Association of ALOX12 gene polymorphism with all-cause and cardiovascular mortality in diabetic nephropathy

Elevated ALOX12 in renal tissue predicts progression in diabetic kidney disease

Diabetic kidney disease (DKD) is one of the major causes of end-stage renal disease and one of the significant complications of diabetes. This study aims to identify the main differentially expressed genes in DKD from transcriptome sequencing results and analyze their diagnostic value. The present study sequenced db/m mouse and db/db mouse to determine the ALOX12 genetic changes related to DKD. After preliminary validation, ALOX12 levels were significantly elevated in the blood of DKD patients, but not during disease progression. Moreover, urine ALOX12 was increased only in macroalbuminuria patients. Therefore, to visualize the diagnostic efficacy of ALOX12 on the onset and progression of renal injury in DKD, we collected kidney tissue from patients for immunohistochemical staining. ALOX12 was increased in the kidneys of patients with DKD and was more elevated in macroalbuminuria patients. Clinical chemical and pathological data analysis indicated a correlation between ALOX12 protein expression and renal tubule injury. Further immunofluorescence double staining showed that ALOX12 was expressed in both proximal tubules and distal tubules. Finally, the diagnostic value of the identified gene in the progression of DKD was assessed using receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC) value for ALOX12 in the diagnosis of DKD entering the macroalbuminuria stage was 0.736, suggesting that ALOX12 has good diagnostic efficacy. During the development of DKD, the expression levels of ALOX12 in renal tubules were significantly increased and can be used as one of the predictors of the progression to macroalbuminuria in patients with DKD.

Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis

BACKGROUND

Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with Type 2 diabetes (T2D).

METHODS

We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies.

RESULTS

Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort.

CONCLUSIONS

Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Precision Prognostics for Cardiovascular Disease in Type 2 Diabetes: A Systematic Review and Meta-analysis

Background Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with type 2 diabetes (T2D). Methods We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies. Results Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort. Conclusions Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Construction of a Prediction Model for the Mortality of Elderly Patients with Diabetic Nephropathy

To construct a prediction model for all-cause mortality in elderly diabetic nephropathy (DN) patients, in this cohort study, the data of 511 DN patients aged ≥65 years were collected and the participants were divided into the training set (n = 358) and the testing set (n = 153). The median survival time of all participants was 2 years. The data in the training set were grouped into the survival group (n = 203) or the death group (n = 155). Variables with P ≤ 0.1 between the two groups were selected as preliminary predictors and involved into the multivariable logistic regression model and the covariables were gradually adjusted. The receiver operator characteristic (ROC), Kolmogorov-Smirnov (KS), and calibration curves were plotted for evaluating the predictive performance of the model. Internal validation of the performance of the model was verified in the testing set. The predictive values of the model were also conducted in terms of people with different genders and ages or accompanied with chronic kidney disease (CKD) or cardiovascular diseases (CVD), respectively. In total, 216 (42.27%) elderly DN patients were dead within 2 years. The prediction model for the 2-year mortality of elderly patients with DN was established based on length of stay (LOS), temperature, heart rate, peripheral oxygen saturation (SpO₂), serum creatinine (Scr), red cell distribution width (RDW), the simplified acute physiology score-II (SAPS-II), hyperlipidemia, and the Chronic Kidney Disease Epidemiology Collaboration equation for estimated glomerular filtration rate (eGFR-CKD-EPI). The AUC of the model was 0.78 (95% CI: 0.73–0.83) in the training set and 0.72 (95% CI: 0.63–0.80) in the testing set. The AUC of the model was 0.78 (95% CI: 0.65–0.91) in females and 0.78 (95%CI: 0.68–0.88) in patients ≤75 years. The AUC of the model was 0.74 (95% CI: 0.64–0.84) in patients accompanied with CKD. The model had good predictive value for the mortality of elderly patients with DN within 2 years. In addition, the model showed good predictive values for female DN patients, DN patients ≤75 years, and DN patients accompanied with CKD.

Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

Type 1 diabetes mellitus (T1DM) is a metabolic disorder for which the underlying molecular mechanisms remain largely unclear. This investigation aimed to elucidate essential candidate genes and pathways in T1DM by integrated bioinformatics analysis. In this study, differentially expressed genes (DEGs) were analyzed using DESeq2 of R package from GSE162689 of the Gene Expression Omnibus (GEO). Gene ontology (GO) enrichment analysis, REACTOME pathway enrichment analysis, and construction and analysis of protein–protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network, and validation of hub genes were performed. A total of 952 DEGs (477 up regulated and 475 down regulated genes) were identified in T1DM. GO and REACTOME enrichment result results showed that DEGs mainly enriched in multicellular organism development, detection of stimulus, diseases of signal transduction by growth factor receptors and second messengers, and olfactory signaling pathway. The top hub genes such as MYC, EGFR, LNX1, YBX1, HSP90AA1, ESR1, FN1, TK1, ANLN and SMAD9 were screened out as the critical genes among the DEGs from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Receiver operating characteristic curve (ROC) analysis confirmed that these genes were significantly associated with T1DM. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the advancement and progression of T1DM, and certain genes might be used as candidate target molecules to diagnose, monitor and treat T1DM.

