The Role of TCF7L2 in Type 2 Diabetes

Machine learning reveals connections between preclinical type 2 diabetes subtypes and brain health

Previous research has established type 2 diabetes mellitus as a significant risk factor for various disorders, adversely impacting human health. While evidence increasingly links type 2 diabetes to cognitive impairment and brain disorders, understanding the causal effects of its preclinical stage on brain health is yet to be fully known. This knowledge gap hinders advancements in screening and preventing neurological and psychiatric diseases. To address this gap, we employed a robust machine learning algorithm (Subtype and Stage Inference, SuStaIn) with cross-sectional clinical data from the UK Biobank (20 277 preclinical type 2 diabetes participants and 20 277 controls) to identify underlying subtypes and stages for preclinical type 2 diabetes. Our analysis revealed one subtype distinguished by elevated circulating leptin levels and decreased leptin receptor levels, coupled with increased body mass index, diminished lipid metabolism, and heightened susceptibility to psychiatric conditions such as anxiety disorder, depression disorder, and bipolar disorder. Conversely, individuals in the second subtype manifested typical abnormalities in glucose metabolism, including rising glucose and haemoglobin A1c levels, with observed correlations with neurodegenerative disorders. A >10-year follow-up of these individuals revealed differential declines in brain health and significant clinical outcome disparities between subtypes. The first subtype exhibited faster progression and higher risk for psychiatric conditions, while the second subtype was associated with more severe progression of Alzheimer's disease and Parkinson's disease and faster progression to type 2 diabetes. Our findings highlight that monitoring and addressing the brain health needs of individuals in the preclinical stage of type 2 diabetes is imperative.

A Genetics-guided Integrative Framework for Drug Repurposing: Identifying Anti-hypertensive Drug Telmisartan for Type 2 Diabetes

Drug development is a long and costly process, and repurposing existing drugs for use toward a different disease or condition may serve as a cost-effective alternative. As drug targets with genetic support have a doubled success rate, genetics-informed drug repurposing holds promise in translating genetic findings into therapeutics. In this study, we developed a Genetics Informed Network-based Drug Repurposing via in silico Perturbation (GIN-DRIP) framework and applied the framework to repurpose drugs for type-2 diabetes (T2D). In GIN-DRIP for T2D, it integrates multi-level omics data to translate T2D GWAS signals into a genetics-informed network that simultaneously encodes gene importance scores and a directional effect (up/down) of risk genes for T2D; it then bases on the GIN to perform signature matching with drug perturbation experiments to identify drugs that can counteract the effect of T2D risk alleles. With this approach, we identified 3 high-confidence FDA-approved candidate drugs for T2D, and validated telmisartan, an anti-hypertensive drug, in our EHR data with over 3 million patients. We found that telmisartan users were associated with a reduced incidence of T2D compared to users of other anti-hypertensive drugs and non-users, supporting the therapeutic potential of telmisartan for T2D. Our framework can be applied to other diseases for translating GWAS findings to aid drug repurposing for complex diseases.

Distinct Roles of Common Genetic Variants and Their Contributions to Diabetes: MODY and Uncontrolled T2DM

Type 2 diabetes mellitus (T2DM) encompasses a range of clinical manifestations, with uncontrolled diabetes leading to progressive or irreversible damage to various organs. Numerous genes associated with monogenic diabetes, exhibiting classical patterns of inheritance (autosomal dominant or recessive), have been identified. Additionally, genes involved in complex diabetes, which interact with environmental factors to trigger the disease, have also been discovered. These genetic findings have raised hopes that genetic testing could enhance diagnostics, disease surveillance, treatment selection, and family counseling. However, the accurate interpretation of genetic data remains a significant challenge, as variants may not always be definitively classified as either benign or pathogenic. Research to date, however, indicates that periodic reevaluation of genetic variants in diabetes has led to more consistent findings, with biases being steadily eliminated. This has improved the interpretation of variants across diverse ethnicities. Clinical studies suggest that genetic risk information may motivate patients to adopt behaviors that promote the prevention or management of T2DM. Given that the clinical features of certain monogenic diabetes types overlap with T2DM, and considering the significant role of genetic variants in diabetes, healthcare providers caring for prediabetic patients should consider genetic testing as part of the diagnostic process. This review summarizes current knowledge of the most common genetic variants associated with T2DM, explores novel therapeutic targets, and discusses recent advancements in the pharmaceutical management of uncontrolled T2DM.

Changes in triglyceride-rich lipoprotein particle profiles in response to one-week on a low fat or Mediterranean diet by TCF7L2 rs7903146 genotype: a randomized crossover dietary intervention trial

BACKGROUND

The TCF7L2 gene is a significant genetic factor contributing to the risk of metabolic and cardiovascular diseases (CVD). We previously found that subjects with the TT genotype of TCF7L2 rs7903146 variant, who consume a low-fat diet (LF) had a higher incidence of stroke than subjects with the CC genotype. Yet this association was abolished in subjects with the TT genotype who consumed a Mediterranean-type diet (MetD). However, the mechanism by which MetD diet modulates the association between TCF7L2 and CVD risk is unclear. This study aims to validate these findings under real-world conditions and clinical practice to elucidate the biological mechanisms involved in this correlation.

METHODS

Thirty-five participants with BMI ranging from 27 to 34 kg/m2 were recruited based on rs7903146 genotype. Of those consented to participate, 21 had the CC and 14 had the TT genotype. Participants were randomly assigned to two dietary intervention groups, ensuring an equal distribution of CC and TT carriers. Each participant followed one of two diets (LF or MetD) for one week, followed by a 10-day washout period before switching to the other diet for one week. Blood samples were collected before and after each diet for metabolomic analysis using nuclear magnetic resonance (NMR) spectroscopy. The differential effect of the diets on triglyceride-rich lipoproteins was determined based on TCF7L2 genotype.

RESULTS

The MetD significantly reduced triglyceride-rich lipoprotein concentrations compared to the LF diet. After consuming the LF diet, TT carriers exhibited more small VLDL particles, potentially contributing to CVD risk compared to CC carriers. However, this difference in risk was not observed with the MetD. Furthermore, the order in which the two diets were crossed affected the triglyceride-rich lipoprotein profile, with LF-MetD regimen showing a stronger effect on triglyceride-rich lipoproteins (TRL) levels than the MetD-LF regimen.

CONCLUSIONS

Our findings suggest that rs7903146 TT carriers benefit more from a MetD than a LF diet in terms of their triglyceride-rich lipoprotein profile, which may reduce their risk of CVD. These results support the notion that genotype is a factor in determining the extent to which the MetD affects cardiovascular health.

Double Diabetes: A Converging Metabolic and Autoimmune Disorder Redefining the Classification and Management of Diabetes

This review explores the pathophysiology, clinical implications, and management of double diabetes. The increasing prevalence of obesity, sedentary lifestyles, and genetic predisposition has blurred the difference between type 1 and type 2 diabetes, leading to diagnostic and therapeutic challenges. Double diabetes presents with overlapping symptoms from both diabetes types, making accurate diagnosis crucial. Biomarkers, such as C-peptide levels, autoantibody testing, and insulin resistance markers, help differentiate double diabetes from classic diabetes subtypes. Early intervention is necessary because of the condition's elevated risk of microvascular and macrovascular consequences, such as retinopathy, nephropathy, and cardiovascular disease. Effective management integrates pharmacological and lifestyle approaches. Metformin, sodium-glucose cotransporter 2 (SGLT2) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, and insulin therapy adjustments all boost glycemic control and metabolic results. Additionally, structured exercise, dietary modifications, and weight management are essential for reducing insulin resistance and preserving beta-cell activity. The potential of precision medicine, artificial intelligence (AI)-driven healthcare, and continuous glucose monitoring (CGM) offers promising advancements for personalized treatment strategies. Future research should focus on targeted immunotherapies, genetic profiling, and refined clinical guidelines to improve early detection and individualized treatment, with long-term outcomes. The review emphasizes the need for a multidisciplinary approach in managing double diabetes, ensuring early diagnosis, optimized treatment, and improved metabolic health to mitigate long-term complications.

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.

Association Between ABCG1/TCF7L2 and Type 2 Diabetes Mellitus: An Intervention Trial Based on a Case-Control Study

Background and Objective: Type 2 diabetes mellitus (T2DM) is the result of both genetic and environmental factors. Environmental factors may contribute to the occurrence and development of T2DM by influencing epigenetic modification. The objective of this study was to explore the potential functions of two SNP-CG sites (rs7901695 of TCF7L2 and cg06500161 of ABCG1) that are most strongly associated with T2DM. Given that Uyghur population has been less studied, we conducted an intervention trial in Uyghur people to provide evidence for personalized health management of T2DM in them. Methods: From May to July 2022, 320 patients with T2DM and 332 patients without T2DM were treated with dietary pagoda-based health education intervention. The demographic data were collected before intervention and basic physical biochemical indexes before and after intervention by questionnaire and physical biochemical examination. SNP typing was performed by the TaqMan-MGB probe method, and gene methylation was detected by the pyrosequencing method. Results: The rs7901695 genotype difference of TCF7L2 was statistically significant between the case group and the control group (p < 0.05). After adjusting for covariates (smoking, alcohol consumption, exercise, fasting blood glucose (FPG), obesity, and hypertension), the genotype of rs7901695 in the TCF7L2 gene was associated with genetic susceptibility to T2DM in additive (TC vs. TT,p = 0.047; CC vs. TT,p = 0.010), dominant (p = 0.015), and recessive (p = 0.039) models. Before intervention, there were significant differences in the intake of water between the case group and the control group (p < 0.05). After intervention, there was statistical significance in the intake of coarse grains, fruits, aquatic products, eggs, dairy products, soy products, nuts, edible oils, and water between the case group and the control group (ps < 0.05). Logistic regression analysis showed that methylation of the ABCG1 gene was correlated with T2DM susceptibility after adjustment of covariable before intervention (p = 0.015, odds ratio (OR): 1.023; 95% confidence interval (CI): 1.004~1.041) but not after intervention. Generalized multifactor dimensionality reduction (GMDR) showed that the rs7901695 locus of the TCF7L2 gene and the cg06500161 locus of the ABCG1 gene had interaction with hypertension, dyslipidemia, abdominal obesity, and obesity and also had interaction with drinking, smoking, and exercise. Conclusions: The interaction of the rs7901695 site of the TCF7L2 gene and the cg06500161 site of the ABCG1 gene with environmental factors may increase the risk of T2DM in Uyghurs. The interaction between the cg06500161 site of the ABCG1 gene and environmental factors on T2DM varied with the intervention. The cg06500161 site of ABCG1 may serve as a biomarker to evaluate the effect of T2DM interventions.

Targeted inhibition of menin promotes β-catenin-mediated GLP-1 expression and improves glucose tolerance in high-fat diet-induced obese mice

Glucagon-like peptide-1 (GLP-1), derived from enteroendocrine cells, is a pivotal hormone crucial for blood glucose regulation. Menin, encoded by the MEN1 gene and known for its tumor suppressor role, is abundantly expressed in the intestine. Previous research has demonstrated that acute Men1 excision reverses preexisting glucose intolerance in high-fat diet-fed mice. However, its impact on GLP-1 expression in enteroendocrine cells has not been investigated. In the present study, both the knockdown of Men1 and the administration of the MI-463 menin inhibitor increased GLP-1 expression in glucose-stimulated STC-1 cells. Additionally, administering MI-463 to obese mice significantly elevated GLP-1 levels in both ileal epithelial cells and serum. Mechanistically, menin inhibition enhanced the nuclear accumulation of β-catenin, allowing it to bind TCF7L2, thereby increasing glucagon gene (Gcg) transcription. Furthermore, compared with control mice, mice with intestinal epithelial cell-specific Men1 knockdown exhibited significant improvements in glucose tolerance under fat challenge, which was correlated with elevated GLP-1 levels. These findings suggest that menin-mediated regulation of GLP-1 expression may be an important mechanism through which menin inhibiton alleviates type 2 diabetes.

Knockoff procedure improves causal gene identifications in conditional transcriptome-wide association studies

Transcriptome-wide association studies (TWASs) have been developed to nominate candidate genes associated with complex traits by integrating genome-wide association studies (GWASs) with expression quantitative trait loci (eQTL) data. However, most existing TWAS methods evaluate the marginal association between a single gene and the trait of interest without accounting for other genes within the same genomic region or the same gene from different tissues. Additionally, false-positive gene-trait pairs can arise due to correlations with the direct effects of genetic variants. In this study, we introduce TWASKnockoff, a new knockoff-based framework for detecting causal gene-tissue pairs using GWAS summary statistics and eQTL data. Unlike marginal testing in traditional TWAS methods, TWASKnockoff examines the conditional independence for each gene-trait pair, considering both correlations in cis-predicted expression across genes and correlations between gene expression levels and genetic variants. TWASKnockoff estimates the theoretical correlation matrix for all genetic elements (cis-predicted expression across genes and genotypes for genetic variants) by averaging estimations from parametric boot-strap samples and then performs knockoff-based inference to detect causal gene-trait pairs while controlling the false discovery rate (FDR). Through empirical simulations and an application to type 2 diabetes (T2D) data, we demonstrate that TWASKnockoff achieves superior FDR control and improves the average power in detecting causal gene-trait pairs at a fixed FDR level.

Atlas of multilineage stem cell differentiation reveals TMEM88 as a developmental regulator of blood pressure

Pluripotent stem cells provide a scalable approach to analyse molecular regulation of cell differentiation across developmental lineages. Here, we engineer barcoded induced pluripotent stem cells to generate an atlas of multilineage differentiation from pluripotency, encompassing an eight-day time course with modulation of WNT, BMP, and VEGF signalling pathways. Annotation of in vitro cell types with reference to in vivo development reveals diverse mesendoderm lineage cell types including lateral plate and paraxial mesoderm, neural crest, and primitive gut. Interrogation of temporal and signalling-specific gene expression in this atlas, evaluated against cell type-specific gene expression in human complex trait data highlights the WNT-inhibitor gene TMEM88 as a regulator of mesendodermal lineages influencing cardiovascular and anthropometric traits. Genetic TMEM88 loss of function models show impaired differentiation of endodermal and mesodermal derivatives in vitro and dysregulated arterial blood pressure in vivo. Together, this study provides an atlas of multilineage stem cell differentiation and analysis pipelines to dissect genetic determinants of mammalian developmental physiology.

Associations Between TCF7L2, PPARγ, and KCNJ11 Genotypes and Insulin Response to an Oral Glucose Tolerance Test: A Systematic Review

SCOPE

Insulin responses to standardized meals differ between individuals. This variability may in part be explained by genotype. This systematic review evaluates associations between genotype and insulin response to an oral glucose tolerance test (OGTT) in terms of insulin area under the curve (AUC).

METHODS AND RESULTS

Three electronic databases (Web of Science, Embase, PubMed) were searched for studies investigating associations between insulin AUC after an OGTT and single nucleotide polymorphisms (SNPs) belonging to the transcription factor 7 like 2 (TCF7L2) gene, the peroxisome proliferator-activated receptor gamma (PPARγ) gene, or the potassium inwardly rectifying channel subfamily J member 11 (KCNJ11) gene in persons without diabetes. A total of 5199 articles were identified, of which 38 were included. Among them were family-based studies (9), twin studies (2), and studies with unrelated participants (27). Seventeen articles investigated TCF7L2 (7 SNPs), 14 investigated PPARγ (1 SNP), and 8 investigated KCNJ11 (5 SNPs). For all investigated SNPs, at least half of the reports indicated no statistically significant association with postprandial insulin AUC.

CONCLUSION

No evidence was found for associations between TCF7L2, PPARγ, and KCNJ11 genotypes and insulin AUC after an OGTT. Future studies should investigate the effect of genetic risk scores on postprandial insulin.

The cryptic lncRNA-encoded microprotein TPM3P9 drives oncogenic RNA splicing and tumorigenesis

Emerging evidence demonstrates that cryptic translation from RNAs previously annotated as noncoding might generate microproteins with oncogenic functions. However, the importance and underlying mechanisms of these microproteins in alternative splicing-driven tumor progression have rarely been studied. Here, we show that the novel protein TPM3P9, encoded by the lncRNA tropomyosin 3 pseudogene 9, exhibits oncogenic activity in clear cell renal cell carcinoma (ccRCC) by enhancing oncogenic RNA splicing. Overexpression of TPM3P9 promotes cell proliferation and tumor growth. Mechanistically, TPM3P9 binds to the RRM1 domain of the splicing factor RBM4 to inhibit RBM4-mediated exon skipping in the transcription factor TCF7L2. This results in increased expression of the oncogenic splice variant TCF7L2-L, which activates NF-κB signaling via its interaction with SAM68 to transcriptionally induce RELB expression. From a clinical perspective, TPM3P9 expression is upregulated in cancer tissues and is significantly correlated with the expression of TCF7L2-L and RELB. High TPM3P9 expression or low RBM4 expression is associated with poor survival in patients with ccRCC. Collectively, our findings functionally and clinically characterize the "noncoding RNA"-derived microprotein TPM3P9 and thus identify potential prognostic and therapeutic factors in renal cancer.

The transcription factor TCF4 regulates the miR-494-3p/THBS1 axis in the fibrosis of pathologic scars

BACKGROUND

The fibrosis of pathologic scar (PS) is formed by the excessive deposition of extracellular matrix, resulting in an abnormal scar. Recent clinical tests have indicated that the regulation of PS fibroblast cells (PSF cells) proliferation can serve as an intervention measure for PS. Our work aimed to elucidate the specific mechanism of action of TCF4 on the progression of PS fibrosis.

