion.

An Overview of the Role of Furin in Type 2 Diabetes

Post-translational modifications (PTMs) play important roles in regulating several human diseases, like cancer, neurodegenerative disorders, and metabolic disorders. Investigating PTMs' contribution to protein functions is critical for modern biology and medicine. Proprotein convertases (PCs) are irreversible post-translational modifiers that have been extensively studied and are considered as key targets for novel therapeutics. They cleave proteins at specific sites causing conformational changes affecting their functions. Furin is considered as a PC model in regulating growth factors and is involved in regulating many pro-proteins. The mammalian target of the rapamycin (mTOR) signaling pathway is another key player in regulating cellular processes and its dysregulation is linked to several diseases including type 2 diabetes (T2D). The role of furin in the context of diabetes has been rarely explored and is currently lacking. Moreover, furin variants have altered activity that could have implications on overall health. In this review, we aim to highlight the role of furin in T2D in relation to mTOR signaling. We will also address furin genetic variants and their potential effect on T2D and β-cell functions. Understanding the role of furin in prediabetes and dissecting it from other confounding factors like obesity is crucial for future therapeutic interventions in metabolic disorders.

Genetic associations of TMEM154, PRC1 and ZFAND6 loci with type 2 diabetes in an endogamous business community of North India

BACKGROUND

More than 250 loci have been identified by genome-wide scans for type 2 diabetes in different populations. South Asians have a very different manifestation of the diseases and hence role of these loci need to be investigated among Indians with huge burden of cardio-metabolic disorders. Thus the present study aims to validate the recently identified GWAS loci in an endogamous caste population in North India.

METHODS

219 T2D cases and 184 controls were recruited from hospitals and genotyped for 15 GWAS loci of T2D. Regression models adjusted for covariates were run to examine the association for T2D and fasting glucose levels.

RESULTS

We validated three variants for T2D namely, rs11634397 at ZFAND6 (OR = 3.05, 95%CI = 1.02-9.19, p = 0.047) and rs8042680 at PRC1 (OR = 3.67, 95%CI = 1.13-11.93, p = 0.031) showing higher risk and rs6813195 at TMEM154 (OR = 0.28, 95%CI = 0.09-0.90, p = 0.033) showing protective effect. The combined risk of 9 directionally consistent variants was also found to be significantly associated with T2D (OR = 1.91, 95%CI = 1.18-3.08, p = 0.008). One variant rs10842994 at KLHDC5 was validated for 9.15mg/dl decreased fasting glucose levels (SE = -17.25-1.05, p = 0.027).

CONCLUSION

We confirm the role of ZFAND6, PRC1 and TMEM154 in the pathophysiology of type 2 diabetes among Indians. More efforts are needed with larger sample sizes to validate the diabetes GWAS loci in South Asian populations for wider applicability.

Identifying latent genetic interactions in genome-wide association studies using multiple traits

Genome-wide association studies of complex traits frequently find that SNP-based estimates of heritability are considerably smaller than estimates from classic family-based studies. This 'missing' heritability may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. To circumvent these challenges, we propose a new method to detect genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Our approach, Latent Interaction Testing (LIT), uses the observation that correlated traits with shared latent genetic interactions have trait variance and covariance patterns that differ by genotype. LIT examines the relationship between trait variance/covariance patterns and genotype using a flexible kernel-based framework that is computationally scalable for biobank-sized datasets with a large number of traits. We first use simulated data to demonstrate that LIT substantially increases power to detect latent genetic interactions compared to a trait-by-trait univariate method. We then apply LIT to four obesity-related traits in the UK Biobank and detect genetic variants with interactive effects near known obesity-related genes. Overall, we show that LIT, implemented in the R package lit, uses shared information across traits to improve detection of latent genetic interactions compared to standard approaches.

