A Big Data investigation of the influence of life-long metabolic factors that contribute to age-related eye diseases

有大量证据将老年性眼病(AREDS)，特别是老年性黄斑变性(AMD)、青光眼和老年性白内障与代谢功能障碍联系起来。以前的AREDS代谢组研究已经用一系列不同的代谢物评估技术在不同的队列中进行了代谢组范围的分析。然而，许多队列是以临床为基础的，动力不足，使用了各种平台，文献中几乎没有收敛。例如，对13项AMD研究的荟萃综述(1)显示，广泛的潜在生物标志物，包括苯丙氨酸、腺苷、次黄嘌呤、酪氨酸、肌酸、柠檬酸、肉碱、脯氨酸和麦芽糖，以及脂、碳水化合物、氨基酸和核苷酸代谢可能在受影响的个体中处于失调状态。荟萃分析的研究结果缺乏趋同性，支持进一步研究的需要，以更高的置信度选择和验证AMD的代谢物生物标记物。在青光眼中，最近对18项代谢组学研究的综述(2)表明，血浆中的羟脯氨酸和蛋氨酸持续增加，而房水中的结果显示，丙氨酸、肌酸、甘氨酸和赖氨酸在青光眼中增加。作者认为，基于血浆的研究结果与使用水中体液的研究结果不太一致。由于血浆是大人群研究中唯一实用和相对非侵入性的数据来源，因此有必要进一步改进AREDS的血浆代谢组学研究。该项目的主管发表了来自TwinsUK的2019年对313种代谢物的研究(3)，发现维生素C代谢物O-甲基抗坏血酸与青光眼的主要风险因素眼压有关，并因此降低眼压，突显了抗氧化剂在抗击光氧化应激方面的力量。这项特殊的研究也为该项目的方法论方法树立了榜样，使用随机森林机器学习来识别最有影响力的代谢物，并使用孟德尔随机化来调查代谢物对眼压的因果影响。到目前为止，年龄相关性白内障受到的关注较少，几乎没有高通量的代谢组学研究：搜索基于血浆的代谢组谱没有得到任何发表的数据，除了一篇关于青光眼的文章，其中使用了性别匹配的白内障对照。虽然有一种有效的手术治疗方法，但揭示白内障患者的代谢特征将提高对分子机制的理解，如果干预可以推迟白内障，那么随后可能会显著减少手术量和节省成本。该项目的监督人员可以访问包括英国生物库在内的四个超过50万人的大型队列的数据，这意味着这项研究有能力确定新的关联，以进一步了解与年龄相关的眼病的病理生理学，潜在地检查提高新疗法前景的因果关系，并使用机器学习模型来使用代谢组学平台预测那些有疾病风险的人。侯旭伟等人。(2020)老年性黄斑变性的代谢组学：系统评价。投资眼科VS Sci。61、13-13唐勇等人。《原发性开角型青光眼的代谢组学：系统回顾和荟萃分析》(2022)。前部神经科。16,835736 Hysi PG等人。(2019)抗坏血酸代谢物参与普通人群的眼压控制。氧化还原生物。20,349-353

There is a body of evidence linking age-related eye diseases (AREDs), and in particular age-related macular degeneration (AMD), glaucoma and age-related cataract to metabolic dysfunction. Previous metabolomic studies of AREDs have performed metabolome-wide analyses in different cohorts with a range of different metabolite evaluation techniques. However, many cohorts were clinic-based, underpowered and used a variety of platforms and there has been little convergence in the literature. For example, a meta-review of 13 AMD studies(1) revealed that a broad range of potential biomarkers, including phenylalanine, adenosine, hypoxanthine, tyrosine, creatine, citrate, carnitine, proline and maltose, and that lipid, carbohydrate, amino acid and nucleotide metabolism may be dysregulated in affected individuals. The lack of convergence of results from the studies in the meta-analysis supports the need for further studies to select and validate metabolite biomarkers of AMD with higher confidence. In glaucoma, a recent review of 18 metabolomics studies(2) suggested that hydroxyproline and methionine were consistently increased in plasma, while results from aqueous humour revealed alanine, creatine, glycine and lysine increased in glaucoma. The authors argued that plasma-based findings were less consistent than those using aqueous humour. Since plasma is the only practical and relatively non-invasive source of data for large population studies, there is therefore need for further improvements in plasma metabolomics studies of AREDs. The supervisors of this project published a 2019 study(3) from TwinsUK of 313 metabolites and found a vitamin C metabolite O-methylascorbate is associated with and causally lowers IOP, the main risk factor for glaucoma, highlighting the power of antioxidants in combating photooxidative stress. This particular study also sets an example for the methodological approach in this project by using random forest machine learning to identify the most influential metabolites, and Mendelian randomization to investigate a causal effect of metabolites on IOP. Age-related cataract has to date received less attention, with few high-throughput metabolomic studies: a search for plasma-based metabolomic profiling returned no published data except for one article on glaucoma where sex-matched controls with cataract were used. While there is an effective surgical remedy, revealing metabolic signatures of cataract patients would improve understanding of molecular mechanisms and if an intervention could delay cataract, then significant surgical volume reduction and cost savings could follow. The supervisors of this project have access to data from four large cohorts of over 500,000 people, including the UK Biobank, meaning that this study has the power to identify novel associations to further understand the pathophysiology of age-related eye diseases, to potentially examine for causality raising the prospect of novel therapies, and to use machine learning models to predict those at risk of disease using metabolomics platforms. Hou XW et al. (2020) Metabolomics in Age-Related Macular Degeneration: A Systematic Review. Invest Ophthalmol Vis Sci. 61, 13-13 Tang Y et al. (2022) Metabolomics in Primary Open Angle Glaucoma: A Systematic Review and Meta-Analysis. Front Neurosci. 16, 835736 Hysi PG et al. (2019) Ascorbic acid metabolites are involved in intraocular pressure control in the general population. Redox Biol. 20, 349-353