Pharmacogenomics study of thiazide diuretics and QT interval in multi-ethnic populations: the cohorts for heart and aging research in genomic epidemiology

Thiazide diuretics, commonly used antihypertensives, may cause QT interval (QT) prolongation, a risk factor for highly fatal and difficult to predict ventricular arrhythmias. We examined whether common single-nucleotide polymorphisms (SNPs) modified the association between thiazide use and QT or its component parts (QRS interval, JT interval) by performing ancestry-specific, transethnic and cross-phenotype genome-wide analyses of European (66%), African American (15%) and Hispanic (19%) populations (N = 78 199), leveraging longitudinal data, incorporating corrected standard errors to account for underestimation of interaction estimate variances and evaluating evidence for pathway enrichment. Although no loci achieved genome-wide significance (P < 5 x 10(-8)), we found suggestive evidence (P < 5 x 10(-6)) for SNPs modifying the thiazide-QT association at 22 loci, including ion transport loci (for example, NELL1, KCNQ3). The biologic plausibility of our suggestive results and simulations demonstrating modest power to detect interaction effects at genome-wide significant levels indicate that larger studies and innovative statistical methods are warranted in future efforts evaluating thiazide-SNP interactions

Acetylation Pharmacogenetics and Renal Function in Diabetes Mellitus Patients

Activities of human hepatic drug metabolizing enzymes N-acetyl transferase (NATS) had earlier been recognized as a cause of inter-individual variation in the metabolism of drugs. Therefore acetylation of many drugs in human exhibit genetic polymorphism. The aim of the study was to investigate if acetylator status predispose diabetic mellitus patients more to the complications of renal disease, One hundred and twenty (120) diabetics consisting of (50) Type 1 (T1) and 70 Type 2 (T2) diabetes mellitus patients and 100 healthy individuals as controls were classified as slow or rapid acetylator using sulphamethazine (SMZ) as an in vivo probe. The percentage acetylation, recovery of SMZ, creatinine clearance and presence of urinary albumin were determined. A significant difference (P < 0.05) was observed in the percentage of SMZ acetylated between slow and rapid acetylators in control, T1 and T2 subjects. The ratios of slow to rapid acetylators for T1, T2 and control subjects were 1:4, 3:2 and 2:3 respectively. No significant differences were observed in the percentage of SMZ recovered in the urine of slow and rapid acetylators that are diabetics. The difference in creatinine clearance of slow and rapid acetylators in T1 and T2 were significant (P < 0.05). 29% out of 120 (24.2%) diabetics (T1 and T2) exhibited albuminuria out of which 25 (86.2%) had slow acetylator status. These findings suggest that slow acetylator status in diabetes mellitus could be a predisposing factor in the development of renal complications. This underscores the need for a rapid pharmacogenetic testing and therapeutic drug monitoring in such patients. However this inference could be further validated with a larger sample size

I Hear You Eat and Speak: Automatic Recognition of Eating Condition and Food Type, Use-Cases, and Impact on ASR Performance

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient