Benefit of treatment based on indapamide mostly combined with perindopril on mortality and cardiovascular outcomes: a pooled analysis of four trials

Machine Learning Insights Into Uric Acid Elevation With Thiazide Therapy Commencement and Intensification

Background Elevated serum uric acid, associated with cardiovascular conditions such as atherosclerotic heart disease, hypertension, and heart failure, can be elevated by thiazide or thiazide-like drugs (THZ), essential in hypertension management. Identifying clinical determinants affecting THZ-related uric acid elevation is critical. Methods In this retrospective cross-sectional study, we explored the clinical determinants influencing uric acid elevation related to THZ, focusing on patients where THZ was initiated or the dose escalated. A cohort of 143 patients was analyzed, collecting baseline and control uric acid levels, alongside basic biochemical studies and clinical data. Feature selection was conducted utilizing criteria based on mean squared error increase and enhancement in node purity. Four machine learning algorithms - Random Forest, Neural Network, Support Vector Machine, and Gradient Boosting regressions - were applied to pinpoint clinical influencers. Results Significant features include uncontrolled diabetes, index estimated Glomerular Filtration Rate (eGFR) level, absence of insulin, action of indapamide, and absence of statin treatment, with absence of Sodium-glucose cotransporter 2 inhibitors (SGLT2i), low dose aspirin exposure, and older age also being noteworthy. Among the applied models, the Gradient Boosting regression model outperformed the others, exhibiting the lowest Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) values, and the highest R2 value (0.779). While Random Forest and Neural Network regression models were able to fit the data adequately, the Support Vector Machine demonstrated inferior metrics. Conclusions Machine learning algorithms are adept at accurately identifying the factors linked to uric acid fluctuations caused by THZ. This proficiency aids in customizing treatments more effectively, reducing the need to unnecessarily avoid THZ, and providing guidance on its use to prevent instances where uric acid levels could become problematic.

Association between Food/UGT2B7 Polymorphisms and Pharmacokinetics/Pharmacodynamics Properties of Indapamide in Healthy Humans

Indapamide is an effective and safe antihypertensive medication showing a beneficial effect in combination with other antihypertensive agents regarding morbidity and mortality. A comparative study was performed under fasting and fed conditions to investigate the effect of food and selected single nucleotide polymorphisms in the uridine diphosphate glucuronyl transferase (UGT2B7) gene on the pharmacokinetics and pharmacodynamics behavior of indapamide 1.5 mg sustained release. Forty-nine healthy volunteers aged 18-55 years were randomized into two groups; 25 volunteers were administered indapamide under fasting conditions and 24 under fed conditions. Genotyping of the UGT2B7 rs7438135 and rs11740316 was done before commencing the study using predesigned TaqMan assays. Results showed that food independently decreased the value of indapamide' T_max by 5.5 h and increased the value of C_max by 8.7 ng/mL. On the other hand, all genetic variants of both UGT2B7 SNPs had no significant impact on the values of T_max, C_max, and AUC_0-t; however, it was found that rs11740316 variant AG was correlated with a 2.8 h lower MRT_inf. Finally, BMI positively correlated with longer MRT_inf. It was concluded that none of rs7438135, rs11740316, or food had a significant impact on the pharmacodynamic properties. Food had a modest impact on indapamide C_max and T_max values, while there were unremarkable differences in safety and efficacy.