Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
Am J Clin Nutr. 2022 Mar 4;115(3):799-810. doi: 10.1093/ajcn/nqab392.
Globally, the burden of obesity and associated nonalcoholic fatty liver disease (NAFLD) are rising, but little is known about the role that circulating metabolomic biomarkers play in mediating their association.
We aimed to examine the observational and genetic associations of adiposity with metabolomic biomarkers and the observational associations of metabolomic biomarkers with incident NAFLD.
A case-subcohort study within the prospective China Kadoorie Biobank included 176 NAFLD cases and 180 subcohort individuals and measured 1208 metabolites in stored baseline plasma using a Metabolon assay. In the subcohort the observational and genetic associations of BMI with biomarkers were assessed using linear regression, with adjustment for multiple testing. Cox regression was used to estimate adjusted HRs for NAFLD associated with biomarkers.
In observational analyses, BMI (kg/m2; mean: 23.9 in the subcohort) was associated with 199 metabolites at a 5% false discovery rate. The effects of genetically elevated BMI with specific metabolites were directionally consistent with the observational associations. Overall, 35 metabolites were associated with NAFLD risk, of which 15 were also associated with BMI, including glutamate (HR per 1-SD higher metabolite: 1.95; 95% CI: 1.48, 2.56), cysteine-glutathione disulfide (0.44; 0.31, 0.62), diaclyglycerol (C32:1) (1.71; 1.24, 2.35), behenoyl dihydrosphingomyelin (C40:0) (1.92; 1.42, 2.59), butyrylcarnitine (C4) (1.91; 1.38, 2.35), 2-hydroxybehenate (1.81; 1.34, 2.45), and 4-cholesten-3-one (1.79; 1.27, 2.54). The discriminatory performance of known risk factors was increased when 28 metabolites were also considered simultaneously in the model (weighted C-statistic: 0.84 to 0.90; P < 0.001).
Among relatively lean Chinese adults, a range of metabolomic biomarkers are associated with NAFLD risk and these biomarkers may lie on the pathway between adiposity and NAFLD.
在全球范围内，肥胖症和相关的非酒精性脂肪性肝病（NAFLD）的负担正在增加，但人们对循环代谢组学生物标志物在介导其相关性方面的作用知之甚少。
我们旨在研究肥胖症与代谢组学生物标志物的观察和遗传关联，以及代谢组学生物标志物与 NAFLD 发病的观察关联。
在中国前瞻性科霍里生物银行的病例亚队列研究中，包括 176 例 NAFLD 病例和 180 例亚队列个体，并使用代谢组学分析在储存的基线血浆中测量了 1208 种代谢物。在亚队列中，使用线性回归评估 BMI 与生物标志物的观察和遗传关联，并进行了多次检验调整。使用 Cox 回归估计与生物标志物相关的 NAFLD 的调整后 HR。
在观察性分析中，BMI（kg/m2；亚队列中的平均值为 23.9）与 199 种代谢物在 5%的错误发现率下相关。遗传上升高的 BMI 与特定代谢物的影响与观察性关联方向一致。总体而言，有 35 种代谢物与 NAFLD 风险相关，其中 15 种代谢物也与 BMI 相关，包括谷氨酸（每增加一个代谢物 SD 的 HR：1.95；95%CI：1.48，2.56）、半胱氨酸-谷胱甘肽二硫键（0.44；0.31，0.62）、二酰基甘油（C32:1）（1.71；1.24，2.35）、二十二酰基二氢鞘氨醇（C40:0）（1.92；1.42，2.59）、丁酰肉碱（C4）（1.91；1.38，2.35）、2-羟基硬脂酸（1.81；1.34，2.45）和 4-胆甾烯-3-酮（1.79；1.27，2.54）。当同时在模型中考虑 28 种代谢物时，已知危险因素的判别性能提高（加权 C 统计量：0.84 至 0.90；P ＜0.001）。
在相对较瘦的中国成年人中，一系列代谢组学生物标志物与 NAFLD 风险相关，这些生物标志物可能位于肥胖症和 NAFLD 之间的通路。
