Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach
Abstract Background Given the complex and progressive nature of mild cognitive impairment (MCI), the ability to delineate and understand the heterogeneous cognitive trajectories is crucial for developing personalized medicine and informing trial design. The primary goals of this study were to examin...
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| Format: | Article |
| Language: | English |
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BMC
2023-03-01
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| Series: | Alzheimer’s Research & Therapy |
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| Online Access: | https://doi.org/10.1186/s13195-023-01205-w |
| _version_ | 1827985012596670464 |
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| author | Xiwu Wang Teng Ye Wenjun Zhou Jie Zhang for the Alzheimer’s Disease Neuroimaging Initiative |
| author_facet | Xiwu Wang Teng Ye Wenjun Zhou Jie Zhang for the Alzheimer’s Disease Neuroimaging Initiative |
| author_sort | Xiwu Wang |
| collection | DOAJ |
| description | Abstract Background Given the complex and progressive nature of mild cognitive impairment (MCI), the ability to delineate and understand the heterogeneous cognitive trajectories is crucial for developing personalized medicine and informing trial design. The primary goals of this study were to examine whether different cognitive trajectories can be identified within subjects with MCI and, if present, to characterize each trajectory in relation to changes in all major Alzheimer’s disease (AD) biomarkers over time. Methods Individuals with a diagnosis of MCI at the first visit and ≥ 1 follow-up cognitive assessment were selected from the Alzheimer’s Disease Neuroimaging Initiative database (n = 936; age 73 ± 8; 40% female; 16 ± 3 years of education; 50% APOE4 carriers). Based on the Alzheimer’s Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) total scores from baseline up to 5 years follow-up, a non-parametric k-means longitudinal clustering method was performed to obtain clusters of individuals with similar patterns of cognitive decline. We further conducted a series of linear mixed-effects models to study the associations of cluster membership with longitudinal changes in other cognitive measures, neurodegeneration, and in vivo AD pathologies. Results Four distinct cognitive trajectories emerged. Cluster 1 consisted of 255 individuals (27%) with a nearly non-existent rate of change in the ADAS-Cog-13 over 5 years of follow-up and a healthy-looking biomarker profile. Individuals in the cluster 2 (n = 336, 35%) and 3 (n = 240, 26%) groups showed relatively mild and moderate cognitive decline trajectories, respectively. Cluster 4, comprising about 11% of our study sample (n = 105), exhibited an aggressive cognitive decline trajectory and was characterized by a pronouncedly abnormal biomarker profile. Conclusions Individuals with MCI show substantial heterogeneity in cognitive decline. Our findings may potentially contribute to improved trial design and patient stratification. |
| first_indexed | 2024-04-09T23:08:03Z |
| format | Article |
| id | doaj.art-f9d4f31d5c304ad0a4b20515e162bbb8 |
| institution | Directory Open Access Journal |
| issn | 1758-9193 |
| language | English |
| last_indexed | 2024-04-09T23:08:03Z |
| publishDate | 2023-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Alzheimer’s Research & Therapy |
| spelling | doaj.art-f9d4f31d5c304ad0a4b20515e162bbb82023-03-22T10:37:41ZengBMCAlzheimer’s Research & Therapy1758-91932023-03-0115111610.1186/s13195-023-01205-wUncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approachXiwu Wang0Teng Ye1Wenjun Zhou2Jie Zhang3for the Alzheimer’s Disease Neuroimaging InitiativeDepartment of Psychiatry, Wenzhou Seventh People’s HospitalDepartment of Ultrasound, The First Affiliated Hospital of Wenzhou Medical UniversityResearch and Development, Hangzhou Shansier Medical Technologies Co., Ltd.Department of Data Science, Hangzhou Shansier Medical Technologies Co., Ltd.Abstract Background Given the complex and progressive nature of mild cognitive impairment (MCI), the ability to delineate and understand the heterogeneous cognitive trajectories is crucial for developing personalized medicine and informing trial design. The primary goals of this study were to examine whether different cognitive trajectories can be identified within subjects with MCI and, if present, to characterize each trajectory in relation to changes in all major Alzheimer’s disease (AD) biomarkers over time. Methods Individuals with a diagnosis of MCI at the first visit and ≥ 1 follow-up cognitive assessment were selected from the Alzheimer’s Disease Neuroimaging Initiative database (n = 936; age 73 ± 8; 40% female; 16 ± 3 years of education; 50% APOE4 carriers). Based on the Alzheimer’s Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) total scores from baseline up to 5 years follow-up, a non-parametric k-means longitudinal clustering method was performed to obtain clusters of individuals with similar patterns of cognitive decline. We further conducted a series of linear mixed-effects models to study the associations of cluster membership with longitudinal changes in other cognitive measures, neurodegeneration, and in vivo AD pathologies. Results Four distinct cognitive trajectories emerged. Cluster 1 consisted of 255 individuals (27%) with a nearly non-existent rate of change in the ADAS-Cog-13 over 5 years of follow-up and a healthy-looking biomarker profile. Individuals in the cluster 2 (n = 336, 35%) and 3 (n = 240, 26%) groups showed relatively mild and moderate cognitive decline trajectories, respectively. Cluster 4, comprising about 11% of our study sample (n = 105), exhibited an aggressive cognitive decline trajectory and was characterized by a pronouncedly abnormal biomarker profile. Conclusions Individuals with MCI show substantial heterogeneity in cognitive decline. Our findings may potentially contribute to improved trial design and patient stratification.https://doi.org/10.1186/s13195-023-01205-wMild cognitive impairmentClusteringHeterogeneityCognitive trajectoryLongitudinal study |
| spellingShingle | Xiwu Wang Teng Ye Wenjun Zhou Jie Zhang for the Alzheimer’s Disease Neuroimaging Initiative Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach Alzheimer’s Research & Therapy Mild cognitive impairment Clustering Heterogeneity Cognitive trajectory Longitudinal study |
| title | Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach |
| title_full | Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach |
| title_fullStr | Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach |
| title_full_unstemmed | Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach |
| title_short | Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach |
| title_sort | uncovering heterogeneous cognitive trajectories in mild cognitive impairment a data driven approach |
| topic | Mild cognitive impairment Clustering Heterogeneity Cognitive trajectory Longitudinal study |
| url | https://doi.org/10.1186/s13195-023-01205-w |
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