Elmas Abdulkadir, Spehar Kevin, Do Ron, Castellano Joseph M, Huang Kuan-Lin
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
medRxiv. 2024 Jul 1:2024.06.30.24309729. doi: 10.1101/2024.06.30.24309729.
Circulating biomarkers play a pivotal role in personalized medicine, offering potential for disease screening, prevention, and treatment. Despite established associations between numerous biomarkers and diseases, elucidating their causal relationships is challenging. Mendelian Randomization (MR) can address this issue by employing genetic instruments to discern causal links. Additionally, using multiple MR methods with overlapping results enhances the reliability of discovered relationships.
Here we report an MR study using multiple methods, including inverse variance weighted, simple mode, weighted mode, weighted median, and MR Egger. We use the MR-base resource (v0.5.6) to evaluate causal relationships between 212 circulating biomarkers (curated from UK Biobank analyses by Neale lab and from Shin et al. 2014, Roederer et al. 2015, and Kettunen et al. 2016) and 99 complex diseases (curated from several consortia by MRC IEU and Biobank Japan).
We report novel causal relationships found by 4 or more MR methods between glucose and bipolar disorder (Mean Effect Size estimate across methods: 0.39) and between cystatin C and bipolar disorder (Mean Effect Size: -0.31). Based on agreement in 4 or more methods, we also identify previously known links between urate with gout and creatine with chronic kidney disease, as well as biomarkers that may be causal of cardiovascular conditions: apolipoprotein B, cholesterol, LDL, lipoprotein A, and triglycerides in coronary heart disease, as well as lipoprotein A, LDL, cholesterol, and apolipoprotein B in myocardial infarction.
This Mendelian Randomization study not only corroborates known causal relationships between circulating biomarkers and diseases but also uncovers two novel biomarkers associated with bipolar disorder that warrant further investigation. Our findings provide insight into understanding how biological processes reflecting circulating biomarkers and their associated effects may contribute to disease etiology, which can eventually help improve precision diagnostics and intervention.
循环生物标志物在个性化医疗中发挥着关键作用，为疾病筛查、预防和治疗提供了潜力。尽管众多生物标志物与疾病之间已建立了关联，但阐明它们的因果关系具有挑战性。孟德尔随机化（MR）可以通过使用基因工具来识别因果联系来解决这个问题。此外，使用多种结果重叠的MR方法可提高所发现关系的可靠性。
在此，我们报告一项使用多种方法的MR研究，包括逆方差加权法、简单模式、加权模式、加权中位数法和MR Egger法。我们使用MR-base资源（v0.5.6）来评估212种循环生物标志物（由Neale实验室对英国生物银行分析整理，以及来自Shin等人2014年、Roederer等人2015年和Kettunen等人2016年的研究）与99种复杂疾病（由医学研究理事会综合流行病学单位和日本生物银行从多个联盟整理）之间的因果关系。
我们报告了通过4种或更多MR方法发现的葡萄糖与双相情感障碍之间（各方法平均效应大小估计值：0.39）以及胱抑素C与双相情感障碍之间（平均效应大小：-0.31）的新因果关系。基于4种或更多方法的一致性，我们还确定了先前已知的尿酸与痛风以及肌酸与慢性肾病之间的联系，以及可能导致心血管疾病的生物标志物：冠心病中的载脂蛋白B、胆固醇、低密度脂蛋白、脂蛋白A和甘油三酯，以及心肌梗死中的脂蛋白A、低密度脂蛋白、胆固醇和载脂蛋白B。
这项孟德尔随机化研究不仅证实了循环生物标志物与疾病之间已知的因果关系，还发现了两种与双相情感障碍相关的新生物标志物，值得进一步研究。我们的发现为理解反映循环生物标志物的生物学过程及其相关效应如何可能导致疾病病因提供了见解，最终有助于改善精准诊断和干预。
