Prevalence and Predictors of Albuminuria in Type 2 Diabetes Mellitus Patients: A Cross-Sectional Study from the United Arab Emirates.

BACKGROUND AND AIM: Albuminuria in Diabetes mellitus (DM) patients may lead to nephropathy and end-stage renal disease. Our study aimed to assess the prevalence of albuminuria and its associated predictors among type 2 DM patients in the United Arab Emirates. METHODS: A retrospective cross-sectional study was conducted among type 2 DM patients in the diabetic clinic, Fujairah Hospital from 1st January 2016 to 30th January 2020 after getting the ethical clearance. Data were collected electronically from the health information system and analyzed using SPSS version 26. Regression analysis and ANOVA were used for inferential analysis. A P-value of ≤0.05 has been considered significant. RESULTS: Among the 200 patients included in the study, the mean age of the study population was 56 years and the majority of them were females (71%). The prevalence of albuminuria was found to be 44%. By using regression analysis, glycated hemoglobin (HbA1c; P=0.038) and systolic blood pressure (SBP; P=0.003) were found to be predictors of albuminuria. One way ANOVA revealed that there were significant associations between the albumin levels and HbA1c (P=0.004), SBP (P= 0.002), diastolic blood pressure (DBP; P=0.028), serum creatinine (Scr) (P=0.039), and glomerular filtration rate (GFR; P=0.013). CONCLUSION: To the best of our knowledge, this is the first study from Fujairah emirate that explored the prevalence and predictors of albuminuria in type 2 DM patients. We found a high prevalence of albuminuria among type 2 DM patients. HbA1c and SBP were directly contributing to albuminuria. To improve glycemic control, patients need to improve physical activity, reduce overweight and, adherence to medications that improve overall therapeutic outcome.

Influence of N-acetyltransferase 2 (NAT2) genotype/single nucleotide polymorphisms on clearance of isoniazid in tuberculosis patients: a systematic review of population pharmacokinetic models.

PURPOSE: Significant pharmacokinetic variabilities have been reported for isoniazid across various populations. We aimed to summarize population pharmacokinetic studies of isoniazid in tuberculosis (TB) patients with a specific focus on the influence of N-acetyltransferase 2 (NAT2) genotype/single-nucleotide polymorphism (SNP) on clearance of isoniazid. METHODS: A systematic search was conducted in PubMed and Embase for articles published in the English language from inception till February 2022 to identify population pharmacokinetic (PopPK) studies of isoniazid. Studies were included if patient population had TB and received isoniazid therapy, non-linear mixed effects modelling, and parametric approach was used for building isoniazid PopPK model and NAT2 genotype/SNP was tested as a covariate for model development. RESULTS: A total of 12 articles were identified from PubMed, Embase, and hand searching of articles. Isoniazid disposition was described using a two-compartment model with first-order absorption and linear elimination in most of the studies. Significant covariates influencing the pharmacokinetics of isoniazid were NAT2 genotype, body weight, lean body weight, body mass index, fat-free mass, efavirenz, formulation, CD4 cell count, and gender. Majority of studies conducted in adult TB population have reported a twofold or threefold increase in isoniazid clearance for NAT2 rapid acetylators compared to slow acetylators. CONCLUSION: The variability in disposition of isoniazid can be majorly attributed to NAT2 genotype. This results in a trimodal clearance pattern with a multi-fold increase in clearance of NAT2 rapid acetylators compared to slow acetylators. Further studies exploring the generalizability/adaptability of developed PopPK models in different clinical settings are required.

Pharmacogenomics: the right drug to the right person.

UNLABELLED: Pharmacogenomics is the branch of pharmacology which deals with the influence of genetic variation on drug response in patients by correlating gene expression or single-nucleotide polymorphisms with a drug's efficacy or toxicity. It aims to develop rational means to optimize drug therapy, with respect to the patients genotype, to ensure maximum efficacy with minimal adverse effects. Such approaches promise the advent of personalized medicine, in which drugs and drug combinations are optimized for each individual's unique genetic makeup. Pharmacogenomics is the whole genome application of pharmacogenetics, which examines the single gene interactions with drugs. KEYWORDS: Pharmacogenetics; Single nucleotide polymorphisms; Genomics; Genotype.