METHODS

Our study used qRT-PCR and Western blot to search for the expression of key proteins in PS clinical samples and cells. Transwell, CCK-8, and wound scratch assays were employed to analyze the proliferation and migration of PSF cells. CHIP, dual-luciferase reporter experiments, and bio-informatics analysis were used to analyze the interactions between molecules.

RESULTS

The analysis of PS clinical samples confirmed a positive correlation between TCF4 and miR-494-3p. This regulatory mechanism was related to the progression of PS. We verified that the overexpression of miR-494-3p or the knockdown of THBS1 both suppressed the proliferation and migration of PSF cells. Furthermore, we also confirmed the binding relationships between TCF4, miR-494-3p, and THBS1. Simultaneously, we verified the existence of the TCF4/miR-494-3p/THBS1 regulatory network in PS. This regulatory process affects the development of PS fibrosis.

CONCLUSION

Our study results indicate that TCF4, miR-494-3p, and THBS1 are abnormally expressed in PS. TCF4 increases the proliferation and migration ability of PSF cells through the miR-494-3p/THBS1 signaling pathway, which promotes the fibrosis of PS.

Association of TCF7L2 genetic variants rs12255372 and rs7903146 with the polycystic ovary syndrome risk: systemic review and meta-analysis

BACKGROUND

A significant overlap in the pathophysiological features of polycystic ovary syndrome (PCOS) and type 2 diabetes mellitus (T2DM) has been reported; and insulin resistance is considered a central driver in both. The expression and hepatic clearance of insulin and subsequent glucose homeostasis are mediated by TCF7L2 via Wnt signaling. Studies have persistently associated TCF7L2 genetic variations with T2DM, however, its results on PCOS are sparse and inconsistent.

METHODS

We performed a comprehensive literature review of the data published till June 2024, on rs7903146, rs12255372, and PCOS in PubMed, Medline, the Cochrane Library, Google Scholar, Science Direct, Scopus, and Web of Science, followed by a meta-analysis to evaluate the association between these genetic variations and the PCOS risk. Using a random effects model, the pooled odds ratio (OR) and confidence intervals (95%CI) were computed using STATA statistical software.

RESULTS

The genotypic data from 3052 controls and 2291 women with PCOS from ten published studies were analysed. The results indicated no cumulative association between the rs7903146 variant and PCOS risk in either the allelic (C vs. T: OR = 1.21; 95% CI: 0.96-1.47, p > 0.05) or genotypic models (CC vs. CT + TT: OR = 1.06; 95% CI: 0.90-1.23, p > 0.05). Similarly, the genetic variant rs12255372 was not associated with PCOS risk both in the allelic and the dominant inheritance model(p > 0.05). Unlike East Asians (MAF < 0.025), both variants are highly frequent across other global populations including America, South Asia, and Europe (MAF ≥ 0.19).

CONCLUSION

Unlike T2DM, our results showed that rs7903146 and rs12255372 variants of the TCF7L2 gene do not modulate the PCOS risk. However, the role of other TCF7L2 variants remains to be studied in future studies.

Genetic Associations of TCF7L2 (rs7903146) and PPARG (rs1801282) with Prediabetes in the Ethnic Kazakh Population

BACKGROUND

This study aims to investigate the genetic contribution of polymorphic variants of the TCF7L2 (rs7903146) and PPARG (rs1801282) genes to the risk of developing prediabetes in individuals of Kazakh ethnicity.

MATERIALS AND METHODS

This was a case-control study involving 200 cases with prediabetes and 200 prediabetes-free controls, aged 16-60 years (n = 400). Real-time polymerase chain reaction on a StepOnePlus instrument (Applied Biosystems, USA), employing the TaqMan method for site-specific amplification and genotyping of the TCF7L2 (rs7903146) and PPARG (rs1801282) genes was used.

RESULTS

Patients with prediabetes had a higher birth weight, increased BMI, larger waist and hip circumferences, and a higher waist-to-hip ratio compared to healthy patients in the control group. There was a significant increase in the risk of developing prediabetes for both the rs1801282 polymorphism of the PPARG gene and the rs7903146 polymorphism of the TCF7L2 gene. The risk was 9.8 times higher in carriers of the GG genotype of PPARG (rs1801282) (OR = 9.769, 95% CI: 2.124-44.922, p = 0.003) and 10.7 times higher for carriers of the TT genotype of TCF7L2 (rs7903146) (OR = 10.731, 95% CI: 1.309-87.939, p < 0.001).

CONCLUSIONS

These findings highlight the need for tailored early screening and preventive strategies for prediabetes in the Kazakh population, focusing on individuals with high-risk genotypes. Such efforts could improve targeted interventions and reduce the burden of prediabetes. Future research should adopt a longitudinal design, include diverse ethnic groups, and investigate additional genetic markers to provide a more comprehensive understanding of the genetic underpinnings of prediabetes.

The Complex Connection Between Myocardial Dysfunction and Cancer Beyond Cardiotoxicity: Shared Risk Factors and Common Molecular Pathways

Cardiovascular diseases and cancer represent the largest disease burden worldwide. Previously, these two conditions were considered independent, except in terms of cardiotoxicity, which links cancer treatment to subsequent cardiovascular issues. However, recent studies suggest that there are further connections between cancer and heart disease beyond cardiotoxicity. It has been revealed that myocardial dysfunction may promote carcinogenesis, indicating that additional common pathophysiological mechanisms might be involved in the relationship between cardiology and oncology, rather than simply a connection through cardiotoxic effects. These mechanisms may include shared risk factors and common molecular pathways, such as persistent inflammation and neurohormonal activation. This review explores the connection between myocardial dysfunction and cancer, emphasizing their shared risk factors, similar biological mechanisms, and causative factors like cardiotoxicity, along with their clinical implications.

Assessment of potential genetic markers for diabetic foot ulcer among Moscow residents

PURPOSE

Diabetic foot ulcer (DFU) is one of the most severe complications of type 2 diabetes, which is manifested in chronic skin ulcers of lower extremities. DFU treatment remains complex and expensive despite the availability of well-established protocols. Early prediction of potential DFU development at the onset of type 2 diabetes can greatly improve the aftermath of this complication.

METHODS

To assess potential genetic markers for DFU, a group of diabetic patients from Moscow region with and without DFU was genotyped for a number of SNPs previously reported to be associated with the DFU.

RESULTS

Obtained results did not confirm previously claimed association of rs1024611, rs3918242, rs2073618, rs1800629, rs4986790, rs179998, rs1963645 and rs11549465 (respectively, in MCP1, MMP9, TNFRSF11B, TNFα, TLR4, eNOS, NOS1AP and HIF1α genes) with the DFU. Surprisingly, the t allele of rs7903146 in the TCF7l2 gene known as one of the most prominent risk factors for type 2 diabetes has shown a protective effect on DFU with OR(95%) = 0.68(0.48-0.96).

CONCLUSION

Non-replication of previously published SNP associations with DFU suggests that the role of genetic factors in the DFU onset is either highly variable in different populations or is not as significant as the role of non-genetic factors.

Single-nucleus transcriptional profiling reveals TCF7L2 as a key regulator in adipogenesis in goat skeletal muscle development

Intramuscular adipogenesis plays an important role in muscle development, which determines the quality of goat meat. However, its underlying cellular and molecular mechanisms remain poorly understood. In this study, we provided detailed cellular atlases of goat longissimus dorsi during muscle development at single-nucleus resolution, and identified the subpopulations of fibroblasts/fibro-adipogenic progenitors (FAPs) and muscle satellite cell (MuSC), as well as the differentiation trajectory of FAPs subpopulations. Cellular ligand-receptor interaction analysis revealed enriched BMP and IGF pathways implicated in within-tissue crosstalk centered around FAPs. Through single-nucleus gene regulatory network analysis and in vitro interference verification, we found that TCF7L2 was a critical transcriptional factor (TF) in early adipogenesis in skeletal muscle. Overall, our work reveals the cellular intricacies and diversity of goat longissimus dorsi during muscle development, implementing insights into the critical roles of BMP, IGF pathways and TCF7L2 TF in intramuscular adipogenesis.

