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Background: Racial and ethnic disparities exist for hepatitis C virus (HCV) treatment and hepatocellular carcinoma (HCC) survival., Aim: To evaluate the impact of HCV treatment on such disparities., Methods: In a retrospective cohort study, we analysed 6069 patients with HCV-related HCC (54.2% Asian, 30.1% White, 8.5% Black, and 7.3% Hispanic) from centres in the United States and Asia., Results: The mean age was 61, 60, 59 and 68, respectively, for White, Black, Hispanic and Asian patients. Black patients were most likely to have Barcelona Clinic Liver Cancer stage D, vascular invasion and distant metastasis (23% vs. 5%-15%, 20% vs. 10%-17% and 10% vs. 5%-7%, respectively; all p < 0.0001). Treatment rate with direct-acting antiviral agents (DAA) was 35.9% for Asian, 34.9% for White, 30.3% for Hispanic (30.3%), and 18.7% for Black patients (p < 0.0001). Among those untreated or without sustained virologic response (SVR), 10-year survival rates were 35.4, 27.5, 19.3 and 14.0, respectively, for Asian, Hispanic, White and Black patients (p < 0.0001). There were no statistically significant differences among those with SVR (p = 0.44). On multivariable analysis adjusted for relevant confounders, there was no statistically significant association between survival and being Hispanic (aHR: 0.68, p = 0.26) or Black (aHR: 1.18, p = 0.60) versus White. There was a significant association between being Asian American and survival (aHR: 0.24, p = 0.001; non-U.S. Asian: aHR: 0.66, p = 0.05), and for SVR (aHR: 0.30, p < 0.0001)., Conclusion: DAA treatment rates were suboptimal. Racial and ethnic disparities resolved with HCV cure. Early diagnosis and improved access to HCV treatment is needed for all patients with HCV infection., (© 2024 John Wiley & Sons Ltd.)

Backgrounds: There is a discrepancy between west and east on the relationship between non-alcoholic fatty liver disease (NAFLD) and chronic kidney disease (CKD). This study aimed to find out the possible reason for this and to clarify the association between NAFLD and CKD by analyzing two population-based datasets from the US and China., Methods: Two health examination datasets from China and the US were used. CKD was defined as an estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73m 2 or and/or abnormal albuminuria and/or overt proteinuria. Binary logistic regression was used to examine the association between NAFLD and CKD., Results: A total of 60,965 participants were analyzed, including 11,844 from the US and 51,229 from China. The prevalence of NAFLD was 27.12% in the Chinese population and 36.08% in the US population (p < 0.001). The proportions of CKD and late stage CKD (stages 3-5) were higher in the US population than the Chinese one. NAFLD was independently associated with an increased risk of CKD in Chinese population, whereas in the US population, the NAFLD was not an independent risk factor of CKD. In subgroup analyses which excluded late stages CKD (stages 3-5), the risks of mild renal function decline became consistent: NAFLD was associated with early stages of CKD but not the late stages of CKD in both populations., Conclusion: NAFLD increased the risk of early stages of CKD in both Chinese and the US population. The conflicting results reported by previous studies might result from the different proportion of late stages of CKD.

Leveraging emerging opportunities in data science to open new frontiers in heart, lung, blood, and sleep research is one of the major strategic objectives of the National Heart, Lung, and Blood Institute (NHLBI), one of the 27 Institutes/Centers within the National Institutes of Health (NIH). To assess NHLBI's recent funding of research grants in data science and to identify its relative areas of focus within data science, a portfolio analysis from fiscal year 2008 to fiscal year 2017 was performed. In this portfolio analysis, an efficient and reliable methodology was used to identify data science research grants by utilizing several NIH databases and search technologies (iSearch, Query View Reporting system, and IN-SPIRE [Pacific Northwest National Laboratory, Richland, WA]). Six hundred thirty data science-focused extramural research grants supported by NHLBI were identified using keyword searches based primarily on NIH's working definitions of bioinformatics and computational biology. Further analysis characterized the distribution of these grants among the heart, lung, blood, and sleep disease areas as well as the subtypes of data science projects funded by NHLBI. Information was also collected for data science research grants funded by other NIH institutes/centers using the same search and analysis methodology. The funding comparison among different NIH institutes/centers highlighted relative data science areas of emphasis and further identified opportunities for potential data science areas in which NHLBI could foster research advances.

The National Institutes of Health have made substantial investments in genomic studies and technologies to identify DNA sequence variants associated with human disease phenotypes. The National Heart, Lung, and Blood Institute has been at the forefront of these commitments to ascertain genetic variation associated with heart, lung, blood, and sleep diseases and related clinical traits. Genome-wide association studies, exome- and genome-sequencing studies, and exome-genotyping studies of the National Heart, Lung, and Blood Institute-funded epidemiological and clinical case-control studies are identifying large numbers of genetic variants associated with heart, lung, blood, and sleep phenotypes. However, investigators face challenges in identification of genomic variants that are functionally disruptive among the myriad of computationally implicated variants. Studies to define mechanisms of genetic disruption encoded by computationally identified genomic variants require reproducible, adaptable, and inexpensive methods to screen candidate variant and gene function. High-throughput strategies will permit a tiered variant discovery and genetic mechanism approach that begins with rapid functional screening of a large number of computationally implicated variants and genes for discovery of those that merit mechanistic investigation. As such, improved variant-to-gene and gene-to-function screens-and adequate support for such studies-are critical to accelerating the translation of genomic findings. In this White Paper, we outline the variety of novel technologies, assays, and model systems that are making such screens faster, cheaper, and more accurate, referencing published work and ongoing work supported by the National Heart, Lung, and Blood Institute's R21/R33 Functional Assays to Screen Genomic Hits program. We discuss priorities that can accelerate the impressive but incomplete progress represented by big data genomic research., (© 2018 American Heart Association, Inc.)