In this Seminar, we highlight the main developments in the field of Alzheimer's disease. The most recent data indicate that, by 2050, the prevalence of dementia will double in Europe and triple worldwide, and that estimate is 3 times higher when based on a biological (rather than clinical) definition of Alzheimer's disease. The earliest phase of Alzheimer's disease (cellular phase) happens in parallel with accumulating amyloid β, inducing the spread of tau pathology. The risk of Alzheimer's disease is 60-80% dependent on heritable factors, with more than 40 Alzheimer's disease-associated genetic risk loci already identified, of which the APOE alleles have the strongest association with the disease. Novel biomarkers include PET scans and plasma assays for amyloid β and phosphorylated tau, which show great promise for clinical and research use. Multidomain lifestyle-based prevention trials suggest cognitive benefits in participants with increased risk of dementia. Lifestyle factors do not directly affect Alzheimer's disease pathology, but can still contribute to a positive outcome in individuals with Alzheimer's disease. Promising pharmacological treatments are poised at advanced stages of clinical trials and include anti-amyloid β, anti-tau, and anti-inflammatory strategies.Copyright © 2021 Elsevier Ltd. All rights reserved.

It has been more than 10 years since it was first proposed that the neurodegeneration in Alzheimer's disease (AD) may be caused by deposition of amyloid beta-peptide (Abeta) in plaques in brain tissue. According to the amyloid hypothesis, accumulation of Abeta in the brain is the primary influence driving AD pathogenesis. The rest of the disease process, including formation of neurofibrillary tangles containing tau protein, is proposed to result from an imbalance between Abeta production and Abeta clearance.

Assessments of brain glucose metabolism (18F-FDG-PET) and cerebral amyloid burden (11C-PiB-PET) in mild cognitive impairment (MCI) have shown highly variable performances when adopted to predict progression to dementia due to Alzheimer's disease (ADD). This study investigates, in a clinical setting, the separate and combined values of 18F-FDG-PET and 11C-PiB-PET in ADD conversion prediction with optimized data analysis procedures. Respectively, we investigate the accuracy of an optimized SPM analysis for 18F-FDG-PET and of standardized uptake value ratio semiquantification for 11C-PiB-PET in predicting ADD conversion in 30 MCI subjects (age 63.57±7.78 years). Fourteen subjects converted to ADD during the follow-up (median 26.5 months, inter-quartile range 30 months). Receiver operating characteristic analyses showed an area under the curve (AUC) of 0.89 and of 0.81 for, respectively, 18F-FDG-PET and 11C-PiB-PET. 18F-FDG-PET, compared to 11C-PiB-PET, showed higher specificity (1.00 versus 0.62, respectively), but lower sensitivity (0.79 versus 1.00). Combining the biomarkers improved classification accuracy (AUC = 0.96). During the follow-up time, all the MCI subjects positive for both PET biomarkers converted to ADD, whereas all the subjects negative for both remained stable. The difference in survival distributions was confirmed by a log-rank test (p = 0.002). These results indicate a very high accuracy in predicting MCI to ADD conversion of both 18F-FDG-PET and 11C-PiB-PET imaging, the former showing optimal performance based on the SPM optimized parametric assessment. Measures of brain glucose metabolism and amyloid load represent extremely powerful diagnostic and prognostic biomarkers with complementary roles in prodromal dementia phase, particularly when tailored to individual cases in clinical settings.

In 1975, tau protein was isolated as a microtubule-associated factor from the porcine brain. In the previous year, a paired helical filament (PHF) protein had been identified in neurofibrillary tangles in the brains of individuals with Alzheimer disease (AD), but it was not until 1986 that the PHF protein and tau were discovered to be one and the same. In the AD brain, tau was found to be abnormally hyperphosphorylated, and it inhibited rather than promoted in vitro microtubule assembly. Almost 80 disease-causing exonic missense and intronic silent mutations in the tau gene have been found in familial cases of frontotemporal dementia but, to date, no such mutation has been found in AD. The first phase I clinical trial of an active tau immunization vaccine in patients with AD was recently completed. Assays for tau levels in cerebrospinal fluid and plasma are now available, and tau radiotracers for PET are under development. In this article, we provide an overview of the pivotal discoveries in the tau research field over the past 40 years. We also review the current status of the field, including disease mechanisms and therapeutic approaches.

