Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.
HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Int J Obes (Lond). 2019 Mar;43(3):487-502. doi: 10.1038/s41366-018-0132-z. Epub 2018 Jun 15.
Obesity is related to a myriad of cardiometabolic outcomes, each of which may have a specific metabolomic signature and a genetic basis. We identified plasma metabolites associating with different cardiometabolic risk factors (adiposity, cholesterol, insulin resistance, and inflammation) in monozygotic (MZ) twins. Additionally, we assessed if metabolite profiling can identify subgroups differing by cardiometabolic risk factors.
We quantified 111 plasma metabolites (Acquity UPLC-triple quadrupole mass spectrometry), and measured blood lipids, HOMA index, CRP, and adiposity (BMI, %bodyfat by DEXA, fat distribution by MRI) in 40 MZ twin pairs (mean BMI 27.9 kg/m, age 30.7). We determined associations among individuals (via linear regression) between metabolites and clinical phenotypes, and assessed, with within-twin pair analysis, if these associations were free from genetic confounding. We also performed cluster analysis to identify distinct subgroups based on subjects' metabolite profiles.
We identified 42 metabolite-phenotype associations (FDR < 0.05), 19 remained significant after controlling for shared factors within the twin pairs. Aspartate, propionylcarnitine, tyrosine hexanoylcarnitine, and deoxycytidine associated positively with two or more adiposity measures. HDL cholesterol (HDL-C) associated negatively and BMI positively with the most numbers of metabolites; 12 were unique for HDL-C and 3 for BMI. Metabolites associating with HDL-C had the strongest effect size. Metabolite profiling revealed two distinct subgroups of individuals, differing by 32 metabolites (p < 0.05), and by total and LDL cholesterol (LDL-C). Forty-two metabolites predicted subgroup membership in correlation with total cholesterol and 45 metabolites predicted subgroup membership in correlation with LDL-C.
Different fat depots share metabolites associating with general adiposity. BMI and HDL-C associated with the most pronounced and specific metabolomic signature. Metabolomics profiling can be used to identify distinct subgroups of individuals that differ by cholesterol measures. Most of the observed metabolite-phenotype associations are free of confounding by genetics and environmental factors shared by the co-twins.
肥胖与众多心血管代谢结局相关，其中每一种结局可能都有其特定的代谢组学特征和遗传基础。我们在同卵双胞胎中鉴定出与不同心血管代谢危险因素（肥胖、胆固醇、胰岛素抵抗和炎症）相关的血浆代谢物。此外，我们评估了代谢组学特征分析是否可以识别出心血管代谢危险因素不同的亚组。
我们对 40 对同卵双胞胎（平均 BMI 为 27.9kg/m，年龄 30.7 岁）进行了 111 种血浆代谢物（ Acquity UPLC-三重四极杆质谱法）的定量检测，并测量了血脂、HOMA 指数、CRP 和体脂（DEXA 测定的 BMI、MRI 测定的脂肪分布）。我们通过线性回归在个体之间确定了代谢物与临床表型之间的关联，并通过双胞胎内分析评估了这些关联是否不受遗传因素的影响。我们还进行了聚类分析，根据受试者的代谢组学特征来识别不同的亚组。
我们确定了 42 个代谢物-表型关联（FDR < 0.05），其中 19 个在控制双胞胎内共享因素后仍然显著。天冬氨酸、丙酰肉碱、酪氨酸己酰肉碱和脱氧胞苷与两种或多种肥胖指标呈正相关。高密度脂蛋白胆固醇（HDL-C）与最多数量的代谢物呈负相关，而 BMI 则与最多数量的代谢物呈正相关；其中 12 个与 HDL-C 相关，3 个与 BMI 相关。与 HDL-C 相关的代谢物具有最强的效应大小。代谢组学特征分析揭示了两个不同的个体亚组，这些亚组在 32 种代谢物（p < 0.05）上存在差异，并且在总胆固醇和 LDL 胆固醇（LDL-C）上存在差异。42 种代谢物可以预测与总胆固醇相关的亚组成员，45 种代谢物可以预测与 LDL-C 相关的亚组成员。
不同的脂肪储存部位具有与整体肥胖相关的代谢物。BMI 和 HDL-C 与最显著和最特异的代谢组学特征相关。代谢组学特征分析可以用于识别不同的胆固醇测量亚组。大多数观察到的代谢物-表型关联不受双胞胎间遗传和环境因素的影响。
