Metabolomic profiling of metoprolol hypertension treatment reveals altered gut microbiota-derived urinary metabolites.

INTRODUCTION: Metoprolol succinate is a long-acting beta-blocker prescribed for the management of hypertension (HTN) and other cardiovascular diseases. Metabolomics, the study of end-stage metabolites of upstream biologic processes, yield insight into mechanisms of drug effectiveness and safety. Our aim was to determine metabolomic profiles associated with metoprolol effectiveness for the treatment of hypertension. METHODS: We performed a prospective pragmatic trial (NCT02293096) that enrolled patients between 30 and 80 years with uncontrolled HTN. Patients were started on metoprolol succinate at a dose based upon systolic blood pressure (SBP). Urine and blood pressure measurements were collected weekly. Individuals with a 10% decline in SBP or heart rate (HR) were considered responsive. Genotype for the CYP2D6 enzyme, the primary metabolic pathway for metoprolol, was evaluated for each subject. Unbiased metabolomic analyses were performed on urine samples using UPLC-QTOF mass spectrometry. RESULTS: Urinary metoprolol metabolite ratios are indicative of patient CYP2D6 genotypes. Patients taking metoprolol had significantly higher urinary levels of many gut microbiota-dependent metabolites including hydroxyhippuric acid, hippuric acid, and methyluric acid. Urinary metoprolol metabolite profiles of normal metabolizer (NM) patients more closely correlate to ultra-rapid metabolizer (UM) patients than NM patients. Metabolites did not predict either 10% SBP or HR decline. CONCLUSION: In summary, urinary metabolites predict CYP2D6 genotype in hypertensive patients taking metoprolol. Metoprolol succinate therapy affects the microbiome-derived metabolites.

Predicting response to lisinopril in treating hypertension: a pilot study.

INTRODUCTION: Only ~ 50% of hypertensive patients will respond to treatment. OBJECTIVE: This pilot study aims to identify clinical and metabolite markers that predict response to lisinopril. METHODS: Hypertensive patients (n = 45) received lisinopril (10 mg) at their baseline visit. Blood pressures were reevaluated one week later. Responders to lisinopril (n = 19) were defined by a 10% decline in systolic blood pressure. Plasma metabolites were evaluated with mass spectrometry. RESULTS: BMI (p = 0.009), GFR (p = 0.015) and 2-oxoglutarate were included in a logistic regression model to predict response to lisinopril. CONCLUSIONS: Further validation cohorts are needed to confirm the predictive values of these clinical and metabolic markers.

Mismatch repair gene mutations in renal cell carcinoma.

We investigated the spectrum and genetic basis for mismatch repair (MMR) deficiency in renal cell carcinoma (RCC) by examining expression of four MMR genes important for hereditary and sporadic carcinogenesis. MMR deficiency was assessed using microsatellite instability (MSI) and genetic analyses of 25 cell lines derived from renal tumors. MMR gene alterations were detected using reverse transcription of RNA coupled with polymerase chain reaction (RT-PCR) and DNA sequencing. Three RCC lines with undetectable MLH1 were identified and investigated for MSI and inactivating mutations in the hMLH1 MMR gene. Genetic instability and hMLH1 mutations were identified in two RCC lines and their corresponding tumors. Genetic alterations affecting expression were limited to MLH1 since other MMR proteins (MSH2, MSH6 and PMS2) were detectable in our RCC lines. Complete inactivation of MMR is apparently uncommon in RCC and occurs predominantly through inactivating mutations in the hMLH1 gene.