Alzheimer’s disease causes a progressive dementia that currently affects over 35 million individuals worldwide and is expected to affect 115 million by 2050 (ref. 1). There are no cures or disease-modifying therapies, and this may be due to our inability to detect the disease before it has progressed to produce evident memory loss and functional decline. Biomarkers of preclinical disease will be critical to the development of disease-modifying or even preventative therapies2. Unfortunately, current biomarkers for early disease, including cerebrospinal fluid tau and amyloid-β levels3, structural and functional magnetic resonance imaging4 and the recent use of brain amyloid imaging5 or inflammaging6, are limited because they are either invasive, time-consuming or expensive. Blood-based biomarkers may be a more attractive option, but none can currently detect preclinical Alzheimer’s disease with the required sensitivity and specificity7. Herein, we describe our lipidomic approach to detecting preclinical Alzheimer’s disease in a group of cognitively normal older adults. We discovered and validated a set of ten lipids from peripheral blood that predicted phenoconversion to either amnestic mild cognitive impairment or Alzheimer’s disease within a 2–3 year timeframe with over 90% accuracy. This biomarker panel, reflecting cell membrane integrity, may be sensitive to early neurodegeneration of preclinical Alzheimer’s disease.

Background Proteins pathogenic in Alzheimer’s disease (AD) were extracted from neurally-derived blood exosomes and quantified to develop biomarkers for staging of sporadic AD. Methods Blood exosomes obtained at one time-point from patients with AD (n=57) or frontotemporal dementia (FTD) (n=16), and at two time-points from others (n=24) when cognitively normal and one-ten years later when diagnosed with AD were enriched for neural sources by immunoabsorption. AD-pathogenic exosomal proteins were extracted and quantified by ELISAs. Results Mean exosomal levels of total Tau, P-T181-tau, P-S396-tau and Aβ1-42 for AD and levels of P-T181-tau and Aβ1-42 for FTD were significantly higher than for case-controls. Stepwise discriminant modeling incorporated P-T181-tau, P-S396-tau and Aβ1-42 in AD, but only P-T181-tau in FTD. Classification of 96.4% of AD patients and 87.5% of FTD patients was correct. In 24 AD patients, exosomal levels of P-S396-tau, P-T181-tau and Aβ1-42 were significantly higher than for controls both one to ten years before and when diagnosed with AD. Conclusions Levels of P-S396-tau, P-T181-tau and Aβ1-42 in extracts of neurally-derived blood exosomes predict development of AD up to 10 years prior to clinical onset.

Background Accurate diagnosis and early detection of complex disease has the potential to be of enormous benefit to clinical trialists, patients, and researchers alike. We sought to create a non-invasive, low-cost, and accurate classification model for diagnosing Parkinson’s disease risk to serve as a basis for future disease prediction studies in prospective longitudinal cohorts. Methods We developed a simple disease classifying model within 367 patients with Parkinson’s disease and phenotypically typical imaging data and 165 controls without neurological disease of the Parkinson’s Progression Marker Initiative (PPMI) study. Olfactory function, genetic risk, family history of PD, age and gender were algorithmically selected as significant contributors to our classifying model. This model was developed using the PPMI study then tested in 825 patients with Parkinson’s disease and 261 controls from five independent studies with varying recruitment strategies and designs including the Parkinson’s Disease Biomarkers Program (PDBP), Parkinson’s Associated Risk Study (PARS), 23andMe, Longitudinal and Biomarker Study in PD (LABS-PD), and Morris K. Udall Parkinson’s Disease Research Center of Excellence (Penn-Udall). Findings Our initial model correctly distinguished patients with Parkinson’s disease from controls at an area under the curve (AUC) of 0.923 (95% CI = 0.900 – 0.946) with high sensitivity (0.834, 95% CI = 0.711 – 0.883) and specificity (0.903, 95% CI = 0.824 – 0.946) in PPMI at its optimal AUC threshold (0.655). The model is also well-calibrated with all Hosmer-Lemeshow simulations suggesting that when parsed into random subgroups, the actual data mirrors that of the larger expected data, demonstrating that our model is robust and fits well. Likewise external validation shows excellent classification of PD with AUCs of 0.894 in PDBP, 0.998 in PARS, 0.955 in 23andMe, 0.929 in LABS-PD, and 0.939 in Penn-Udall. Additionally, when our model classifies SWEDD as PD, they convert within one year to typical PD more than would be expected by chance, with 4 out of 17 classified as PD converting to PD during brief follow-up; while SWEDD not classified as PD showed one conversion to PD out of 38 participants (test of proportions, p-value = 0.003). Interpretation This model may serve as a basis for future investigations into the classification, prediction and treatment of Parkinson’s disease, particularly those planning on attempting to identify prodromal or preclinical etiologically typical PD cases in prospective cohorts for efficient interventional and biomarker studies. Funding Please see the acknowledgements and funding section at the end of the manuscript.

