Effects of AGXT2 variants on blood pressure and blood sugar among 750 older Japanese subjects recruited by the complete enumeration survey method

Functional AGXT2 SNP rs180749 variant and depressive symptoms: Baseline data from the Aidai Cohort Study in Japan

No study has shown the relationship between alanine-glyoxylate aminotransferase 2 (AGXT2) single nucleotide polymorphisms (SNPs) and depressive symptoms. The present case-control study examined this relationship in Japanese adults. Cases and control participants were selected from those who participated in the baseline survey of the Aidai Cohort Study, which is an ongoing cohort study. Cases comprised 280 participants with depressive symptoms based on a Center for Epidemiologic Studies Depression Scale (CES-D) score ≥ 16. Control participants comprised 2034 participants without depressive symptoms based on the CES-D who had not been diagnosed by a physician as having depression or who had not been currently taking medication for depression. Adjustment was made for age, sex, smoking status, alcohol consumption, leisure time physical activity, education, body mass index, hypertension, dyslipidemia, and diabetes mellitus. Compared with the GG genotype of rs180749, both the GA and AA genotypes were significantly positively associated with the risk of depressive symptoms assessed by the CES-D: the adjusted odds ratios for the GA and AA genotypes were 2.83 (95% confidence interval [CI] 1.23-8.24) and 3.10 (95% CI 1.37-8.92), respectively. The TGC haplotype of rs37370, rs180749, and rs16899974 was significantly inversely related to depressive symptoms (crude OR 0.67; 95% CI 0.49-0.90), whereas the TAC haplotype was significantly positively associated with depressive symptoms (crude OR 1.24; 95% CI 1.01-1.52). This is the first study to show significant associations between AGXT2 SNP rs180749, the TGC haplotype, and the TAC haplotype and depressive symptoms.

Novel model predicts diastolic cardiac dysfunction in type 2 diabetes

OBJECTIVE

Diabetes mellitus complicated with heart failure has high mortality and morbidity, but no reliable diagnoses and treatments are available. This study aimed to develop and verify a new model nomogram based on clinical parameters to predict diastolic cardiac dysfunction in patients with Type 2 diabetes mellitus (T2DM).

METHODS

3030 patients with T2DM underwent Doppler echocardiography at the First Affiliated Hospital of Shenzhen University between January 2014 and December 2021. The patients were divided into the training dataset (n = 1701) and the verification dataset (n = 1329). In this study, a predictive diastolic cardiac dysfunction nomogram is developed using multivariable logical regression analysis, which contains the candidates selected in a minor absolute shrinkage and selection operator regression model. Discrimination in the prediction model was assessed using the area under the receiver operating characteristic curve (AUC-ROC). The calibration curve was applied to evaluate the calibration of the alignment nomogram, and the clinical decision curve was used to determine the clinical practicability of the alignment map. The verification dataset was used to evaluate the prediction model's performance.

RESULTS

A multivariable model that included age, body mass index (BMI), triglyceride (TG), creatine phosphokinase isoenzyme (CK-MB), serum sodium (Na), and urinary albumin/creatinine ratio (UACR) was presented as the nomogram. We obtained the model for estimating diastolic cardiac dysfunction in patients with T2DM. The AUC-ROC of the training dataset in our model was 0.8307, with 95% CI of 0.8109-0.8505. Similar to the results obtained with the training dataset, the AUC-ROC of the verification dataset in our model was 0.8083, with 95% CI of 0.7843-0.8324, thus demonstrating robust. The function of the predictive model was as follows: Diastolic Dysfunction = -4.41303 + 0.14100*Age(year)+0.10491*BMI (kg/m2) +0.12902*TG (mmol/L) +0.03970*CK-MB (ng/mL) -0.03988*Na(mmol/L) +0.65395 * (UACR > 30 mg/g) + 1.10837 * (UACR > 300 mg/g). The calibration plot diagram of predicted probabilities against observed DCM rates indicated excellent concordance. Decision curve analysis demonstrated that the novel nomogram was clinically useful.

