Challenges associated with biomarker-based classification systems for Alzheimer's disease

Amyloid-β but not tau accumulation is strongly associated with longitudinal cognitive decline

OBJECTIVE

Alzheimer's disease (AD) pathology is featured by the extracellular accumulation of amyloid-β (Aβ) plaques and intracellular tau neurofibrillary tangles in the brain. We studied whether Aβ and tau accumulation are independently associated with future cognitive decline in the AD continuum.

METHODS

Data were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) public database. A total of 1272 participants were selected based on the availability of Aβ-PET and CSF tau at baseline and of those 777 participants with follow-up visits.

RESULTS

We found that Aβ-PET and CSF tau pathology were related to cognitive decline across the AD clinical spectrum, both as potential predictors for dementia progression. Among them, Aβ-PET (A + T- subjects) is an independent reliable predictor of longitudinal cognitive decline in terms of ADAS-13, ADNI-MEM, and MMSE scores rather than tau pathology (A - T+ subjects), indicating tau accumulation is not closely correlated with future cognitive impairment without being driven by Aβ deposition. Of note, a high percentage of APOE ε4 carriers with Aβ pathology (A+) develop poor memory and learning capacity. Interestingly, this condition is not recurrence in terms of the ADNI-MEM domain when adding APOE ε4 status. Finally, the levels of Aβ-PET SUVR related to glucose hypometabolism more strongly in subjects with A + T- than A - T+ both happen at baseline and longitudinal changes.

CONCLUSIONS

In conclusion, Aβ-PET alone without tau pathology (A + T-) measure is an independent reliable predictor of longitudinal cognitive decline but may nonetheless forecast different status of dementia progression. However, tau accumulation alone without Aβ pathology background (A - T+) was not enough to be an independent predictor of cognitive worsening.

Clinical variables contributing to the identification of biologically defined subgroups within cognitively unimpaired and mild cognitive impairment individuals

BACKGROUND

A lack of consensus exists in linking demographic, behavioral, and cognitive characteristics to biological stages of dementia, defined by the ATN (amyloid, tau, neurodegeneration) classification incorporating amyloid, tau, and neuronal injury biomarkers.

METHODS

Using a random forest classifier we investigated whether 27 demographic, behavioral, and cognitive characteristics allowed distinction between ATN-defined groups with the same cognitive profile. This was done separately for three cognitively unimpaired (CU) (112 A-T-N-; 46 A+T+N+/-; 65 A-T+/-N+/-) and three mild cognitive impairment (MCI) (128 A-T-N-; 223 A+T+N+/-; 94 A-T+/-N+/-) subgroups.

RESULTS

Classification-balanced accuracy reached 39% for the CU and 52% for the MCI subgroups. Logical Delayed Recall (explaining 16% of the variance), followed by the Alzheimer's Disease Assessment Scale 13 (14%) and Everyday Cognition Informant (10%), were the most relevant characteristics for classification of the MCI subgroups. Race and ethnicity, marital status, and Everyday Cognition Patient were not relevant (0%).

CONCLUSIONS

The demographic, behavioral, and cognitive measures used in our model were not informative in differentiating ATN-defined CU profiles. Measures of delayed memory, general cognition, and activities of daily living were the most informative in differentiating ATN-defined MCI profiles; however, these measures alone were not sufficient to reach high classification performance.

Predicting mild cognitive impairment in older adults: A machine learning analysis of the Alzheimer's Disease Neuroimaging Initiative

AIM

Mild cognitive impairment (MCI) in older adults is potentially devastating, but an accurate prediction model is still lacking. We hypothesized that neuropsychological tests and MRI-related markers could predict the onset of MCI early.

METHODS

We analyzed data from 306 older adults who were cognitive normal (CN) attending the Alzheimer's Disease Neuroimaging Initiative sequentially (474 pairs of visits) within 3 years. There were 231 pairs of MCI conversion (CN to MCI), and 242 pairs of CN maintenance (CN to CN). Variables on demographic, neuropsychological tests, genetic, and MRI-related markers were collected. Machine learning was used to construct MCI prediction models, comparing the area under the receiver operating characteristic curve (AUC) as the primary metric of performance. Important predictors were ranked for the optimal model.

RESULTS

The baseline age of the study sample was 74.8 years old. The best-performing model (gradient boosting decision tree) with 13 variables predicted MCI with an AUC of 0.819, and the rank of variable importance showed that intracranial volume, hippocampal volume, and score from task 4 (word recognition) of the Alzheimer's Disease Assessment Scale were important predictors of MCI.

CONCLUSIONS

With the help of machine learning, fewer neuropsychological tests and MRI-related markers are required to accurately predict MCI within 3 years, thereby facilitating targeted intervention. Geriatr Gerontol Int 2024; 24: 96-101.

A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers

AIMS

The AT(N) classification system not only improved the biological characterization of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data-driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values.

METHODS

We compared the diagnostic and prognostic abilities of CSF biomarkers clustering results against their AT(N) classification. We studied clinical (patients from our center) and research (Alzheimer's Disease Neuroimaging Initiative) cohorts. The studied CSF biomarkers included Aβ(1-42), Aβ(1-42)/Aβ(1-40) ratio, tTau, and pTau.

RESULTS

The optimal solution yielded three clusters in both cohorts, significantly different in diagnosis, AT(N) classification, values distribution, and survival. We defined these three CSF groups as (i) non-defined or unrelated to AD, (ii) early stages and/or more delayed risk of conversion to dementia, and (iii) more severe cognitive impairment subjects with faster progression to dementia.

CONCLUSION

We propose this data-driven three-group classification as a meaningful and straightforward approach to evaluating the risk of conversion to dementia, complementary to the AT(N) system classification.

Data-Driven Thresholding Statistically Biases ATN Profiling across Cohort Datasets

BACKGROUND

While the amyloid/tau/neurodegeneration (ATN) framework has found wide application in Alzheimer's disease research, it is unclear if thresholds obtained using distinct thresholding methods are concordant within the same dataset and interchangeable across cohorts.

OBJECTIVES

To investigate the robustness of data-driven thresholding methods and ATN profiling across cohort datasets.

DESIGN AND SETTING

We evaluated the impact of thresholding methods on ATN profiles by applying five commonly-used methodologies across cohort datasets. We assessed the generalizability of disease patterns discovered within ATN profiles by clustering individuals from different cohorts who were assigned to the same ATN profile.

PARTICIPANTS AND MEASUREMENTS

Participants with available CSF amyloid-β 1-42, phosphorylated tau, and total tau measurements were included from eleven AD cohort studies.

RESULTS

We observed high variability among obtained ATN thresholds, both across methods and datasets that impacted the resulting profile assignments of participants significantly. Clustering participants from different cohorts within the same ATN category indicated that identified disease patterns were comparable across most cohorts and biases introduced through distinct thresholding and data representations remained insignificant in most ATN profiles.