Genetic associations of neuropathic pain and sensory profile in a deeply phenotyped neuropathy cohort

ABSTRACT

We aimed to investigate the genetic associations of neuropathic pain in a deeply phenotyped cohort. Participants with neuropathic pain were cases and compared with those exposed to injury or disease but without neuropathic pain as control subjects. Diabetic polyneuropathy was the most common aetiology of neuropathic pain. A standardised quantitative sensory testing protocol was used to categorize participants based on sensory profile. We performed genome-wide association study, and in a subset of participants, we undertook whole-exome sequencing targeting analyses of 45 known pain-related genes. In the genome-wide association study of diabetic neuropathy (N = 1541), a top significant association was found at the KCNT2 locus linked with pain intensity (rs114159097, P = 3.55 × 10-8). Gene-based analysis revealed significant associations between LHX8 and TCF7L2 and neuropathic pain. Polygenic risk score for depression was associated with neuropathic pain in all participants. Polygenic risk score for C-reactive protein showed a positive association, while that for fasting insulin showed a negative association with neuropathic pain, in individuals with diabetic polyneuropathy. Gene burden analysis of candidate pain genes supported significant associations between rare variants in SCN9A and OPRM1 and neuropathic pain. Comparison of individuals with the "irritable" nociceptor profile to those with a "nonirritable" nociceptor profile identified a significantly associated variant (rs72669682, P = 4.39 × 10-8) within the ANK2 gene. Our study on a deeply phenotyped cohort with neuropathic pain has confirmed genetic associations with the known pain-related genes KCNT2, OPRM1, and SCN9A and identified novel associations with LHX8 and ANK2, genes not previously linked to pain and sensory profiles, respectively.

An atlas of genetic effects on the monocyte methylome across European and African populations

Elucidating the genetic architecture of DNA methylation is crucial for decoding complex disease etiology. However, current epigenomic studies are often limited by incomplete methylation coverage and heterogeneous tissue samples. Here, we present the first comprehensive, multi-ancestry human methylome atlas of purified human monocytes, generated through integrated whole-genome bisulfite sequencing and whole-genome sequencing from 298 European Americans (EA) and 160 African Americans (AA). By analyzing over 25 million methylation sites, we identified 1,383,250 and 1,721,167 methylation quantitative trait loci (meQTLs) in cis- regions for EA and AA populations, respectively, revealing both shared (880,108 sites) and population-specific regulatory patterns. Furthermore, we developed population-specific DNAm imputation models, enabling methylome-wide association studies (MWAS) for 1,976,046 and 2,657,581 methylation sites in EA and AA, respectively. These models were validated through multi-ancestry analysis of 41 complex traits from the Million Veteran Program. The identified meQTLs, MWAS models, and data resources are freely available at www.gcbhub.org and https://osf.io/gct57/ .

Depot-specific mRNA expression programs in human adipocytes suggest physiological specialization via distinct developmental programs

Adipose tissue is distributed in diverse locations throughout the human body. Not much is known about the extent to which anatomically distinct adipose depots are functionally distinct, specialized organs, nor whether depot-specific characteristics result from intrinsic developmental programs, as opposed to reversible physiological responses to differences in tissue microenvironment. We used DNA microarrays to compare mRNA expression patterns of isolated human adipocytes and cultured adipose stem cells, before and after ex vivo adipocyte differentiation, from seven anatomically diverse adipose tissue depots. Adipocytes from different depots display distinct gene expression programs, which are most closely shared with anatomically related depots. mRNAs whose expression differs between anatomically diverse groups of depots (e.g., subcutaneous vs. internal) suggest important functional specializations. These depot-specific differences in gene expression were recapitulated when adipocyte progenitor cells from each site were differentiated ex vivo, suggesting that progenitor cells from specific anatomic sites are deterministically programmed to differentiate into depot-specific adipocytes. Many developmental transcription factors show striking depot-specific patterns of expression, suggesting that adipocytes in each anatomic depot are programmed during early development in concert with anatomically related tissues and organs. Our results support the hypothesis that adipocytes from different depots are functionally distinct and that their depot-specific specialization reflects distinct developmental programs.

Genetic and Anthropometric Interplay: How Waist-to-Hip Ratio Modulates LDL-c Levels in Mexican Population

BACKGROUND/OBJECTIVES

Genetic factors contribute to the physiopathology of obesity and its comorbidities. This study aimed to investigate the association of the SNPs ABCA1 (rs9282541), ADIPOQ (rs2241766), FTO (rs9939609), GRB14 (rs10195252), and LEPR (rs1805134) with various clinical, anthropometric, and biochemical variables.

METHODS

The study included 396 Mexican mestizo individuals with obesity and 142 individuals with normal weight. Biochemical markers were evaluated from peripheral blood samples, and SNP genotyping was performed using PCR with TaqMan probes. A genetic risk score (GRS) was computed using an additive model.

RESULTS

No significant associations were found between the SNPs ABCA1, ADIPOQ, FTO, and LEPR with obesity. However, the T allele of the GRB14 SNP was significantly associated with obesity (χ2 = 5.93, p = 0.01; OR = 1.52; 95% CI: 1.08-2.12). A multivariate linear regression model (adjusted R-squared: 0.1253; p < 0.001) predicting LDL-c levels among all participants (n = 538) identified significant (p < 0.05) beta coefficients for several anthropometric and biochemical variables, as well as for the GRS. Additionally, the interaction between the GRS and the waist-to-hip ratio (WHR) showed a negative beta coefficient (BC = -26.5307; p = 0.014). Participants with a WHR < 0.839 showed no effect of GRS on LDL-c concentration, while those with a WHR > 0.839 exhibited a greater effect of GRS (~9) at lower LDL-c concentrations (~50 mg/dL) and a lesser effect of GRS (~7) at higher LDL-c concentrations (~250 mg/dL).

CONCLUSIONS

A significant interaction between genetics and WHR influences LDL-c in Mexicans, which may contribute to the prevention and clinical management of dyslipidemia and cardiovascular disease.

Harnessing the power of AI in precision medicine: NGS-based therapeutic insights for colorectal cancer cohort

Purpose

Developing innovative precision and personalized cancer therapeutics is essential to enhance cancer survivability, particularly for prevalent cancer types such as colorectal cancer. This study aims to demonstrate various approaches for discovering new targets for precision therapies using artificial intelligence (AI) on a Polish cohort of colorectal cancer patients.

Methods

We analyzed 71 patients with histopathologically confirmed advanced resectional colorectal adenocarcinoma. Whole exome sequencing was performed on tumor and peripheral blood samples, while RNA sequencing (RNAseq) was conducted on tumor samples. We employed three approaches to identify potential targets for personalized and precision therapies. First, using our in-house neoantigen calling pipeline, ARDentify, combined with an AI-based model trained on immunopeptidomics mass spectrometry data (ARDisplay), we identified neoepitopes in the cohort. Second, based on recurrent mutations found in our patient cohort, we selected corresponding cancer cell lines and utilized knock-out gene dependency scores to identify synthetic lethality genes. Third, an AI-based model trained on cancer cell line data was employed to identify cell lines with genomic profiles similar to selected patients. Copy number variants and recurrent single nucleotide variants in these cell lines, along with gene dependency data, were used to find personalized synthetic lethality pairs.

Results

We identified approximately 8,700 unique neoepitopes, but none were shared by more than two patients, indicating limited potential for shared neoantigenic targets across our cohort. Additionally, we identified three synthetic lethality pairs: the well-known APC-CTNNB1 and BRAF-DUSP4 pairs, along with the recently described APC-TCF7L2 pair, which could be significant for patients with APC and BRAF variants. Furthermore, by leveraging the identification of similar cancer cell lines, we uncovered a potential gene pair, VPS4A and VPS4B, with therapeutic implications.

Conclusion

Our study highlights three distinct approaches for identifying potential therapeutic targets in cancer patients. Each approach yielded valuable insights into our cohort, underscoring the relevance and utility of these methodologies in the development of precision and personalized cancer therapies. Importantly, we developed a novel AI model that aligns tumors with representative cell lines using RNAseq and methylation data. This model enables us to identify cell lines closely resembling patient tumors, facilitating accurate selection of models needed for in vitro validation.

Targeting the Epigenetic Marks in Type 2 Diabetes Mellitus: Will Epigenetic Therapy Be a Valuable Adjunct to Pharmacotherapy?

Although genetic, environmental, and lifestyle factors largely contribute to type 2 diabetes mellitus (T2DM) risk, the role of epigenetics in its pathogenesis is now well established. The epigenetic mechanisms in T2DM mainly consist of DNA methylation, histone modifications and regulation by noncoding RNAs (ncRNAs). For instance, DNA methylation at CpG islands in the promoter regions of specific genes encoding insulin signaling and glucose metabolism suppresses these genes. Modulating the enzyme mediators of these epigenetic marks aims to restore standard gene expression patterns and improve glycemic control. In targeting these epigenetic marks, using epigenetic drugs such as DNA methyltransferase (DNAMT), histone deacetylase (HDAC) and histone acetyltransferase (HAT) inhibitors has led to variable success in humans and experimental murine models. Specifically, the United States' Food and Drug Administration (US FDA) has approved DNAMT inhibitors like 5-azacytidine and 5-aza-2'-deoxycytidine for use in diabetic retinopathy: a T2DM microvascular complication. These DNAMT inhibitors block the genes for methylation of mitochondrial superoxide dismutase 2 (SOD2) and matrix metallopeptidase 9 (MMP-9): the epigenetic marks in diabetic retinopathy. Traditional pharmacotherapy with metformin also have epigenetic effects in T2DM and positively alter disease outcomes when combined with epigenetic drugs like DNAMT and HDAC inhibitors, raising the prospect of using epigenetic therapy as a valuable adjunct to pharmacotherapy. However, introducing small interfering RNAs (siRNAs) in cells to silence specific target genes remains in the exploratory phase. Future research should focus on regulating gene expression in T2DM using long noncoding RNA (lncRNA) molecules, another type of ncRNA. This review discusses the epigenetics of T2DM and that of its macro- and microvascular complications, and the potential benefits of combining epigenetic therapy with pharmacotherapy for optimal results.