CONCLUSION

Diastolic cardiac dysfunction in patients with T2DM can be predicted by clinical parameters. Our prediction model may represent an effective tool for large-scale epidemiological study of diastolic cardiac dysfunction in T2DM patients and provide a reliable method for early screening of T2DM patients with cardiac complications.KEY MESSAGESThis study used clinical parameters to predict diastolic cardiac dysfunction in patients with T2DM. This study established a nomogram for predicting diastolic cardiac dysfunction by multivariate logical regression analysis. Our predictive model can be used as an effective tool for large-scale epidemiological study of diastolic cardiac dysfunction in patients with T2DM and provides a reliable method for early screening of cardiac complications in patients with T2DM.

An individualized Bayesian method for estimating genomic variants of hypertension

BACKGROUND

Genomic variants of the disease are often discovered nowadays through population-based genome-wide association studies (GWAS). Identifying genomic variations potentially underlying a phenotype, such as hypertension, in an individual is important for designing personalized treatment; however, population-level models, such as GWAS, may not capture all the important, individualized factors well. In addition, GWAS typically requires a large sample size to detect the association of low-frequency genomic variants with sufficient power. Here, we report an individualized Bayesian inference (IBI) algorithm for estimating the genomic variants that influence complex traits, such as hypertension, at the level of an individual (e.g., a patient). By modeling at the level of the individual, IBI seeks to find genomic variants observed in the individual's genome that provide a strong explanation of the phenotype observed in this individual.

RESULTS

We applied the IBI algorithm to the data from the Framingham Heart Study to explore the genomic influences of hypertension. Among the top-ranking variants identified by IBI and GWAS, there is a significant number of shared variants (intersection); the unique variants identified only by IBI tend to have relatively lower minor allele frequency than those identified by GWAS. In addition, IBI discovered more individualized and diverse variants that explain hypertension patients better than GWAS. Furthermore, IBI found several well-known low-frequency variants as well as genes related to blood pressure that GWAS missed in the same cohort. Finally, IBI identified top-ranked variants that predicted hypertension better than GWAS, according to the area under the ROC curve.

CONCLUSIONS

The results support IBI as a promising approach for complementing GWAS, especially in detecting low-frequency genomic variants as well as learning personalized genomic variants of clinical traits and disease, such as the complex trait of hypertension, to help advance precision medicine.

Functional AGXT2 SNP rs37369 Variant Is a Risk Factor for Diabetes Mellitus: Baseline Data From the Aidai Cohort Study in Japan

OBJECTIVES

The relationship between alanine-glyoxylate aminotransferase 2 (AGXT2) single-nucleotide polymorphisms (SNPs) and diabetes mellitus (DM) has not been investigated. Therefore, we performed a case-control study to examine this relationship.

METHODS

The study subjects included 2,390 Japanese men and women aged 34 to 88 years. In total, 190 cases were defined as having a fasting plasma glucose level ≥126 mg/dL, having a glycated hemoglobin ≥6.5% or currently using diabetic medication. The 2,200 remaining participants served as control subjects.

RESULTS

Compared with study subjects with the CC genotype of AGXT2 SNP rs37369, those with the TT, but not CT, genotype had a significantly increased risk of DM: the adjusted odds ratio (OR) for the TT genotype was 1.83 (95% confidence interval [CI], 1.04 to 3.47). AGXT2 SNPs rs37370 and rs180749 were not significantly associated with the risk of DM. The CTA haplotype of rs37370, rs37369 and rs180749 was significantly positively associated with the risk of DM (crude OR, 1.25; 95% CI, 1.01 to 1.56), whereas the CCA haplotype was significantly inversely related to DM (crude OR, 0.53; 95% CI, 0.27 to 0.95). The multiplicative interaction between AGXT2 SNP rs37369 and smoking status with regard to the risk of DM was not significant (p=0.32 for interaction).

CONCLUSIONS

This is the first study to show significant associations between AGXT2 SNP rs37369, the CTA haplotype, and the CCA haplotype and DM. No interaction with regard to the risk of DM was observed between rs37369 and smoking.