Alzheimer disease (AD) is a heterogeneous disease with a complex pathobiology. The presence of extracellular β-amyloid deposition as neuritic plaques and intracellular accumulation of hyperphosphorylated tau as neurofibrillary tangles remains the primary neuropathologic criteria for AD diagnosis. However, a number of recent fundamental discoveries highlight important pathological roles for other critical cellular and molecular processes. Despite this, no disease-modifying treatment currently exists, and numerous phase 3 clinical trials have failed to demonstrate benefits. Here, we review recent advances in our understanding of AD pathobiology and discuss current treatment strategies, highlighting recent clinical trials and opportunities for developing future disease-modifying therapies.Copyright © 2019 Elsevier Inc. All rights reserved.

Recent advances in disease-modifying treatments highlight the need for accurately identifying individuals in early Alzheimer's disease (AD) stages and for monitoring of treatment effects. Plasma measurements of phosphorylated tau (p-tau) are a promising biomarker for AD, but different assays show varying diagnostic and prognostic accuracies. The objective of this study was to determine the clinical performance of a novel plasma p-tau217 (p-tau217) assay, p-tau217+, and perform a head-to-head comparison to an established assay, plasma p-tau217, within two independent cohorts METHODS: The study consisted of two cohorts, cohort 1 (27 controls and 25 individuals with mild-cognitive impairment [MCI]) and cohort 2 including 147 individuals with MCI at baseline who were followed for an average of 4.92 (SD 2.09) years. Receiver operating characteristic analyses were used to assess the performance of both assays to detect amyloid-β status (+/-) in CSF, distinguish MCI from controls, and identify subjects who will convert from MCI to AD dementia. General linear and linear mixed-effects analyses were used to assess the associations between p-tau and baseline, and annual change in Mini-Mental State Examination (MMSE) scores. Spearman correlations were used to assess the associations between the two plasma measures, and Bland-Altmann plots were examined to assess the agreement between the assays.Both assays showed similar performance in detecting amyloid-β status in CSF (plasma p-tau217+ AUC = 0.91 vs plasma p-tau217 AUC = 0.89), distinguishing MCI from controls (plasma p-tau217+ AUC = 0.91 vs plasma p-tau217 AUC = 0.91), and predicting future conversion from MCI to AD dementia (plasma p-tau217+ AUC = 0.88 vs p-tau217 AUC = 0.89). Both assays were similarly related to baseline (plasma p-tau217+ rho = -0.39 vs p-tau217 rho = -0.35), and annual change in MMSE scores (plasma p-tau217+r = -0.45 vs p-tau217r = -0.41). Correlations between the two plasma measures were rho = 0.69, p < 0.001 in cohort 1 and rho = 0.70, p < 0.001 in cohort 2. Bland-Altmann plots revealed good agreement between plasma p-tau217+ and plasma p-tau217 in both cohorts (cohort 1, 51/52 [98%] within 95%CI; cohort 2, 139/147 [95%] within 95%CI).Taken together, our results indicate good diagnostic and prognostic performance of the plasma p-tau217+ assay, similar to the p-tau217 assay.© 2022. The Author(s).

Our aim was to compare the predictive accuracy of 4 different medial temporal lobe measurements for Alzheimer's disease (AD) in subjects with mild cognitive impairment (MCI). Manual hippocampal measurement, automated atlas-based hippocampal measurement, a visual rating scale (MTA-score), and lateral ventricle measurement were compared. Predictive accuracy for AD 2 years after baseline was assessed by receiver operating characteristics analyses with area under the curve as outcome. Annual cognitive decline was assessed by slope analyses up to 5 years after baseline. Correlations with biomarkers in cerebrospinal fluid (CSF) were investigated. Subjects with MCI were selected from the Development of Screening Guidelines and Clinical Criteria for Predementia AD (DESCRIPA) multicenter study (n = 156) and the single-center VU medical center (n = 172). At follow-up, area under the curve was highest for automated atlas-based hippocampal measurement (0.71) and manual hippocampal measurement (0.71), and lower for MTA-score (0.65) and lateral ventricle (0.60). Slope analysis yielded similar results. Hippocampal measurements correlated with CSF total tau and phosphorylated tau, not with beta-amyloid 1-42. MTA-score and lateral ventricle volume correlated with CSF beta-amyloid 1-42. We can conclude that volumetric hippocampal measurements are the best predictors of AD conversion in subjects with MCI.Copyright © 2013 Elsevier Inc. All rights reserved.

