Brain-age is associated with progression to dementia in memory clinic patients.
Brain-age is associated with progression to dementia in memory clinic patients.
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DOI：
10.1016/j.nicl.2022.103175
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发表时间：
2022
影响因子：
4.2
通讯作者：
Cole, James H.
中科院分区：
文献类型：
--
作者：
Biondo, Francesca;Jewell, Amelia;Pritchard, Megan;Aarsland, Dag;Steves, Claire J.;Mueller, Christoph;Cole, James H.
大脑年龄是基于T1加权MRI数据的大脑“生物”年龄指数。记忆诊所的患者大脑看起来更老，患痴呆症的风险更高。结果与病史、年龄、性别、MMSE评分和脑体积无关。大脑年龄有可能帮助患者及早发现痴呆症。早期检测痴呆症风险的生物标记物有望更好地进行疾病监测和有针对性的干预。然而，大多数生物标记物研究，特别是神经成像方面的研究，都分析了人工“干净”的研究组，没有共病、错误的转介、禁忌症和狭窄的社会人口库。这种偏见意味着，神经成像样本往往不能代表痴呆症风险的目标人群(例如，被提到记忆诊所的人)，限制了这些研究在现实世界临床环境中的推广。为了更好地将研究转化为临床，需要更能代表痴呆症患者群体的数据集。我们分析了参考英国记忆诊所服务的患者(n=1140；60.2%女性，平均[SD]年龄为70.0[10.8]岁)的真实环境中的T1加权MRI扫描，以得出“大脑年龄”。脑龄是基于结构神经成像定量分析的与年龄相关的脑健康指数，在很大程度上反映脑萎缩。脑预测年龄差(Brain-Pad)的计算方法是脑年龄减去实际年龄。我们使用与电子健康记录的链接来确定哪些患者在神经成像评估后三个月至7.8年间继续发展为痴呆症(n=1476)。使用COX回归进行生存分析，结果表明，在调整了基线年龄、年龄、性别、迷你精神状态检查评分和正常脑体积后，每一脑垫一年患痴呆症的风险增加3%(风险比[95%CI]=1.03[1.02，1.04]，P<0.0001)。在敏感性分析中，当痴呆症发生时间至少3年时(风险比[95%CI]=1.06[1.02，1.09]，p=0.0006)，或当基线≥评分>27分(风险比[95%CI]=111.03[1.01，1.05]，p=0.0006)时，脑垫仍然显著。记忆诊所的患者大脑看起来更老，更有可能随后被诊断为痴呆症。潜在地，脑年龄可以在最初的记忆临床评估中帮助决策，以提高痴呆症的早期发现。即使在确诊前3年多进行神经成像评估，当认知功能没有明显受损时，脑年龄仍然被证明是有信息的。这些真实世界的结果支持在记忆诊所使用定量神经成像生物标记物，如大脑年龄。
Brain-age is an index of the brain’s ‘biological’ age based on T1-weighted MRI data. Memory clinic patients with older-appearing brains have higher risk of dementia. Results are independent of medical history, age, sex, MMSE score and brain volumes. Brain-age has the potential to aid early detection of dementia in patients. Biomarkers for the early detection of dementia risk hold promise for better disease monitoring and targeted interventions. However, most biomarker studies, particularly in neuroimaging, have analysed artificially ‘clean’ research groups, free from comorbidities, erroneous referrals, contraindications and from a narrow sociodemographic pool. Such biases mean that neuroimaging samples are often unrepresentative of the target population for dementia risk (e.g., people referred to a memory clinic), limiting the generalisation of these studies to real-world clinical settings. To facilitate better translation from research to the clinic, datasets that are more representative of dementia patient groups are warranted. We analysed T1-weighted MRI scans from a real-world setting of patients referred to UK memory clinic services (n = 1140; 60.2 % female and mean [SD] age of 70.0[10.8] years) to derive ‘brain-age’. Brain-age is an index of age-related brain health based on quantitative analysis of structural neuroimaging, largely reflecting brain atrophy. Brain-predicted age difference (brain-PAD) was calculated as brain-age minus chronological age. We determined which patients went on to develop dementia between three months and 7.8 years after neuroimaging assessment (n = 476) using linkage to electronic health records. Survival analysis, using Cox regression, indicated a 3 % increased risk of dementia per brain-PAD year (hazard ratio [95 % CI] = 1.03 [1.02,1.04], p < 0.0001), adjusted for baseline age, age2, sex, Mini Mental State Examination (MMSE) score and normalised brain volume. In sensitivity analyses, brain-PAD remained significant when time-to-dementia was at least 3 years (hazard ratio [95 % CI] = 1.06 [1.02, 1.09], p = 0.0006), or when baseline MMSE score ≥ 27 (hazard ratio [95 % CI] = 1.03 [1.01, 1.05], p = 0.0006). Memory clinic patients with older‐appearing brains are more likely to receive a subsequent dementia diagnosis. Potentially, brain-age could aid decision-making during initial memory clinic assessment to improve early detection of dementia. Even when neuroimaging assessment was more than 3 years prior to diagnosis and when cognitive functioning was not clearly impaired, brain-age still proved informative. These real-world results support the use of quantitative neuroimaging biomarkers like brain-age in memory clinics.
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影响因子：
5
作者：
Fry A;Littlejohns TJ;Sudlow C;Doherty N;Adamska L;Sprosen T;Collins R;Allen NE
通讯作者：
Allen NE
影响因子：
9.9
作者：
Dickerson, B. C.;Stoub, T. R.;deToledo-Morrell, L.
通讯作者：
deToledo-Morrell, L.
影响因子：
29
作者：
Gottesman, Rebecca F.;Albert, Marilyn S.;Knopman, David S.
通讯作者：
Knopman, David S.
脑年龄预测：使用基于区域和体素的形态学数据进行机器学习模型的比较。
影响因子：
4.8
作者：
Baecker L;Dafflon J;da Costa PF;Garcia-Dias R;Vieira S;Scarpazza C;Calhoun VD;Sato JR;Mechelli A;Pinaya WHL
通讯作者：
Pinaya WHL
影响因子：
3.1
作者：
Clausen AN;Fercho KA;Monsour M;Disner S;Salminen L;Haswell CC;Rubright EC;Watts AA;Buckley MN;Maron-Katz A;Sierk A;Manthey A;Suarez-Jimenez B;Olatunji BO;Averill CL;Hofmann D;Veltman DJ;Olson EA;Li G;Forster GL;Walter H;Fitzgerald J;Théberge J;Simons JS;Bomyea JA;Frijling JL;Krystal JH;Baker JT;Phan KL;Ressler K;Han LKM;Nawijn L;Lebois LAM;Schmaal L;Densmore M;Shenton ME;van Zuiden M;Stein M;Fani N;Simons RM;Neufeld RWJ;Lanius R;van Rooij S;Koch SBJ;Bonomo S;Jovanovic T;deRoon-Cassini T;Ely TD;Magnotta VA;He X;Abdallah CG;Etkin A;Schmahl C;Larson C;Rosso IM;Blackford JU;Stevens JS;Daniels JK;Herzog J;Kaufman ML;Olff M;Davidson RJ;Sponheim SR;Mueller SC;Straube T;Zhu X;Neria Y;Baugh LA;Cole JH;Thompson PM;Morey RA
通讯作者：
Morey RA