Incremental value of biomarker combinations to predict progression of mild cognitive impairment to Alzheimer's dementia

Risk of conversion to mild cognitive impairment or dementia among subjects with amyloid and tau pathology: a systematic review and meta-analysis

BACKGROUND

Measurement of beta-amyloid (Aβ) and phosphorylated tau (p-tau) levels offers the potential for early detection of neurocognitive impairment. Still, the probability of developing a clinical syndrome in the presence of these protein changes (A+ and T+) remains unclear. By performing a systematic review and meta-analysis, we investigated the risk of mild cognitive impairment (MCI) or dementia in the non-demented population with A+ and A- alone and in combination with T+ and T- as confirmed by PET or cerebrospinal fluid examination.

METHODS

A systematic search of prospective and retrospective studies investigating the association of Aβ and p-tau with cognitive decline was performed in three databases (MEDLINE via PubMed, EMBASE, and CENTRAL) on January 9, 2024. The risk of bias was assessed using the Cochrane QUIPS tool. Odds ratios (OR) and Hazard Ratios (HR) were pooled using a random-effects model. The effect of neurodegeneration was not studied due to its non-specific nature.

RESULTS

A total of 18,162 records were found, and at the end of the selection process, data from 36 cohorts were pooled (n= 7,793). Compared to the unexposed group, the odds ratio (OR) for conversion to dementia in A+ MCI patients was 5.18 [95% CI 3.93; 6.81]. In A+ CU subjects, the OR for conversion to MCI or dementia was 5.79 [95% CI 2.88; 11.64]. Cerebrospinal fluid Aβ42 or Aβ42/40 analysis and amyloid PET imaging showed consistent results. The OR for conversion in A+T+ MCI subjects (11.60 [95% CI 7.96; 16.91]) was significantly higher than in A+T- subjects (2.73 [95% CI 1.65; 4.52]). The OR for A-T+ MCI subjects was non-significant (1.47 [95% CI 0.55; 3.92]). CU subjects with A+T+ status had a significantly higher OR for conversion (13.46 [95% CI 3.69; 49.11]) than A+T- subjects (2.04 [95% CI 0.70; 5.97]). Meta-regression showed that the ORs for Aβ exposure decreased with age in MCI. (beta = -0.04 [95% CI -0.03 to -0.083]).

CONCLUSIONS

Identifying Aβ-positive individuals, irrespective of the measurement technique employed (CSF or PET), enables the detection of the most at-risk population before disease onset, or at least at a mild stage. The inclusion of tau status in addition to Aβ, especially in A+T+ cases, further refines the risk assessment. Notably, the higher odds ratio associated with Aβ decreases with age.

TRIAL REGISTRATION

The study was registered in PROSPERO (ID: CRD42021288100).

Inferring Alzheimer's disease pathologic traits from clinical measures in living adults

Background

Alzheimer's disease neuropathologic changes (AD-NC) are important for identify people with high risk for AD dementia (ADD) and subtyping ADD.

Objective

Develop imputation models based on clinical measures to infer AD-NC.

Methods

We used penalized generalized linear regression to train imputation models for four AD-NC traits (amyloid-β, tangles, global AD pathology, and pathologic AD) in Rush Memory and Aging Project decedents, using clinical measures at the last visit prior to death as predictors. We validated these models by inferring AD-NC traits with clinical measures at the last visit prior to death for independent Religious Orders Study (ROS) decedents. We inferred baseline AD-NC traits for all ROS participants at study entry, and then tested if inferred AD-NC traits at study entry predicted incident ADD and postmortem pathologic AD.

Results

Inferred AD-NC traits at the last visit prior to death were related to postmortem measures with R2=(0.188,0.316,0.262) respectively for amyloid-β, tangles, and global AD pathology, and prediction Area Under the receiver operating characteristic Curve (AUC) 0.765 for pathologic AD. Inferred baseline levels of all four AD-NC traits predicted ADD. The strongest prediction was obtained by the inferred baseline probabilities of pathologic AD with AUC=(0.919,0.896) for predicting the development of ADD in 3 and 5 years from baseline. The inferred baseline levels of all four AD-NC traits significantly discriminated pathologic AD profiled eight years later with p-values<1.4 × 10-10.