Exosomes as biomarkers and therapy in type 2 diabetes mellitus and associated complications

Type 2 diabetes mellitus (T2DM) is one of the most prevalent metabolic disorders worldwide. However, T2DM still remains underdiagnosed and undertreated resulting in poor quality of life and increased morbidity and mortality. Given this ongoing burden, researchers have attempted to locate new therapeutic targets as well as methodologies to identify the disease and its associated complications at an earlier stage. Several studies over the last few decades have identified exosomes, small extracellular vesicles that are released by cells, as pivotal contributors to the pathogenesis of T2DM and its complications. These discoveries suggest the possibility of novel detection and treatment methods. This review provides a comprehensive presentation of exosomes that hold potential as novel biomarkers and therapeutic targets. Additional focus is given to characterizing the role of exosomes in T2DM complications, including diabetic angiopathy, diabetic cardiomyopathy, diabetic nephropathy, diabetic peripheral neuropathy, diabetic retinopathy, and diabetic wound healing. This study reveals that the utilization of exosomes as diagnostic markers and therapies is a realistic possibility for both T2DM and its complications. However, the majority of the current research is limited to animal models, warranting further investigation of exosomes in clinical trials. This review represents the most extensive and up-to-date exploration of exosomes in relation to T2DM and its complications.

Models Predicting Postpartum Glucose Intolerance Among Women with a History of Gestational Diabetes Mellitus: a Systematic Review

PURPOSE OF REVIEW

Despite the crucial role that prediction models play in guiding early risk stratification and timely intervention to prevent type 2 diabetes after gestational diabetes mellitus (GDM), their use is not widespread in clinical practice. The purpose of this review is to examine the methodological characteristics and quality of existing prognostic models predicting postpartum glucose intolerance following GDM.

RECENT FINDINGS

A systematic review was conducted on relevant risk prediction models, resulting in 15 eligible publications from research groups in various countries. Our review found that traditional statistical models were more common than machine learning models, and only two were assessed to have a low risk of bias. Seven were internally validated, but none were externally validated. Model discrimination and calibration were done in 13 and four studies, respectively. Various predictors were identified, including body mass index, fasting glucose concentration during pregnancy, maternal age, family history of diabetes, biochemical variables, oral glucose tolerance test, use of insulin in pregnancy, postnatal fasting glucose level, genetic risk factors, hemoglobin A1c, and weight. The existing prognostic models for glucose intolerance following GDM have various methodological shortcomings, with only a few models being assessed to have low risk of bias and validated internally. Future research should prioritize the development of robust, high-quality risk prediction models that follow appropriate guidelines, in order to advance this area and improve early risk stratification and intervention for glucose intolerance and type 2 diabetes among women who have had GDM.

Utility of genetic risk scores in type 1 diabetes

Iterative advances in understanding of the genetics of type 1 diabetes have identified >70 genetic regions associated with risk of the disease, including strong associations across the HLA class II region that account for >50% of heritability. The increased availability of genetic data combined with the decreased costs of generating these data, have facilitated the development of polygenic scores that aggregate risk variants from associated loci into a single number: either a genetic risk score (GRS) or a polygenic risk score (PRS). PRSs incorporate the risk of many possibly correlated variants from across the genome, even if they do not reach genome-wide significance, whereas GRSs estimate the cumulative contribution of a smaller subset of genetic variants that reach genome-wide significance. Type 1 diabetes GRSs have utility in diabetes classification, aiding discrimination between type 1 diabetes, type 2 diabetes and MODY. Type 1 diabetes GRSs are also being used in newborn screening studies to identify infants at risk of future presentation of the disease. Most early studies of type 1 diabetes genetics have been conducted in European ancestry populations, but, to develop accurate GRSs across diverse ancestries, large case-control cohorts from non-European populations are still needed. The current barriers to GRS implementation within healthcare are mainly related to a lack of guidance and knowledge on integration with other biomarkers and clinical variables. Once these limitations are addressed, there is huge potential for 'test and treat' approaches to be used to tailor care for individuals with type 1 diabetes.