CONLUSION

Thresholding method selection is a decision of statistical relevance that will inevitably bias the resulting profiling and affect its sensitivity and specificity. Thresholds are likely not directly interchangeable between independent cohorts. To apply the ATN framework as an actionable and robust profiling scheme, a comprehensive understanding of the impact of used thresholding methods, their statistical implications, and a validation of results is crucial.

The heritability of blood-based biomarkers related to risk of Alzheimer's disease in a population-based sample of early old-age men

INTRODUCTION

Despite their increased application, the heritability of Alzheimer's disease (AD)-related blood-based biomarkers remains unexplored.

METHODS

Plasma amyloid beta 40 (Aβ40), Aβ42, the Aβ42/40 ratio, total tau (t-tau), and neurofilament light (NfL) data came from 1035 men 60 to 73 years of age (μ = 67.0, SD = 2.6). Twin models were used to calculate heritability and the genetic and environmental correlations between them.

RESULTS

Additive genetics explained 44% to 52% of Aβ42, Aβ40, t-tau, and NfL. The Aβ42/40 ratio was not heritable. Aβ40 and Aβ42 were genetically near identical (rg  = 0.94). Both Aβ40 and Aβ42 were genetically correlated with NfL (rg  = 0.35 to 0.38), but genetically unrelated to t-tau.

DISCUSSION

Except for Aβ42/40, plasma biomarkers are heritable. Aβ40 and Aβ42 share mostly the same genetic influences, whereas genetic influences on plasma t-tau and NfL are largely unique in early old-age men. The absence of genetic associations between the Aβs and t-tau is not consistent with the amyloid cascade hypothesis.

A comparison of diagnostic performance of word-list and story recall tests for biomarker-determined Alzheimer's disease

BACKGROUND

Wordlist and story recall tests are routinely employed in clinical practice for dementia diagnosis. In this study, our aim was to establish how well-standard clinical metrics compared to process scores derived from wordlist and story recall tests in predicting biomarker determined Alzheimer's disease, as defined by CSF ptau/Aβ42 ratio.

METHODS

Data from 295 participants (mean age = 65 ± 9.) were drawn from the University of Wisconsin - Madison Alzheimer's Disease Research Center (ADRC) and Wisconsin Registry for Alzheimer's Prevention (WRAP). Rey's Auditory Verbal Learning Test (AVLT; wordlist) and Logical Memory Test (LMT; story) data were used. Bayesian linear regression analyses were carried out with CSF ptau/Aβ42 ratio as outcome. Sensitivity analyses were carried out with logistic regressions to assess diagnosticity.

RESULTS

LMT generally outperformed AVLT. Notably, the best predictors were primacy ratio, a process score indexing loss of information learned early during test administration, and recency ratio, which tracks loss of recently learned information. Sensitivity analyses confirmed this conclusion.

CONCLUSIONS

Our study shows that story recall tests may be better than wordlist tests for detection of dementia, especially when employing process scores alongside conventional clinical scores.

Investigating predictors of progression from mild cognitive impairment to Alzheimer's disease based on different time intervals

BACKGROUND

Mild cognitive impairment (MCI) is the early stage of AD, and about 10-12% of MCI patients will progress to AD every year. At present, there are no effective markers for the early diagnosis of whether MCI patients will progress to AD. This study aimed to develop machine learning-based models for predicting the progression from MCI to AD within 3 years, to assist in screening and prevention of high-risk populations.

METHODS

Data were collected from the Alzheimer's Disease Neuroimaging Initiative, a representative sample of cognitive impairment population. Machine learning models were applied to predict the progression from MCI to AD, using demographic, neuropsychological test and MRI-related biomarkers. Data were divided into training (56%), validation (14%) and test sets (30%). AUC (area under ROC curve) was used as the main evaluation metric. Key predictors were ranked utilising their importance.

RESULTS

The AdaBoost model based on logistic regression achieved the best performance (AUC: 0.98) in 0-6 month prediction. Scores from the Functional Activities Questionnaire, Modified Preclinical Alzheimer Cognitive Composite with Trails test and ADAS11 (Unweighted sum of 11 items from The Alzheimer's Disease Assessment Scale-Cognitive Subscale) were key predictors.

CONCLUSION

Through machine learning, neuropsychological tests and MRI-related markers could accurately predict the progression from MCI to AD, especially in a short period time. This is of great significance for clinical staff to screen and diagnose AD, and to intervene and treat high-risk MCI patients early.

ATN profile classification across two independent prospective cohorts

BACKGROUND

The ATN model represents a research framework used to describe in subjects the presence or absence of Alzheimer's disease (AD) pathology through biomarkers. The aim of this study was to describe the prevalence of different ATN profiles using quantitative imaging biomarkers in two independent cohorts, and to evaluate the pertinence of ATN biomarkers to identify comparable populations across independent cohorts.

METHODS

A total of 172 subjects from the Geneva Memory Clinic and 113 volunteers from a study on healthy aging at the University Hospital of Zurich underwent amyloid (A) and tau (T) PET, as well as T1-weigthed MRI scans using site-specific protocols. Subjects were classified by cognition (cognitively unimpaired, CU, or impaired, CI) based on clinical assessment by experts. Amyloid data converted into the standardized centiloid scale, tau PET data normalized to cerebellar uptake, and hippocampal volume expressed as a ratio over total intracranial volume ratio were considered as biomarkers for A, T, and neurodegeneration (N), respectively. Positivity for each biomarker was defined based on previously published thresholds. Subjects were then classified according to the ATN model. Differences among profiles were tested using Kruskal-Wallis ANOVA, and between cohorts using Wilcoxon tests.

RESULTS

Twenty-nine percent of subjects from the Geneva cohorts were classified with a normal (A-T-N-) profile, while the Zurich cohort included 64% of subjects in the same category. Meanwhile, 63% of the Geneva and 16% of the Zurich cohort were classified within the AD continuum (being A+ regardless of other biomarkers' statuses). Within cohorts, ATN profiles were significantly different for age and mini-mental state examination scores, but not for years of education. Age was not significantly different between cohorts. In general, imaging A and T biomarkers were significantly different between cohorts, but they were no longer significantly different when stratifying the cohorts by ATN profile. N was not significantly different between cohorts.

CONCLUSION

Stratifying subjects into ATN profiles provides comparable groups of subjects even when individual recruitment followed different criteria.

Exploring the ATN classification system using brain morphology

BACKGROUND

The NIA-AA proposed amyloid-tau-neurodegeneration (ATN) as a classification system for AD biomarkers. The amyloid cascade hypothesis (ACH) implies a sequence across ATN groups that patients might undergo during transition from healthy towards AD: A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+. Here we assess the evidence for monotonic brain volume decline for this particular (amyloid-conversion first, tau-conversion second, N-conversion last) and alternative progressions using voxel-based morphometry (VBM) in a large cross-sectional MRI cohort.

