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Whether the neuropathological profile of mild cognitive impairment (MCI) reflects an intermediate state between normal aging and dementia is not clear. Identifying which phenomena initiate disease and which occur secondary to the neuropathological process is important for targeted disease prevention. Current definitions of MCI include amnestic (aMCI), nonamnestic (nMCI), and multidomain (mMCI) subtypes. In an unbiased population-based cohort of brain donors, we have determined how many individuals fulfill these criteria in the period leading up to death [n=10 (5 multidomain MCI, 4 amnestic MCI, 1 nonamnestic MCI)]. All MCI cases were collapsed into 1 group and we tested whether their pathologic profile, including markers of Alzheimer disease (AD) and vascular disease (VD), is intermediate to individuals (matched for age, sex, and education) without cognitive impairment (n=20) or dementia (n=20). The main findings are of a significant linear trend in the odds of neuritic plaques (entorhinal/hippocampus), atrophy (hippocampal and cortical), infarcts, and small vessel disease (SVD) with increased cognitive impairment. Neuropathologically, MCI is complex, with 10% of MCI brains classified as normal, 10% as VD, 10% as AD, and 40% as mixed AD/VD, with the remaining showing other pathologies. Rather than pure pathologic changes, several different factors seem to contribute to the impairment of MCI. In MCI, both AD and non-AD pathology should be considered as possible intervention targets. [ABSTRACT FROM AUTHOR]

Purpose of Review: A key priority in dementia research is the development of tools to identify individuals at high risk of dementia. This is important to prevent or delay dementia onset and as we move towards personalized medicine., Recent Findings: Numerous models (n > 50) for predicting dementia have been developed. These vary in the number (0 to 20+) and type (e.g. demographics, health, neuropsychological, and genetic) of predictor variables used for risk calculation, follow-up length (1-20 years) and age at screening (mid vs laterlife). Evaluation of the models shows that most have moderate-to-poor predictive accuracy. Few have been externally validated, raising questions about their generalizability outside the cohorts from which they were developed. The results highlight that if additional models are proposed the field will be overwhelmed with many competing risk models, making it difficult to reach consensus on which is best., Summary: Numerous models for predicting dementia have been proposed but are limited by a lack of external validation and evaluation of economic impact. Innovative methods and data designs may be needed to improve derivation of dementia risk scores. Having a method for predicting dementia risk could transform medical research and allow for earlier testing of intervention strategies.