Although detailed volumetric MRI assessment of medial temporal lobe atrophy (MTA) can predict dementia in patients with mild cognitive impairment (MCI), it is not easily applied to routine clinical practice.To test the predictive accuracy of visually assessed MTA in MCI patients using a standardized visual rating scale.Seventy-five MCI patients (mean age 63 years) underwent a coronal three-dimensional magnetization-prepared rapid gradient echo brain MRI sequence. MTA was rated visually using a 5-point rating scale.The mean follow-up period for the cohort was 34 months. At follow-up, 49% of the enrolled MCI patients fulfilled criteria for dementia. MTA assessed using a standardized visual rating scale was significantly associated with dementia at follow-up, with a hazard ratio of 1.5 for every point increase in atrophy score (p < 0.001) and of 3.1 for the presence of atrophy based on the dichotomized atrophy score (p = 0.003). The predictive accuracy of visually assessed MTA was independent of age, gender, education, Mini-Mental State Examination score, Clinical Dementia Rating Sum of Boxes score, Verbal Delayed Recall, and the presence of hypertension, depression, the APOE epsilon4 allele, and white matter hyperintensities.Visual assessment of MTA on brain MRI using a standardized rating scale is a powerful and independent predictor of conversion to dementia in relatively young MCI patients. As overlap existed in MTA scores between patients with and without dementia at follow-up, the results should be interpreted in the light of the odds for the individual patient.

A previous Cochrane systematic review concluded there is insufficient evidence to support the routine use of 18F-FDG PET in clinical practice in people with mild cognitive impairment (MCI).To update the evidence and reassess the accuracy of 18F-FDG-PET for detecting people with MCI at baseline who would clinically convert to Alzheimer's disease (AD) dementia at follow-up.A systematic review including comprehensive search of electronic databases from January 2013 to July 2017, to update original searches (1999 to 2013). All key review steps, including quality assessment using QUADAS 2, were performed independently and blindly by two review authors. Meta-analysis could not be conducted due to heterogeneity across studies.When all included studies were examined across all semi-quantitative and quantitative metrics, exploratory analysis for conversion of MCI to AD dementia (n = 24) showed highly variable accuracy; half the studies failed to meet four or more of the seven sets of QUADAS 2 criteria. Variable accuracy for all metrics was also found across eleven newly included studies published in the last 5 years (range: sensitivity 56-100%, specificity 24-100%). The most consistently high sensitivity and specificity values (approximately ≥80%) were reported for the sc-SPM (single case statistical parametric mapping) metric in 6 out of 8 studies.Systematic and comprehensive assessment of studies of 18FDG-PET for prediction of conversion from MCI to AD dementia reveals many studies have methodological limitations according to Cochrane diagnostic test accuracy gold standards, and shows accuracy remains highly variable, including in the most recent studies. There is some evidence, however, of higher and more consistent accuracy in studies using computer aided metrics, such as sc-SPM, in specialized clinical settings. Robust, methodologically sound prospective longitudinal cohort studies with long (≥5 years) follow-up, larger consecutive samples, and defined baseline threshold(s) are needed to test these promising results. Further evidence of the clinical validity and utility of 18F-FDG PET in people with MCI is needed.