METHODS

We used baseline data of the DELCODE cohort of 437 subjects (127 controls, 168 SCD, 87 MCI, 55 AD patients) which underwent lumbar puncture, MRI scanning, and neuropsychological assessment. ATN classification was performed using CSF-Aβ42/Aβ40 (A+/-), CSF phospho-tau (T+/-), and adjusted hippocampal volume or CSF total-tau (N+/-). We compared voxel-wise model evidence for monotonic decline of gray matter volume across various sequences over ATN groups using the Bayesian Information Criterion (including also ROIs of Braak stages). First, face validity of the ACH transition sequence A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+ was compared against biologically less plausible (permuted) sequences among AD continuum ATN groups. Second, we evaluated evidence for 6 monotonic brain volume progressions from A-T-N- towards A+T+N+ including also non-AD continuum ATN groups.

RESULTS

The ACH-based progression A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+ was consistent with cognitive decline and clinical diagnosis. Using hippocampal volume for operationalization of neurodegeneration (N), ACH was most evident in 9% of gray matter predominantly in the medial temporal lobe. Many cortical regions suggested alternative non-monotonic volume progressions over ACH progression groups, which is compatible with an early amyloid-related tissue expansion or sampling effects, e.g., due to brain reserve. Volume decline in 65% of gray matter was consistent with a progression where A status converts before T or N status (i.e., ACH/ANT) when compared to alternative sequences (TAN/TNA/NAT/NTA). Brain regions earlier affected by tau tangle deposition (Braak stage I-IV, MTL, limbic system) present stronger evidence for volume decline than late Braak stage ROIs (V/VI, cortical regions). Similar findings were observed when using CSF total-tau for N instead.

CONCLUSION

Using the ATN classification system, early amyloid status conversion (before tau and neurodegeneration) is associated with brain volume loss observed during AD progression. The ATN system and the ACH are compatible with monotonic progression of MTL atrophy.

TRIAL REGISTRATION

DRKS00007966, 04/05/2015, retrospectively registered.

Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020.

METHODS

We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion.

RESULTS

Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity.

DISCUSSION

ADNI has had a profound impact in improving clinical trials for AD.

Dementia treatment versus prevention

Alzheimer disease (AD) and dementia are becoming increasingly prevalent due to the aging of the global populations. Currently available treatment options, including acetylcholinesterase inhibitors and memantine, only have symptomatic effects and no drugs with disease-modifying properties are available. Research on the amyloid cascade indicates that amyloid-β (Aβ) clearance from the brain may be the main pathophysiological change in late-onset AD and the key driver of neurodegeneration, which ultimately results in progressive cognitive deterioration and dementia. Most new AD drug candidates target different aspects of Aβ clearance, eg, using passive anti-Aβ immunization, but so far, all efforts to develop more effective drugs have failed. In parallel, nonpharmacological prevention trials are being conducted to modify dementia risk associated with known epidemiological risk factors. Some initial results are promising, but replication across independent cohorts remains a challenge.

Prediction of Cognitive Progression in Individuals with Mild Cognitive Impairment Using Radiomics as an Improvement of the ATN System: A Five-Year Follow-Up Study

Objective

To improve the N biomarker in the amyloid/tau/neurodegeneration system by radiomics and study its value for predicting cognitive progression in individuals with mild cognitive impairment (MCI).

Materials and Methods

A group of 147 healthy controls (HCs) (72 male; mean age ± standard deviation, 73.7 ± 6.3 years), 197 patients with MCI (114 male; 72.2 ± 7.1 years), and 128 patients with Alzheimer’s disease (AD) (74 male; 73.7 ± 8.4 years) were included. Optimal A, T, and N biomarkers for discriminating HC and AD were selected using receiver operating characteristic (ROC) curve analysis. A radiomics model containing comprehensive information of the whole cerebral cortex and deep nuclei was established to create a new N biomarker. Cerebrospinal fluid (CSF) biomarkers were evaluated to determine the optimal A or T biomarkers. All MCI patients were followed up until AD conversion or for at least 60 months. The predictive value of A, T, and the radiomics-based N biomarker for cognitive progression of MCI to AD were analyzed using Kaplan-Meier estimates and the log-rank test.

Results

The radiomics-based N biomarker showed an ROC curve area of 0.998 for discriminating between AD and HC. CSF Aβ42 and p-tau proteins were identified as the optimal A and T biomarkers, respectively. For MCI patients on the Alzheimer’s continuum, isolated A+ was an indicator of cognitive stability, while abnormalities of T and N, separately or simultaneously, indicated a high risk of progression. For MCI patients with suspected non-Alzheimer’s disease pathophysiology, isolated T+ indicated cognitive stability, while the appearance of the radiomics-based N+ indicated a high risk of progression to AD.

Conclusion

We proposed a new radiomics-based improved N biomarker that could help identify patients with MCI who are at a higher risk for cognitive progression. In addition, we clarified the value of a single A/T/N biomarker for predicting the cognitive progression of MCI.

Loss of corneal nerves and brain volume in mild cognitive impairment and dementia

Introduction

This study compared the capability of corneal confocal microscopy (CCM) with magnetic resonance imaging (MRI) brain volumetry for the diagnosis of mild cognitive impairment (MCI) and dementia.

Methods

In this cross‐sectional study, participants with no cognitive impairment (NCI), MCI, and dementia underwent assessment of Montreal Cognitive Assessment (MoCA), MRI brain volumetry, and CCM.

Results

Two hundred eight participants with NCI (n = 42), MCI (n = 98), and dementia (n = 68) of comparable age and gender were studied. For MCI, the area under the curve (AUC) of CCM (76% to 81%), was higher than brain volumetry (52% to 70%). For dementia, the AUC of CCM (77% to 85%), was comparable to brain volumetry (69% to 93%). Corneal nerve fiber density, length, branch density, whole brain, hippocampus, cortical gray matter, thalamus, amygdala, and ventricle volumes were associated with cognitive impairment after adjustment for confounders (All P’s < .01).

Discussion

The diagnostic capability of CCM compared to brain volumetry is higher for identifying MCI and comparable for dementia, and abnormalities in both modalities are associated with cognitive impairment.

Phosphorylated tau181 in plasma as a potential biomarker for Alzheimer's disease in adults with Down syndrome

Plasma tau phosphorylated at threonine 181 (p-tau181) predicts Alzheimer’s disease (AD) pathology with high accuracy in the general population. In this study, we investigated plasma p-tau181 as a biomarker of AD in individuals with Down syndrome (DS). We included 366 adults with DS (240 asymptomatic, 43 prodromal AD, 83 AD dementia) and 44 euploid cognitively normal controls. We measured plasma p-tau181 with a Single molecule array (Simoa) assay. We examined the diagnostic performance of p-tau181 for the detection of AD and the relationship with other fluid and imaging biomarkers. Plasma p-tau181 concentration showed an area under the curve of 0.80 [95% CI 0.73–0.87] and 0.92 [95% CI 0.89–0.95] for the discrimination between asymptomatic individuals versus those in the prodromal and dementia groups, respectively. Plasma p-tau181 correlated with atrophy and hypometabolism in temporoparietal regions. Our findings indicate that plasma p-tau181 concentration can be useful to detect AD in DS.