Analgesics Induce Alterations in the Expression of SARS-CoV-2 Entry and Arachidonic-Acid-Metabolizing Genes in the Mouse Lungs

Paracetamol and nonsteroidal anti-inflammatory drugs are widely used in the management of respiratory viral infections. This study aimed to determine the effects of the most commonly used analgesics (paracetamol, ibuprofen, and diclofenac) on the mRNA expression of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) entry and arachidonic-acid-metabolizing genes in mouse lungs. A total of twenty eight Balb/c mice were divided into four groups and treated separately with vehicle, paracetamol, ibuprofen, and diclofenac in clinically equivalent doses for 14 days. Then, the expressions of SARS-CoV-2 entry, ACE2, TMPRSS2, and Ctsl genes, in addition to the arachidonic-acid-metabolizing cyp450, cox, and alox genes, were analyzed using real-time PCR. Paracetamol increased the expressions of TMPRSS2 and Ctsl genes by 8.5 and 5.6 folds, respectively, while ibuprofen and diclofenac significantly decreased the expression of the ACE2 gene by more than 2.5 folds. In addition, all tested drugs downregulated (p < 0.05) cox2 gene expression, and paracetamol reduced the mRNA levels of cyp4a12 and 2j5. These molecular alterations in diclofenac and ibuprofen were associated with pathohistological alterations, where both analgesics induced the infiltration of inflammatory cells and airway wall thickening. It is concluded that analgesics such as paracetamol, ibuprofen, and diclofenac alter the expression of SARS-CoV-2 entry and arachidonic-acid-metabolizing genes in mouse lungs.

Novel Cardiovascular Risk Factors in Patients with Diabetic Kidney Disease

Patients with diabetic kidney disease (DKD) are at very high risk for cardiovascular events. Only part of this increased risk can be attributed to the presence of diabetes mellitus (DM) and to other DM-related comorbidities, including hypertension and obesity. The identification of novel risk factors that underpin the association between DKD and cardiovascular disease (CVD) is essential for risk stratification, for individualization of treatment and for identification of novel treatment targets.In the present review, we summarize the current knowledge regarding the role of emerging cardiovascular risk markers in patients with DKD. Among these biomarkers, fibroblast growth factor-23 and copeptin were studied more extensively and consistently predicted cardiovascular events in this population. Therefore, it might be useful to incorporate them in risk stratification strategies in patients with DKD to identify those who would possibly benefit from more aggressive management of cardiovascular risk factors.

Oxidative Stress Genes in Diabetes Mellitus Type 2: Association with Diabetic Kidney Disease

Diabetic type 2 patients compared to nondiabetic patients exhibit an increased risk of developing diabetic kidney disease (DKD), the leading cause of end-stage renal disease. Hyperglycemia, hypertension, oxidative stress (OS), and genetic background are some of the mechanisms and pathways implicated in DKD pathogenesis. However, data on OS pathway susceptibility genes show limited success and conflicting or inconclusive results. Our study is aimed at exploring OS pathway genes and variants which could be associated with DKD. We recruited 121 diabetes mellitus type 2 (DM2) patients with DKD (cases) and 220 DM2, non-DKD patients (control) of Greek origin and performed a case-control association study using genome-wide association data. PLINK and EIGENSOFT were used to analyze the data. Our results indicate 43 single nucleotide polymorphisms with their 21 corresponding genes on the OS pathway possibly contributing or protecting from DKD: SPP1, TPO, TTN, SGO2, NOS3, PDLIM1, CLU, CCS, GPX4, TXNRD2, EPHX2, MTL5, EPX, GPX3, ALOX12, IPCEF1, GSTA, OXR1, GPX6, AOX1, and PRNP. Therefore, a genetic OS background might underlie the complex pathogenesis of DKD in DM2 patients.

A Patent Review on the Current Developments of Benzoxazoles in Drug Discovery

The benzoxazole moiety is widely found in various natural compounds, which are often found to be biologically active. Due to its versatile biological properties, benzoxazole has been incorporated as an essential pharmacophore and substructure in many medicinal compounds. In the past years, numerous benzoxazole derivatives have been synthesised and evaluated for their biological potential. The wide range in therapeutic potential of benzoxazole derivatives is related to the favourable interactions of the benzoxazole moiety with different protein targets. Herein we review the biological activities of benzoxazole derivatives patented within the past six years. Using the Lens database, granted patents issued from 2015 to 2020 were retrieved. The patented benzoxazole derivatives demonstrated excellent activity against various protein targets and diseases, with some reaching clinical trial stage. Pharmacological and medicinal aspects of patented benzoxazole derivatives are discussed. The recent development and drawbacks are also reviewed.