Schizophrenia and type 2 diabetes risk: a systematic review and meta-analysis

Objectives

The metabolic syndrome in patients with schizophrenia has consistently been a challenge for clinicians. Previous studies indicate that individuals with schizophrenia are highly prone to developing type 2 diabetes mellitus (T2DM). In recent years, a continuous stream of new observational studies has been reported, emphasizing the pressing need for clinicians to gain a more precise understanding of the association between schizophrenia and T2DM. The objective of this meta-analysis is to integrate new observational studies and further explore the potential link between schizophrenia and the risk of T2DM.

Methods

We conducted a comprehensive search of PubMed, Cochrane Library, Embase, and Web of Science using medical subject headings (MeSH) and relevant keywords. The risk of bias in cohort studies and case-control studies was assessed using the Newcastle-Ottawa Scale (NOS), while cross-sectional studies were evaluated using the Agency for Healthcare Research and Quality scale (AHRQ), scoring was based on the content of the original studies. A fixed-effects model was employed if P > 0.1 and I2 ≤ 50%, indicating low heterogeneity. Conversely, a random-effects model was utilized if I2 > 50%, indicating substantial heterogeneity. Publication bias was assessed using funnel plots and Egger's test. Statistical analyses were carried out using Stata statistical software version 14.0.

Results

This meta-analysis comprised 32 observational studies, involving a total of 2,007,168 patients with schizophrenia and 35,883,980 without schizophrenia, published from 2004 to 2023. The pooled analysis revealed a significant association between a history of schizophrenia and an increased risk of T2DM (Odds Ratio [OR] = 2.15; 95% Confidence Interval [CI]: 1.83-2.52; I2 = 98.9%, P < 0.001). Stratified by gender, females with schizophrenia (OR = 2.12; 95% CI: 1.70-2.64; I2 = 90.7%, P < 0.001) had a significantly higher risk of T2DM than males (OR = 1.68; 95% CI: 1.39-2.04; I2 = 91.3%, P < 0.001). Regarding WHO regions, EURO (OR = 2.73; 95% CI: 2.23-3.35; I2 = 97.5%, P < 0.001) exhibited a significantly higher risk of T2DM compared to WPRO (OR = 1.72; 95% CI: 1.32-2.23; I2 = 95.2%, P < 0.001) and AMRO (OR = 1.82; 95% CI: 1.40-2.37; I2 = 99.1%, P < 0.001). In terms of follow-up years, the >20 years subgroup (OR = 3.17; 95% CI: 1.24-8.11; I2 = 99.4%, P < 0.001) showed a significantly higher risk of T2DM than the 10-20 years group (OR = 2.26; 95% CI: 1.76-2.90; I2 = 98.6%, P < 0.001) and <10 years group (OR = 1.68; 95% CI: 1.30-2.19; I2 = 95.4%, P < 0.001).

Conclusions

This meta-analysis indicates a strong association between schizophrenia and an elevated risk of developing diabetes, suggesting that schizophrenia may function as an independent risk factor for T2DM.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023465826.

The associations between single nucleotide polymorphisms and diabetic retinopathy risk: an umbrella review

This umbrella review was conducted aiming to assess the association between genetic variations and the development of diabetic retinopathy (DR) by collecting and evaluating available systematic reviews and meta-analysis results. We evaluated the methodological quality using the Measurement Tool to Assess Systematic Reviews (AMSTAR) 2.0, estimated the summary effect size by using the random effects model and calculated the 95% prediction intervals (PIs). Evidence from the included meta-analyses was graded according to established criteria as follows: convincing, highly suggestive, suggestive, weak, or not significant. This umbrella review included 32 meta-analyses of 52 candidate SNPs. The 12 selected meta-analyses were rated as "high," 2 studies were rated as "moderate," 11 studies were graded as "low," and the remaining 7 studies were graded as "critically low" in terms of methodological quality. Carriers of specific genotypes and alleles of the transcription Factor 7-like 2 C/T (TCF7L2 C/T) polymorphism (rs7903146, p < 0.001) might be more susceptible to the occurrence of DR in the homozygous and recessive models, and these associations were supported by "convincing" evidence. Significant associations were also found between interleukin-6 (IL-6) -174 G/C (rs1800795; p < 0.05) or vascular endothelial growth factor (VEGF) polymorphisms (rs2010963, rs699947, rs1570360, rs2010963, rs699947, rs2146323; all p values <0.05) and DR risk, but these associations were supported by "weak" evidence. The TCF7L2 C/T variant could be identified as a definitive genetic risk factor for the development and progression of DR. Data from additional in-depth studies are needed to establish robust evidence for the associations between polymorphisms of IL-6 or VEGF and DR.

Adaptive selection at G6PD and disparities in diabetes complications

Diabetes complications occur at higher rates in individuals of African ancestry. Glucose-6-phosphate dehydrogenase deficiency (G6PDdef), common in some African populations, confers malaria resistance, and reduces hemoglobin A1c (HbA1c) levels by shortening erythrocyte lifespan. In a combined-ancestry genome-wide association study of diabetic retinopathy, we identified nine loci including a G6PDdef causal variant, rs1050828 -T (Val98Met), which was also associated with increased risk of other diabetes complications. The effect of rs1050828 -T on retinopathy was fully mediated by glucose levels. In the years preceding diabetes diagnosis and insulin prescription, glucose levels were significantly higher and HbA1c significantly lower in those with versus without G6PDdef. In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, participants with G6PDdef had significantly higher hazards of incident retinopathy and neuropathy. At the same HbA1c levels, G6PDdef participants in both ACCORD and the Million Veteran Program had significantly increased risk of retinopathy. We estimate that 12% and 9% of diabetic retinopathy and neuropathy cases, respectively, in participants of African ancestry are due to this exposure. Across continentally defined ancestral populations, the differences in frequency of rs1050828 -T and other G6PDdef alleles contribute to disparities in diabetes complications. Diabetes management guided by glucose or potentially genotype-adjusted HbA1c levels could lead to more timely diagnoses and appropriate intensification of therapy, decreasing the risk of diabetes complications in patients with G6PDdef alleles.

Multi-Omics Analysis Revealed the rSNPs Potentially Involved in T2DM Pathogenic Mechanism and Metformin Response

The goal of our study was to identify and assess the functionally significant SNPs with potentially important roles in the development of type 2 diabetes mellitus (T2DM) and/or their effect on individual response to antihyperglycemic medication with metformin. We applied a bioinformatics approach to identify the regulatory SNPs (rSNPs) associated with allele-asymmetric binding and expression events in our paired ChIP-seq and RNA-seq data for peripheral blood mononuclear cells (PBMCs) of nine healthy individuals. The rSNP outcomes were analyzed using public data from the GWAS (Genome-Wide Association Studies) and Genotype-Tissue Expression (GTEx). The differentially expressed genes (DEGs) between healthy and T2DM individuals (GSE221521), including metformin responders and non-responders (GSE153315), were searched for in GEO RNA-seq data. The DEGs harboring rSNPs were analyzed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We identified 14,796 rSNPs in the promoters of 5132 genes of human PBMCs. We found 4280 rSNPs to associate with both phenotypic traits (GWAS) and expression quantitative trait loci (eQTLs) from GTEx. Between T2DM patients and controls, 3810 rSNPs were detected in the promoters of 1284 DEGs. Based on the protein-protein interaction (PPI) network, we identified 31 upregulated hub genes, including the genes involved in inflammation, obesity, and insulin resistance. The top-ranked 10 enriched KEGG pathways for these hubs included insulin, AMPK, and FoxO signaling pathways. Between metformin responders and non-responders, 367 rSNPs were found in the promoters of 131 DEGs. Genes encoding transcription factors and transcription regulators were the most widely represented group and many were shown to be involved in the T2DM pathogenesis. We have formed a list of human rSNPs that add functional interpretation to the T2DM-association signals identified in GWAS. The results suggest candidate causal regulatory variants for T2DM, with strong enrichment in the pathways related to glucose metabolism, inflammation, and the effects of metformin.

