Integration and relative value of biomarkers for prediction of MCI to AD progression: spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers.
Integration and relative value of biomarkers for prediction of MCI to AD progression: spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers.
复制标题
DOI：
10.1016/j.nicl.2013.11.010
复制
发表时间：
2014
影响因子：
4.2
通讯作者：
Davatzikos, Christos
中科院分区：
文献类型：
--
作者：
Da, Xiao;Toledo, Jon B.;Zee, Jarcy;Wolk, David A.;Xie, Sharon X.;Ou, Yangming;Shacklett, Amanda;Parmpi, Paraskevi;Shaw, Leslie;Trojanowski, John Q.;Davatzikos, Christos
关键词：
本研究评估了在不同的随访期内从轻度认知障碍 (MCI) 发展为阿尔茨海默病 (AD) 的过程中，从脑萎缩的磁共振成像 (MRI) 模式（通过 SPARE-AD 指数量化）、脑脊液 (CSF) 生物标志物、APOE 基因型和认知表现 (ADAS-Cog) 中获得的个体以及相对和联合价值。 使用阿尔茨海默病神经影像计划-1 (ADNI-1) 的数据，最长可达 6 年。 SPARE-AD 最初被确立为 AD 与认知正常 (CN) 受试者的高度敏感和特异性 MRI 标记物 (AUC = 0.98)。然后使用 381 名 MCI 受试者的生存分析来比较所有上述指数的基线预测值。 SPARE-AD 和 ADAS-Cog 被发现具有相似的预测价值，并且它们的组合明显优于它们单独的表现。尽管 SPARE-AD、ADAS-Cog 和 APOE ε4 的组合提供了最高的风险比估计值 17.8（最后四分位数与第一个四分位数），但 APOE 基因型并未显着改善预测。在也有 CSF 生物标志物的 192 名 MCI 患者的子集中，与 SPARE-AD 和 ADAS-Cog 组合相比，在之前的模型中添加 Aβ1-42、t-tau 和 p-tau181p 并没有显着提高预测价值。重要的是，在淀粉样蛋白阴性的 MCI 患者中，SPARE-AD 对临床进展具有很高的预测能力。我们的研究结果表明，SPARE-AD 和 ADAS-Cog 组合提供了从 MCI 到 AD 转化的最高预测能力，APOE 基因型对此有所改善，尽管不显着。淀粉样蛋白阴性 MCI 患者的 SPARE-AD 可以预测临床进展这一发现在淀粉样蛋白假说下是出乎意料的，值得进一步研究。使用模式识别方法对 813 名 ADNI-1 受试者进行了分析。 SPARE-AD 和 ADAS-Cog 的组合为 MCI 进展提供了高预测指数。 Cox PH 模型显示预测因子与 AD 转换时间高度相关。淀粉样蛋白阴性 MCI 患者的 SPARE-AD 可预测临床进展。
This study evaluates the individual, as well as relative and joint value of indices obtained from magnetic resonance imaging (MRI) patterns of brain atrophy (quantified by the SPARE-AD index), cerebrospinal fluid (CSF) biomarkers, APOE genotype, and cognitive performance (ADAS-Cog) in progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) within a variable follow-up period up to 6 years, using data from the Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1). SPARE-AD was first established as a highly sensitive and specific MRI-marker of AD vs. cognitively normal (CN) subjects (AUC = 0.98). Baseline predictive values of all aforementioned indices were then compared using survival analysis on 381 MCI subjects. SPARE-AD and ADAS-Cog were found to have similar predictive value, and their combination was significantly better than their individual performance. APOE genotype did not significantly improve prediction, although the combination of SPARE-AD, ADAS-Cog and APOE ε4 provided the highest hazard ratio estimates of 17.8 (last vs. first quartile). In a subset of 192 MCI patients who also had CSF biomarkers, the addition of Aβ1–42, t-tau, and p-tau181p to the previous model did not improve predictive value significantly over SPARE-AD and ADAS-Cog combined. Importantly, in amyloid-negative patients with MCI, SPARE-AD had high predictive power of clinical progression. Our findings suggest that SPARE-AD and ADAS-Cog in combination offer the highest predictive power of conversion from MCI to AD, which is improved, albeit not significantly, by APOE genotype. The finding that SPARE-AD in amyloid-negative MCI patients was predictive of clinical progression is not expected under the amyloid hypothesis and merits further investigation. 813 ADNI-1 subjects are analyzed using pattern recognition methods. Combination of SPARE-AD and ADAS-Cog offer high predictive index on MCI progression. Cox PH models showed predictors were highly associated with time to AD conversion. SPARE-AD in amyloid-negative MCI patients predicts clinical progression.
登录
查看更多内容
DOI：
10.1016/s1474-4422(12)70227-2
发表时间：
2012-12
期刊：
The Lancet. Neurology
影响因子：
--
作者：
Fleisher AS;Chen K;Quiroz YT;Jakimovich LJ;Gomez MG;Langois CM;Langbaum JB;Ayutyanont N;Roontiva A;Thiyyagura P;Lee W;Mo H;Lopez L;Moreno S;Acosta-Baena N;Giraldo M;Garcia G;Reiman RA;Huentelman MJ;Kosik KS;Tariot PN;Lopera F;Reiman EM
通讯作者：
Reiman EM
影响因子：
3.7
作者：
Dickerson, Bradford C.;Bakkour, Akram;Salat, David H.;Feczko, Eric;Pacheco, Jenni;Greve, Douglas N.;Grodstein, Fran;Wright, Christopher I.;Blacker, Deborah;Rosas, H. Diana;Sperling, Reisa A.;Atri, Alireza;Growdon, John H.;Hyman, Bradley T.;Morris, John C.;Fischl, Bruce;Buckner, Randy L.
通讯作者：
Buckner, Randy L.
影响因子：
3.7
作者：
Aksu Y;Miller DJ;Kesidis G;Bigler DC;Yang QX
通讯作者：
Yang QX
影响因子：
5.7
作者：
Costafreda, Sergi G.;Dinov, Ivo D.;Tu, Zhuowen;Shi, Yonggang;Liu, Cheng-Yi;Kloszewska, Iwona;Mecocci, Patrizia;Soininen, Hilkka;Tsolaki, Magda;Vellas, Bruno;Wahlund, Lars-Olof;Spenger, Christian;Toga, Arthur W.;Lovestone, Simon;Simmons, Andrew
通讯作者：
Simmons, Andrew
影响因子：
--
作者：
Gomar, Jesus J.;Bobes-Bascaran, Maria T.;Goldberg, Terry E.
通讯作者：
Goldberg, Terry E.