Background: Herb–drug interactions are nowadays an important decision factor in many healthcare interventions. Patients with cardiovascular risk factors such as hyperlipidemia and hypertension are usually prescribed long-term treatments. We need more informed decision tools to direct future clinical research and decision making to avoid HDI occurrences in this group. Methods: A scoping review was conducted using data from online databases such as PUBMED, the National Library of Medicine, and the electronic Medicines Compendium. Included studies consisted of the reported effects on Phase 1/2 and P-glycoprotein of herbal medicines listed in the medicines agencies of Latin America and Europe and drugs used for cardiovascular conditions (statins, diuretics, beta blockers, calcium channel blockers, and ACE inhibitors). The cross tabulation of the results allowed for finding potential HDI. Results and conclusions: as per the preclinical data reviewed here, we encourage more clinical research on whether drugs with apparently very low interaction risk, such as pravastatin, nadolol, and nimodipine/nitrendipine, may help prevent HDI when statins, beta blockers, and calcium channel blockers, respectively, are prescribed for long-term treatments.

Background: Historically, previous research demonstrating associations between self-rated health (SRH) and metabolic anomalies have rarely controlled for metabolic covariates. Thus, there is currently poor understanding of the unique contribution of SRH to metabolic syndrome (MetS) over and beyond underlying cardiometabolic abnormalities. Objective: This study explored unique associations between SRH and multiple cardiometabolic factors, after controlling for metabolic covariates. Methods: This study was based on an analysis of archived population-based data from the 2019 Health Survey for England. A total of 352 MetS cases were extracted from 10299 participants in the survey. Bootstrapped adjusted regression methods were used to predict MetS status and cardiometabolic abnormalities (HDL (high-density lipoprotein), waist/hip ratio, body mass index (BMI), systolic and diastolic blood pressure, and glycated haemoglobin (HbA1c) from SRH. Structural Equation Modelling (SEM) was used to explore direct and indirect associations between SRH and cardiometabolic factors, with SRH treated as a mediating factor. Results: SRH predicted MetS status but this was negated after cardiometabolic adjustments. Poor SRH independently predicted HDL (high-density lipoprotein) deficiency, and elevated waist/hip ratio, BMI, and HbA1c, even after cardiometabolic adjustments. SEM generated two models with equivalent fit indices, but different structural pathways. In one model SRH mediated relations between anthropometric risk factors (waist/hip ratio and BMI). Conclusions: SRH can help identify people at risk of developing MetS, irrespective of cardiometabolic abnormalities. Poor SRH may represent a non-intrusive easily measurable risk factor for adiposity in MetS, especially where direct measurement of body fat is impractical or socially challenging.