Investigation of genetic markers associated to type 2 diabetes mellitus in Santarém-Pará

Genetic, epigenetic and environmental factors play an important role in the genesis of Type 2 Diabetes Mellitus (T2D). In the genetic context, one of the strategies used to investigate possible associations with diabetes is the search for Single Nucleotide Polymorphisms (SNPs), involving the comparison of alelle frequencies, the phenotypic variations and other relevant factors, such as environmental influences and lifestyle choices, Thus, the aim of this study was to find the relationship of risk variants for T2D in SNPs (rs4994) in the ADRB3 gene; (rs1799854) in the ABCC8 gene; (rs7901695 and rs12255372) in the TCF7L2 gene; and (rs8050136) in the FTO gene in a sample of the population of the municipality of Santarém (PA), Brazilian Amazon, in the northern region of Brazil. ABCC8 (rs1799854 C>T) showed a statistically significant association with T2D. Each chosen gene and SNP has been previously implicated in T2D risk according to existing scientific literature, owing to their roles in glucose regulation and body fat.

Identification of core genes in intervertebral disc degeneration using bioinformatics and machine learning algorithms

Background

Intervertebral Disc Degeneration (IDD) is a major cause of lower back pain and a significant global health issue. However, the specific mechanisms of IDD remain unclear. This study aims to identify key genes and pathways associated with IDD using bioinformatics and machine learning algorithms.

Methods

Gene expression profiles, including those from 35 LDH patients and 43 healthy volunteers, were downloaded from the GEO database (GSE124272, GSE150408, GSE23130, GSE153761). After merging four microarray datasets, differentially expressed genes (DEGs) were selected for GO and KEGG pathway enrichment analysis. Weighted Gene Co-expression Network Analysis (WGCNA) was then applied to the merged dataset to identify relevant modules and intersect with DEGs to discover candidate genes with diagnostic value. A LASSO model was established to select appropriate genes, and ROC curves were drawn to elucidate the diagnostic value of genetic markers. A Protein-Protein Interaction (PPI) network was constructed and visualized to determine central genes, followed by external validation using qRT-PCR.

Results

Differential analysis of the preprocessed dataset identified 244 genes, including 183 upregulated and 61 downregulated genes. WGCNA analysis revealed the most relevant module intersecting with DEGs, yielding 9 candidate genes. The lasso-cox method was used for regression analysis, ultimately identifying 6 genes: ASPH, CDC42EP3, FOSL2, IL1R1, NFKBIZ, TCF7L2. A Protein-Protein Interaction (PPI) network created with GENEMANIA identified IL1R1 and TCF7L2 as central genes.

Conclusion

Our study shows that IL1R1 and TCF7L2 are the core genes of IDD, offering new insights into the pathogenesis and therapeutic development of IDD.

Association of TCF7L2 Gene Variant (rs12255372) with Polycystic Ovary Syndrome and its Effect Modification of the Disease Phenotype

Polycystic ovary syndrome (PCOS) and type-2 diabetes mellitus (T2DM) share common genetic features. Transcription factor 7-like-2 (TCF7L2) is consistently studied T2DM susceptibility locus. However, limited studies on TCF7L2 have failed to demonstrate any link with the PCOS risk. Therefore, we investigated the association of TCF7L2 polymorphic variant (rs12255372) with the PCOS risk. We recruited 120 PCOS cases, diagnosed as per Rotterdam 2003 criteria, and an equal number of age-matched controls. Besides a detailed clinical assessment, subjects underwent biochemical and hormonal profiling. Genotyping for rs12255372 was done by PCR-RFLP. Conditional logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (95%CIs) of genotype-phenotype correlations. The PCOS cases reported fewer menstrual cycles per year and exhibited signs of hyperandrogenism. The heterozygous genotype of rs12255372 was strongly associated with the PCOS risk (OR = 2.00; 95%CI: 1.07-3.76). Unlike controls, only 3 cases harbored TT genotype, and the PCOS risk persisted in the dominant model (GT + TT) as well. Moreover, we found a synergistic effect modification by the variant genotype in the subjects who had family histories of T2DM, hirsutism, or menstrual irregularities. We report a significant association of the TCF7L2 polymorphic variant rs12255372 with the PCOS risk.

Correlation between TCF7L2 gene expression and certain biochemical parameters in type 2 diabetes mellitus

Objectives

The transcription factor 7-like 2 gene (TCF7L2) is associated with a predisposition to type 2 diabetes mellitus (T2DM) in different ethnic populations. This article investigated the relationship between TCF7L2 gene expression and several biochemical indexes among different age categories of T2DM in a sample of the Iraqi population.

Methods

One hundred and fifty blood samples were collected from three groups: young T2DM (10-35 years), old T2DM (40-80 years), and healthy control (10-65 years) groups. Both sexes were enrolled. qPCR was performed to explore the expression of the TCF7L2 gene. Biochemical tests were performed to assess hemoglobin A1C (HbA1c), triglyceride (TG), low-density lipoprotein (LDL), and high-density lipoprotein (HDL) levels. The body mass index (BMI) was calculated. The results were statistically analyzed.

Results

Patients with T2DM had higher BMI, TG, and LDL, and lower HDL than the control group. There was a strong positive correlation between hemoglobin A1C (HbA1c) and BMI, TG, and LDL and a negative correlation between HbA1c and HDL. Expression of the TCF7L2 gene showed a significant difference between old and young patients by 1.68 and 0.207 fold, respectively. These results showed that old patients had higher gene expression than young patients.

Conclusion

TCF7L2 gene expression was affected by age, with higher expression in old T2DM patients. This may influence beta cell functions and insulin secretion.

Genetic and inflammatory factors underlying gestational diabetes mellitus: a review

Gestational diabetes mellitus (GDM) poses a significant global health concern, impacting both maternal and fetal well-being. Early detection and treatment are imperative to mitigate adverse outcomes during pregnancy. This review delves into the pivotal role of insulin function and the influence of genetic variants, including SLC30A8, CDKAL1, TCF7L2, IRS1, and GCK, in GDM development. These genetic variations affect beta-cell function and insulin activity in crucial tissues, such as muscle, disrupting glucose regulation during pregnancy. We propose a hypothesis that this variation may disrupt zinc transport, consequently impairing insulin production and secretion, thereby contributing to GDM onset. Furthermore, we discussed the involvement of inflammatory pathways, such as TNF-alpha and IL-6, in predisposing individuals to GDM. Genetic modulation of these pathways may exacerbate glucose metabolism dysregulation observed in GDM patients. We also discussed how GDM affects cardiovascular disease (CVD) through a direct correlation between pregnancy and cardiometabolic function, increasing atherosclerosis, decreased vascular function, dyslipidemia, and hypertension in women with GDM history. However, further research is imperative to unravel the intricate interplay between inflammatory pathways, genetics, and GDM. This understanding is pivotal for devising targeted gene therapies and pharmacological interventions to rectify genetic variations in SLC30A8, CDKAL1, TCF7L2, IRS1, GCK, and other pertinent genes. Ultimately, this review offers insights into the pathophysiological mechanisms of GDM, providing a foundation for developing strategies to mitigate its impact.

Conditional Deletion of β-Catenin in the Mediobasal Hypothalamus Impairs Adaptive Energy Expenditure in Response to High-Fat Diet and Exacerbates Diet-Induced Obesity

β-Catenin is a bifunctional molecule that is an effector of the wingless-related integration site (Wnt) signaling to control gene expression and contributes to the regulation of cytoskeleton and neurotransmitter vesicle trafficking. In its former role, β-catenin binds transcription factor 7-like 2 (TCF7L2), which shows strong genetic associations with the pathogenesis of obesity and type-2 diabetes. Here, we sought to determine whether β-catenin plays a role in the neuroendocrine regulation of body weight and glucose homeostasis. Bilateral injections of adeno-associated virus type-2 (AAV2)-mCherry-Cre were placed into the arcuate nucleus of adult male and female β-catenin flox mice, to specifically delete β-catenin expression in the mediobasal hypothalamus (MBH-β-cat KO). Metabolic parameters were then monitored under conditions of low-fat (LFD) and high-fat diet (HFD). On LFD, MBH-β-cat KO mice showed minimal metabolic disturbances, but on HFD, despite having only a small difference in weekly caloric intake, the MBH-β-cat KO mice were significantly heavier than the control mice in both sexes (p < 0.05). This deficit seemed to be due to a failure to show an adaptive increase in energy expenditure seen in controls, which served to offset the increased calories by HFD. Both male and female MBH-β-cat KO mice were highly glucose intolerant when on HFD and displayed a significant reduction in both leptin and insulin sensitivity compared with controls. This study highlights a critical role for β-catenin in the hypothalamic circuits regulating body weight and glucose homeostasis and reveals potential mechanisms by which genetic variation in this pathway could impact on development of metabolic disease.