Plasma tau phosphorylated at threonine 181 (p-tau181) predicts Alzheimer’s disease (AD) pathology. Here, the authors investigated whether plasma ptau181 could be a potential biomarker of AD in individuals with Down syndrome (DS) and find plasma p-tau181 can detect AD in DS adults.

Association of plasma P-tau181 with memory decline in non-demented adults

Alzheimer's disease is the leading cause of dementia worldwide and is characterized by a long preclinical phase in which amyloid-β and tau accumulate in the absence of cognitive decline. In vivo biomarkers for Alzheimer's disease are expensive, invasive and inaccessible, yet are critical for accurate disease diagnosis and patient management. Recent ultrasensitive methods to measure plasma phosphorylated tau 181 (p-tau181) display strong correlations with tau positron emission tomography, p-tau181 in CSF, and tau pathology at autopsy. The clinical utility of plasma-based p-tau181 biomarkers is unclear. In a longitudinal multicentre observational study, we assessed 1113 non-demented individuals (509 cognitively unimpaired elderly and 604 individuals with mild cognitive impairment) from the Alzheimer's Disease Neuroimaging Initiative who underwent neuropsychological assessments and were evaluated for plasma p-tau181. The primary outcome was a memory composite z-score. Mixed-effect models assessed rates of memory decline in relation to baseline plasma p-tau181, and whether plasma p-tau181 significantly predicted memory decline beyond widely available clinical and genetic data (age, sex, years of education, cardiovascular and metabolic conditions, and APOEε4 status). Participants were followed for a median of 4.1 years. Baseline plasma p-tau181 was associated with lower baseline memory (β estimate: -0.49, standard error: 0.06, t-value: -7.97), as well as faster rates of memory decline (β estimate: -0.11, standard error: 0.01, t-value: -7.37). Moreover, the inclusion of plasma p-tau181 resulted in improved prediction of memory decline beyond clinical and genetic data (marginal R 2 of 16.7-23%, χ2 = 100.81, P < 0.00001). Elevated baseline plasma p-tau181 was associated with higher rates of clinical progression to mild cognitive impairment (hazard ratio = 1.82, 95% confidence interval: 1.2-2.8) and from mild cognitive impairment to dementia (hazard ratio = 2.06, 95% confidence interval: 1.55-2.74). Our results suggest that in elderly individuals without dementia at baseline, plasma p-tau181 biomarkers were associated with greater memory decline and rates of clinical progression to dementia. Plasma p-tau181 improved prediction of memory decline above a model with currently available clinical and genetic data. While the clinical importance of this improvement in the prediction of memory decline is unknown, these results highlight the potential of plasma p-tau181 as a cost-effective and scalable Alzheimer's disease biomarker.

Prognostic value of amyloid/tau/neurodegeneration (ATN) classification based on diagnostic cerebrospinal fluid samples for Alzheimer's disease

Objective

The primary study objective of this retrospective academic memory clinic-based observational longitudinal study was to investigate the prognostic value of a cerebrospinal fluid (CSF)-based ATN classification for subsequent cognitive decline during the 3 years following lumbar puncture in a clinical, real-life setting. The secondary objective was to investigate the prognostic value of CSF biomarkers as continuous variables.

Methods

Data from 228 patients (median age 67 (47–85) years), who presented at the Neurology Memory Clinic UZ/KU Leuven between September 2011 and December 2016, were included with a follow-up period of up to 36 months. Patients underwent a CSF AD biomarker test for amyloid-beta 1–42 (Aβ₄₂)_, hyperphosphorylated tau (p₁₈₁-tau) and total tau (t-tau) in the clinical work-up for diagnostic reasons. Patients were divided into ATN classes based on CSF biomarkers: Aβ₄₂ for amyloid (A), p₁₈₁-tau for tau (T), and t-tau as a measure for neurodegeneration (N). Based on retrospective data analysis, cognitive performance was evaluated by Mini Mental State Examination (MMSE) scores every 6 months over a period up to 36 months following the lumbar puncture. The statistical analysis was based on linear mixed-effects modeling (LME).

Results

The distribution in the current clinical sample was as follows: A−/T−/N− 32.02%, A+/T−/N− 33.33%, A+/T+/N+ 17.11%, A+/T−/N+ 11.84%, A−/T−/N+ 4.39%, A−/T+/N+ 1.32% (3 cases), with no cases in the A−/T+/N− and A+/T+/N− class. Hence, the latter 3 classes were excluded from further analyses. The change of MMSE relative to A−/T−/N− over a 36-month period was significant in all four ATN classes: A+/T+/N+ = − 4.78 points on the MMSE; A−/T−/N+ = − 4.76; A+/T−/N+ = − 2.83; A+/T−/N− = − 1.96. The earliest significant difference was seen in the A+/T+/N+ class at 12 months after baseline. The effect of ATN class on future cognitive decline was confirmed for a different set of CSF thresholds. All individual baseline CSF biomarkers including the Aβ₄₂/t-tau ratio showed a significant correlation with subsequent cognitive decline, with the highest correlation seen for Aβ₄₂/t-tau.

Conclusion

ATN classification based on CSF biomarkers has a statistically significant and clinically relevant prognostic value for the course of cognitive decline in a 3-year period in a clinical practice setting.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-021-00817-4.

Plasma Tau and Neurofilament Light in Frontotemporal Lobar Degeneration and Alzheimer Disease

OBJECTIVE

To test the hypothesis that plasma total tau (t-tau) and neurofilament light chain (NfL) concentrations may have a differential role in the study of frontotemporal lobar degeneration syndromes (FTLD-S) and clinically diagnosed Alzheimer disease syndromes (AD-S), we determined their diagnostic and prognostic value in FTLD-S and AD-S and their sensitivity to pathologic diagnoses.

METHODS

We measured plasma t-tau and NfL with the Simoa platform in 265 participants: 167 FTLD-S, 43 AD-S, and 55 healthy controls (HC), including 82 pathology-proven cases (50 FTLD-tau, 18 FTLD-TDP, 2 FTLD-FUS, and 12 AD) and 98 participants with amyloid PET. We compared cross-sectional and longitudinal biomarker concentrations between groups, their correlation with clinical measures of disease severity, progression, and survival, and cortical thickness.

RESULTS

Plasma NfL, but not plasma t-tau, discriminated FTLD-S from HC and AD-S from HC. Both plasma NfL and t-tau were poor discriminators between FLTD-S and AD-S. In pathology-confirmed cases, plasma NfL was higher in FTLD than AD and in FTLD-TDP compared to FTLD-tau, after accounting for age and disease severity. Plasma NfL, but not plasma t-tau, predicted clinical decline and survival and correlated with regional cortical thickness in both FTLD-S and AD-S. The combination of plasma NfL with plasma t-tau did not outperform plasma NfL alone.

CONCLUSION

Plasma NfL is superior to plasma t-tau for the diagnosis and prediction of clinical progression of FTLD-S and AD-S.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that plasma NfL has superior diagnostic and prognostic performance vs plasma t-tau in FTLD and AD.

