RESUMO
Background: Cataract is one of the most prevalent causes of blindness worldwide. Whilst surgery is the primary treatment for cataracts, it is not always an available option, particularly in developing countries. Non-surgical methods of treatment would increase treatment availability for more patients. Several studies have investigated how topical application of oxysterols, such as lanosterol, may break down aggregated proteins and restore lens transparency. However, the results are conflicting and inconclusive. Aim: In this study, we focus on combining genetic evidence for associations between lanosterol related genetic variation and cataract to explore whether lanosterol is a potentially suitable drug treatment option. Method: Using data from 45,449 available cataract cases from the UK Biobank, with participant ages ranging from 40-69, we conducted a genetic association study (GWAS) to assess the risk of cataract. Cataract cases were defined using diagnostic and operation codes. We focused on genetic variants in the lanosterol synthase gene region. We also compared our results with previously published genetic associations of phytosterol-to-lanosterol ratios. Finally, we performed a genetic risk score analysis to test the association between lanosterol within the cholesterol synthesis pathway and the risk of cataract. Results: No statistically significant single nucleotide polymorphisms (SNPs) associations with cataract were observed in the gene region of lanosterol synthase at a multiple testing adjusted significance threshold of p < 0.05/13. The comparison between cataract risk and genetic association of 8 phytosterol-to-lanosterol GWAS results also showed no evidence to support lanosterol's protective properties for cataract risk. No statistically significant association was found between the lanosterol within the cholesterol synthesis pathway genetic risk score and cataract outcomes (OR = 1.002 p = 0.568). Conclusion: There was no evidence observed for genetic associations between lanosterol and cataract risk. Our results do not support lanosterol's potential role in treating cataracts. Further research may be needed to address the effect of lanosterol on specific cataract subtypes.
RESUMO
Introduction: Trusted Research Environments (TREs) are secure computing environments that provide access to data for approved researchers to use in studies that can save and improve lives. TREs rely on Data Access Agreements (DAAs) to bind researchers and their organisations to the terms and conditions of accessing the infrastructure and data use. However, DAAs can be overly lengthy, complex, and can contain outdated terms from historical data sharing agreements for physical exchange of data. This is often cited as a cause of significant delays to legal review and research projects starting. Objectives: The aim was to develop a standardised DAA optimised for data science in TREs across the UK and framed around the 'Five Safes framework' for trustworthy data use. The DAA is underpinned by principles of data access in TREs, the development of which is described in this paper. Methods: The Pan-UK Data Governance Steering Group of the UK Health Data Research Alliance led the development of a core set of data access principles. This was informed by a benchmarking exercise of DAAs used by established TREs and consultation with public members and stakeholders. Results: We have defined a core set of principles for TRE data access that can be mapped to a common set of DAA terms for UK-based TREs. Flexibility will be ensured by including terms specific to TREs or specific data/data owners in customisable annexes. Public views obtained through public involvement and engagement (PIE) activities are also reported. Conclusions: These principles provide the foundation for a standardised UK TRE DAA template, designed to support the growing ecosystem of TREs. By providing a familiar structure and terms, this template aims to build trust among data owners and the UK public and to provide clarity to researchers on their obligations to protect the data. Widespread adoption is intended to accelerate health data research by enabling faster approval of projects, ultimately enabling more timely and effective research.
