Pravastatin

The data set was prepared based on an extensive literature survey taking IC₅₀ values of in-vitro enzyme inhibition assays against XO and HMGR by various secondary metabolites. Based on IC₅₀ values, sixteen plant- and fungus-based secondary metabolites (Tables 1 and 2) were chosen for the ligand-protein docking study. The docking study was performed against commercial drugs such as atorvastatin, simvastatin, lovastatin, and pravastatin for HMGR. On the other hand, commercial drugs such as allopurinol, febuxostat, topiroxostat, and probenecid were used for molecular docking studies with XO. The structures of the ligand molecules and the control drugs of both enzymes were retrieved from the PubChem database [38 (link)] and verified from SciFinder. The structures were retrieved in SDF format and were converted to PDB and MOL2 format using Discovery Studio Visualizer 4.0 software. The structure and complete chemical properties, torsional energy, van der Waals potential energy, electrostatic energy, weight, log P, total polar surface area (TPSA), donor atoms, and acceptor atoms of the ligands were listed (Supplementary Table 4S) by the help of MOE Module [39 (link)].

All samples in our analysis were derived from the Pharmacogenomics and Risk of Cardiovascular Disease (PARC) study. The study population, experimental design, and genotyping procedures have been described in detail previously [6 (link)]. Briefly, this study contains individuals from two statin trials: the Cholesterol and Pharmacogenetics (CAP) study [8 (link)], and the Pravastatin Inflammation/CRP Evaluation (PRINCE) study [9 (link)]. The PRINCE study consists of two cohorts, one containing individuals with history of CVD (secondary prevention cohort) and the other containing individuals with no history of CVD (primary prevention cohort). Participant characteristics are summarized in Table 1.
Genotyping was conducted in two stages. The first stage individuals were genotyped on the Illumina HumanHap300 bead chip and the second stage individuals were genotyped on the Illumina HumanQuad610 bead chip and a custom-made iSelect chip. The HumanHap300 and the HumanQuad610 chips (henceforth referred to as the 300K chip and the 610K chip) were designed to tag common variation among individuals of European ancestry while 12,959 SNPs in the iSelect chip were selected to increase coverage of candidate SNPs for cardiovascular disease regardless of minor allele frequency (MAF). Our analyses reported here utilized a total of 1,868 Caucasian individuals for whom complete LDL subfraction phenotype data were available (see below).
To maximize genomic coverage and combine the multiple groups genotyped on different SNP chips, we performed genotype imputation [10 (link)] [11 (link)], using an imputation protocol that has been previously described [12 (link)]. Briefly, genotype imputation was performed using IMPUTE2 [10 (link)] with an integrated reference panel that included 120 CEU haplotypes from the 1000 Genomes Pilot Project (“1000G”) [13 (link)] and 1910 worldwide haplotypes from the HapMap Phase 3 Project (“HM3”) [14 (link)]. This procedure generated genotypes (either genotyped or imputed) for 7,836,525 SNPs.

In addition to these overall analyses, we performed sensitivity analyses to investigate the robustness of the associations considering different subgroups of the population. First, we performed separate analyses for placebo and pravastatin treatment groups. Next, it has been shown that high blood pressure variability was associated with worse cognitive function [10 (link)]. As variability in weight and blood pressure may have a common cause, we additionally adjusted our models for systolic blood pressure variability and mean systolic blood pressure from baseline to month 30. This allows for the separation of effects of weight variability from those originating from blood pressure variability. Systolic blood pressure variability was defined as the intraindividual SD from baseline to month 30, where blood pressure was measured every 3 months, as previously done [10 (link), 17 (link)]. Furthermore, we included both weight loss (slope) and visit-to-visit body weight variables in the same multivariable-adjusted linear regression model to test independence of the two phenotypes. Last, we performed analyses excluding individuals who developed any of the following diseases during follow-up, to ensure weight loss did not follow as a result: incident diabetes, non-fatal cancer, non-fatal stroke or TIA, hospitalisation because of heart failure and non-fatal coronary or cardiovascular events.

The data employed in the present study originates from the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER). In all, 5,804 men and women aged 70–82 years from three countries (the Netherlands, Scotland and Ireland) were enrolled between December 1997 and May 1999 in a prospective, multicentre randomised trial in order to assess the safety and efficacy of pravastatin in reducing the risk of major vascular events. Participants were eligible for enrolment if they had pre-existing vascular disease or increased risk because of smoking, hypertension or diabetes.
During recruitment, the following exclusion criteria were applied: cognitive impairment (Mini-Mental Score Examination score < 24); history of malignancy within the past 5 years except localised basal cell carcinoma; recent stroke, transient ischemic attack (TIA), myocardial infarction, surgery or amputation for vascular disease ≤6 months before study entry. More details regarding exclusion criteria of PROSPER have been described elsewhere [12 (link), 13 (link)]. The PROSPER study was approved by the Medical Ethics Committees of the three collaborating centres and complied with the Declaration of Helsinki. All participants gave written informed consent.
In the present study, the following inclusion criteria were applied: ≥1 out of the four cognitive tests scores at month 30 of follow-up; ≥2 weight measurements recorded between baseline and month 30 of follow-up with a maximum of 11 repetitive measurements.