Chemokine gene polymorphisms association with increased risk of type 2 diabetes mellitus in Tatar ethnic group, Russia

Polygenic Score Approach to Predicting Risk of Metabolic Syndrome

BACKGROUND/OBJECTIVES

Metabolic syndrome (MetS) is a complex condition linking obesity, diabetes, and hypertension, representing a major challenge in clinical care. Its rising global prevalence, driven by urbanization, sedentary lifestyles, and dietary changes, underscores the need for effective management. This study aims to explore the genetic mechanisms behind MetS, including adiposity, inflammation, neurotransmitters, and β-cell function, to develop a prognostic tool for MetS risk.

METHODS

We genotyped 40 genetic variants across these pathways in 279 MetS patients and 397 healthy individuals. Using logistic regression, we evaluated the prognostic capability of a polygenic score model for MetS risk, both independently and with other factors like sex and age.

RESULTS

Logistic regression analysis identified 18 genetic variants significantly associated with MetS. The optimal predictive model used polygenic scores calculated with weights assigned to the 18 loci (AUC 82.5%, 95% CI 79.4-85.6%), with age and sex providing a minimal, non-significant improvement (AUC 83.3%, 95% CI 80.2-86.3%). The addition of the polygenic score significantly improved net reclassification (NRI = 1.03%, p = 3.42 × 10-50). Including all 40 variants did not enhance prediction (NRI = -0.11, p = 0.507).

CONCLUSIONS

Polygenic scores could aid in predicting MetS risk and health outcomes, emphasizing the need for diagnostic tools tailored to specific populations. Additional research is warranted to corroborate these conclusions and explore the molecular mechanisms of MetS.

A New Application for Cenicriviroc, a Dual CCR2/CCR5 Antagonist, in the Treatment of Painful Diabetic Neuropathy in a Mouse Model

The ligands of chemokine receptors 2 and 5 (CCR2 and CCR5, respectively) are associated with the pathomechanism of neuropathic pain development, but their role in painful diabetic neuropathy remains unclear. Therefore, the aim of our study was to examine the function of these factors in the hypersensitivity accompanying diabetes. Additionally, we analyzed the analgesic effect of cenicriviroc (CVC), a dual CCR2/CCR5 antagonist, and its influence on the effectiveness of morphine. An increasing number of experimental studies have shown that targeting more than one molecular target is advantageous compared with the coadministration of individual pharmacophores in terms of their analgesic effect. The advantage of using bifunctional compounds is that they gain simultaneous access to two receptors at the same dose, positively affecting their pharmacokinetics and pharmacodynamics and consequently leading to improved analgesia. Experiments were performed on male and female Swiss albino mice with a streptozotocin (STZ, 200 mg/kg, i.p.) model of diabetic neuropathy. We found that the blood glucose level increased, and the mechanical and thermal hypersensitivity developed on the 7th day after STZ administration. In male mice, we observed increased mRNA levels of Ccl2, Ccl5, and Ccl7, while in female mice, we observed additional increases in Ccl8 and Ccl12 levels. We have demonstrated for the first time that a single administration of cenicriviroc relieves pain to a similar extent in male and female mice. Moreover, repeated coadministration of cenicriviroc with morphine delays the development of opioid tolerance, while the best and longest-lasting analgesic effect is achieved by repeated administration of cenicriviroc alone, which reduces pain hypersensitivity in STZ-exposed mice, and unlike morphine, no tolerance to the analgesic effects of CVC is observed until Day 15 of treatment. Based on these results, we suggest that targeting CCR2 and CCR5 with CVC is a potent therapeutic option for novel pain treatments in diabetic neuropathy patients.

Mendelian Randomization Analysis Identifies Inverse Causal Relationship between External Eating and Metabolic Phenotypes

Disordered eating contributes to weight gain, obesity, and type 2 diabetes (T2D), but the precise mechanisms underlying the development of different eating patterns and connecting them to specific metabolic phenotypes remain unclear. We aimed to identify genetic variants linked to eating behaviour and investigate its causal relationships with metabolic traits using Mendelian randomization (MR). We tested associations between 30 genetic variants and eating patterns in individuals with T2D from the Volga-Ural region and investigated causal relationships between variants associated with eating patterns and various metabolic and anthropometric traits using data from the Volga-Ural population and large international consortia. We detected associations between HTR1D and CDKAL1 and external eating; between HTR2A and emotional eating; between HTR2A, NPY2R, HTR1F, HTR3A, HTR2C, CXCR2, and T2D. Further analyses in a separate group revealed significant associations between metabolic syndrome (MetS) and the loci in CRP, ADCY3, GHRL, CDKAL1, BDNF, CHRM4, CHRM1, HTR3A, and AKT1 genes. MR results demonstrated an inverse causal relationship between external eating and glycated haemoglobin levels in the Volga-Ural sample. External eating influenced anthropometric traits such as body mass index, height, hip circumference, waist circumference, and weight in GWAS cohorts. Our findings suggest that eating patterns impact both anthropometric and metabolic traits.