Association of rs11780592 Polymorphism in the Human Soluble Epoxide Hydrolase Gene (EPHX2) with Oxidized LDL and Mortality in Patients with Diabetic Chronic Kidney Disease

Soluble epoxide hydrolase 2 (EPHX2) is an enzyme promoting increased cellular apoptosis through induction of oxidative stress (OS) and inflammation. The EPHX2 gene which encodes soluble EPHX2 might be implicated in the pathogenesis and development of OS and atherosclerosis. We aimed to assess the possible association between two functional polymorphisms of the EPHX2 gene (rs2741335 and rs11780592) with oxidized LDL (ox-LDL), carotid atherosclerosis, mortality, and cardiovascular (CV) disease in 118 patients with diabetic chronic kidney disease (CKD). At baseline, ox-LDL and carotid intima-media thickness (cIMT) were evaluated and all patients were followed for seven years with outcomes all-cause mortality and CV events. rs11780592 EPHX2 polymorphism was associated with ox-LDL, cIMT, albuminuria, and hypertension. Compared to AG and GG, AA homozygotes had higher values of albuminuria, ox-LDL, and cIMT (p = 0.046, p = 0.003, and p = 0.038, respectively). These associations remained significant, even after grouping for the G allele. After the follow-up period, 42/118 patients died (30/60 with AA genotype, 11/42 with AG genotype, and 1/12 with GG genotype) and 49/118 experienced a new CV event (fatal or nonfatal). The Kaplan-Meier analysis revealed that patients with the AA genotype exhibited a significantly higher mortality risk, compared to patients with AG and GG genotypes (p = 0.006). This association became even stronger, when AG and GG genotypes were grouped (AA vs. AG/GG, p = 0.002). AA homozygotes were strongly associated with all-cause mortality in both univariate (hazard ratio (HR) = 2.74, confidence interval (CI) = 1.40-5.35, p = 0.003) and multivariate Cox regression analysis (HR = 2.61, CI = 1.32-5.17, p = 0.006). In conclusion, our study demonstrated that genetic variations of EPHX2 gene were associated with increased circulating ox-LDL, increased cIMT, and all-cause mortality in diabetic CKD. Since EPHX2 regulates the cholesterol efflux and the oxidation of LDL in foam cells and macrophages, our study suggests that a genetic basis to endothelial dysfunction and OS might be present in diabetic CKD.

Distinctive Under-Expression Profile of Inflammatory and Redox Genes in the Blood of Elderly Patients with Cardiovascular Disease

Purpose

Chronic low-grade inflammation and oxidative stress are present in most of the pathologic mechanisms underlying non-communicable diseases. Inflammation and redox biomarkers might therefore have a value in disease prognosis and therapy response. In this context, we performed a case–control study for assessing in whole blood the expression profile of inflammation and redox-related genes in elderly subjects with various comorbidities.

Patients and Methods

In the blood of 130 elderly subjects with various pathologies (cardiovascular disease, hypertension, dyslipidemia including hypercholesterolemia, type 2 diabetes mellitus), kept under control by polyvalent disease-specific medication, we investigated by pathway-focused qRT-PCR a panel comprising 84 inflammation-related and 84 redox-related genes.

Results

The study highlights a distinctive expression profile of genes critically involved in NF-κB-mediated inflammation and redox signaling in the blood of patients with cardiovascular disease, characterized by significant down-regulation of the genes NFKB2, NFKBIA, RELA, RELB, AKT1, IRF1, STAT1, CD40, LTA, TRAF2, PTGS1, ALOX12, DUOX1, DUOX2, MPO, GSR, TXNRD2, HSPA1A, MSRA, and PDLIM1. This gene expression profile defines the transcriptional status of blood leukocytes in stable disease under medication control, without discriminating between disease- and therapy-related changes.

Conclusion

The study brings preliminary proof on a minimally invasive strategy for monitoring disease in patients with cardiovascular pathology, from the point of view of inflammation or redox dysregulation in whole blood.

Genetic variation in CARD8, a gene coding for an NLRP3 inflammasome-associated protein, alters the genetic risk for diabetic nephropathy in the context of type 2 diabetes mellitus

BACKGROUND

Approximately one third of type 2 diabetes mellitus (T2DM) cases present with diabetic nephropathy (DN), the leading cause of end-stage renal disease. Inflammation plays an important role in T2DM disease and DN pathogenesis. NLRP3 inflammasomes are complexes that regulate interleukin-1B (IL-1B) and IL-18 secretion, both involved in inflammatory responses. Activation of NLRP3 is associated with DN onset and progression. Here, we explore whether DN is associated with variants in genes encoding key members of the NLRP3 inflammasome pathway.