We examined the frequency of Parkinson disease with mild cognitive impairment (PD-MCI) and its subtypes and the accuracy of 3 cognitive scales for detecting PD-MCI using the new criteria for PD-MCI proposed by the Movement Disorders Society. Nondemented patients with Parkinson’s disease completed a clinical visit with the 3 screening tests followed 1 to 3 weeks later by neuropsychological testing. Of 139 patients, 46 met Level 2 Task Force criteria for PD-MCI when impaired performance was based on comparisons with normative scores. Forty-two patients (93%) had multi-domain MCI. At the lowest cutoff levels that provided at least 80% sensitivity, specificity was 44% for the Montreal Cognitive Assessment and 33% for the Scales for Outcomes in Parkinson’s Disease-Cognition. The Mini-Mental State Examination could not achieve 80% sensitivity at any cutoff score. At the highest cutoff levels that provided specificity of at least 80%, sensitivities were low (≤44%) for all tests. When decline from estimated premorbid levels was considered evidence of cognitive impairment, 110 of 139 patients were classified with PD-MCI, and 103 (94%) had multi-domain MCI. We observed dramatic differences in the proportion of patients who had PD-MCI using the new Level 2 criteria, depending on whether or not decline from premorbid level of intellectual function was considered. Recommendations for methods of operationalizing decline from premorbid levels constitute an unmet need. Among the 3 screening tests examined, none of the instruments provided good combined sensitivity and specificity for PD-MCI. Other tests recommended by the Task Force Level 1 criteria may represent better choices, and these should be the subject of future research.

Impaired visual motion processing may accompany memory deficits in MCI or AD, or may occur alone in otherwise intact ON subjects. This suggests that visuospatial impairment may develop as an independent sign of neurodegenerative disease, possibly preceding the clinical onset of AD.

The increasing number of afflicted individuals with late-onset Alzheimer's disease (AD) poses significant emotional and financial burden on the world's population. Therapeutics designed to treat symptoms or alter the disease course have failed to make an impact, despite substantial investments by governments, pharmaceutical industry, and private donors. These failures in treatment efficacy have led many to believe that symptomatic disease, including both mild cognitive impairment (MCI) and AD, may be refractory to therapeutic intervention. The recent focus on biomarkers for defining the preclinical state of MCI/AD is in the hope of defining a therapeutic window in which the neural substrate remains responsive to treatment. The ability of biomarkers to adequately define the at-risk state may ultimately allow novel or repurposed therapeutic agents to finally achieve the disease-modifying status for AD. In this review, we examine current preclinical AD biomarkers and suggest how to generalize their use going forward.

We recently documented plasma lipid dysregulation in preclinical late-onset Alzheimer's disease (LOAD). A 10 plasma lipid panel, predicted phenoconversion and provided 90% sensitivity and 85% specificity in differentiating an at-risk group from those that would remain cognitively intact. Despite these encouraging results, low positive predictive values limit the clinical usefulness of this panel as a screening tool in subjects aged 70-80 years or younger. In this report, we re-examine our metabolomic data, analyzing baseline plasma specimens from our group of phenoconverters (n = 28) and a matched set of cognitively normal subjects (n = 73), and discover and internally validate a panel of 24 plasma metabolites. The new panel provides a classifier with receiver operating characteristic area under the curve for the discovery and internal validation cohort of 1.0 and 0.995 (95% confidence intervals of 1.0-1.0, and 0.981-1.0), respectively. Twenty-two of the 24 metabolites were significantly dysregulated lipids. While positive and negative predictive values were improved compared to our 10-lipid panel, low positive predictive values provide a reality check on the utility of such biomarkers in this age group (or younger). Through inclusion of additional significantly dysregulated analyte species, our new biomarker panel provides greater accuracy in our cohort but remains limited by predictive power. Unfortunately, the novel metabolite panel alone may not provide improvement in counseling and management of at-risk individuals but may further improve selection of subjects for LOAD secondary prevention trials. We expect that external validation will remain challenging due to our stringent study design, especially compared with more diverse subject cohorts. We do anticipate, however, external validation of reduced plasma lipid species as a predictor of phenoconversion to either prodromal or manifest LOAD.

Background/Objectives Vision-based speed of processing (VSOP) training is a promising cognitive intervention for older adults. However, it is unknown whether VSOP training can affect cognitive processing in individuals at high risk for dementia. Here, we examined cognitive and neural effects of VSOP training in older adults with amnestic mild cognitive impairment (aMCI) and contrasted those effects with an active control (mental leisure activities; MLA). Design A randomized single-blinded controlled pilot trial. Setting An academic medical center. Participants Twenty-one participants with aMCI. Intervention A 6-week computerized VSOP training. Measurements Multiple cognitive processing measures, instrumental activities of daily living (IADL), and two key resting state neural networks regulating cognitive processing: central executive network (CEN) and default mode network (DMN). Results We found that, compared to MLA control, VSOP training led to significant improvements in trained (processing speed and attention: F1,19 = 6.61, Partial η2 = 0.26, p = .019) and untrained cognitive domains (working memory: F1,19 = 7.33, Partial η2 = 0.28, p = .014; IADL: F1,19 = 5.16, Partial η2 = 0.21, p = .035), and protective maintenance in DMN (F1, 9 = 14.63, Partial η2 = 0.62, p = .004). Additionally, VSOP training, but not MLA, resulted in a significant improvement in CEN connectivity (Z = −2.37, p = .018). Conclusion We identified both target and transfer effects of VSOP training and revealed links between VSOP training and two key neural networks associated with aMCI. These findings highlight the potential of VSOP training to slow cognitive decline in aMCI. Further delineation of mechanisms underlying VSOP-induced plasticity is necessary to understand in what populations and conditions such training may be most effective.