To determine whether the Alzheimer disease (AD) dementia conversion-related pattern (ADCRP) on [F]FDG PET can serve as a valid predictor for the development of AD dementia, the individual expression of the ADCRP (subject score) and its prognostic value were examined in patients with mild cognitive impairment (MCI) and biologically defined AD.A total of 269 patients with available [F]FDG PET, [F]AV-45 PET, phosphorylated and total tau in CSF, and neurofilament light chain in plasma were included. Following the AT(N) classification scheme, where AD is defined biologically by in vivo biomarkers of β-amyloid (Aβ) deposition ("A") and pathologic tau ("T"), patients were categorized to the A-T-, A+T-, A+T+ (AD), and A-T+ groups.The mean subject score of the ADCRP was significantly higher in the A+T+ group compared to each of the other group (all < 0.05) but was similar among the latter (all > 0.1). Within the A+T+ group, the subject score of ADCRP was a significant predictor of conversion to dementia (hazard ratio, 2.02 per score increase; < 0.001), with higher predictive value than of alternative biomarkers of neurodegeneration (total tau and neurofilament light chain). Stratification of A+T+ patients by the subject score of ADCRP yielded well-separated groups of high, medium, and low conversion risks.The ADCRP is a valuable biomarker of neurodegeneration in patients with MCI and biologically defined AD. It shows great potential for stratifying the risk and estimating the time to conversion to dementia in patients with MCI and underlying AD (A+T+).This study provides Class I evidence that [F]FDG PET predicts the development of AD dementia in individuals with MCI and underlying AD as defined by the AT(N) framework.© 2021 American Academy of Neurology.

Plasma glial fibrillary acidic protein (GFAP) is a marker of astroglial activation and astrocytosis. We assessed the ability of plasma GFAP to detect Alzheimer's disease (AD) pathology in the form of AD-related amyloid-β (Aβ) pathology and conversion to AD dementia in a mild cognitive impairment (MCI) cohort.One hundred sixty MCI patients were followed for 4.7 years (average). AD pathology was defined using cerebrospinal fluid (CSF) Aβ42/40 and Aβ42/total tau (T-tau). Plasma GFAP was measured at baseline and follow-up using Simoa technology.Baseline plasma GFAP could detect abnormal CSF Aβ42/40 and CSF Aβ42/T-tau with an AUC of 0.79 (95% CI 0.72-0.86) and 0.80 (95% CI 0.72-0.86), respectively. When also including APOE ε4 status as a predictor, the accuracy of the model to detect abnormal CSF Aβ42/40 status improved (AUC = 0.86, p = 0.02). Plasma GFAP predicted subsequent conversion to AD dementia with an AUC of 0.84 (95% CI 0.77-0.91), which was not significantly improved when adding APOE ε4 or age as predictors to the model. Longitudinal GFAP slopes for Aβ-positive and MCI who progressed to dementia (AD or other) were significantly steeper than those for Aβ-negative (p = 0.007) and stable MCI (p < 0.0001), respectively.Plasma GFAP can detect AD pathology in patients with MCI and predict conversion to AD dementia.

To capitalize on data from different clinical series to compare sensitivity and specificity of individual biomarkers for predicting mild cognitive impairment (MCI) progression to Alzheimer's disease (AD).Medial temporal atrophy, cortical hypometabolism, and cerebrospinal fluid biomarkers were assessed in 18 patients with mild cognitive impairment (MCI) with prodromal AD (pAD; conversion time, 26 ± 12 months) and 18 stable MCI (sMCI) patients from the Translational Outpatient Memory Clinic cohort, as well as in 24 pAD patients (conversion time, 36 ± 12 months) and 33 sMCI patients from the Alzheimer's Disease Neuroimaging Initiative cohort. Medial temporal atrophy was measured by manual, semi-automated, and automated hippocampal volumetry; cortical hypometabolism was measured using several indices of AD-related hypometabolism pattern; and cerebrospinal fluid markers were amyloid β (Aβ)42 and total tau protein concentrations. For each biomarker, sensitivity for pAD, specificity for sMCI, and diagnostic accuracy were computed.Sensitivity to predict MCI conversion to AD in the Alzheimer's Disease Neuroimaging Initiative and Translational Outpatient Memory Clinic cohorts was 79% and 94% based on Aβ42, 46% and 28% based on hippocampal volumes, 33% to 66% and 56% to 78% based on different hypometabolism indices, and 46% and 61% based on total tau levels, respectively. Specificity to exclude sMCI was 27% and 50% based on Aβ42, 76% and 94% based on hippocampal volumes, 58% to 67% and 55% to 83% based on different hypometabolism indices, and 61% and 83% based on total tau levels, respectively.Current findings suggest that Aβ42 concentrations and hippocampal volumes may be used in combination to best identify pAD.Copyright © 2013 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