Altered DNA methylation underlies monocyte dysregulation and immune exhaustion memory in sepsis

Monocytes can develop an exhausted memory state characterized by reduced differentiation, pathogenic inflammation, and immune suppression that drives immune dysregulation during sepsis. Chromatin alterations, notably via histone modifications, underlie innate immune memory, but the contribution of DNA methylation remains poorly understood. Using an ex vivo sepsis model, we show altered DNA methylation throughout the genome of exhausted monocytes, including genes implicated in immune dysregulation during sepsis and COVID-19 infection (e.g., Plac8). These changes are recapitulated in septic mice induced by cecal slurry injection. Methylation profiles developed in septic mice are maintained during ex vivo culture, supporting the involvement of DNA methylation in stable monocyte exhaustion memory. Methylome reprogramming is driven in part by Wnt signaling inhibition in exhausted monocytes and can be reversed with DNA methyltransferase inhibitors, Wnt agonists, or immune training molecules. Our study demonstrates the significance of altered DNA methylation in the maintenance of stable monocyte exhaustion memory.

Insights on effects of Wnt pathway modulation on insulin signaling and glucose homeostasis for the treatment of type 2 diabetes mellitus: Wnt activation or Wnt inhibition?

Type 2 diabetes mellitus (T2DM) is a major worldwide chronic disease and can lead to serious diabetic complications. Despite the availability of many anti-diabetic agents in the market, they are unable to meet the long-term treatment goals. Also, they cause many side effects which justify the need for novel class of anti-diabetic drugs with newer mechanism of action. Wnt signaling is one of such novel target pathways which can be explored for metabolic disorders. Many key components of the Wnt signaling are involved in the regulation of glucose homeostasis. Polymorphism in the Transcription factor 7-like 2 (TCF7L2) gene, and mutations in the LRP5 (LDL Receptor Related Protein 5) gene lead to disturbed glucose metabolism and obesity. Despite of several years of research in this field, there is no concrete proof of concept available on whether Wnt activation or Wnt inhibition is the beneficial approach for the treatment of T2DM. Here, we have summarized the conclusions of relevant published research studies to give structured insights into possibilities to explore Wnt modulation as a novel target pathway for the treatment of T2DM. The review also highlights the present challenges and future opportunities towards the development of anti-diabetic small molecules targeting the Wnt signaling pathway.

Association between variants in TCF7L2, CTRB1/2, and GLP-1R genes and response to therapy with glucagon-like peptide-1 receptor agonists

OBJECTIVES

The factors determining the response to treatment with glucagon-like peptide-1 receptor agonists (GLP-1- RAs) have not been clarified. The present study investigated the association between polymorphisms in TCF7L2, CTRB1/2, and GLP-1 R genes and response to GLP-1 RAs regarding glycemic control and weight loss among Greek patients with type 2 diabetes mellitus (T2DM).

METHODS

Patients (n = 191) treated with GLP-1 RAs for at least 6 months were included. Participants were genotyped for TCF7L2 rs7903146 (C>T), CTRB1/2 rs7202877 (T>G) and GLP-1 R rs367543060 (C>T) polymorphisms. Clinical and laboratory parameters were measured before, 3, and 6 months after treatment initiation. The patients were classified into responders and non-responders according to specific criteria.

RESULTS

Carriers of at least one rs7903146 'T' allele and rs7202877 'G' allele presented similar glucose control and weight loss response to GLP-1 RAs with the respective homozygous wild-type genotypes [odds ratio (OR): 1.08, 95% confidence interval (CI): 0.5, 2.31, p = 0.85 and OR: 1.35, 95% CI: 0.66, 2.76, p = 0.42; OR: 1.4, 95% CI: 0.56, 3.47, p = 0.47 and OR: 1.28, 95% CI: 0.55, 2.98, p = 0.57, respectively]. Regarding the GLP-1 R polymorphism, all participants were homozygous for the wild-type allele; thus, no comparisons were feasible. Female sex (p = 0.03) and lower baseline weight (p = 0.024) were associated with an improved glycemic and weight loss response, respectively.

CONCLUSION

There is no evidence suggesting a role for the variants studied in response to GLP-1 RA therapy in people with T2DM. However, specific demographic and clinical factors may be related to a better response to treatment with these agents.

Mechanisms and Physiological Roles of Polymorphisms in Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is a significant pregnancy complication linked to perinatal complications and an elevated risk of future metabolic disorders for both mothers and their children. GDM is diagnosed when women without prior diabetes develop chronic hyperglycemia due to β-cell dysfunction during gestation. Global research focuses on the association between GDM and single nucleotide polymorphisms (SNPs) and aims to enhance our understanding of GDM's pathogenesis, predict its risk, and guide patient management. This review offers a summary of various SNPs linked to a heightened risk of GDM and explores their biological mechanisms within the tissues implicated in the development of the condition.

RedRibbon: A new rank-rank hypergeometric overlap for gene and transcript expression signatures

High-throughput omics technologies have generated a wealth of large protein, gene, and transcript datasets that have exacerbated the need for new methods to analyse and compare big datasets. Rank-rank hypergeometric overlap is an important threshold-free method to combine and visualize two ranked lists of P-values or fold-changes, usually from differential gene expression analyses. Here, we introduce a new rank-rank hypergeometric overlap-based method aimed at gene level and alternative splicing analyses at transcript or exon level, hitherto unreachable as transcript numbers are an order of magnitude larger than gene numbers. We tested the tool on synthetic and real datasets at gene and transcript levels to detect correlation and anticorrelation patterns and found it to be fast and accurate, even on very large datasets thanks to an evolutionary algorithm-based minimal P-value search. The tool comes with a ready-to-use permutation scheme allowing the computation of adjusted P-values at low time cost. The package compatibility mode is a drop-in replacement to previous packages. RedRibbon holds the promise to accurately extricate detailed information from large comparative analyses.

Multigenerational diabetes mellitus

Gestational diabetes (GDM) changes the maternal metabolic and uterine environment, thus increasing the risk of short- and long-term adverse outcomes for both mother and child. Children of mothers who have GDM during their pregnancy are more likely to develop Type 2 Diabetes (T2D), early-onset cardiovascular disease and GDM when they themselves become pregnant, perpetuating a multigenerational increased risk of metabolic disease. The negative effect of GDM is exacerbated by maternal obesity, which induces a greater derangement of fetal adipogenesis and growth. Multiple factors, including genetic, epigenetic and metabolic, which interact with lifestyle factors and the environment, are likely to contribute to the development of GDM. Genetic factors are particularly important, with 30% of women with GDM having at least one parent with T2D. Fetal epigenetic modifications occur in response to maternal GDM, and may mediate both multi- and transgenerational risk. Changes to the maternal metabolome in GDM are primarily related to fatty acid oxidation, inflammation and insulin resistance. These might be effective early biomarkers allowing the identification of women at risk of GDM prior to the development of hyperglycaemia. The impact of the intra-uterine environment on the developing fetus, "developmental programming", has a multisystem effect, but its influence on adipogenesis is particularly important as it will determine baseline insulin sensitivity, and the response to future metabolic challenges. Identifying the critical window of metabolic development and developing effective interventions are key to our ability to improve population metabolic health.

Insulin Resistance/Diabetes and Schizophrenia: Potential Shared Genetic Factors and Implications for Better Management of Patients with Schizophrenia

Schizophrenia is a complex psychotic disorder with co-occurring conditions, including insulin resistance and type 2 diabetes (T2D). It is well established that T2D and its precursors (i.e., insulin resistance) are more prevalent in patients with schizophrenia who are treated with antipsychotics, as well as in antipsychotic-naïve patients experiencing their first episode of psychosis, compared with the general population. However, the mechanism(s) underlying the increased susceptibility, shared genetics, and possible cause-effect relationship between schizophrenia and T2D remain largely unknown. The objective of this narrative review was to synthesize important studies, including Mendelian randomization (MR) analyses that have integrated genome-wide association studies (GWAS), as well as results from in vitro models, in vivo models, and observational studies of patients with schizophrenia. Both GWAS and MR studies have found that schizophrenia and T2D/insulin resistance share genetic risk factors, and this may mediate a connection between peripheral or brain insulin resistance and T2D with cognition impairment and an increased risk of developing prediabetes and T2D in schizophrenia. Moreover, accumulating evidence supports a causal role for insulin resistance in schizophrenia and emphasizes the importance of a genetic basis for susceptibility to T2D in patients with schizophrenia before they receive psychotic treatment. The present findings and observations may have clinical implications for the development of better strategies to treat patients with schizophrenia, with both pharmacological (i.e., samidorphan, empagliflozin) and/or nonpharmacological (i.e., lifestyle changes) approaches. Additionally, this review may benefit the design of future studies by physicians and clinical investigators.

