128 Chapter 6 dementia risk. The large sample size of community-dwelling individuals in our study aids the generalizability of our results. Our study is further strengthened by the long follow-up time to the assessment of occurrence of dementia. Because of our study design, in the future, our method could help in assessing an individuals increased dementia risk at an early stage to determine patient prognosis in clinical practice and may aid in patient selection for future treatment studies. A hierarchical clustering model allows combining of a spectrum of brain MRI markers and to find patterns in these data. We have previously applied the hierarchical clustering method in different data sets with different MRI markers to identify MRI phenotypes of the brain related to future stroke and mortality in patients with manifest arterial disease,7 as well as related to increased postoperative delirium risk in preoperative patients.36 To the best of our knowledge, this study assessed brain MRI phenotypes in relation to long-term dementia risk in community-dwelling older adults. Our results identified 15 distinct subgroups of individuals with different distributions of brain MRI markers of neurodegenerative and neurovascular disease. The multi-burden group with the highest long-term risk for dementia (subgroup 12) does show markers of SVD, such as high WMH volumes and an irregular WMH shape, but includes only few individuals with brain infarcts. In addition, subgroup 12 showed the most severe cerebral atrophy, which may suggest that this subgroup has more underlying neurodegenerative pathology. Subgroup 2 has, similar to subgroup 12, high WMH volumes and an irregular WMH shape but also includes a high number of participants with subcortical, cerebellar, and cortical infarcts. Atrophy is less prominent in subgroup 2 compared with subgroup 12. Subgroup 15 may include mostly patients with large vessel disease, as WMH volumes and shape are only moderately abnormal, while all participants in this group have cortical infarcts. We showed that different brain MRI phenotypes, characterized by a distinct combination of brain MRI markers, predispose to occurrence of dementia and are related to different long-term dementia risks. Strengths of our study include the use of multiple brain MRI markers in one framework, a large sample size, and a long follow-up period for dementia outcome. Furthermore, we mostly included markers that can be (semi) automatically detected on brain MRI scans (e.g., WMH volumes and brain atrophy) and the inclusion of novel brain MRI markers (such as WMH shape). An automated, unsupervised approach to identify groups was applied that allowed us to identify novel patterns of brain MRI markers. Limitations of this study might be the somewhat subjective cut-offs within the model, such as the dendrogram cut-off. However, to increase objectivity, we used the Dunn index and the heatmap to determine the cut-off for the number of
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