据7月2日的《科学》杂志报道说，科学家们已经在那些活到100岁或以上的人中发现了一系列的与一般人群相比特别常见的遗传学特征。 这些发现提出了也许在某一天人们可以预先知道他们是否有可能活到非常老的年龄的可能性，尽管生活方式的选择以及环境因子也是非常重要的因素。 这些结果同时也为人们研究多种基因影响我们如何衰老的方式打下了某些重要的基础。
Paola Sebastiani及其同僚对超过1000名的百岁或百岁以上的老人以及相同数目的作为对照的人的基因组进行了检测。他们找到了在百岁或以上的老人与随机选择的个人之间有着最大差异的多个基因标志。 因为人要活到非常老的年龄一定会有多个基因的参与，文章的作者接下来根据150个基因标志研发出了一个可计算一个人达到异常高寿概率的模型。应用这一模型，研究人员可以预测某人是否可以活到百岁或以上，而且精确性达77%。
研究人员还将基因预测分解成为与超过100岁的不同寿限相关及与不同模式的与年龄有关疾病（诸如痴呆症、高血压和心血管疾病等）相关的19个特征组。 未来对这些基因特征的研究可使人们了解特异的、不同模式的健康衰老，而且它们最终可能会有助于个体化医学及量身打造的疾病预防和治疗策略。
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Article #17: "Genetic Signatures of Exceptional Longevity in Humans," by P. Sebastiani; N. Solovieff; S.W. Hartley at Boston University School of Public Health in Boston, MA; A. Puca at Istituto di Tecnologie Biomediche in Segrate, Italy; E. Melista; S. Andersen; D.A. Dworkis; J.B. Wilk; R.H. Meyers; M.H. Steinberg; M. Montano; C.T. Baldwin; T.T. Perls at Boston University School of Medicine in Boston, MA; S. Andersen; M.H. Steinberg; C.T. Baldwin at Boston Medical Center in Boston, MA.
Published Online July 1, 2010
Science DOI: 10.1126/science.1190532
Healthy aging is thought to reflect the combined influence of environmental factors (lifestyle choices) and genetic factors. To explore the genetic contribution, we undertook a genome-wide association study of exceptional longevity (EL) in 1055 centenarians and 1267 controls. Using these data, we built a genetic model that includes 150 single-nucleotide polymorphisms (SNPs) and found that it could predict EL with 77% accuracy in an independent set of centenarians and controls. Further in silico analysis revealed that 90% of centenarians can be grouped into 19 clusters characterized by different combinations of SNP genotypes—or genetic signatures—of varying predictive value. The different signatures, which attest to the genetic complexity of EL, correlated with differences in the prevalence and age of onset of age-associated diseases (e.g., dementia, hypertension, and cardiovascular disease) and may help dissect this complex phenotype into subphenotypes of healthy aging.