To evaluate the utility of MRI hippocampal and entorhinal cortex atrophy in predicting conversion from mild cognitive impairment (MCI) to Alzheimer disease (AD).Baseline brain MRI was performed in 139 patients with MCI, broadly defined, and 63 healthy controls followed for an average of 5 years (range 1 to 9 years).Hippocampal and entorhinal cortex volumes were each largest in controls, intermediate in MCI nonconverters, and smallest in MCI converters to AD (37 of 139 patients converted to AD). In separate Cox proportional hazards models, covarying for intracranial volume, smaller hippocampal volume (risk ratio [RR] 3.62, 95% CI 1.93 to 6.80, p < 0.0001), and entorhinal cortex volume (RR 2.43, 95% CI 1.56 to 3.79, p < 0.0001) each predicted time to conversion to AD. Similar results were obtained for hippocampal and entorhinal cortex volume in patients with MCI with Mini-Mental State Examination (MMSE) scores > or = 27 out of 30 (21% converted to AD) and in the subset of patients with amnestic MCI (35% converted to AD). In the total patient sample, when both hippocampal and entorhinal volume were entered into an age-stratified Cox model with sex, MMSE, education, and intracranial volume, smaller hippocampal volume (RR 2.21, 95% CI 1.14 to 4.29, p < 0.02) and entorhinal cortex volume (RR 2.48, 95% CI 1.54 to 3.97, p < 0.0002) predicted time to conversion to AD. Similar results were obtained in a Cox model that also included Selective Reminding Test (SRT) delayed recall and Wechsler Adult Intelligence Scale-Revised (WAIS-R) Digit Symbol as predictors. Based on logistic regression models in the 3-year follow-up sample, for a fixed specificity of 80%, the sensitivities for MCI conversion to AD were as follows: age 43.3%, MMSE 43.3%, age + MMSE 63.7%, age + MMSE + SRT delayed recall + WAIS-R Digit Symbol 80.6% (79.6% correctly classified), hippocampus + entorhinal cortex 66.7%, age + MMSE + hippocampus + entorhinal cortex 76.7% (85% correctly classified), age + MMSE + SRT delayed recall + WAIS-R Digit Symbol + hippocampus + entorhinal cortex 83.3% (86.8% correctly classified).Smaller hippocampal and entorhinal cortex volumes each contribute to the prediction of conversion to Alzheimer disease. Age and cognitive variables also contribute to prediction, and the added value of hippocampal and entorhinal cortex volumes is small. Nonetheless, combining these MRI volumes with age and cognitive measures leads to high levels of predictive accuracy that may have potential clinical application.

The objective of this work was to assess the predictive accuracy of targeted neuroimaging and neuropsychological measures for the detection of incipient dementia in individuals with mild cognitive impairment (MCI), and to examine the potential benefit of combining both classes of measures. Baseline MRI measures included hippocampal volume, cortical thickness, and white matter hyperintensities. Neuropsychological assessment focused on different aspects of episodic memory (i.e., familiarity, free recall, and associative memory) and executive control functions (i.e., working memory, switching, and planning). Global and regional cortical thinning was observed in MCI patients who progressed to dementia compared to those who remained stable, whereas no differences were found between groups for baseline hippocampal volume and white matter hyperintensities. The strongest neuroimaging predictors were baseline cortical thickness in the right anterior cingulate and middle frontal gyri. For cognitive predictors, we found that deficits in both free recall and recognition episodic memory tasks were highly suggestive of progression to dementia. Cortical thinning in the right anterior cingulate gyrus, combined to controlled and familiarity-based retrieval deficits, achieved a classification accuracy of 87.5%, a specificity of 90.9%, and a sensitivity of 83.3%. This predictive model including both classes of measures provided more accurate predictions than those based on neuroimaging or cognitive measures alone. Overall, our findings suggest that detecting preclinical Alzheimer's disease is probably best accomplished by combining complementary information from targeted neuroimaging and cognitive classifiers, and highlight the importance of taking into account both structural and functional changes associated with the disease.

