Insulin resistance in Alzheimer's disease: The genetics and metabolomics links.

Alzheimer's disease (AD) is a neurodegenerative disease with significant socioeconomic burden worldwide. Although genetics and environmental factors play a role, AD is highly associated with insulin resistance (IR) disorders such as metabolic syndrome (MS), obesity, and type two diabetes mellitus (T2DM). These findings highlight a shared pathogenesis. The use of metabolomics as a downstream systems' biology (omics) approach can help to identify these shared metabolic traits and assist in the early identification of at-risk groups and potentially guide therapy. Targeting the shared AD-IR metabolic trait with lifestyle interventions and pharmacological treatments may offer promising AD therapeutic approach. In this narrative review, we reviewed the literature on the AD-IR pathogenic link, the shared genetics and metabolomics biomarkers between AD and IR disorders, as well as the lifestyle interventions and pharmacological treatments which target this pathogenic link.

Metabolomics applications in coronary artery disease personalized medicine.

Coronary artery disease (CAD), the most common cardiovascular disease (CVD), contributes to significant mortality worldwide. CAD is a multifactorial disease wherein various factors contribute to its pathogenesis often complicating management. Biomarker based personalized medicine may provide a more effective way to individualize therapy in multifactorial diseases in general and CAD specifically. Systems' biology "Omics" biomarkers have been investigated for this purpose. These biomarkers provide a more comprehensive understanding on pathophysiology of the disease process and can help in identifying new therapeutic targets and tailoring therapy to achieve optimum outcome. Metabolomics biomarkers usually reflect genetic and non-genetic factors involved in the phenotype. Metabolomics analysis may provide better understanding of the disease pathogenesis and drug response variation. This will help in guiding therapy, particularly for multifactorial diseases such as CAD. In this chapter, advances in metabolomics analysis and its role in personalized medicine will be reviewed with comprehensive focus on CAD. Assessment of risk, diagnosis, complications, drug response and nutritional therapy will be discussed. Together, this chapter will review the current application of metabolomics in CAD management and highlight areas that warrant further investigation.

Metabolomics profiling and pathway analysis of human plasma and urine reveal further insights into the multifactorial nature of coronary artery disease.

BACKGROUND: Coronary artery disease (CAD) claims lives yearly. Nuclear magnetic resonance (1H NMR) metabolomics analysis is efficient in identifying metabolic biomarkers which lend credence to diagnosis. We aimed to identify CAD metabotypes and its implicated pathways using 1H NMR analysis. METHODS: We analysed plasma and urine samples of 50 stable CAD patients and 50 healthy controls using 1H NMR. Orthogonal partial least square discriminant analysis (OPLS-DA) followed by multivariate logistic regression (MVLR) models were developed to indicate the discriminating metabotypes. Metabolic pathway analysis was performed to identify the implicated pathways. RESULTS: Both plasma and urine OPLS-DA models had specificity, sensitivity and accuracy of 100%, 96% and 98%, respectively. Plasma MVLR model had specificity, sensitivity, accuracy and AUROC of 92%, 86%, 89% and 0.96, respectively. The MVLR model of urine had specificity, sensitivity, accuracy and AUROC of 90%, 80%, 85% and 0.92, respectively. 35 and 12 metabolites were identified in plasma and urine metabotypes, respectively. Metabolic pathway analysis revealed that urea cycle, aminoacyl-tRNA biosynthesis and synthesis and degradation of ketone bodies pathways were significantly disturbed in plasma, while methylhistidine metabolism and galactose metabolism pathways were significantly disturbed in urine. The enrichment over representation analysis against SNPs-associated-metabolite sets library revealed that 85 SNPs were significantly enriched in plasma metabotype. CONCLUSIONS: Cardiometabolic diseases, dysbiotic gut-microbiota and genetic variabilities are largely implicated in the pathogenesis of CAD.

Pharmacometabolomics analysis of plasma to phenotype clopidogrel high on treatment platelets reactivity in coronary artery disease patients.