Exploring metformin monotherapy response in Type-2 diabetes: Computational insights through clinical, genomic, and proteomic markers using machine learning algorithms

BACKGROUND

In 2016, the UK had 4.5 million people with diabetes, predominantly Type-2 Diabetes Mellitus (T2DM). The NHS allocates £10 billion (9% of its budget) to manage diabetes. Metformin is the primary treatment for T2DM, but 35% of patients don't benefit from it, leading to complications. This study aims to delve into metformin's efficacy using clinical, genomic, and proteomic data to uncover new biomarkers and build a Machine Learning predictor for early metformin response detection.

METHODS

Here we report analysis from a T2DM dataset of individuals prescribed metformin monotherapy from the Diastrat cohort recruited at the Altnagelvin Area Hospital, Northern Ireland.

RESULTS

In the clinical data analysis, comparing responders (those achieving HbA1c ≤ 48 mmol/mol) to non-responders (with HbA1c > 48 mmol/mol), we identified that creatinine levels and bodyweight were more negatively correlated with response than non-response. In genomic analysis, we identified statistically significant (p-value <0.05) variants rs6551649 (LPHN3), rs6551654 (LPHN3), rs4495065 (LPHN3) and rs7940817 (TRPC6) which appear to differentiate the responders and non-responders. In proteomic analysis, we identified 15 statistically significant (p-value <0.05, q-value <0.05) proteomic markers that differentiate controls, responders, non-responders and treatment groups, out of which the most significant were HAOX1, CCL17 and PAI that had fold change ∼2. A machine learning model was build; the best model predicted non-responders with 83% classification accuracy.

CONCLUSION

Further testing in prospective validation cohorts is required to determine the clinical utility of the proposed model.

Evidence from genetic studies among rs2107538 variant in the CCL5 gene and Saudi patients diagnosed with type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is a chronic and metabolic disorder that affects the adult population. Chemokines are proinflammatory cytokines that play a role in the development of chronic diseases such as obesity, gestational diabetes, and T2DM. The C-C Motif Chemokine Ligand 5 (CCL5) gene plays a role in antiviral immunity, tumor development, obesity, impaired glucose tolerance, and T2DM. This study aimed to investigate the genetic role of the rs2107538 variant in the CCL5 gene in Saudi patients with T2DM. Sixty subjects with T2DM patients and 60 healthy controls participated in this prospective case-control study. Prior to Sanger sequencing, genomic DNA was extracted and amplified with Polymerase chain reaction (PCR), after which the PCR products were purified. The collected data were used to conduct various statistical analyses to determine the relationship between T2DM and control subjects. The findings of the current study revealed a positive association for most parameters between T2DM and control subjects (p < 0.05). The frequency of genotypes (p = 0.002, AA vs.GG: p = 0.008, GA + AA vs. GG: p = 0.0002) and alleles (A vs. G: p = 0.0007) revealed a strong risk association. Multiple logistic regression with individual effects revealed a link between SBP and HDLc levels (p = 0.03). In patients with T2DM, waist (p = 0.001), TG (p = 0.0007), and LDLc (p = 0.0004) levels were all associated with the ANOVA. Finally, the rs2107538 variant was linked to an increased risk of T2DM in the Saudi Population. The GA and AA genotypes were strongly connected to the T2DM subjects. In order to rule out disease-causing variants in the global population, future research should use a large sample size.

Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes

We tested associations between 13 established genetic variants and type 2 diabetes (T2D) in 1371 study participants from the Volga-Ural region of the Eurasian continent, and evaluated the predictive ability of the model containing polygenic scores for the variants associated with T2D in our dataset, alone and in combination with other risk factors such as age and sex. Using logistic regression analysis, we found associations with T2D for the CCL20 rs6749704 (OR = 1.68, P_FDR = 3.40 × 10^-5), CCR5 rs333 (OR = 1.99, P_FDR = 0.033), ADIPOQ rs17366743 (OR = 3.17, P_FDR = 2.64 × 10^-4), TCF7L2 rs114758349 (OR = 1.77, P_FDR = 9.37 × 10^-5), and CCL2 rs1024611 (OR = 1.38, P_FDR = 0.033) polymorphisms. We showed that the most informative prognostic model included weighted polygenic scores for these five loci, and non-genetic factors such as age and sex (AUC 85.8%, 95%CI 83.7-87.8%). Compared to the model containing only non-genetic parameters, adding the polygenic score for the five T2D-associated loci showed improved net reclassification (NRI = 37.62%, 1.39 × 10^-6). Inclusion of all 13 tested SNPs to the model with age and sex did not improve the predictive ability compared to the model containing five T2D-associated variants (NRI = -17.86, p = 0.093). The five variants associated with T2D in people from the Volga-Ural region are linked to inflammation (CCR5, CCL2, CCL20) and glucose metabolism regulation (TCF7L, ADIPOQ2). Further studies in independent groups of T2D patients should validate the prognostic value of the model and elucidate the molecular mechanisms of the disease development.

