Genetically-informed prediction of short-term Parkinson's disease progression.
Genetically-informed prediction of short-term Parkinson's disease progression.
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短期帕金森氏病进展的遗传信息预测。
DOI：
10.1038/s41531-022-00412-w
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发表时间：
2022-10-28
影响因子：
8.7
通讯作者：
Torkamani, Ali
中科院分区：
文献类型：
--
作者：
Sadaei, Hossein J.;Cordova-Palomera, Aldo;Lee, Jonghun;Padmanabhan, Jaya;Chen, Shang-Fu;Wineinger, Nathan E.;Dias, Raquel;Prilutsky, Daria;Szalma, Sandor;Torkamani, Ali
帕金森氏病(PD)的治疗可以改变疾病的症状，但并没有显示出减缓进展的迹象，其特点是随着时间的推移，逐渐和变化的运动和非运动变化。帕金森病进展的差异阻碍了临床研究，导致长期且昂贵的临床试验容易失败。开发短期帕金森病进展预测模型可能有助于缩短临床研究中检测疾病修饰药物效果所需的时间。PD进展者的定义是基线后12、24和36个月MDS-UPDRS评分的增加。只使用基线特征，在独立的、优化的XGBoost模型中，分别预测所有时间点和MDS-UPDRS子部分的PD进展。这些预测加上基线特征被组合成12个月MDS UPDRS总进展的元预测因子。帕金森进展标记物计划(PPMI)的数据用于训练，并对帕金森病生物标记物计划(PDBP)队列进行独立测试。当在坚持的PPMI集合上进行测试时，预测12个月PD总进展的F测量为0.77，ROC AUC为0.77，PR AUC为0.76。在PDBP上进行测试时，我们的F-MEASURE为0.75，ROC AUC为0.74，PR AUC为0.73。排除遗传预测因子导致预测准确性的最大损失；对于PPMI和PDBP测试，ROC AUC为0.66，PR AUC为0.66-0.68。可以结合基于调查、神经成像、内科检查和遗传预测因素来预测短期帕金森病进展。剖析遗传风险、运动症状、非运动症状和较长期的预期进展速度之间的相互作用，可以进行概括性的预测。
Parkinson’s disease (PD) treatments modify disease symptoms but have not been shown to slow progression, characterized by gradual and varied motor and non-motor changes overtime. Variation in PD progression hampers clinical research, resulting in long and expensive clinical trials prone to failure. Development of models for short-term PD progression prediction could be useful for shortening the time required to detect disease-modifying drug effects in clinical studies. PD progressors were defined by an increase in MDS-UPDRS scores at 12-, 24-, and 36-months post-baseline. Using only baseline features, PD progression was separately predicted across all timepoints and MDS-UPDRS subparts in independent, optimized, XGBoost models. These predictions plus baseline features were combined into a meta-predictor for 12-month MDS UPDRS Total progression. Data from the Parkinson’s Progression Markers Initiative (PPMI) were used for training with independent testing on the Parkinson’s Disease Biomarkers Program (PDBP) cohort. 12-month PD total progression was predicted with an F-measure 0.77, ROC AUC of 0.77, and PR AUC of 0.76 when tested on a hold-out PPMI set. When tested on PDBP we achieve a F-measure 0.75, ROC AUC of 0.74, and PR AUC of 0.73. Exclusion of genetic predictors led to the greatest loss in predictive accuracy; ROC AUC of 0.66, PR AUC of 0.66–0.68 for both PPMI and PDBP testing. Short-term PD progression can be predicted with a combination of survey-based, neuroimaging, physician examination, and genetic predictors. Dissection of the interplay between genetic risk, motor symptoms, non-motor symptoms, and longer-term expected rates of progression enable generalizable predictions.
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全基因组生存研究确定了帕金森病认知进展的新突触位点和多基因评分
影响因子：
30.8
作者：
Liu G;Peng J;Liao Z;Locascio JJ;Corvol JC;Zhu F;Dong X;Maple-Grødem J;Campbell MC;Elbaz A;Lesage S;Brice A;Mangone G;Growdon JH;Hung AY;Schwarzschild MA;Hayes MT;Wills AM;Herrington TM;Ravina B;Shoulson I;Taba P;Kõks S;Beach TG;Cormier-Dequaire F;Alves G;Tysnes OB;Perlmutter JS;Heutink P;Amr SS;van Hilten JJ;Kasten M;Mollenhauer B;Trenkwalder C;Klein C;Barker RA;Williams-Gray CH;Marinus J;International Genetics of Parkinson Disease Progression (IGPP) Consortium;Scherzer CR
通讯作者：
Scherzer CR
基因发现和多基因预测，从基因组全基因组协会的教育程度研究中，有110万个人。
影响因子：
30.8
作者：
Lee JJ;Wedow R;Okbay A;Kong E;Maghzian O;Zacher M;Nguyen-Viet TA;Bowers P;Sidorenko J;Karlsson Linnér R;Fontana MA;Kundu T;Lee C;Li H;Li R;Royer R;Timshel PN;Walters RK;Willoughby EA;Yengo L;23andMe Research Team;COGENT (Cognitive Genomics Consortium);Social Science Genetic Association Consortium;Alver M;Bao Y;Clark DW;Day FR;Furlotte NA;Joshi PK;Kemper KE;Kleinman A;Langenberg C;Mägi R;Trampush JW;Verma SS;Wu Y;Lam M;Zhao JH;Zheng Z;Boardman JD;Campbell H;Freese J;Harris KM;Hayward C;Herd P;Kumari M;Lencz T;Luan J;Malhotra AK;Metspalu A;Milani L;Ong KK;Perry JRB;Porteous DJ;Ritchie MD;Smart MC;Smith BH;Tung JY;Wareham NJ;Wilson JF;Beauchamp JP;Conley DC;Esko T;Lehrer SF;Magnusson PKE;Oskarsson S;Pers TH;Robinson MR;Thom K;Watson C;Chabris CF;Meyer MN;Laibson DI;Yang J;Johannesson M;Koellinger PD;Turley P;Visscher PM;Benjamin DJ;Cesarini D
通讯作者：
Cesarini D
影响因子：
12.3
作者：
Chen SF;Dias R;Evans D;Salfati EL;Liu S;Wineinger NE;Torkamani A
通讯作者：
Torkamani A
影响因子：
5.2
作者：
Chahine, Lana M.;Siderowf, Andrew;Tauscher, Johannes
通讯作者：
Tauscher, Johannes
影响因子：
3.9
作者：
Chung SJ;Lee JJ;Lee PH;Sohn YH
通讯作者：
Sohn YH