Biomarkers have become increasingly important in understanding neurodegenerative processes associated with Alzheimer disease. Markers include regional brain volumes, cerebrospinal fluid measures of pathological Aβ1-42 and total tau, cognitive measures, and individual risk factors.To determine the discriminative utility of different classes of biomarkers and cognitive markers by examining their ability to predict a change in diagnostic status from mild cognitive impairment to Alzheimer disease.Longitudinal study.We analyzed the Alzheimer's Disease Neuroimaging Initiative database to study patients with mild cognitive impairment who converted to Alzheimer disease (n = 116) and those who did not convert (n = 204) within a 2-year period. We determined the predictive utility of 25 variables from all classes of markers, biomarkers, and risk factors in a series of logistic regression models and effect size analyses.The Alzheimer's Disease Neuroimaging Initiative public database.Primary outcome measures were odds ratios, pseudo- R(2)s, and effect sizes.In comprehensive stepwise logistic regression models that thus included variables from all classes of markers, the following baseline variables predicted conversion within a 2-year period: 2 measures of delayed verbal memory and middle temporal lobe cortical thickness. In an effect size analysis that examined rates of decline, change scores for biomarkers were modest for 2 years, but a change in an everyday functional activities measure (Functional Assessment Questionnaire) was considerably larger. Decline in scores on the Functional Assessment Questionnaire and Trail Making Test, part B, accounted for approximately 50% of the predictive variance in conversion from mild cognitive impairment to Alzheimer disease.Cognitive markers at baseline were more robust predictors of conversion than most biomarkers. Longitudinal analyses suggested that conversion appeared to be driven less by changes in the neurobiologic trajectory of the disease than by a sharp decline in functional ability and, to a lesser extent, by declines in executive function.

Our objective in this study was to develop a point-based tool to predict conversion from amnestic mild cognitive impairment (MCI) to probable Alzheimer's disease (AD).Subjects were participants in the first part of the Alzheimer's Disease Neuroimaging Initiative. Cox proportional hazards models were used to identify factors associated with development of AD, and a point score was created from predictors in the final model.The final point score could range from 0 to 9 (mean 4.8) and included: the Functional Assessment Questionnaire (2‒3 points); magnetic resonance imaging (MRI) middle temporal cortical thinning (1 point); MRI hippocampal subcortical volume (1 point); Alzheimer's Disease Cognitive Scale-cognitive subscale (2‒3 points); and the Clock Test (1 point). Prognostic accuracy was good (Harrell's c = 0.78; 95% CI 0.75, 0.81); 3-year conversion rates were 6% (0‒3 points), 53% (4‒6 points), and 91% (7‒9 points).A point-based risk score combining functional dependence, cerebral MRI measures, and neuropsychological test scores provided good accuracy for prediction of conversion from amnestic MCI to AD.Copyright © 2014 The Alzheimer's Association. All rights reserved.

