研究人員發(fā)現(xiàn)，4個(gè)基因似乎能決定人們消化食物的速度,，這項(xiàng)發(fā)現(xiàn)將來(lái)也許能幫助醫(yī)生給病人提供更個(gè)性化的護(hù)理,。
據(jù)柏林媒體11月28日?qǐng)?bào)道，新陳代謝情況的不同會(huì)導(dǎo)致一些人更易患上糖尿病之類(lèi)的疾病,，這也解釋了飲食,、鍛煉、藥物對(duì)不同病人產(chǎn)生的結(jié)果各不相同的原因,。
研究人員共掃描了284個(gè)人的基因,，發(fā)現(xiàn)FADS1、LIPC,、SCAD和MCAD這4個(gè)基因能決定人體的新陳代謝速度,。
德國(guó)慕尼黑的黑爾姆霍爾茨中心研究人員卡斯滕·祖雷說(shuō)：“這些基因似乎與新陳代謝有關(guān)，或者能對(duì)新陳代謝起重要作用,。”
祖雷說(shuō),，這方面的可能為更個(gè)性化的護(hù)理開(kāi)辟了道路，醫(yī)生可以根據(jù)病人的基因構(gòu)成來(lái)研究他們的新陳代謝情況,，再根據(jù)這些情況決定如何進(jìn)行治療,。這對(duì)于治療與新陳代謝有關(guān)的疾病，如冠狀動(dòng)脈疾病和肥胖可能尤其有效,。
祖雷和同事在《公共科學(xué)圖書(shū)館·遺傳學(xué)》（PLoS Genetics）月刊上撰文說(shuō)：“這些發(fā)現(xiàn)使我們可以根據(jù)基因和新陳代謝兩方面的特點(diǎn)來(lái)作出判斷,，從而帶領(lǐng)我們向個(gè)性化護(hù)理和營(yíng)養(yǎng)供給邁進(jìn)。”（生物谷Bioon.com）
生物谷推薦原始出處：
PLoS Genet 4(11): e1000282. doi:10.1371/journal.pgen.1000282
Genetics Meets Metabolomics: A Genome-Wide Association Study of Metabolite Profiles in Human Serum
Christian Gieger1,2, Ludwig Geistlinger1, Elisabeth Altmaier3,4, Martin Hrabé de Angelis5,6, Florian Kronenberg7, Thomas Meitinger8,9, Hans-Werner Mewes3,10, H.-Erich Wichmann1,2, Klaus M. Weinberger11, Jerzy Adamski5,6, Thomas Illig1, Karsten Suhre3,4*
1 Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,
2 Institute of Medical Informatics, Biometry, and Epidemiology, Ludwig-Maximilians-Universit?t, Munich, Germany,
3 Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,
4 Faculty of Biology, Ludwig-Maximilians-Universit?t, Planegg-Martinsried, Germany,
5 Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,
6 Institute of Experimental Genetics, Life and Food Science Center Weihenstephan, Technische Universit?t München, Freising-Weihenstephan, Germany,
7 Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria,
8 Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,
9 Institute of Human Genetics, Klinikum rechts der Isar, Technische Universit?t München, Munich, Germany,
10 Department of Genome-Oriented Bioinformatics, Life and Food Science Center Weihenstephan, Technische Universit?t München, Freising-Weihenstephan, Germany,
11 Biocrates Life Sciences AG, Innsbruck, Austria
The rapidly evolving field of metabolomics aims at a comprehensive measurement of ideally all endogenous metabolites in a cell or body fluid. It thereby provides a functional readout of the physiological state of the human body. Genetic variants that associate with changes in the homeostasis of key lipids, carbohydrates, or amino acids are not only expected to display much larger effect sizes due to their direct involvement in metabolite conversion modification, but should also provide access to the biochemical context of such variations, in particular when enzyme coding genes are concerned. To test this hypothesis, we conducted what is, to the best of our knowledge, the first GWA study with metabolomics based on the quantitative measurement of 363 metabolites in serum of 284 male participants of the KORA study. We found associations of frequent single nucleotide polymorphisms (SNPs) with considerable differences in the metabolic homeostasis of the human body, explaining up to 12% of the observed variance. Using ratios of certain metabolite concentrations as a proxy for enzymatic activity, up to 28% of the variance can be explained (p-values 10−16 to 10−21). We identified four genetic variants in genes coding for enzymes (FADS1, LIPC, SCAD, MCAD) where the corresponding metabolic phenotype (metabotype) clearly matches the biochemical pathways in which these enzymes are active. Our results suggest that common genetic polymorphisms induce major differentiations in the metabolic make-up of the human population. This may lead to a novel approach to personalized health care based on a combination of genotyping and metabolic characterization. These genetically determined metabotypes may subscribe the risk for a certain medical phenotype, the response to a given drug treatment, or the reaction to a nutritional intervention or environmental challenge.