Personalized Healthcare for Dementia

Dementia is one of the most common health problems affecting older adults, and the population with dementia is growing. Dementia refers to a comprehensive syndrome rather than a specific disease and is characterized by the loss of cognitive abilities. Many factors are related to dementia, such as aging, genetic profile, systemic vascular disease, unhealthy diet, and physical inactivity. As the causes and types of dementia are diverse, personalized healthcare is required. In this review, we first summarize various diagnostic approaches associated with dementia. Particularly, clinical diagnosis methods, biomarkers, neuroimaging, and digital biomarkers based on advances in data science and wearable devices are comprehensively reviewed. We then discuss three effective approaches to treating dementia, including engineering design, exercise, and diet. In the engineering design section, recent advances in monitoring and drug delivery systems for dementia are introduced. Additionally, we describe the effects of exercise on the treatment of dementia, especially focusing on the effects of aerobic and resistance training on cognitive function, and the effects of diets such as the Mediterranean diet and ketogenic diet on dementia.

Developing univariate neurodegeneration biomarkers with low-rank and sparse subspace decomposition

Cognitive decline due to Alzheimer's disease (AD) is closely associated with brain structure alterations captured by structural magnetic resonance imaging (sMRI). It supports the validity to develop sMRI-based univariate neurodegeneration biomarkers (UNB). However, existing UNB work either fails to model large group variances or does not capture AD dementia (ADD) induced changes. We propose a novel low-rank and sparse subspace decomposition method capable of stably quantifying the morphological changes induced by ADD. Specifically, we propose a numerically efficient rank minimization mechanism to extract group common structure and impose regularization constraints to encode the original 3D morphometry connectivity. Further, we generate regions-of-interest (ROI) with group difference study between common subspaces of Aβ+AD and Aβ-cognitively unimpaired (CU) groups. A univariate morphometry index (UMI) is constructed from these ROIs by summarizing individual morphological characteristics weighted by normalized difference between Aβ+AD and Aβ-CU groups. We use hippocampal surface radial distance feature to compute the UMIs and validate our work in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25% reduction in the mean annual change with 80% power and two-tailed P=0.05are 116, 279 and 387 for the longitudinal Aβ+AD, Aβ+mild cognitive impairment (MCI) and Aβ+CU groups, respectively. Additionally, for MCI patients, UMIs well correlate with hazard ratio of conversion to AD (4.3, 95% CI = 2.3-8.2) within 18 months. Our experimental results outperform traditional hippocampal volume measures and suggest the application of UMI as a potential UNB.

Biomarkers and phenotypic expression in Alzheimer's disease: exploring the contribution of frailty in the Alzheimer's Disease Neuroimaging Initiative

The present study aimed at investigating if the main biomarkers of Alzheimer’s disease (AD) neuropathology and their association with cognitive disturbances and dementia are modified by the individual’s frailty status. We performed a cross-sectional analysis of data from participants with normal cognition, mild cognitive impairment (MCI), and AD dementia enrolled in the Alzheimer’s Disease Neuroimaging Initiative 2 (ADNI2) study. Frailty was operationalized by computing a 40-item Frailty Index (FI). The following AD biomarkers were considered and analyzed according to the participants’ frailty status: CSF Aβ_1-42, ¹⁸¹P-tau, and T-tau; MRI-based hippocampus volume; cortical glucose metabolism at the FDG PET imaging; amyloid deposition at the ¹⁸F-AV-45 PET imaging. Logistic regression models, adjusted for age, sex, and education, were performed to explore the association of biomarkers with cognitive status at different FI levels. Subjects with higher FI scores had lower CSF levels of Aβ_1-42, hippocampus volumes at the MRI, and glucose metabolism at the FDG PET imaging, and a higher amyloid deposition at the ¹⁸F-AV-45 PET. No significant differences were observed among the two frailty groups concerning ApoE genotype, CSF T-tau, and P-tau. Increasing frailty levels were associated with a weakened relationship between dementia and ¹⁸F-AV-45 uptake and hippocampus volume and with a stronger relationship of dementia with FDG PET. Frailty contributes to the discrepancies between AD pathology and clinical manifestations and influences the association of AD pathological modifications with cognitive changes. AD and dementia should increasingly be conceived as “complex diseases of aging,” determined by multiple, simultaneous, and interacting pathophysiological processes.

Discordant Alzheimer's neurodegenerative biomarkers and their clinical outcomes

OBJECTIVE

In the 2018 ATN framework, Alzheimer's neurodegenerative biomarkers comprised cerebrospinal fluid (CSF) total tau, 18 F-fluorodeoxyglucose-positron emission tomography, and brain atrophy. We aimed to assess the clinical outcomes of having discordant Alzheimer's neurodegenerative biomarkers.

METHODS

A total of 721 non-demented individuals from the Alzheimer's Disease Neuroimaging Initiative database were included and then further categorized into concordant-negative, discordant, and concordant-positive groups. Demographic distributions of the groups were compared. Longitudinal changes in clinical outcomes and risk of conversion were assessed using linear mixed-effects models and multivariate Cox proportional hazard models, respectively.

RESULTS

Discordant group was intermediate to concordant-negative and concordant-positive groups in terms of APOE ε4 positivity, CSF amyloid-beta, and phosphorylated tau. Compared with concordant-negative group, discordant group deteriorated faster in cognitive scores (Mini-Mental State Examination, the Clinical Dementia Rating Scale-Sum of Boxes, and the Functional Activities Questionnaire) and demonstrated greater rates of atrophy in brain structures (hippocampus, entorhinal cortex, and whole brain), and concordant-positive group performed worse over time than discordant group. Moreover, the risk of cognitive decline increased from concordant-negative to discordant to concordant-positive. The results from longitudinal analyses were validated in A+T+, cognitively normal, and mild cognitive impairment individuals, and were also validated by applying different cutoffs and neurodegenerative biomarkers.

INTERPRETATION

Discordant neurodegenerative status denotes a stage of cognitive function which is intermediate between concordant-negative and concordant-positive. Identification of discordant cases would provide insights into intervention and new therapy approaches, particularly in A+T+ individuals. Moreover, this work may be a complement to the ATN scheme.

Computing Univariate Neurodegenerative Biomarkers with Volumetric Optimal Transportation: A Pilot Study

Changes in cognitive performance due to neurodegenerative diseases such as Alzheimer's disease (AD) are closely correlated to the brain structure alteration. A univariate and personalized neurodegenerative biomarker with strong statistical power based on magnetic resonance imaging (MRI) will benefit clinical diagnosis and prognosis of neurodegenerative diseases. However, few biomarkers of this type have been developed, especially those that are robust to image noise and applicable to clinical analyses. In this paper, we introduce a variational framework to compute optimal transportation (OT) on brain structural MRI volumes and develop a univariate neuroimaging index based on OT to quantify neurodegenerative alterations. Specifically, we compute the OT from each image to a template and measure the Wasserstein distance between them. The obtained Wasserstein distance, Wasserstein Index (WI) for short to specify the distance to a template, is concise, informative and robust to random noise. Comparing to the popular linear programming-based OT computation method, our framework makes use of Newton's method, which makes it possible to compute WI in large-scale datasets. Experimental results, on 314 subjects (140 Aβ + AD and 174 Aβ- normal controls) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset, provide preliminary evidence that the proposed WI is correlated with a clinical cognitive measure (the Mini-Mental State Examination (MMSE) score), and it is able to identify group difference and achieve a good classification accuracy, outperforming two other popular univariate indices including hippocampal volume and entorhinal cortex thickness. The current pilot work suggests the application of WI as a potential univariate neurodegenerative biomarker.