Revised diagnostic criteria for Alzheimer disease (AD) acknowledge a key role of imaging biomarkers for early diagnosis. Diagnostic accuracy depends on which marker (i.e., amyloid imaging, ¹⁸F-fluorodeoxyglucose [FDG]-PET, SPECT, MRI) as well as how it is measured ("metric": visual, manual, semiautomated, or automated segmentation/computation). We evaluated diagnostic accuracy of marker vs metric in separating AD from healthy and prognostic accuracy to predict progression in mild cognitive impairment. The outcome measure was positive (negative) likelihood ratio, LR+ (LR-), defined as the ratio between the probability of positive (negative) test outcome in patients and the probability of positive (negative) test outcome in healthy controls. Diagnostic LR+ of markers was between 4.4 and 9.4 and LR- between 0.25 and 0.08, whereas prognostic LR+ and LR- were between 1.7 and 7.5, and 0.50 and 0.11, respectively. Within metrics, LRs varied up to 100-fold: LR+ from approximately 1 to 100; LR- from approximately 1.00 to 0.01. Markers accounted for 11% and 18% of diagnostic and prognostic variance of LR+ and 16% and 24% of LR-. Across all markers, metrics accounted for an equal or larger amount of variance than markers: 13% and 62% of diagnostic and prognostic variance of LR+, and 29% and 18% of LR-. Within markers, the largest proportion of diagnostic LR+ and LR- variability was within ¹⁸F-FDG-PET and MRI metrics, respectively. Diagnostic and prognostic accuracy of imaging AD biomarkers is at least as dependent on how the biomarker is measured as on the biomarker itself. Standard operating procedures are key to biomarker use in the clinical routine and drug trials.

Biomarkers do not determine conversion to Alzheimer disease (AD) perfectly, and criteria do not specify how to take patient characteristics into account. Consequently, biomarker use may be challenging for clinicians, especially in patients with mild cognitive impairment (MCI).To construct biomarker-based prognostic models that enable determination of future AD dementia in patients with MCI.This study is part of the Alzheimer's Biomarkers in Daily Practice (ABIDE) project. A total of 525 patients with MCI from the Amsterdam Dementia Cohort (longitudinal cohort, tertiary referral center) were studied. All patients had their baseline visit to a memory clinic from September 1, 1997, through August 31, 2014. Prognostic models were constructed by Cox proportional hazards regression with patient characteristics (age, sex, and Mini-Mental State Examination [MMSE] score), magnetic resonance imaging (MRI) biomarkers (hippocampal volume, normalized whole-brain volume), cerebrospinal fluid (CSF) biomarkers (amyloid-β1-42, tau), and combined biomarkers. Data were analyzed from November 1, 2015, to October 1, 2016.Clinical end points were AD dementia and any type of dementia after 1 and 3 years.Of the 525 patients, 210 (40.0%) were female, and the mean (SD) age was 67.3 (8.4) years. On the basis of age, sex, and MMSE score only, the 3-year progression risk to AD dementia ranged from 26% (95% CI, 19%-34%) in younger men with MMSE scores of 29 to 76% (95% CI, 65%-84%) in older women with MMSE scores of 24 (1-year risk: 6% [95% CI, 4%-9%] to 24% [95% CI, 18%-32%]). Three- and 1-year progression risks were 86% (95% CI, 71%-95%) and 27% (95% CI, 17%-41%) when MRI results were abnormal, 82% (95% CI, 73%-89%) and 26% (95% CI, 20%-33%) when CSF test results were abnormal, and 89% (95% CI, 79%-95%) and 26% (95% CI, 18%-36%) when the results of both tests were abnormal. Conversely, 3- and 1-year progression risks were 18% (95% CI, 13%-27%) and 3% (95% CI, 2%-5%) after normal MRI results, 6% (95% CI, 3%-9%) and 1% (95% CI, 0.5%-2%) after normal CSF test results, and 4% (95% CI, 2%-7%) and 0.5% (95% CI, 0.2%-1%) after combined normal MRI and CSF test results. The prognostic value of models determining any type of dementia were in the same order of magnitude although somewhat lower. External validation in Alzheimer's Disease Neuroimaging Initiative 2 showed that our models were highly robust.This study provides biomarker-based prognostic models that may help determine AD dementia and any type of dementia in patients with MCI at the individual level. This finding supports clinical decision making and application of biomarkers in daily practice.