Senescent adipocytes and type 2 diabetes - current knowledge and perspective concepts

Among civilization diseases, the number of individuals suffering from type 2 diabetes (T2DM) is expected to increase to more than a billion in less than 20 years, which is associated with, e.g., populational aging, poor diet, sedentary lifestyle, genetic predispositions, and immunological factors. T2DM affects many organs and is characterized by insulin resistance, high glucose levels, and adipocyte dysfunction, which are related to senescence. Although this type of cellular aging has beneficial biological functions, it can also act unfavorable since senescent adipocytes resist apoptosis, enhance cytokine secretion, downregulate cell identity genes, and acquire the senescence-associated secretory phenotype that renders a more oxidative environment. Opposing T2DM is possible via a wide variety of senotherapies, including senolytics and senomorphics; nevertheless, further research is advised to expand therapeutic possibilities and benefits. Consequences that ought to be deeply researched include secretory phenotype, chronic inflammation, increasing insulin resistance, as well as impairment of adipogenesis and functioning of adipocyte cells. Herein, despite reviewing T2DM and fat tissue senescence, we summarized the latest adipocyte-related anti-diabetes solutions and suggested further research directions.

Transform diabetes care with precision medicine

Background and Aims

Diabetes is a global concern. This article took a closer look at diabetes and precision medicine.

Methods

A literature search of studies related to the use of precision medicine in diabetes care was conducted in various databases (PubMed, Google Scholar, and Scopus).

Results

Precision medicine encompasses the integration of a wide array of personal data, including clinical, lifestyle, genetic, and various biomarker information. Its goal is to facilitate tailored treatment approaches using contemporary diagnostic and therapeutic techniques that specifically target patients based on their genetic makeup, molecular markers, phenotypic traits, or psychosocial characteristics. This article not only highlights significant advancements but also addresses key challenges, particularly focusing on the technologies that contribute to the realization of personalized and precise diabetes care.

Conclusion

For the successful implementation of precision diabetes medicine, collaboration and coordination among multiple stakeholders are crucial.

Altered DNA methylation underlies monocyte dysregulation and innate exhaustion memory in sepsis

Innate immune memory is the process by which pathogen exposure elicits cell-intrinsic states to alter the strength of future immune challenges. Such altered memory states drive monocyte dysregulation during sepsis, promoting pathogenic behavior characterized by pro-inflammatory, immunosuppressive gene expression in concert with emergency hematopoiesis. Epigenetic changes, notably in the form of histone modifications, have been shown to underlie innate immune memory, but the contribution of DNA methylation to this process remains poorly understood. Using an ex vivo sepsis model, we discovered broad changes in DNA methylation throughout the genome of exhausted monocytes, including at several genes previously implicated as major drivers of immune dysregulation during sepsis and Covid-19 infection (e.g. Plac8 ). Methylome alterations are driven in part by Wnt signaling inhibition in exhausted monocytes, and can be reversed through treatment with DNA methyltransferase inhibitors, Wnt agonists, or immune training molecules. Importantly, these changes are recapitulated in septic mice following cecal slurry injection, resulting in stable changes at critical immune genes that support the involvement of DNA methylation in acute and long-term monocyte dysregulation during sepsis.

The Role of Artificial Intelligence in Deciphering Diet-Disease Relationships: Case Studies

Modernization of society from a rural, hunter-gatherer setting into an urban and industrial habitat, with the associated dietary changes, has led to an increased prevalence of cardiometabolic and additional noncommunicable diseases, such as cancer, inflammatory bowel disease, and neurodegenerative and autoimmune disorders. However, while dietary sciences have been rapidly evolving to meet these challenges, validation and translation of experimental results into clinical practice remain limited for multiple reasons, including inherent ethnic, gender, and cultural interindividual variability, among other methodological, dietary reporting-related, and analytical issues. Recently, large clinical cohorts with artificial intelligence analytics have introduced new precision and personalized nutrition concepts that enable one to successfully bridge these gaps in a real-life setting. In this review, we highlight selected examples of case studies at the intersection between diet-disease research and artificial intelligence. We discuss their potential and challenges and offer an outlook toward the transformation of dietary sciences into individualized clinical translation.

Genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis

Multimorbidity is a rising public health challenge with important implications for health management and policy. The most common multimorbidity pattern is the combination of cardiometabolic and osteoarticular diseases. Here, we study the genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis. We find genome-wide genetic correlation between the two diseases and robust evidence for association-signal colocalization at 18 genomic regions. We integrate multi-omics and functional information to resolve the colocalizing signals and identify high-confidence effector genes, including FTO and IRX3, which provide proof-of-concept insights into the epidemiologic link between obesity and both diseases. We find enrichment for lipid metabolism and skeletal formation pathways for signals underpinning the knee and hip osteoarthritis comorbidities with type 2 diabetes, respectively. Causal inference analysis identifies complex effects of tissue-specific gene expression on comorbidity outcomes. Our findings provide insights into the biological basis for the type 2 diabetes-osteoarthritis disease co-occurrence.

Signaling pathways and intervention for therapy of type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) represents one of the fastest growing epidemic metabolic disorders worldwide and is a strong contributor for a broad range of comorbidities, including vascular, visual, neurological, kidney, and liver diseases. Moreover, recent data suggest a mutual interplay between T2DM and Corona Virus Disease 2019 (COVID-19). T2DM is characterized by insulin resistance (IR) and pancreatic β cell dysfunction. Pioneering discoveries throughout the past few decades have established notable links between signaling pathways and T2DM pathogenesis and therapy. Importantly, a number of signaling pathways substantially control the advancement of core pathological changes in T2DM, including IR and β cell dysfunction, as well as additional pathogenic disturbances. Accordingly, an improved understanding of these signaling pathways sheds light on tractable targets and strategies for developing and repurposing critical therapies to treat T2DM and its complications. In this review, we provide a brief overview of the history of T2DM and signaling pathways, and offer a systematic update on the role and mechanism of key signaling pathways underlying the onset, development, and progression of T2DM. In this content, we also summarize current therapeutic drugs/agents associated with signaling pathways for the treatment of T2DM and its complications, and discuss some implications and directions to the future of this field.

Functional interrogation of twenty type 2 diabetes-associated genes using isogenic hESC-derived β-like cells

Genetic studies have identified numerous loci associated with type 2 diabetes (T2D), but the functional role of many loci has remained unexplored. In this study, we engineered isogenic knockout human embryonic stem cell (hESC) lines for 20 genes associated with T2D risk. We systematically examined β-cell differentiation, insulin production and secretion, and survival. We performed RNA-seq and ATAC-seq on hESC-β cells from each knockout line. Analyses of T2D GWAS signals overlapping with HNF4A-dependent ATAC peaks identified a specific SNP as a likely causal variant. In addition, we performed integrative association analyses and identified four genes ( CP, RNASE1, PCSK1N and GSTA2 ) associated with insulin production, and two genes ( TAGLN3 and DHRS2 ) associated with sensitivity to lipotoxicity. Finally, we leveraged deep ATAC-seq read coverage to assess allele-specific imbalance at variants heterozygous in the parental hESC line, to identify a single likely functional variant at each of 23 T2D GWAS signals.

Is TNF alpha a mediator in the co-existence of malaria and type 2 diabetes in a malaria endemic population?

Malaria remains a disease of public health importance globally, especially in sub-Saharan Africa. Malaria deaths reduced globally steadily between 2000-2019, however there was a 10% increase in 2020 due to disruptions in medical service during the COVID-19 pandemic. Globally, about 96% of malaria deaths occurred in 29 countries; out of which, four countries (Nigeria, the Democratic Republic of the Congo, the Niger, and the United Republic of Tanzania) accounted for just over half of the malaria deaths. Nigeria leads the four countries with the highest malaria deaths (accounting for 31% globally). Parallelly, sub-Saharan Africa is faced with a rise in the incidence of Type 2 diabetes (T2D). Until recently, T2D was a disease of adulthood and old age. However, this is changing as T2D in children and adolescents is becoming an increasingly important public health problem. Nigeria has been reported to have the highest burden of diabetes in Africa with a prevalence of 5.77% in the country. Several studies conducted in the last decade investigating the interaction between malaria and T2D in developing countries have led to the emergence of the intra-uterine hypothesis. The hypothesis has arisen as a possible explanation for the rise of T2D in malaria endemic areas; malaria in pregnancy could lead to intra-uterine stress which could contribute to low birth weight and may be a potential cause of T2D later in life. Hence, previous, and continuous exposure to malaria infection leads to a higher risk of T2D. Current and emerging evidence suggests that an inflammation-mediated link exists between malaria and eventual T2D emergence. The inflammatory process thus, is an important link for the co-existence of malaria and T2D because these two diseases are inflammatory-related. A key feature of T2D is systemic inflammation, characterized by the upregulation of inflammatory cytokines such as tumor necrosis factor alpha (TNF-α) which leads to impaired insulin signaling. Malaria infection is an inflammatory disease in which TNF-α also plays a major role. TNF-α plays an important role in the pathogenesis and development of malaria and T2D. We therefore hypothesize that TNF-α is an important link in the increasing co-existence of T2D.