Characterization of Alzheimer Disease Biomarker Discrepancies Using Cerebrospinal Fluid Phosphorylated Tau and AV1451 Positron Emission Tomography

Importance

Fluid and imaging biomarkers of Alzheimer disease (AD) are often used interchangeably, but some biomarkers may reveal earlier stages of disease.

Objective

To characterize individuals with tau abnormality indicated by cerebrospinal fluid (CSF) assay or positron emission tomography (PET).

Design, Setting, and Participants

Between 2010 and 2019, 322 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) underwent CSF and PET assessments of tau pathology. Data-driven, clinically relevant thresholds for CSF phosphorylated tau (P-tau) (≥26.64 pg/mL) and flortaucipir-PET meta-regions of interest (ROI) (standard uptake value ratio ≥1.37) indicated participants' tau status as CSF-/PET-, CSF+/PET-, CSF-/PET+, and CSF+/PET+. Of 1659 ADNI participants with a CSF or flortaucipir assessment, 588 had both measures (1071 were excluded). Among these, 266 were further excluded because they did not have flortaucipir and CSF testing within less than 25 months, leaving 322 for analysis. Of these, 213 were cognitively unimpaired (CU); 98 had mild cognitive impairment (MCI); and 11 had AD dementia.

Main Outcomes and Measures

We compared tau-positive vs tau-negative groups as indicated by either modality or demographic and clinical variables, amyloid β-PET burden, and flortaucipir-PET binding across Braak stage-related ROIs. We also compared 5-year rates of CSF P-tau accumulation and cognitive decline prior to flortaucipir-PET scanning.

Results

Among the 322 study participants, 180 were women (56%), and the mean (SD) age was 73.08 (7.37) years. Two hundred ten participants were CSF-/PET- (65%); 63 were CSF+/PET- (19.5%); 15 were CSF-/PET+ (4.6%); and 34 were CSF+/PET+ (10.5%). Most CSF-/PET+ participants had measures near CSF or PET tau thresholds. The CSF+/PET- participants showed faster 5-year accrual of P-tau and increased flortaucipir-PET binding in early Braak ROIs but similar memory decline compared with CSF-/PET- participants. Tau-positive individuals by either measure showed increased amyloid β-PET burden. All CSF+/PET+ individuals were amyloid-positive, and 26 had MCI or AD dementia (76%). Compared with the CSF-/PET- group, CSF+/PET+ individuals had experienced faster 5-year accrual of CSF P-tau and decline in memory and executive function, resulting in reduced cognitive abilities at the time of flortaucipir-PET assessment.

Conclusions and Relevance

Suprathreshold CSF P-tau without flortaucipir-PET abnormality may indicate a stage of AD development characterized by early tau abnormality without measurable loss in cognitive performance. Persons with both tau CSF and PET abnormality appear to have reduced cognitive capacities resulting from faster antecedent cognitive decline. Elevation of CSF P-tau appears to precede flortaucipir-PET positivity in the progression of AD pathogenesis and related cognitive decline.

ATN classification and clinical progression in subjective cognitive decline: The SCIENCe project

Objective

To investigate the relationship between the ATN classification system (amyloid, tau, neurodegeneration) and risk of dementia and cognitive decline in individuals with subjective cognitive decline (SCD).

Methods

We classified 693 participants with SCD (60 ± 9 years, 41% women, Mini-Mental State Examination score 28 ± 2) from the Amsterdam Dementia Cohort and Subjective Cognitive Impairment Cohort (SCIENCe) project according to the ATN model, as determined by amyloid PET or CSF β-amyloid (A), CSF p-tau (T), and MRI-based medial temporal lobe atrophy (N). All underwent extensive neuropsychological assessment. For 342 participants, follow-up was available (3 ± 2 years). As a control population, we included 124 participants without SCD.

Results

Fifty-six (n = 385) participants had normal Alzheimer disease (AD) biomarkers (A–T–N–), 27% (n = 186) had non-AD pathologic change (A–T–N+, A–T+N–, A–T+N+), 18% (n = 122) fell within the Alzheimer continuum (A+T–N–, A+T–N+, A+T+N–, A+T+N+). ATN profiles were unevenly distributed, with A–T+N+, A+T–N+, and A+T+N+ containing very few participants. Cox regression showed that compared to A–T–N–, participants in A+ profiles had a higher risk of dementia with a dose–response pattern for number of biomarkers affected. Linear mixed models showed participants in A+ profiles showed a steeper decline on tests addressing memory, attention, language, and executive functions. In the control group, there was no association between ATN and cognition.

Conclusions

Among individuals presenting with SCD at a memory clinic, those with a biomarker profile A–T+N+, A+T–N–, A+T+N–, and A+T+N+ were at increased risk of dementia, and showed steeper cognitive decline compared to A–T–N– individuals. These results suggest a future where biomarker results could be used for individualized risk profiling in cognitively normal individuals presenting at a memory clinic.

Is Alzheimer's Disease Risk Modifiable?

Population-based clinic-pathological studies have established that the most common pathological substrate of dementia in community-dwelling elderly people is mixed, especially Alzheimer's disease (AD) and cerebrovascular ischemic disease (CVID), rather than pure AD. While these could be just two frequent unrelated comorbidities in the elderly, epidemiological research has reinforced the idea that mid-life (age <65 years) vascular risk factors increase the risk of late-onset (age ≥ 65 years) dementia, and specifically AD. By contrast, healthy lifestyle choices such as leisure activities, physical exercise, and Mediterranean diet are considered protective against AD. Remarkably, several large population-based longitudinal epidemiological studies have recently indicated that the incidence and prevalence of dementia might be decreasing in Western countries. Although it remains unclear whether these positive trends are attributable to neuropathologically definite AD versus CVID, based on these epidemiological data it has been estimated that a sizable proportion of AD cases could be preventable. In this review, we discuss the current evidence about modifiable risk factors for AD derived from epidemiological, preclinical, and interventional studies, and analyze the opportunities for therapeutic and preventative interventions.

The implications of different approaches to define AT(N) in Alzheimer disease

Objective

To compare different β-amyloid (Aβ), tau, and neurodegeneration (AT[N]) variants within the Swedish BioFINDER studies.

