Three sets of research criteria are available for diagnosis of Alzheimer’s disease in subjects with mild cognitive impairment: the International Working Group-1, International Working Group-2, and National Institute of Aging-Alzheimer Association criteria. We compared the prevalence and prognosis of Alzheimer’s disease at the mild cognitive impairment stage according to these criteria. Subjects with mild cognitive impairment (n = 1607), 766 of whom had both amyloid and neuronal injury markers, were recruited from 13 cohorts. We used cognitive test performance and available biomarkers to classify subjects as prodromal Alzheimer’s disease according to International Working Group-1 and International Working Group-2 criteria and in the high Alzheimer’s disease likelihood group, conflicting biomarker groups (isolated amyloid pathology or suspected non-Alzheimer pathophysiology), and low Alzheimer’s disease likelihood group according to the National Institute of Ageing-Alzheimer Association criteria. Outcome measures were the proportion of subjects with Alzheimer’s disease at the mild cognitive impairment stage and progression to Alzheimer’s disease-type dementia. We performed survival analyses using Cox proportional hazards models. According to the International Working Group-1 criteria, 850 (53%) subjects had prodromal Alzheimer’s disease. Their 3-year progression rate to Alzheimer’s disease-type dementia was 50% compared to 21% for subjects without prodromal Alzheimer’s disease. According to the International Working Group-2 criteria, 308 (40%) subjects had prodromal Alzheimer’s disease. Their 3-year progression rate to Alzheimer’s disease-type dementia was 61% compared to 22% for subjects without prodromal Alzheimer’s disease. According to the National Institute of Ageing-Alzheimer Association criteria, 353 (46%) subjects were in the high Alzheimer’s disease likelihood group, 49 (6%) in the isolated amyloid pathology group, 220 (29%) in the suspected non-Alzheimer pathophysiology group, and 144 (19%) in the low Alzheimer’s disease likelihood group. The 3-year progression rate to Alzheimer’s disease-type dementia was 59% in the high Alzheimer’s disease likelihood group, 22% in the isolated amyloid pathology group, 24% in the suspected non-Alzheimer pathophysiology group, and 5% in the low Alzheimer’s disease likelihood group. Our findings support the use of the proposed research criteria to identify Alzheimer’s disease at the mild cognitive impairment stage. In clinical settings, the use of both amyloid and neuronal injury markers as proposed by the National Institute of Ageing-Alzheimer Association criteria offers the most accurate prognosis. For clinical trials, selection of subjects in the National Institute of Ageing-Alzheimer Association high Alzheimer’s disease likelihood group or the International Working Group-2 prodromal Alzheimer’s disease group could be considered.

Background: Recently, the LIfestyle for BRAin health (LIBRA) index was developed to assess an individual’s prevention potential for dementia. Objective: We investigated the predictive validity of the LIBRA index for incident dementia in midlife, late life, and the oldest-old. Methods: 9,387 non-demented individuals were recruited from the European population-based DESCRIPA study. An individual’s LIBRA index was calculated solely based on modifiable risk factors: depression, diabetes, physical activity, hypertension, obesity, smoking, hypercholesterolemia, coronary heart disease, and mild/moderate alcohol use. Cox regression was used to test the predictive validity of LIBRA for dementia at follow-up (mean 7.2 y, range 1–16). Results: In midlife (55–69 y, n = 3,256) and late life (70–79 y, n = 4,320), the risk for dementia increased with higher LIBRA scores. Individuals in the intermediate- and high-risk groups had a higher risk of dementia than those in the low-risk group. In the oldest-old (80–97 y, n = 1,811), higher LIBRA scores did not increase the risk for dementia. Conclusion: LIBRA might be a useful tool to identify individuals for primary prevention interventions of dementia in midlife, and maybe in late life, but not in the oldest-old.

Objective: To compare the predictive accuracy of β-amyloid (Aβ)1–42 and total tau in CSF, hippocampal volume (HCV), and APOE genotype for Alzheimer disease (AD)-type dementia in subjects with amnestic mild cognitive impairment (aMCI) and nonamnestic mild cognitive impairment (naMCI). Methods: We selected 399 subjects with aMCI and 226 subjects with naMCI from a multicenter memory clinic–based cohort. We measured CSF Aβ1–42 and tau by ELISA (n = 231), HCV on MRI (n = 388), and APOE ε4 (n = 523). Follow-up was performed annually up to 5 years. Outcome measures were progression to AD-type dementia and cognitive decline. Results: At least 1 follow-up was available for 538 subjects (86%). One hundred thirty-two subjects with aMCI (38%) and 39 subjects with naMCI (20%) progressed to AD-type dementia after an average follow-up of 2.5 years. CSF Aβ1–42, tau, Aβ1–42/tau ratio, HCV, and APOE ε4 predicted AD-type dementia in each MCI subgroup with the same overall diagnostic accuracy. However, CSF Aβ1–42 concentration was higher and hippocampal atrophy less severe in subjects with naMCI compared with aMCI. This reduced the sensitivity but increased the specificity of these markers for AD-type dementia in subjects with naMCI. Conclusions: AD biomarkers are useful to predict AD-type dementia in subjects with aMCI and naMCI. However, biomarkers might not be as sensitive for early diagnosis of AD in naMCI compared with aMCI. This may have implications for clinical implementation of the National Institute on Aging and Alzheimer9s Association criteria.

There is an urgent need for novel, noninvasive biomarkers to diagnose Alzheimer’s disease (AD) in the predementia stages and to predict the rate of decline. Therefore, we set up the European Medical Information Framework for Alzheimer’s Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study. In this report we describe the design of the study, the methods used and the characteristics of the participants. Participants were selected from existing prospective multicenter and single-center European studies. Inclusion criteria were having normal cognition (NC) or a diagnosis of mild cognitive impairment (MCI) or AD-type dementia at baseline, age above 50 years, known amyloid-beta (Aβ) status, availability of cognitive test results and at least two of the following materials: plasma, DNA, magnetic resonance imaging (MRI) or cerebrospinal fluid (CSF). Targeted and untargeted metabolomic and proteomic analyses were performed in plasma, and targeted and untargeted proteomics were performed in CSF. Genome-wide SNP genotyping, next-generation sequencing and methylation profiling were conducted in DNA. Visual rating and volumetric measures were assessed on MRI. Baseline characteristics were analyzed using ANOVA or chi-square, rate of decline analyzed by linear mixed modeling. We included 1221 individuals (NC n = 492, MCI n = 527, AD-type dementia n = 202) with a mean age of 67.9 (SD 8.3) years. The percentage Aβ+ was 26% in the NC, 58% in the MCI, and 87% in the AD-type dementia groups. Plasma samples were available for 1189 (97%) subjects, DNA samples for 929 (76%) subjects, MRI scans for 862 (71%) subjects and CSF samples for 767 (63%) subjects. For 759 (62%) individuals, clinical follow-up data were available. In each diagnostic group, the APOE ε4 allele was more frequent amongst Aβ+ individuals (p < 0.001). Only in MCI was there a difference in baseline Mini Mental State Examination (MMSE) score between the A groups (p < 0.001). Aβ+ had a faster rate of decline on the MMSE during follow-up in the NC (p < 0.001) and MCI (p < 0.001) groups. The characteristics of this large cohort of elderly subjects at various cognitive stages confirm the central roles of Aβ and APOE ε4 in AD pathogenesis. The results of the multimodal analyses will provide new insights into underlying mechanisms and facilitate the discovery of new diagnostic and prognostic AD biomarkers. All researchers can apply for access to the EMIF-AD MBD data by submitting a research proposal via the EMIF-AD Catalog.