Lacruz Maria Elena, Kluttig Alexander, Tiller Daniel, Medenwald Daniel, Giegling Ina, Rujescu Dan, Prehn Cornelia, Adamski Jerzy, Frantz Stefan, Greiser Karin Halina, Emeny Rebecca Thwing, Kastenmüller Gabi, Haerting Johannes
From the Institute of Medical Epidemiology, Biostatistics and Informatics (M.E.L., A.K., D.T., D.M., J.H.), Clinic of Psychiatry, Psychotherapy, and Psychosomatics (I.G., D.R.), and Department of Medicine III, Martin-Luther University Halle-Wittenberg, Halle Saale, Germany (S.F.); Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (C.P., J.A.); Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising-Weihenstephan, Germany (J.A.); German Center for Diabetes Research (DZD), Neuherberg (J.A.); Division of Cancer Epidemiology, German Cancer Research Centre, Heidelberg (K.H.G.); Laboratory of Immunology, Wadsworth Center, New York State Department of Health, Albany (R.T.E.); and Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (G.K.).
Circ Cardiovasc Genet. 2016 Dec;9(6):487-494. doi: 10.1161/CIRCGENETICS.116.001444. Epub 2016 Oct 26.
The effects of lifestyle risk factors considered collectively on the human metabolism are to date unknown. We aim to investigate the association of these risk factors with metabolites and their changes during 4 years.
One hundred and sixty-three metabolites were measured in serum samples with the AbsoluteIDQ kit p150 (Biocrates) following a targeted metabolomics approach, in a population-based cohort of 1030 individuals, aged 45 to 83 years at baseline. We evaluated associations between metabolite concentrations (28 acylcarnitines, 14 amino acids, 9 lysophosphocholines, 72 phosphocholines, 10 sphingomyelins and sum of hexoses) and 5 lifestyle risk factors (body mass index [BMI], alcohol consumption, smoking, diet, and exercise). Multilevel or simple linear regression modeling adjusted for relevant covariates was used for the evaluation of cross-sectional or longitudinal associations, respectively; multiple testing correction was based on false discovery rate. BMI, alcohol consumption, and smoking were associated with lipid metabolism (reduced lyso- and acyl-alkyl-phosphatidylcholines and increased diacylphosphatidylcholines concentrations). Smoking showed positive associations with acylcarnitines, and BMI correlated inversely with nonessential amino acids. Fewer metabolites showed relative changes that were associated with baseline risk factors: increases in 5 different acyl-alkyl phosphatidylcholines were associated with lower alcohol consumption and BMI and with a healthier diet. Increased levels of tyrosine were associated with BMI. Sex-specific effects of smoking and BMI were found specifically related to acylcarnitine metabolism: in women higher BMI and in men more pack-years were associated with increases in acylcarnitines.
This study showed sex-specific effects of lifestyle risks factors on human metabolism and highlighted their long-term metabolic consequences.
目前尚不清楚综合考虑的生活方式风险因素对人体新陈代谢的影响。我们旨在研究这些风险因素与代谢物的关联以及它们在4年中的变化。
采用靶向代谢组学方法,使用AbsoluteIDQ试剂盒p150(百泰克)对1030名年龄在45至83岁的人群队列中的血清样本进行了163种代谢物的测量。我们评估了代谢物浓度(28种酰基肉碱、14种氨基酸、9种溶血磷脂酰胆碱、72种磷酸胆碱、10种鞘磷脂和己糖总和)与5种生活方式风险因素(体重指数[BMI]、饮酒、吸烟、饮食和运动)之间的关联。分别使用针对相关协变量进行调整的多水平或简单线性回归模型来评估横断面或纵向关联;多重检验校正基于错误发现率。BMI、饮酒和吸烟与脂质代谢相关(溶血和酰基烷基磷脂酰胆碱减少,二酰基磷脂酰胆碱浓度增加)。吸烟与酰基肉碱呈正相关,BMI与非必需氨基酸呈负相关。较少的代谢物显示出与基线风险因素相关的相对变化:5种不同的酰基烷基磷脂酰胆碱增加与较低的饮酒量、BMI以及更健康的饮食相关。酪氨酸水平升高与BMI相关。发现吸烟和BMI的性别特异性效应与酰基肉碱代谢特别相关:在女性中较高的BMI和在男性中较多的吸烟包年数与酰基肉碱增加相关。
本研究显示了生活方式风险因素对人体新陈代谢的性别特异性影响,并突出了它们的长期代谢后果。