Methods

A total of 490 participants were classified into AT(N) groups. These include 53 cognitively unimpaired (CU) and 48 cognitively impaired (CI) participants (14 mild cognitive impairment [MCI] and 34 Alzheimer disease [AD] dementia) from BioFINDER-1 and 389 participants from BioFINDER-2 (245 CU and 144 CI [138 MCI and 6 AD dementia]). Biomarkers for A were CSF Aβ₄₂ and amyloid-PET ([¹⁸F]flutemetamol); for T, CSF phosphorylated tau (p-tau) and tau PET ([¹⁸F]flortaucipir); and for (N), hippocampal volume, temporal cortical thickness, and CSF neurofilament light (NfL). Binarization of biomarkers was achieved using cutoffs defined in other cohorts. The relationship between different AT(N) combinations and cognitive trajectories (longitudinal Mini-Mental State Examination scores) was examined using linear mixed modeling and coefficient of variation.

Results

Among CU participants, A−T−(N)− or A+T−(N)− variants were most common. However, more T+ cases were seen using p-tau than tau PET. Among CI participants, A+T+(N)+ was more common; however, more (N)+ cases were seen for MRI measures relative to CSF NfL. Tau PET best predicted longitudinal cognitive decline in CI and p-tau in CU participants. Among CI participants, continuous T (especially tau PET) and (N) measures improved the prediction of cognitive decline compared to binary measures.

Conclusions

Our findings show that different AT(N) variants are not interchangeable, and that optimal variants differ by clinical stage. In some cases, dichotomizing biomarkers may result in loss of important prognostic information.

Synaptic biomarkers in CSF aid in diagnosis, correlate with cognition and predict progression in MCI and Alzheimer's disease

INTRODUCTION

Amyloid, Tau, and neurodegeneration biomarkers can stage Alzheimer's Disease (AD). Synaptic biomarkers may help track cognition.

METHODS

In cognitively normal controls, Mild Cognitive Impairment (MCI) and AD, we investigated CSF biomarkers in relation to cognitive measures and as predictors of cognitive and global decline.

RESULTS

There were 90 normal controls (mean age 73.0, 58% women), 57 MCI (mean age 74.3, 35% women), and 46 AD (mean age 70.7, 41% women). CSF Aβ1-42 and Neuronal Pentraxin 2 (NPTX2) were decreased, and CSF Tau, neurogranin, and SNAP25 increased in AD versus controls. Aβ1-42/Tau or NPTX2/Tau discriminated AD and controls best. NPTX2/Tau correlated strongly with cognition in AD and MCI and predicted a 2-3-year decline. We replicated findings in the ADNI cohort.

DISCUSSION

CSF synaptic biomarkers, particularly NPTX2, which regulates synaptic homeostasis, relate to cognition and predict progression in AD beyond Aβ1-42 and Tau. This is relevant for prognosis and clinical trials.

APP-derived peptides reflect neurodegeneration in frontotemporal dementia

Objective

We aimed to investigate the relationship between cerebrospinal fluid levels (CSF) of amyloid precursor protein (APP)‐derived peptides related to the amyloidogenic pathway, cortical thickness, neuropsychological performance, and cortical gene expression profiles in frontotemporal lobar degeneration (FTLD)‐related syndromes, Alzheimer’s disease (AD), and healthy controls.

Methods

We included 214 participants with CSF available recruited at two centers: 93 with FTLD‐related syndromes, 57 patients with AD, and 64 healthy controls. CSF levels of amyloid β (Aβ)1‐42, Aβ1‐40, Aβ1‐38, and soluble β fragment of APP (sAPPβ) were centrally analyzed. We compared CSF levels of APP‐derived peptides between groups and, we studied the correlation between CSF biomarkers, cortical thickness, and domain‐specific cognitive composites in each group. Then, we explored the relationship between cortical thickness, CSF levels of APP‐derived peptides, and regional gene expression profile using a brain‐wide regional gene expression data in combination with gene set enrichment analysis.

Results

The CSF levels of Aβ1‐40, Aβ1‐38, and sAPPβ were lower in the FTLD‐related syndromes group than in the AD and healthy controls group. CSF levels of all APP‐derived peptides showed a positive correlation with cortical thickness and the executive cognitive composite in the FTLD‐related syndromes group but not in the healthy control or AD groups. In the cortical regions where we observed a significant association between cortical thickness and CSF levels of APP‐derived peptides, we found a reduced expression of genes related to synaptic function.

Interpretation

APP‐derived peptides in CSF may reflect FTLD‐related neurodegeneration. This observation has important implications as Aβ1‐42 levels are considered an indirect biomarker of cerebral amyloidosis.

Testing the 2018 NIA-AA research framework in a retrospective large cohort of patients with cognitive impairment: from biological biomarkers to clinical syndromes

Background

According to the 2018 NIA-AA research framework, Alzheimer’s disease (AD) is not defined by the clinical consequences of the disease, but by its underlying pathology, measured by biomarkers. Evidence of both amyloid-β (Aβ) and phosphorylated tau protein (p-tau) deposition—assessed interchangeably with amyloid-positron emission tomography (PET) and/or cerebrospinal fluid (CSF) analysis—is needed to diagnose AD in a living person. Our aim was to test the new NIA-AA research framework in a large cohort of cognitively impaired patients to evaluate correspondence between the clinical syndromes and the underlying pathologic process testified by biomarkers.

Methods

We retrospectively analysed 628 subjects referred to our centre in suspicion of dementia, who underwent CSF analysis, together with neuropsychological assessment and neuroimaging, and were diagnosed with different neurodegenerative dementias according to current criteria, or as cognitively unimpaired. Subjects were classified considering CSF biomarkers, and the prevalence of normal, AD-continuum and non-AD profiles in each clinical syndrome was calculated. The positivity threshold of each CSF biomarker was first assessed by receiver operating characteristic analysis, using Aβ-positive/negative status as determined by amyloid-PET visual reads. The agreement between CSF and amyloid-PET data was also evaluated.

Results

Among patients with a clinical diagnosis of AD, 94.1% were in the AD-continuum, whereas 5.5% were classified as non-AD and 0.4% were normal. The AD-continuum profile was found also in 26.2% of frontotemporal dementia, 48.6% of Lewy body dementia, 25% of atypical parkinsonism and 44.7% of vascular dementia. Biomarkers’ profile did not differ in amnestic and not amnestic mild cognitive impairment. CSF Aβ levels and amyloid-PET tracer binding negatively correlated, and the concordance between the two Aβ biomarkers was 89%.

Conclusions

The examination of the 2018 NIA-AA research framework in our clinical setting revealed a good, but incomplete, correspondence between the clinical syndromes and the underlying pathologic process measured by CSF biomarkers. The AD-continuum profile resulted to be a sensitive, but non-specific biomarker with regard to the clinical AD diagnosis. CSF and PET Aβ biomarkers were found to be not perfectly interchangeable to quantify the Aβ burden, possibly because they measure different aspects of AD pathology.

Agreement of amyloid PET and CSF biomarkers for Alzheimer's disease on Lumipulse

OBJECTIVE

To determine the cutoffs that optimized the agreement between ¹⁸ F-Florbetapir positron emission tomography (PET) and Aβ1-42, Aβ1-40, tTau, pTau and their ratios measured in cerebrospinal fluid (CSF) on the LUMIPULSE G600II instrument, we quantified the levels of these four biomarkers in 94 CSF samples from participants of the Sant Pau Initiative on Neurodegeneration (SPIN cohort) using the Lumipulse G System with available ¹⁸ F-Florbetapir imaging.