Potassium channels in behavioral brain disorders. Molecular mechanisms and therapeutic potential: A narrative review

Potassium channels (K+-channels) selectively control the passive flow of potassium ions across biological membranes and thereby also regulate membrane excitability. Genetic variants affecting many of the human K+-channels are well known causes of Mendelian disorders within cardiology, neurology, and endocrinology. K+-channels are also primary targets of many natural toxins from poisonous organisms and drugs used within cardiology and metabolism. As genetic tools are improving and larger clinical samples are being investigated, the spectrum of clinical phenotypes implicated in K+-channels dysfunction is rapidly expanding, notably within immunology, neurosciences, and metabolism. K+-channels that previously were considered to be expressed in only a few organs and to have discrete physiological functions, have recently been found in multiple tissues and with new, unexpected functions. The pleiotropic functions and patterns of expression of K+-channels may provide additional therapeutic opportunities, along with new emerging challenges from off-target effects. Here we review the functions and therapeutic potential of K+-channels, with an emphasis on the nervous system, roles in neuropsychiatric disorders and their involvement in other organ systems and diseases.

Inferring causal genes at type 2 diabetes GWAS loci through chromosome interactions in islet cells

Background: Resolving causal genes for type 2 diabetes at loci implicated by genome-wide association studies (GWAS) requires integrating functional genomic data from relevant cell types. Chromatin features in endocrine cells of the pancreatic islet are particularly informative and recent studies leveraging chromosome conformation capture (3C) with Hi-C based methods have elucidated regulatory mechanisms in human islets. However, these genome-wide approaches are less sensitive and afford lower resolution than methods that target specific loci. Methods: To gauge the extent to which targeted 3C further resolves chromatin-mediated regulatory mechanisms at GWAS loci, we generated interaction profiles at 23 loci using next-generation (NG) capture-C in a human beta cell model (EndoC-βH1) and contrasted these maps with Hi-C maps in EndoC-βH1 cells and human islets and a promoter capture Hi-C map in human islets. Results: We found improvements in assay sensitivity of up to 33-fold and resolved ~3.6X more chromatin interactions. At a subset of 18 loci with 25 co-localised GWAS and eQTL signals, NG Capture-C interactions implicated effector transcripts at five additional genetic signals relative to promoter capture Hi-C through physical contact with gene promoters. Conclusions: High resolution chromatin interaction profiles at selectively targeted loci can complement genome- and promoter-wide maps.

Human gain-of-function variants in HNF1A confer protection from diabetes but independently increase hepatic secretion of atherogenic lipoproteins

Loss-of-function mutations in hepatocyte nuclear factor 1A (HNF1A) are known to cause rare forms of diabetes and alter hepatic physiology through unclear mechanisms. In the general population, 1:100 individuals carry a rare, protein-coding HNF1A variant, most of unknown functional consequence. To characterize the full allelic series, we performed deep mutational scanning of 11,970 protein-coding HNF1A variants in human hepatocytes and clinical correlation with 553,246 exome-sequenced individuals. Surprisingly, we found that ∼1:5 rare protein-coding HNF1A variants in the general population cause molecular gain of function (GOF), increasing the transcriptional activity of HNF1A by up to 50% and conferring protection from type 2 diabetes (odds ratio [OR] = 0.77, p = 0.007). Increased hepatic expression of HNF1A promoted a pro-atherogenic serum profile mediated in part by enhanced transcription of risk genes including ANGPTL3 and PCSK9. In summary, ∼1:300 individuals carry a GOF variant in HNF1A that protects carriers from diabetes but enhances hepatic secretion of atherogenic lipoproteins.