Investigation of the Correlation Between the Polymorphism/Expression Level of RANTES and Its Receptor CCR5 Gene Promoter and Type 2 Diabetes Mellitus

BACKGROUND

This study aimed to explore relationship among RANTES -28 (rs2280788) C/G polymorphism or CCR5 59029 (rs1799987) A/G polymorphism, level of self-expression, and type 2 diabetes mellitus (T2DM).

MATERIALS AND METHODS

Clinical data were collected from 92 subjects with normal blood glucose (NC) and 97 patients with T2DM (DM). CCR5 levels on the surface of monocyte/lymphocyte and plasma RANTES levels were detected by flow cytometry. TaqMan real-time fluorescent quantitative PCR was used to detect genetic polymorphisms of RANTES rs2280788 and CCR5 rs1799987.

RESULTS

There were no significant differences in frequencies of CCR5 rs1799987 genotype and A/G allele and frequencies of RANTES rs2280788 genotype and C/G allele, between subjects in NC and DM group (P > 0.05). Plasma RANTES level in DM group was significantly lower than NC group (P < 0.05), and difference came from patients with T2DM using insulin and subjects with normal blood glucose. CCR5 levels on the surface of monocytes and lymphocytes of patients in DM group were higher than NC group (P < 0.05). There was no significant difference in CCR5 level on the surface of monocytes and lymphocytes (or plasma RANTES level) among different genotypes of CCR5 rs1799987 (or RANTES rs2280788) (P > 0.05). RANTES level was positively correlated with age and TC and negatively correlated with diabetes course and HbA1c. CCR5 level on the surface of monocytes was positively correlated with drinking years, HbA1c, course of diabetes, and negatively correlated with TC. CCR5 on lymphocyte surface was positively correlated with diabetes course, smoking years, HbA1c, and negatively correlated with LDL, TC, HDL (P < 0.05).

CONCLUSION

RANTES -28 (rs2280788) C/G polymorphism or CCR5 59029 (rs1799987) A/G polymorphism may not be associated with T2DM of Han nationality in Kunming and cannot affect RANTES and CCR5 expression. RANTES and CCR5 levels may be related to T2DM but may also be affected by age, blood lipids, HbA1c, diabetes course, drugs, and other factors.

Innate-Immunity Genes in Obesity

The main functions of adipose tissue are thought to be storage and mobilization of the body’s energy reserves, active and passive thermoregulation, participation in the spatial organization of internal organs, protection of the body from lipotoxicity, and ectopic lipid deposition. After the discovery of adipokines, the endocrine function was added to the above list, and after the identification of crosstalk between adipocytes and immune cells, an immune function was suggested. Nonetheless, it turned out that the mechanisms underlying mutual regulatory relations of adipocytes, preadipocytes, immune cells, and their microenvironment are complex and redundant at many levels. One possible way to elucidate the picture of adipose-tissue regulation is to determine genetic variants correlating with obesity. In this review, we examine various aspects of adipose-tissue involvement in innate immune responses as well as variants of immune-response genes associated with obesity.

RANTES 59029A/G Polymorphisms Associated with Diabetic Compilations in Korean Patients with Type 2 Diabetes for over 15 Years

Background: Polymorphisms in the RANTES gene are known to be associated with several diseases related to insulin resistance. In this study, we investigated the association between RANTES 59029A/G polymorphisms and the prevalence of diabetic complications relative to obesity in Korean patients who had type 2 diabetes (T2D) for over 15 years. Methods: A single-center, retrospective case-control study was performed. We included 271 patients with a duration of diabetes greater than 15 years. Polymerase chain reaction-restriction fragment length polymorphism was used to analyze RANTES polymorphisms, identifying genotypes as GG, AG, or AA. Obesity was defined using the body mass index with a cutoff value of 25 kg/m². Both microvascular (retinopathy and nephropathy) and macrovascular (coronary artery disease and cerebrovascular disease) complications were evaluated. Results: The duration of T2D and hemoglobin A1c values at enrollment were 24.4 ± 5.0 years and 7.8 ± 1.6%, respectively, in the non-obese group, and 25.4 ± 6.1 years and 7.7 ± 1.7%, respectively, in the obese group. The prevalence of microvascular complications was significantly higher in the obese group compared with that in the non-obese group (83.5% vs. 72.0%, p = 0.039). Compared to the non-obese group, the obese group showed a higher proportion of the patients with AA or AG genotypes (64.3% vs. 84.5%, p = 0.001). Conclusions: The A allele of the RANTES gene is associated with obesity and may affect diabetic microvascular complications in patients with T2D for over 15 years.