METHODS

Participants had mild cognitive impairment (n = 35), AD dementia (n = 12), other dementias or neurodegenerative diseases (n = 41), or were cognitively normal controls (n = 6). Levels of Aβ1-42 were standardized to certified reference material. Amyloid scans were assessed visually and through automated quantification. We determined the cutoffs of CSF biomarkers that optimized their agreement with ¹⁸ F-Florbetapir PET and evaluated concordance between markers of the amyloid category.

RESULTS

Aβ1-42, tTau and pTau (but not Aβ1-40) and the ratios with Aβ1-42 had good diagnostic agreement with ¹⁸ F-Florbetapir PET. As a marker of amyloid pathology, the Aβ1-42/Aβ1-40 ratio had higher agreement and better correlation with amyloid PET than Aβ1-42 alone.

INTERPRETATION

CSF biomarkers measured with the Lumipulse G System show good agreement with amyloid imaging in a clinical setting with heterogeneous presentations of neurological disorders. Combination of Aβ1-42 with Aβ1-40 increases the agreement between markers of amyloid pathology.

Plasma Levels of Soluble AβPPβ as a Biomarker for Alzheimer's Disease with Dementia

Cost- and time-effective markers of Alzheimer's disease (AD), reliable and feasible at the population level are urgently needed. Soluble amyloid-β protein precursor β (sAβPPβ) in plasma has attracted scientific attention as a potential AD biomarker candidate. Here we report that plasma sAβPPβ levels in patients with AD dementia and typical for AD cerebrospinal fluid (CSF) biomarker profiles (N = 33) are significantly lower (p < 0.01) than those of cognitively healthy elderly individuals without AD (N = 39), while CSF sAβPPβ levels did not differ between the studied groups. This provides further evidence for the potential of sAβPPβ in plasma as an AD biomarker candidate.

Clinical impact of [18F]flutemetamol PET among memory clinic patients with an unclear diagnosis

Purpose

To investigate the impact of amyloid PET with [¹⁸F]flutemetamol on diagnosis and treatment management in a cohort of patients attending a tertiary memory clinic in whom, despite extensive cognitive assessment including neuropsychological testing, structural imaging, CSF biomarker analysis and in some cases [¹⁸F]FDG PET, the diagnosis remained unclear.

Methods

The study population consisted of 207 patients with a clinical diagnosis prior to [¹⁸F]flutemetamol PET including mild cognitive impairment (MCI; n = 131), Alzheimer’s disease (AD; n = 41), non-AD (n = 10), dementia not otherwise specified (dementia NOS; n = 20) and subjective cognitive decline (SCD; n = 5).

Results

Amyloid positivity was found in 53% of MCI, 68% of AD, 20% of non-AD, 20% of dementia NOS, and 60% of SCD patients. [¹⁸F]Flutemetamol PET led, overall, to a change in diagnosis in 92 of the 207 patients (44%). A high percentage of patients with a change in diagnosis was observed in the MCI group (n = 67, 51%) and in the dementia NOS group (n = 11; 55%), followed by the non-AD and AD (30% and 20%, respectively). A significant increase in cholinesterase inhibitor treatment was observed after [¹⁸F]flutemetamol PET (+218%, 34 patients before and 108 patients after).

Conclusion

The present study lends support to the clinical value of amyloid PET in patients with an uncertain diagnosis in the tertiary memory clinic setting.

Association of short-term cognitive decline and MCI-to-AD dementia conversion with CSF, MRI, amyloid- and 18F-FDG-PET imaging

Disease-modifying treatment trials are increasingly advanced to the prodromal or preclinical phase of Alzheimer's disease (AD), and inclusion criteria are based on biomarkers rather than clinical symptoms. Therefore, it is of great interest to determine which biomarkers should be combined to accurately predict conversion from mild cognitive impairment (MCI) to AD dementia. However, up to date, only few studies performed a complete A/T/N subject characterization using each of the CSF and imaging markers, or they only investigated long-term (≥ 2 years) prognosis. This study aimed to investigate the association between cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), amyloid- and ¹⁸F-FDG positron emission tomography (PET) measures at baseline, in relation to cognitive changes and conversion to AD dementia over a short-term (12-month) period. We included 13 healthy controls, 49 MCI and 16 AD dementia patients with a clinical-based diagnosis and a complete A/T/N characterization at baseline. Global cortical amyloid-β (Aβ) burden was quantified using the ¹⁸F-AV45 standardized uptake value ratio (SUVR) with two different reference regions (cerebellar grey and subcortical white matter), whereas metabolism was assessed based on ¹⁸F-FDG SUVR. CSF measures included Aβ_1–42, Aβ_1–40, T-tau, P-tau₁₈₁, and their ratios, and MRI markers included hippocampal volumes (HV), white matter hyperintensities, and cortical grey matter volumes. Cognitive functioning was measured by MMSE and RBANS index scores. All statistical analyses were corrected for age, sex, education, and APOE ε4 genotype. As a result, faster cognitive decline was most strongly associated with hypometabolism (posterior cingulate) and smaller hippocampal volume (e.g., Δstory recall: β = +0.43 [p < 0.001] and + 0.37 [p = 0.005], resp.) at baseline. In addition, faster cognitive decline was significantly associated with higher baseline Aβ burden only if SUVR was referenced to the subcortical white matter (e.g., Δstory recall: β = −0.28 [p = 0.020]). Patients with MCI converted to AD dementia at an annual rate of 31%, which could be best predicted by combining neuropsychological testing (visuospatial construction skills) with either MRI-based HV or ¹⁸F-FDG-PET. Combining all three markers resulted in 96% specificity and 92% sensitivity. Neither amyloid-PET nor CSF biomarkers could discriminate short-term converters from non-converters.

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FDG-PET and MRI HV are the strongest predictors of cognitive decline and conversion to AD.

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Combination of visuospatial construction testing with FDG-PET or MRI HV present high predicting power of conversion.

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CSF and amyloid-PET seem less suitable markers of disease progression.

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Increased AV45-PET predicts short-term cognitive decline if SUVR is referenced to WM instead of CB.

Dementia treatment versus prevention

Alzheimer disease (AD) and dementia are becoming increasingly prevalent due to the aging of the global populations. Currently available treatment options, including acetylcholinesterase inhibitors and memantine, only have symptomatic effects and no drugs with disease-modifying properties are available. Research on the amyloid cascade indicates that amyloid-β (Aβ) clearance from the brain may be the main pathophysiological change in late-onset AD and the key driver of neurodegeneration, which ultimately results in progressive cognitive deterioration and dementia. Most new AD drug candidates target different aspects of Aβ clearance, eg, using passive anti-Aβ immunization, but so far, all efforts to develop more effective drugs have failed. In parallel, nonpharmacological prevention trials are being conducted to modify dementia risk associated with known epidemiological risk factors. Some initial results are promising, but replication across independent cohorts remains a challenge.