Conclusion

Inferred AD-NC traits based on clinical measures may provide effective AD biomarkers that can estimate the burden of AD-NC traits in aging adults.

Cerebrospinal and Blood Biomarkers in Alzheimer's Disease: Did Mild Cognitive Impairment Definition Affect Their Clinical Usefulness?

Despite Alzheimer's Disease (AD) being known from the times of Alois Alzheimer, who lived more than one century ago, many aspects of the disease are still obscure, including the pathogenesis, the clinical spectrum definition, and the therapeutic approach. Well-established biomarkers for AD come from the histopathological hallmarks of the disease, which are Aβ and phosphorylated Tau protein aggregates. Consistently, cerebrospinal fluid (CSF) Amyloid β (Aβ) and phosphorylated Tau level measurements are currently used to detect AD presence. However, two central biases affect these biomarkers. Firstly, incomplete knowledge of the pathogenesis of diseases legitimates the search for novel molecules that, reasonably, could be expressed by neurons and microglia and could be detected in blood simpler and earlier than the classical markers and in a higher amount. Further, studies have been performed to evaluate whether CSF biomarkers can predict AD onset in Mild Cognitive Impairment (MCI) patients. However, the MCI definition has changed over time. Hence, the studies on MCI patients seem to be biased at the beginning due to the imprecise enrollment and heterogeneous composition of the miscellaneous MCI subgroup. Plasma biomarkers and novel candidate molecules, such as microglia biomarkers, have been tentatively investigated and could represent valuable targets for diagnosing and monitoring AD. Also, novel AD markers are urgently needed to identify molecular targets for treatment strategies. This review article summarizes the main CSF and blood AD biomarkers, underpins their advantages and flaws, and mentions novel molecules that can be used as potential biomarkers for AD.

Neuroimaging and machine learning for studying the pathways from mild cognitive impairment to alzheimer's disease: a systematic review

BACKGROUND

This systematic review synthesizes the most recent neuroimaging procedures and machine learning approaches for the prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia.

METHODS

We systematically searched PubMed, SCOPUS, and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review guidelines.

RESULTS

Our search returned 2572 articles, 56 of which met the criteria for inclusion in the final selection. The multimodality framework and deep learning techniques showed potential for predicting the conversion of MCI to AD dementia.

CONCLUSION

Findings of this systematic review identified that the possibility of using neuroimaging data processed by advanced learning algorithms is promising for the prediction of AD progression. We also provided a detailed description of the challenges that researchers are faced along with future research directions. The protocol has been registered in the International Prospective Register of Systematic Reviews- CRD42019133402 and published in the Systematic Reviews journal.

The QPLEX™ Plus Assay Kit for the Early Clinical Diagnosis of Alzheimer's Disease

We recently developed a multiplex diagnostic kit, QPLEX™ Alz plus assay kit, which captures amyloid-β1-40, galectin-3 binding protein, angiotensin-converting enzyme, and periostin simultaneously using microliters of peripheral blood and utilizes an optimized algorithm for screening Alzheimer's disease (AD) by correlating with cerebral amyloid deposition. Owing to the demand for early AD detection, we investigate the potential of our kit for the early clinical diagnosis of AD. A total of 1395 participants were recruited, and their blood samples were analyzed with the QPLEX™ kit. The average of QPLEX™ algorithm values in each group increased gradually in the order of the clinical progression continuum of AD: cognitively normal (0.382 ± 0.150), subjective cognitive decline (0.452 ± 0.130), mild cognitive impairment (0.484 ± 0.129), and AD (0.513 ± 0.136). The algorithm values between each group showed statistically significant differences among groups divided by Mini-Mental State Examination and Clinical Dementia Rating. The QPLEX™ algorithm values could be used to distinguish the clinical continuum of AD or cognitive function. Because blood-based diagnosis is more accessible, convenient, and cost- and time-effective than cerebral spinal fluid or positron emission tomography imaging-based diagnosis, the QPLEX™ kit can potentially be used for health checkups and the early clinical diagnosis of AD.

Volume changes of hippocampal and amygdala subfields in patients with mild cognitive impairment and Alzheimer's disease

BACKGROUND

Automated segmentation of hippocampal and amygdala subfields could improve classification accuracy of Mild Cognitive Impairments (MCI) and Alzheimer's Disease (AD) individuals.

