Genetic risk prediction for CKD: a journey of a thousand miles.
Genetic risk prediction for CKD: a journey of a thousand miles.
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CKD 的遗传风险预测：千里之行。
DOI：
10.1053/j.ajkd.2011.11.011
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Winkler,CherylA
中科院分区：
文献类型：
--
作者：
Kopp,JeffreyB;Winkler,CherylA
通过针对高危人群亚组的干预措施，提供特定临床结局风险定量估计的工具有可能改善临床决策并降低发病率和死亡率。临床风险评分的典型示例是Fragrance风险评分，其结合人口统计学（年龄、种族、性别）和临床（吸烟和糖尿病状态、血清总胆固醇和血压值）变量来估计心肌梗死的10年风险。[1]类似地，一些研究小组报告了慢性肾脏病（CKD）的风险评分。2-5受试者工作特征曲线C统计值范围为0.67 - 0.84，但最高值并非来自重复队列（表1）。其他人报告了发生终末期肾病的风险评分，6-8，并对肾脏风险评分的作用进行了审查。9遗传风险评分是否能通过更精确地定义风险特征，为临床风险评分提供额外信息，从而在个性化肾病护理中找到一席之地？根据公元前世纪中国圣人老子的说法，千里之行始于足下。在本期《美国肾脏病杂志》中，O 'Seaghdha和同事们朝着帮助临床医生使用遗传信息来补充临床评分系统以预测CKD风险的目标迈出了第一步。作者使用了来自心脏研究原始和后代队列的数据，涉及2，489名参与者。CKD 3期定义为使用肾病饮食改良研究4变量方程基于血清肌酐估计的肾小球滤过率（eGFR）< 60 mL/min/1.73 m2。在平均10.8年的随访中，有270例CKD 3期病例。作者使用了一种临床风险评分，包括年龄和性别变量，并使用16个单核苷酸多态性开发了一种遗传风险评分，16个基因座中的每一个都有1个，这些基因座先前与欧洲血统人群中eGFR< 60 mLmin/1.73 m2相关。其中11个基因座中，2个涉及非同义变异（即非密码子改变），2个位于转录起始位点上游，9个位于内含子内，3个位于基因间或位置不明。这些变体都没有显示出影响功能，而是它们可能跟踪功能等位基因。
By targeting interventions to high-risk population subgroups, tools that provide quantitative estimates of the risk for particular clinical outcomes have the potential to improve clinical decision making and reduce morbidity and mortality. The paradigmatic example of a clinical risk score is the Framingham risk score, which incorporates demographic (age, race, sex) and clinical (smoking and diabetes status, serum total cholesterol, and blood pressure values) variables to estimate 10-year risk for myocardial infarction. 1 Similarly, several research groups have reported chronic kidney disease (CKD) risk scores. 2–5 The receiver operating characteristic curve C statistic values ranged from 0.67 to 0.84, although the highest value did not derive from a replication cohort (Table 1). Others have reported risk scores for developing end-stage renal disease, 6–8 and the role of renal risk scores has been reviewed. 9Will genetic risk scores provide additional information to clinical risk scores, by defining the risk profile more precisely, and thus find a place in personalized nephrology care? A journey of a thousand miles begins beneath one’s feet, according to Lao-Tzu, a Chinese sage of the 6th century BCE. In this issue of the American Journal of Kidney Diseases, O’Seaghdha and colleagues10 have taken a first step toward the goal of assisting clinicians to use genetic information to supplement clinical scoring systems for prediction of CKD risk. The authors used data from the Framingham Heart Study Original and Offspring cohorts, involving 2,489 participants. CKD stage 3 was defined as estimated glomerular filtration rate (eGFR)< 60 mL/min/1.73 m2 based on serum creatinine using the 4-variable Modification of Diet in Renal Disease Study equation. Over a mean of 10.8 years of followup, 270 cases of CKD stage 3 developed. The authors used a clinical risk score, with the variables age and sex, and developed a genetic risk score, using 16 single-nucleotide polymorphisms, 1 from each of 16 loci that have previously been associated with eGFR< 60 mLmin/1.73 m2 in European descent populations. 11 Of these loci, 2 involved nonsynomous variants (ie, were non–codon changing), 2 were upstream of the transcriptional start site, 9 were located within introns, and 3 were intergenic or of uncertain location. None of these variants has been shown to affect function but instead they likely track functional alleles.
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影响因子：
--
作者：
Kshirsagar, Abhijit V.;Bang, Heejung;Bomback, Andrew S.;Vupputuri, Suma;Shoham, David A.;Kern, Lisa M.;Klemmer, Philip J.;Mazumdar, Madhu;August, Phyllis A.
通讯作者：
August, Phyllis A.
影响因子：
8.8
作者：
Calonge, Ned;Berg, Jonathan S.;Douglas, Michael P.
通讯作者：
Douglas, Michael P.
影响因子：
19.6
作者：
McDonough CW;Palmer ND;Hicks PJ;Roh BH;An SS;Cooke JN;Hester JM;Wing MR;Bostrom MA;Rudock ME;Lewis JP;Talbert ME;Blevins RA;Lu L;Ng MC;Sale MM;Divers J;Langefeld CD;Freedman BI;Bowden DW
通讯作者：
Bowden DW
心血管风险预测的改进：是时候回顾一下有关如何评估测试特征的当前知识、争论和基础知识了
影响因子：
8.3
作者：
Michel J. Romanens;Franz Ackermann;J. Spence;R. Darioli;N. Rodondi;R. Corti;G. Noll;M. Schwenkglenks;M. Pencina
通讯作者：
M. Pencina
DOI：
10.1056/nejmoa0804742
发表时间：
2008-11-20
期刊：
The New England journal of medicine
影响因子：
--
作者：
Meigs JB;Shrader P;Sullivan LM;McAteer JB;Fox CS;Dupuis J;Manning AK;Florez JC;Wilson PW;D'Agostino RB Sr;Cupples LA
通讯作者：
Cupples LA