Biomarker-based risk predictions of dementia in people with mild cognitive impairment are highly relevant for care planning and to select patients for treatment when disease-modifying drugs become available. We aimed to establish robust prediction models of disease progression in people at risk of dementia.In this modelling study, we included people with mild cognitive impairment (MCI) from single-centre and multicentre cohorts in Europe and North America: the European Medical Information Framework for Alzheimer's Disease (EMIF-AD; n=883), Alzheimer's Disease Neuroimaging Initiative (ADNI; n=829), Amsterdam Dementia Cohort (ADC; n=666), and the Swedish BioFINDER study (n=233). Inclusion criteria were a baseline diagnosis of MCI, at least 6 months of follow-up, and availability of a baseline Mini-Mental State Examination (MMSE) and MRI or CSF biomarker assessment. The primary endpoint was clinical progression to any type of dementia. We evaluated performance of previously developed risk prediction models-a demographics model, a hippocampal volume model, and a CSF biomarkers model-by evaluating them across cohorts, incorporating different biomarker measurement methods, and determining prognostic performance with Harrell's C statistic. We then updated the models by re-estimating parameters with and without centre-specific effects and evaluated model calibration by comparing observed and expected survival. Finally, we constructed a model combining markers for amyloid deposition, tauopathy, and neurodegeneration (ATN), in accordance with the National Institute on Aging and Alzheimer's Association research framework.We included all 2611 individuals with MCI in the four cohorts, 1007 (39%) of whom progressed to dementia. The validated demographics model (Harrell's C 0·62, 95% CI 0·59-0·65), validated hippocampal volume model (0·67, 0·62-0·72), and updated CSF biomarkers model (0·72, 0·68-0·74) had adequate prognostic performance across cohorts and were well calibrated. The newly constructed ATN model had the highest performance (0·74, 0·71-0·76).We generated risk models that are robust across cohorts, which adds to their potential clinical applicability. The models could aid clinicians in the interpretation of CSF biomarker and hippocampal volume results in individuals with MCI, and help research and clinical settings to prepare for a future of precision medicine in Alzheimer's disease. Future research should focus on the clinical utility of the models, particularly if their use affects participants' understanding, emotional wellbeing, and behaviour.ZonMW-Memorabel.Copyright © 2019 Elsevier Ltd. All rights reserved.

We developed models for individualized risk prediction of cognitive decline in mild cognitive impairment (MCI) using plasma biomarkers of β-amyloid (Aβ), tau and neurodegeneration. A total of 573 patients with MCI from the Swedish BioFINDER study and the Alzheimer's Disease Neuroimaging Initiative (ADNI) were included in the study. The primary outcomes were longitudinal cognition and conversion to Alzheimer's disease (AD) dementia. A model combining tau phosphorylated at threonine 181 (P-tau181) and neurofilament light (NfL), but not Aβ/Aβ, had the best prognosis performance of all models (area under the curve = 0.88 for 4-year conversion to AD in BioFINDER, validated in ADNI), was stronger than a basic model of age, sex, education and baseline cognition, and performed similarly to cerebrospinal fluid biomarkers. A publicly available online tool for individualized prognosis in MCI based on our combined plasma biomarker models is introduced. Combination of plasma biomarkers may be of high value to identify individuals with MCI who will progress to AD dementia in clinical trials and in clinical practice.© 2020. The Author(s), under exclusive licence to Springer Nature America, Inc.

Previous studies reported the value of blood-based biomarkers in predicting Alzheimer disease (AD) progression among individuals with different disease stages. However, evidence regarding the value of these markers in those with amnestic mild cognitive impairment (aMCI) is insufficient.A cohort with 251 aMCI individuals were followed for up to 8 years. Baseline blood biomarkers were measured on a single-molecule array platform. Multipoint clinical diagnosis and domain-specific cognitive functions were assessed to investigate the longitudinal relationship between blood biomarkers and clinical AD progression.Individuals with low Aβ42/Aβ40 and high p-tau181 at baseline demonstrated the highest AD risk (hazard ratio = 4.83, 95% CI 2.37-9.86), and the most dramatic decline across cognitive domains. Aβ42/Aβ40 and p-tau181, combined with basic characteristics performed the best in predicting AD conversion (AUC = 0.825, 95% CI 0.771-0.878).Combining Aβ42/Aβ40 and p-tau181 may be a feasible indicator for AD progression in clinical practice, and a potential composite marker in clinical trials.© American Association for Clinical Chemistry 2022. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.