Enroth Stefan, Johansson Asa, Enroth Sofia Bosdotter, Gyllensten Ulf
Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, SE-75108 Uppsala, Sweden.
1] Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, SE-75108 Uppsala, Sweden [2] Uppsala Clinical Research Centre, Uppsala University, SE-75237 Uppsala, Sweden.
Nat Commun. 2014 Aug 22;5:4684. doi: 10.1038/ncomms5684.
Ideal biomarkers used for disease diagnosis should display deviating levels in affected individuals only and be robust to factors unrelated to the disease. Here we show the impact of genetic, clinical and lifestyle factors on circulating levels of 92 protein biomarkers for cancer and inflammation, using a population-based cohort of 1,005 individuals. For 75% of the biomarkers, the levels are significantly heritable and genome-wide association studies identifies 16 novel loci and replicate 2 previously known loci with strong effects on one or several of the biomarkers with P-values down to 4.4 × 10(-58). Integrative analysis attributes as much as 56.3% of the observed variance to non-disease factors. We propose that information on the biomarker-specific profile of major genetic, clinical and lifestyle factors should be used to establish personalized clinical cutoffs, and that this would increase the sensitivity of using biomarkers for prediction of clinical end points.
用于疾病诊断的理想生物标志物应仅在受影响个体中显示出偏离水平，并且对与疾病无关的因素具有稳健性。在此，我们使用一个基于人群的包含1005名个体的队列，展示了遗传、临床和生活方式因素对92种癌症和炎症相关循环蛋白生物标志物水平的影响。对于75%的生物标志物，其水平具有显著的遗传性，全基因组关联研究确定了16个新位点，并重复了2个先前已知的对一种或几种生物标志物有强烈影响的位点，P值低至4.4×10(-58)。综合分析将多达56.3%的观察到的变异归因于非疾病因素。我们建议，应利用主要遗传、临床和生活方式因素的生物标志物特异性概况信息来建立个性化的临床临界值，这将提高使用生物标志物预测临床终点的敏感性。
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