Characterizing common and rare variations in non-traditional glycemic biomarkers using multivariate approaches on multi-ancestry ARIC study

Glycated hemoglobin, fasting glucose, glycated albumin, and fructosamine are biomarkers that reflect different aspects of the glycemic process. Genetic studies of these glycemic biomarkers can shed light on unknown aspects of type 2 diabetes genetics and biology. While there exists several GWAS of glycated hemoglobin and fasting glucose, very few GWAS have focused on glycated albumin or fructosamine. We performed a multi-phenotype GWAS of glycated albumin and fructosamine from 7,395 White and 2,016 Black participants in the Atherosclerosis Risk in Communities (ARIC) study on the common variants from genotyped/imputed data. We found 2 genome-wide significant loci, one mapping to known type 2 diabetes gene (ARAP1/STARD10, p = 2.8 × 10-8) and another mapping to a novel gene (UGT1A, p = 1.4 × 10-8) using multi-omics gene mapping strategies in diabetes-relevant tissues. We identified additional loci that were ancestry-specific (e.g., PRKCA from African ancestry individuals, p = 1.7 × 10-8) and sex-specific (TEX29 locus in males only, p = 3.0 × 10-8). Further, we implemented multi-phenotype gene-burden tests on whole-exome sequence data from 6,590 White and 2,309 Black ARIC participants. Eleven genes across different rare variant aggregation strategies were exome-wide significant only in multi-ancestry analysis. Four out of 11 genes had notable enrichment of rare predicted loss of function variants in African ancestry participants despite smaller sample size. Overall, 8 out of 15 loci/genes were implicated to influence these biomarkers via glycemic pathways. This study illustrates improved locus discovery and potential effector gene discovery by leveraging joint patterns of related biomarkers across entire allele frequency spectrum in multi-ancestry analyses. Most of the loci/genes we identified have not been previously implicated in studies of type 2 diabetes, and future investigation of the loci/genes potentially acting through glycemic pathways may help us better understand risk of developing type 2 diabetes.

Biochemical association of regulatory variant of KLF14 genotype in the pathogenesis of cardiodiabetic patients

Background and purpose

The study focuses on examining the relationship between a single nucleotide polymorphism (SNP) in KLF14 rs4731702 and risk of type 2 diabetes mellitus (T2DM) and dyslipidemia in different ethnic populations. The purpose of this study was to evaluate the association between KLF14 rs4731702 and serum lipid profile and to determine the frequency distribution of KLF14 rs4731702 among T2DM and cardiometabolic patients.

Methods

A total of 300 volunteers were recruited, consisting of three groups: 100 healthy individuals, 100 individuals diagnosed with T2DM, and 100 individuals diagnosed with cardiometabolic disorders. Biochemical analysis of blood samples was conducted to assess various biomarkers related to glycemic control and lipid profile. This involved measuring levels of glucose, triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and ApoA1. Genotyping analysis was performed to investigate KLF14 rs4731702 polymorphism. The Tetra ARMS-PCR method was employed for genotyping analysis.

Results

The results of biochemical profiling revealed a significant association between altered glycemic biomarkers and lipid profile in diseased patients compared to healthy participants. The frequencies of KLF14 rs4731702 alleles and genotypes were compared between the control group and T2DM group. A statistically significant difference was observed, indicating a potential association between KLF14 rs4731702 and T2DM. In the dominant inheritance model of KLF14 rs4731702 SNP, a statistically significant difference [odds ratio (95% confidence interval)] of 0.56 (0.34 -0.96) was found between the control and T2DM subjects. This suggests that the presence of certain genotypes influences the risk of T2DM. In T2DM patients, individuals carrying the C allele exhibited compromised insulin sensitivity, decreased HDL-C and ApoA1 levels, and increased serum glucose, TG, and LDL-C concentrations. Conversely, TT genotype carriers demonstrated increased levels of HDL-C and ApoA1, lower insulin resistance, serum glucose, LDL-C, and TG levels.

Conclusion

The study's findings indicate that dyslipidemia in T2DM patients is associated with reduced KLF14 functionality due to CC and CT genotypes, leading to insulin resistance and an increased risk of cardiovascular diseases. Additionally, risk of KLF14 rs4731702 polymorphism was found to increase with age and was more prevalent in female than in male individuals. These insights contribute to understanding genetic factors influencing the development and progression of T2DM and dyslipidemia in different ethnic populations.

