Lau Angus, Beheshti Iman, Modirrousta Mandana, Kolesar Tiffany A, Goertzen Andrew L, Ko Ji Hyun
Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB R3E 0J9, Canada.
Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB R3E 0Z3, Canada.
Diagnostics (Basel). 2021 Nov 1;11(11):2023. doi: 10.3390/diagnostics11112023.
Dementia is broadly characterized by cognitive and psychological dysfunction that significantly impairs daily functioning. Dementia has many causes including Alzheimer's disease (AD), dementia with Lewy bodies (DLB), and frontotemporal lobar degeneration (FTLD). Detection and differential diagnosis in the early stages of dementia remains challenging. Fueled by AD Neuroimaging Initiatives (ADNI) (Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. As such, the investigators within ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.), a number of neuroimaging biomarkers for AD have been proposed, yet it remains to be seen whether these markers are also sensitive to other types of dementia. We assessed AD-related metabolic patterns in 27 patients with diverse forms of dementia (five had probable/possible AD while others had atypical cases) and 20 non-demented individuals. All participants had positron emission tomography (PET) scans on file. We used a pre-trained machine learning-based AD designation (MAD) framework to investigate the AD-related metabolic pattern among the participants under study. The MAD algorithm showed a sensitivity of 0.67 and specificity of 0.90 for distinguishing dementia patients from non-dementia participants. A total of 18/27 dementia patients and 2/20 non-dementia patients were identified as having AD-like patterns of metabolism. These results highlight that many underlying causes of dementia have similar hypometabolic pattern as AD and this similarity is an interesting avenue for future research.
痴呆症的广泛特征是认知和心理功能障碍,这会严重损害日常功能。痴呆症有多种病因,包括阿尔茨海默病(AD)、路易体痴呆(DLB)和额颞叶变性(FTLD)。在痴呆症早期进行检测和鉴别诊断仍然具有挑战性。在阿尔茨海默病神经影像倡议(ADNI)的推动下(本文撰写所使用的数据来自阿尔茨海默病神经影像倡议(ADNI)数据库。因此,ADNI的研究人员参与了ADNI的设计和实施及/或提供了数据,但未参与本报告的分析或撰写),已经提出了一些用于AD的神经影像生物标志物,但这些标志物对其他类型痴呆症是否也敏感仍有待观察。我们评估了27例患有不同形式痴呆症的患者(5例可能/疑似患有AD,其他为非典型病例)和20例非痴呆个体中与AD相关的代谢模式。所有参与者都有存档的正电子发射断层扫描(PET)图像。我们使用一个预先训练的基于机器学习的AD诊断(MAD)框架来研究研究参与者中与AD相关的代谢模式。MAD算法在区分痴呆症患者和非痴呆参与者方面显示出0.67的灵敏度和0.90的特异性。在27例痴呆症患者中,共有18例被确定具有类似AD的代谢模式,在20例非痴呆患者中有2例被确定具有类似AD的代谢模式。这些结果突出表明,许多痴呆症的潜在病因具有与AD相似的代谢减低模式,这种相似性是未来研究的一个有趣方向。