Global burden of atherosclerotic cardiovascular disease in people with hepatitis C virus infection: a systematic review, meta-analysis, and modelling study.

BACKGROUND: More than 70 million people worldwide are estimated to have hepatitis C virus (HCV) infection. Emerging evidence indicates an association between HCV and atherosclerotic cardiovascular disease. We aimed to determine the association between HCV and cardiovascular disease, and estimate the national, regional, and global burden of cardiovascular disease attributable to HCV. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, Ovid Global Health, and Web of Science databases from inception to May 9, 2018, without language restrictions, for longitudinal studies that evaluated the risk ratio (RR) of cardiovascular disease in people with HCV compared with those without HCV. Two investigators independently reviewed and extracted data from published reports. The main outcome was cardiovascular disease, defined as hospital admission with, or mortality from, acute myocardial infarction or stroke. We calculated the pooled RR of cardiovascular disease associated with HCV using a random-effects model. Additionally, we calculated the population attributable fraction and disability-adjusted life-years (DALYs) from HCV-associated cardiovascular disease at the national, regional, and global level. We also used age-stratified and sex-stratified HCV prevalence estimates and cardiovascular DALYs for 100 countries to estimate country-level burden associated with HCV. This study is registered with PROSPERO, number CRD42018091857. FINDINGS: Our search identified 16 639 records, of which 36 studies were included for analysis, including 341 739 people with HCV. The pooled RR for cardiovascular disease was 1·28 (95% CI 1·18-1·39). Globally, 1·5 million (95% CI 0·9-2·1) DALYs per year were lost due to HCV-associated cardiovascular disease. Low-income and middle-income countries had the highest disease burden with south Asian, eastern European, north African, and Middle Eastern regions accounting for two-thirds of all HCV-associated cardiovascular DALYs. INTERPRETATION: HCV infection is associated with an increased risk of cardiovascular disease. The global burden of cardiovascular disease associated with HCV infection was responsible for 1·5 million DALYs, with the highest burden in low-income and middle-income countries. FUNDING: British Heart Foundation and Wellcome Trust.

Distribution based nearest neighbor imputation for truncated high dimensional data with applications to pre-clinical and clinical metabolomics studies.

BACKGROUND: High throughput metabolomics makes it possible to measure the relative abundances of numerous metabolites in biological samples, which is useful to many areas of biomedical research. However, missing values (MVs) in metabolomics datasets are common and can arise due to both technical and biological reasons. Typically, such MVs are substituted by a minimum value, which may lead to different results in downstream analyses. RESULTS: Here we present a modified version of the K-nearest neighbor (KNN) approach which accounts for truncation at the minimum value, i.e., KNN truncation (KNN-TN). We compare imputation results based on KNN-TN with results from other KNN approaches such as KNN based on correlation (KNN-CR) and KNN based on Euclidean distance (KNN-EU). Our approach assumes that the data follow a truncated normal distribution with the truncation point at the detection limit (LOD). The effectiveness of each approach was analyzed by the root mean square error (RMSE) measure as well as the metabolite list concordance index (MLCI) for influence on downstream statistical testing. Through extensive simulation studies and application to three real data sets, we show that KNN-TN has lower RMSE values compared to the other two KNN procedures as well as simpler imputation methods based on substituting missing values with the metabolite mean, zero values, or the LOD. MLCI values between KNN-TN and KNN-EU were roughly equivalent, and superior to the other four methods in most cases. CONCLUSION: Our findings demonstrate that KNN-TN generally has improved performance in imputing the missing values of the different datasets compared to KNN-CR and KNN-EU when there is missingness due to missing at random combined with an LOD. The results shown in this study are in the field of metabolomics but this method could be applicable with any high throughput technology which has missing due to LOD.