Dual antiplatelet therapy (DAPT) of clopidogrel and aspirin is crucial for coronary artery disease (CAD) patients undergoing percutaneous coronary intervention (PCI). However, some patients may endure clopidogrel high on treatment platelets reactivity (HTPR) which may cause thromboembolic events. Clopidogrel HTPR is multifactorial with some genetic and non-genetic factors contributing to it. We aimed to use nuclear magnetic resonance (1H NMR) pharmacometabolomics analysis of plasma to investigate this multifactorial and identify metabolic phenotypes and pathways associated with clopidogrel HTPR. Blood samples were collected from 71 CAD patients planned for interventional angiographic procedure (IAP) before the administration of clopidogrel 600 mg loading dose (LD) and 6 h after the LD. Platelets function testing was done 6 h post-LD using VerifyNow® P2Y12 assay. Pre-dose and post-dose plasma samples were analysed using 1H NMR. Multivariate statistical analysis was used to indicate the discriminating metabolites. Two metabotypes, each with 34 metabolites (pre-dose and post-dose) were associated with clopidogrel HTPR. Pathway analysis of these metabotypes revealed that aminoacyl-tRNA biosynthesis, nitrogen metabolism and glycine-serine-threonine metabolism are the most perturbed metabolic pathways associated with clopidogrel HTPR. Furthermore, the identified biomarkers indicated that clopidogrel HTPR is multifactorial where the metabolic phenotypes of insulin resistance, type two diabetes mellitus, obesity, gut-microbiota and heart failure are associated with it. Pharmacometabolomics analysis of plasma revealed new insights on the implicated metabolic pathways and the predisposing factors of clopidogrel HTPR.

1H NMR based pharmacometabolomics analysis of urine identifies metabolic phenotype of clopidogrel high on treatment platelets reactivity in coronary artery disease patients.

Clopidogrel high on treatment platelets reactivity (HTPR) has burdened achieving optimum therapeutic outcome. Although there are known genetic and non-genetic factors associated with clopidogrel HTPR, which explain in part clopidogrel HTPR, yet, great portion remains unknown, often hindering personalizing antiplatelet therapy. Nuclear magnetic resonance (1H NMR) pharmacometabolomics analysis is useful technique to phenotype drug response. We investigated using 1H NMR analysis to phenotype clopidogrel HTPR in urine. Urine samples were collected from 71 coronary artery disease (CAD) patients who were planned for interventional angiographic procedure prior to taking 600mg clopidogrel loading dose (LD) and 6h post LD. Patients' platelets function testing was assessed with the VerifyNow® P2Y12 assay at 6h after LD. Urine samples were analysed using 1H NMR. Multivariate statistical analysis was used to identify metabolites associated with clopidogrel HTPR. In pre-dose samples, 16 metabolites were associated with clopidogrel HTPR. However, 18 metabolites were associated with clopidogrel HTPR in post-dose samples. The pathway analysis of the identified biomarkers reflected that multifactorial conditions are associated with clopidogrel HTPR. It also revealed the implicated role of gut microbiota in clopidogrel HTPR. Pharmacometabolomics not only discovered novel biomarkers of clopidogrel HTPR but also revealed implicated pathways and conditions.

Plasma metabolic biomarkers for discriminating individuals with alcohol use disorders from social drinkers and alcohol-naive subjects.

BACKGROUND: Alcohol use disorders (AUD) is a phase of alcohol misuse in which the drinker consumes excessive amount of alcohol and have a continuous urge to consume alcohol which may lead to various health complications. The current methods of alcohol use disorders diagnosis such as questionnaires and some biomarkers lack specificity and sensitivity. Metabolomics is a novel scientific field which may provide a novel method for the diagnosis of AUD by using a sensitive and specific technique such as nuclear magnetic resonance (NMR). METHODS: A cross sectional study was conducted on three groups: individuals with alcohol use disorders (n=30), social drinkers (n=54) and alcohol-naive controls (n=60). 1H NMR-based metabolomics was used to obtain the metabolic profiles of plasma samples. Data were processed by multivariate principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) followed by univariate and multivariate logistic regressions to produce the best fit-model for discrimination between groups. RESULTS: The OPLS-DA model was able to distinguish between the AUD group and the other groups with high sensitivity, specificity and accuracy of 64.29%, 98.17% and 91.24% respectively. The logistic regression model identified two biomarkers in plasma (propionic acid and acetic acid) as being significantly associated with alcohol use disorders. The reproducibility of all biomarkers was excellent (0.81-1.0). CONCLUSIONS: The applied plasma metabolomics technique was able to differentiate the metabolites between AUD and the other groups. These metabolites are potential novel biomarkers for diagnosis of alcohol use disorders.

The Personalization of Clopidogrel Antiplatelet Therapy: The Role of Integrative Pharmacogenetics and Pharmacometabolomics.

