Biomarker-guided clustering of Alzheimer’s disease clinical syndromesopen access
- Authors
- Toschi, Nicola; Lista, Simone; Baldacci, Filippo; Cavedo, Enric; Zetterberg, Henrik; Blennow, Kaj; Kilimann, Ingo; Teipel, Stefan J.; Melo dos Santos, Antonio; Kim, Seung Hyun; Harald Hampel
- Issue Date
- Nov-2019
- Publisher
- ELSEVIER SCIENCE INC
- Keywords
- Alzheimer' s disease; Biomarker-guided categorization; Clustering; Pathophysiology; Precision medicine
- Citation
- NEUROBIOLOGY OF AGING, v.83, pp.42 - 53
- Indexed
- SCIE
SCOPUS
- Journal Title
- NEUROBIOLOGY OF AGING
- Volume
- 83
- Start Page
- 42
- End Page
- 53
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/146299
- DOI
- 10.1016/j.neurobiolaging.2019.08.032
- ISSN
- 0197-4580
- Abstract
- Alzheimer's disease (AD) neuropathology is extremely heterogeneous, and the evolution from preclinical to mild cognitive impairment until dementia is driven by interacting genetic/biological mechanisms not fully captured by current clinical/research criteria. We characterized the heterogeneous "construct" of AD through a cerebrospinal fluid biomarker-guided stratification approach. We analyzed 5 validated pathophysiological cerebrospinal fluid biomarkers (A beta(1-42), t-tau, -p-tau(181), NFL, YKL-40) in 113 participants (healthy controls [N = 20], subjective memory complainers [N = 36], mild cognitive impairment [N = 20], and AD dementia [N = 37], age: 66.7 +/- 10.4, 70.4 +/- 7.7, 71.7 +/- 8.4, 76.2 +/- 3.5 years [mean +/- SD], respectively) using Density-Based Spatial Clustering of Applications with Noise, which does not require a priori determination of the number of clusters. We found 5 distinct clusters (sizes: N = 38, 16, 24, 14, and 21) whose composition was independent of phenotypical groups. Two clusters showed biomarker profiles linked to neurodegenerative processes not associated with classical AD-related pathophysiology. One cluster was characterized by the neuroinflammation biomarker YKL-40. Combining nonlinear data aggregation with informative biomarkers can generate novel patient strata which are representative of cellular/molecular pathophysiology and may aid in predicting disease evolution and mechanistic drug response.
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