Beyond eNOS: Genetic influence in NO pathway affecting drug response

Nitric Oxide (NO) has important biological functions, and its production may be influenced by genetic polymorphisms. Since NO mediates the drug response, the same genetic polymorphism that alter NO levels may also impact drug therapy. The vast majority of studies in the literature that assess the genetic influence on NO-related drug response focus on NOS3 (which encodes endothelial nitric oxide synthase), however several other proteins are interconnected in the same pathway and may also impact NO availability and drug response. The aim of this study was to review the literature regarding genetic polymorphisms that influence NO in response to pharmacological agents located in genes other than NOS3. Articles were obtained from Pubmed and consisted of 17 manuscripts that assessed polymorphisms of the following targets: Arginases 1 and 2 (ARG1 and ARG2), dimethylarginine dimethylaminohydrolases 1 and 2 (DDAH1 and DDAH2), and vascular endothelial growth factor (VEGF). Here we analyze the main results of these articles, which show promising evidences that may suggest that the NO-driven pharmacological response is affected by more than the eNOS gene. The search for genetic markers may result in better understanding of the variability of drug response and turn pharmacotherapy involving NO safer and more effective.

Metabolomic alterations in the blood plasma of older adults with mild cognitive impairment and Alzheimer's disease (from the Nakayama Study)

Alzheimer's disease (AD) is a progressive disease, and the number of AD patients is increasing every year as the population ages. One of the pathophysiological mechanisms of AD is thought to be the effect of metabolomic abnormalities. There have been several studies of metabolomic abnormalities of AD, and new biomarkers are being investigated. Metabolomic studies have been attracting attention, and the aim of this study was to identify metabolomic biomarkers associated with AD and mild cognitive impairment (MCI). Of the 927 participants in the Nakayama Study conducted in Iyo City, Ehime Prefecture, 106 were selected for this study as Control (n = 40), MCI (n = 26), and AD (n = 40) groups, matched by age and sex. Metabolomic comparisons were made across the three groups. Then, correlations between metabolites and clinical symptoms were examined. The blood mRNA levels of the ornithine metabolic enzymes were also measured. Of the plasma metabolites, significant differences were found in ornithine, uracil, and lysine. Ornithine was significantly decreased in the AD group compared to the Control and MCI groups (Control vs. AD: 97.2 vs. 77.4; P = 0.01, MCI vs. AD: 92.5 vs. 77.4; P = 0.02). Uracil and lysine were also significantly decreased in the AD group compared to the Control group (uracil, Control vs. AD: 272 vs. 235; P = 0.04, lysine, Control vs. AD: 208 vs. 176; P = 0.03). In the total sample, the MMSE score was significantly correlated with lysine, ornithine, thymine, and uracil. The Barthel index score was significantly correlated with lysine. The instrumental activities of daily living (IADL) score were significantly correlated with lysine, betaine, creatine, and thymine. In the ornithine metabolism pathway, the spermine synthase mRNA level was significantly decreased in AD. Ornithine was decreased, and mRNA expressions related to its metabolism were changed in the AD group compared to the Control and MCI groups, suggesting an association between abnormal ornithine metabolism and AD. Increased betaine and decreased methionine may also have the potential to serve as markers of higher IADL in elderly persons. Plasma metabolites may be useful for predicting the progression of AD.

Association of Variability in the DDAH1, DDAH2, AGXT2 and PRMT1 Genes with Circulating ADMA Concentration in Human Whole Blood

Asymmetric dimethylarginine is an endogenous inhibitor of nitric oxide synthesis and a cardiovascular risk factor. Its regulation has been studied extensively in experimental models, but less in humans. We studied common single-nucleotide polymorphisms (SNPs) in genes encoding for enzymes involved in ADMA biosynthesis and metabolism, i.e., PRMT1, DDAH1, DDAH2, and AGXT2, and assessed their associations with blood ADMA concentration in 377 unselected humans. The minor allele of DDAH1 SNP rs233112 was significantly more frequent in individuals with ADMA in the highest tertile or in the highest quartile, as was the major allele of DDAH2 rs805304. A combined genotype comprising both SNPs showed a significant genotype–phenotype association, with increasing ADMA concentration by an increasing number of inactive alleles. SNPs in the AGXT2 and PRMT1 genes showed no significant associations with blood ADMA concentration. Our study provides comprehensive evidence that DDAH1 and DDAH2 are the major enzymes regulating blood ADMA concentration, whilst PRMT1 indirectly affects ADMA, and AGXT2 may act as a back-up enzyme in ADMA metabolism under pathophysiological conditions only.