The Relationship Between Chemokine and Chemokine Receptor Genes Polymorphisms and Chronic Obstructive Pulmonary Disease Susceptibility in Tatar Population from Russia: A Case Control Study

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease affecting primarily distal respiratory pathways and lung parenchyma. This study aimed to determine possible genetic association of chemokine and chemokine receptor genes polymorphisms with COPD in a Tatar population from Russia. SNPs of CCL20, CCR6, CXCL8, CXCR1, CXCR2, CCL8, CCL23, CCR2, and CX3CL1 genes and their gene-gene interactions were analyzed for association with COPD in cohort of 601 patients and 617 controls. As a result statistically significant associations with COPD in the study group under the biologically plausible assumption of additive genetic model were identified in CCL20 (rs6749704) (P = 0.00001, OR 1.55), CCR6 (rs3093024) (P = 0.0003, OR 0.74), CCL8 (rs3138035) (P = 0.0001, OR 0.67), CX3CL1 (rs170364) (P = 0.023, OR 1.21), CXCL8 (rs4073) (P = 0.007, OR 1.23), CXCR2 (rs2230054) (P = 0.0002, OR 1.32). Following SNPs CCL20 (rs6749704), CX3CL1 (rs170364), CCL8 (rs3138035), CXCL8 (rs4073), CXCR2 (rs2230054) showed statistically significant association with COPD only in smokers. The association of CCR6 (rs3093024) with COPD was confirmed both in smokers and in non-smokers. A relationship between smoking index and CCL20 (rs6749704) (P = 0.04), CCR6 (rs3093024) (P = 0.007), CCL8 (rs3138035) (P = 0.0043), and CX3CL1 (rs170364) (P = 0.04) was revealed. A significant genotype-dependent variation of Forced Vital Capacity was observed for CCL23 (rs854655) (P = 0.04). Forced Expiratory Volume in 1 s / Forced Vital Capacity ratio was affected by CCL23 (rs854655) (P = 0.05) and CXCR2 (rs1126579) (P = 0.02). Using the APSampler algorithm, we obtained nine gene-gene combinations that remained significantly associated with COPD; loci CCR2 (rs1799864) and CCL8 (rs3138035) were involved in the largest number of the combinations. Our results indicate that CCL20 (rs6749704), CCR6 (rs3093024), CCR2 (rs1799864), CCL8 (rs3138035), CXCL8 (rs4073), CXCR1 (rs2234671), CXCR2 (rs2230054), and CX3CL1 (rs170364) polymorphisms are strongly associated with COPD in Tatar population from Russia, alone and in combinations. For the first time combination of the corresponding SNPs were considered and as a result 8 SNP patterns were associated with increased risk of COPD.

Lipid deposition and metaflammation in diabetic kidney disease

A critical link between metabolic disorders and a form of low-grade systemic and chronic inflammation has been recently established and named 'Metaflammation'. Metaflammation has been recognized as a key mediator of both microvascular and macrovascular complications of diabetes and as a significant contributor to the development of diabetic kidney disease (DKD). The goal of this review is to summarize the contribution of diabetes-induced inflammation and the related signaling pathways to diabetic complications, with a particular focus on how innate immunity and lipid metabolism influence each other.

Chemokine Receptor 5, a Double-Edged Sword in Metabolic Syndrome and Cardiovascular Disease

The key characteristic of cardiovascular disease (CVD) is endothelial dysfunction, which is likely the consequence of inflammation. It is well demonstrated that chemokines and their receptors play a crucial role in regulating inflammatory responses, and recently, much attention has been paid to chemokine receptor 5 (CCR5) and its ligands. For example, CCR5 aggravates the inflammatory response in adipose tissue by regulating macrophage recruitment and M1/M2 phenotype switch, thus causing insulin resistance and obesity. Inhibition of CCR5 expression reduces the aggregation of pro-atherogenic cytokines to the site of arterial injury. However, targeting CCR5 is not always effective, and emerging evidence has shown that CCR5 facilitates progenitor cell recruitment and promotes vascular endothelial cell repair. In this paper, we provide recent insights into the role of CCR5 and its ligands in metabolic syndrome as related to cardiovascular disease and the opportunities and roadblocks in targeting CCR5 and its ligands.