METHODS

Using genome-wide association data, we performed a pilot case-control association study, between 101 DN-T2DM and 185 non-DN-T2DM cases from the Hellenic population across six NLRP3 inflammasome pathway genes.

RESULTS

Three common CARD8 variants confer decreased risk for DN, namely rs11665831 (OR = 0.62, p = 0.016), rs11083925 (OR = 0.65, p = 0.021), and rs2043211 (OR = 0.66, p = 0.026), independent of sex or co-inheritance with an IL-1B variant.

CONCLUSION

CARD8 acts as an NLRP3, NF-κB and caspase-1 inhibitor; perhaps, alterations in the cross-talk between CARD8, NF-κB, and NLRP3, which could affect the pro-inflammatory environment in T2DM, render diabetic carriers of certain common CARD8 variants potentially less likely to develop T2DM-related pro-inflammatory responses followed by DN. These preliminary, yet novel, observations will require validation in larger cohorts from several ethnic groups.

Carotid intima-media thickness is an independent predictor of all-cause mortality and cardiovascular morbidity in patients with diabetes mellitus type 2 and chronic kidney disease

BACKGROUND

Intima-Media-Thickness of the carotid artery wall (cIMT) is a strong predictor of cardiovascular (CV) disease. The aim of this study was to investigate the significance of cIMT as an independent prognostic factor for CV morbidity and mortality in patients with chronic kidney disease (CKD) and diabetes mellitus type 2 (DM2).

METHODS

The study included 142 diabetic patients in different stages of CKD. Patients were categorized into two groups according to low (≤0.86 mm) or high cIMT (>0.86 mm), respectively. CV events and death from all causes were registered during a seven-year follow-up.

RESULTS

Mean age, BMI and duration of diabetes were 68 years (range: 45-90), >30 kg/m² and 15 years (range: 5-40), respectively. Patients with increased cIMT were older, suffered from a lower estimated glomerular filtration rate (eGFR), peripheral atherosclerosis and plaque presence in either carotid artery. Increased BMI (beta= -0.29, p = .01), lower eGFR (beta = 0.353, p = .003) and male gender (beta= -0.339, p = .005) were found to predict increased cIMT. Predictors of all-cause mortality in Cox proportional hazard models were low eGFR and high cIMT with HR = 0.96 (CI = 0.94-0.98), p < .001 and HR = 2.9 (CI = 1.03-7.99), p = .04, respectively. The risk of future CV event was determined by albuminuria and cIMT with HR = 1 (CI = 1.0-1.0), p < .001 and HR = 2.04 (CI = 1.1-3.78), p = .02, respectively. Patients with high cIMT presented significantly higher all-cause mortality and a new CV event (p = .005/p = .018, respectively).

CONCLUSIONS

cIMT is a strong and independent predictor of CV morbidity and mortality, and should be considered a valuable tool for the stratification of CV risk in patients with CKD and DM2.

Longitudinal analysis of epigenome-wide DNA methylation reveals novel smoking-related loci in African Americans

Changes in DNA methylation may be a potential mechanism that mediates the effects of smoking on physiological function and subsequent disease risk. Given the dynamic nature of the epigenome, longitudinal studies are indispensable for investigating smoking-induced methylation changes over time. Using blood samples collected approximately five years apart in 380 African Americans (mean age 60.7 years) from the Genetic Epidemiology Network of Arteriopathy (GENOA) study, we measured DNA methylation levels using Illumina HumanMethylation BeadChips. We evaluated the association between Phase 1 smoking status and rate of methylation change, using generalized estimating equation models. Among the 6958 CpG sites examined, smoking status was associated with methylation change for 22 CpGs (false discovery rate q < 0.1), with the majority (91%) becoming less methylated over time. Methylation change was greater in ever smokers than never smokers, and the absolute differences in rates of change ranged from 0.18 to 0.77 per decade in M value, equivalent to a β value change of 0.013 to 0.047 per decade. Significant enrichment was observed for CpG islands, enhancers, and DNAse hypersensitivity sites (p < 0.05). Although biological pathway analyses were not significant, most of the 22 CpGs were within genes known to be associated with cardiovascular disease, cancers, and aging. In conclusion, we identified epigenetic signatures for cigarette smoking that may have been missed in cross-sectional analyses, providing insight into the epigenetic effect of smoking and highlighting the importance of longitudinal analysis in understanding the dynamic human epigenome.