Dual antiplatelet therapy of aspirin and clopidogrel is pivotal for patients undergoing percutaneous coronary intervention. However, the variable platelets reactivity response to clopidogrel may lead to outcome failure and recurrence of cardiovascular events. Although many genetic and nongenetic factors are known, great portion of clopidogrel variable platelets reactivity remain unexplained which challenges the personalization of clopidogrel therapy. Current methods for clopidogrel personalization include CYP2C19 genotyping, pharmacokinetics, and platelets function testing. However, these methods lack precise prediction of clopidogrel outcome, often leading to insufficient prediction. Pharmacometabolomics which is an approach to identify novel biomarkers of drug response or toxicity in biofluids has been investigated to predict drug response. The advantage of pharmacometabolomics is that it does not only predict the response but also provide extensive information on the metabolic pathways implicated with the response. Integrating pharmacogenetics with pharmacometabolomics can give insight on unknown genetic and nongenetic factors associated with the response. This review aimed to review the literature on factors associated with the variable platelets reactivity response to clopidogrel, as well as appraising current methods for the personalization of clopidogrel therapy. We also aimed to review the literature on using pharmacometabolomics approach to predict drug response, as well as discussing the plausibility of using it to predict clopidogrel outcome.

Metabolic phenotyping of urine for discriminating alcohol-dependent from social drinkers and alcohol-naive subjects.

BACKGROUND: Alcohol-dependence (AD) is a ravaging public health and social problem. AD diagnosis depends on questionnaires and some biomarkers, which lack specificity and sensitivity, however, often leading to less precise diagnosis, as well as delaying treatment. This represents a great burden, not only on AD individuals but also on their families. Metabolomics using nuclear magnetic resonance spectroscopy (NMR) can provide novel techniques for the identification of novel biomarkers of AD. These putative biomarkers can facilitate early diagnosis of AD. OBJECTIVES: To identify novel biomarkers able to discriminate between alcohol-dependent, non-AD alcohol drinkers and controls using metabolomics. METHOD: Urine samples were collected from 30 alcohol-dependent persons who did not yet start AD treatment, 54 social drinkers and 60 controls, who were then analysed using NMR. Data analysis was done using multivariate analysis including principal component analysis (PCA) and orthogonal partial least square-discriminate analysis (OPLS-DA), followed by univariate and multivariate logistic regression to develop the discriminatory model. The reproducibility was done using intraclass correlation coefficient (ICC). RESULTS: The OPLS-DA revealed significant discrimination between AD and other groups with sensitivity 86.21%, specificity 97.25% and accuracy 94.93%. Six biomarkers were significantly associated with AD in the multivariate logistic regression model. These biomarkers were cis-aconitic acid, citric acid, alanine, lactic acid, 1,2-propanediol and 2-hydroxyisovaleric acid. The reproducibility of all biomarkers was excellent (0.81-1.0). CONCLUSION: This study revealed that metabolomics analysis of urine using NMR identified AD novel biomarkers which can discriminate AD from social drinkers and controls with high accuracy.

The effectiveness of the McIsaac clinical decision rule in the management of sore throat: an evaluation from a pediatrics ward.

BACKGROUND: Sore throats may be due to either viral or group A beta hemolytic streptococcus (GABHS) infections; but diagnosis of the etiology of a sore throat is difficult, often leading to unnecessary antibiotic prescriptions and consequent increases in bacterial resistance. Scoring symptoms using the McIsaac clinical decision rule can help physicians to diagnose and manage streptococcal infections leading to sore throat and have been recommended by the Ministry of Health, Malaysia. In this paper, we offer the first assessment of the effectiveness of the McIsaac rule in a clinical setting in Malaysia. METHOD: This study is a retrospective review of 116 pediatric patients presenting with sore throat. Group A comprised patients before the implementation of the McIsaac rule and Group B comprised patients after the implementation. RESULTS: Unnecessary throat swab cultures were reduced by 40% (P = 0.003). Redundant antibiotic prescriptions were reduced by 26.5% (P = 0.003) and the overall use of antibiotics was reduced by 22.1% (P = 0.003). The pediatricians' compliance rate to McIsaac rule criteria was 45% before implementation of the McIsaac rule, but improved to 67.9% (P = 0.0005) after implementation. DISCUSSION: The McIsaac rule is an effective tool for the management of sore throat in children in Malaysia.