Lean legacy, heavy heritage: family history of diabetes and its association with young adult body mass index

Substantial intergenerational transmission of diabetes mellitus (DM) risk exists. However, less is known regarding whether parental DM and DM among extended family members relate to adult offspring's body mass index (BMI), and whether any of these associations vary by sex. Using data from the National Longitudinal Study of Youth 1997 cohort (NLSY97), we assess the sex-specific relationship between DM present in first-degree parents and second-degree relatives and BMI among the parents' young adult offspring.Multivariate regressions reveal a positive relationship between parental DM and young adults' BMI for both daughters and sons, and the magnitude of coefficients is somewhat larger for the same-sex parent. Further, we observe that the link between parental DM and young adults' BMI is strongest when both parents have diagnosed diabetes. In contrast, the relationship between second-degree relatives with DM and the respondent's BMI is weaker and appears to be sex-specific, through same-sex parent and respondent. Logistic regressions show the association is especially strong when assessing how parental DM status relates to young adults' obesity risk. These results generally persist when controlling for parental BMI. The findings of this study point to the need to better distinguish the role of shared family environments (e.g., eating and physical activity patterns) from shared genes in order to understand factors that may influence young adults' BMI. Young adult offspring of parents with diabetes should be targeted for obesity prevention efforts in order to reduce their risks of obesity and perhaps diabetes.

Ameliorative effects of gallic acid on GLUT-4 expression and insulin resistance in high fat diet-induced obesity animal model mice, Mus musculus

Reduced activity of glucose transporter type 4 isoform (GLUT-4), an insulin-sensitive glucose transporter distributed on the adipocytes, is associated with impaired insulin signaling. Insulin resistance resulting from alteration in glucose transport is responsible for exacerbating the emergence of metabolic abnormalities. The present study aimed to investigate the effects of the antidote gallic acid (GA) on expression-related changes in GLUT-4 and insulin receptor substrate-1 (IRS-1) in the visceral adipose tissue and on the subsequent development of insulin resistance in a high-fat diet (HFD)-induced obesity animal model. Methods: Twenty-four female Swiss albino mice were used and separated into the following four groups (six animals in each group): control group (standard pellet diet), HFD group, (60% HFD), HFD + GA group (60% HFD and GA 50 mg/kg body weight for 60 days), and GA group (GA 50 mg/kg body weight for 60 days). The effect of HFD on serum glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL), low-density lipoprotein (LDL) cholesterol, and insulin was evaluated. Additionally, homeostasis model assessment for insulin resistance (HOMA-IR) and glucose tolerance test (GTT) was performed. The serum antioxidative profile, which comprises oxidative parameters (superoxide dismutase [SOD], catalase [CAT], and glutathione peroxidase [GPx]) was measured. The effectiveness of GA against HFD-induced alteration in GLUT-4 and IRS-1 expression was also evaluated. Results: The experimental group that fed on GA + HFD had improved levels of serum triglycerides (p˂0.001), cholesterol (p˂0.05), and LDL cholesterol. GA administration also significantly improved hyperinsulinemia and HOMA-IR index (p˂0.001) in HFD mice. GA improved GTT results (p˂0.05); activity of SOD, CAT, and GPx (p˂0.05); and upregulated mRNA expression of GLUT-4 and IRS-1(p˂0.05) in the visceral adipose tissue in the HFD + GA experimental group. Conclusion: A link exists between insulin resistance, GLUT-4, and IRS-1 expression in the adipose tissue, and the initiation of metabolic syndrome, a condition characterized by obesity. GA may promote insulin signaling, glucose uptake, and lipid metabolism in the adipose tissues by mitigating oxidative stress. GA can also be used to manage obesity-related comorbidities including type 2 diabetes and dyslipidemia.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-023-01194-5.