127 Distinct brain MRI phenotypes and their association with long-term dementia risk 6 obstructive pulmonary disease.27,28 Moreover, it is frequently used in the field of genetics to identify genotypes, for example, to identify differences in DNA methylation and gene expression in breast cancer.29 There are some previous studies on combined analysis of brain MRI markers or data-driven approaches in the field of dementia research. In a previous study, using an unsupervised deep learning approach on a large diverse data set of T1- weighted brain MRI scans showed that the difference between predicted brain age and chronological age is associated with the presence of different diseases (e.g., schizophrenia, and Alzheimer disease (AD)).30 In another study, a semi-supervised deep-clustering method identified 4 neurodegenerative brain MRI patterns based on atrophy regions of interests on T1 scans in a data set including cognitively healthy individuals and patients with cognitive impairment and dementia.31 Another study identified 3 brain MRI patterns (neurodegeneration, white matter disease, and typical brain ageing) on T1-weighted scans using a machine learning–based method that can be used to identify individual brain health.32 These previous studies used (semi) supervised deep learning approaches, which is a different approach compared with our unsupervised machine learning approach. There are also some previous studies that have used a more similar approach compared with ours, albeit in different patient populations. For example, a previous study has applied hierarchical clustering to identify patterns of markers (including brain MRI markers, blood values, and CSF markers) related to the conversion from mild cognitive impairment to AD.33 Here, 4 subgroups were identified with a different risk for conversion to AD.33 The subgroup with the highest risk showed the most severe biomarker profile, for example, the highest WMH volumes, the lowest CSF amyloid beta, the highest CSF tau, and the lowest entorhinal cortical thickness.33 Another previous study showed that midlife white matter textural properties were associated with future dementia risk.34 A more heterogeneous normal appearing white matter intensity profile was associated with a higher WMH burden in the future, and a more heterogeneous intensity of normal appearing white matter was related to increased dementia risk. Another study found 2 distinct subgroups of mild cognitive impairment based on radiomics similarity networks.35 Significant differences between the 2 mild cognitive impairment subgroups were found, among others, in the regional radiomics similarity networks of the hippocampus, temporal lobe, parahippocampal gyrus, and amygdala, as well as in the gray matter volume and cortical thickness. Furthermore, the 2 subgroups were significantly different from each other in clinical measures and the number of participants progressing to dementia within 3 years.35 Our study is the first to apply an unsupervised machine learning approach in a large group of communitydwelling individuals to assess the association of brain MRI phenotypes and long-term
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