METHODS

We applied T1-weighted magnetic resonance imaging (MRI) for 21 AD, 39 MCI and 32 normal control (NC) participants at 3-Tesla MRI. Twelve hippocampal subfields and 9 amygdala subfields in each hemisphere were analyzed using FreeSurfer 6.0.

RESULTS

Smaller volumes were observed in right/left whole hippocampus, right/left hippocampal tail, right/left subiculum, right Cornu ammonis 1(CA1), right/left molecular layer, right granule cell-molecular layer-dentate gyrus (GC-ML-DG), right CA4, right fimbria, right whole amygdala, right/left accessory basal, right anterior amygdala area, left central, left medial and right/left cortical nucleus of AD group compared to both MCI and NC groups (p < 0.001). The volumes of right presubiculum, right CA3, right hippocampus-amygdala-transition-area (HATA), right lateral, right basal, right central, right medial, right cortico-amygdaloid transition (CAT) and right paralaminar nucleus were significantly larger in NC than AD group (p ≤ 0.001), while the volumes of right subiculum, right CA1, right molecular layer, right whole hippocampus, right whole amygdala, right basal and right accessory basal were significantly larger in NC than MCI group (p ≤ 0.002). Trend analysis showed that most hippocampus and amygdala subfields have a trend of atrophy with the decline of cognitive function. Six core components were identified by the hierarchical clustering. The combined Receiver operating characteristic (ROC) analysis achieved the diagnostic performances (AUC: 0.81) in differentiating AD from MCI; (AUC: 0.79) in differentiating MCI from NC and (AUC: 0.97) in differentiating AD from NC.

CONCLUSIONS

Volumetric differences of hippocampus and amygdala were at a finer subfields scale, and the volumes of right basal nucleus, left parasubiculum, left medial nucleus, left GC-ML-DG, left hippocampal fissure, and right fimbria can be employed as neuroimaging biomarkers to assist the clinical diagnosis of MCI and AD.

Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer's Disease: A Literature Review from a Machine Learning Perspective

There is a growing interest in the application of machine learning (ML) in Alzheimer's disease (AD) research. However, neuropsychiatric symptoms (NPS), frequent in subjects with AD, mild cognitive impairment (MCI), and other related dementias have not been analyzed sufficiently using ML methods. To portray the landscape and potential of ML research in AD and NPS studies, we present a comprehensive literature review of existing ML approaches and commonly studied AD biomarkers. We conducted PubMed searches with keywords related to NPS, AD biomarkers, machine learning, and cognition. We included a total of 38 articles in this review after excluding some irrelevant studies from the search results and including 6 articles based on a snowball search from the bibliography of the relevant studies. We found a limited number of studies focused on NPS with or without AD biomarkers. In contrast, multiple statistical machine learning and deep learning methods have been used to build predictive diagnostic models using commonly known AD biomarkers. These mainly included multiple imaging biomarkers, cognitive scores, and various omics biomarkers. Deep learning approaches that combine these biomarkers or multi-modality datasets typically outperform single-modality datasets. We conclude ML may be leveraged to untangle the complex relationships of NPS and AD biomarkers with cognition. This may potentially help to predict the progression of MCI or dementia and develop more targeted early intervention approaches based on NPS.

Non-Invasive Nasal Discharge Fluid and Other Body Fluid Biomarkers in Alzheimer’s Disease

The key to current Alzheimer’s disease (AD) therapy is the early diagnosis for prompt intervention, since available treatments only slow the disease progression. Therefore, this lack of promising therapies has called for diagnostic screening tests to identify those likely to develop full-blown AD. Recent AD diagnosis guidelines incorporated core biomarker analyses into criteria, including amyloid-β (Aβ), total-tau (T-tau), and phosphorylated tau (P-tau). Though effective, the accessibility of screening tests involving conventional cerebrospinal fluid (CSF)- and blood-based analyses is often hindered by the invasiveness and high cost. In an attempt to overcome these shortcomings, biomarker profiling research using non-invasive body fluid has shown the potential to capture the pathological changes in the patients’ bodies. These novel non-invasive body fluid biomarkers for AD have emerged as diagnostic and pathological targets. Here, we review the potential peripheral biomarkers, including non-invasive peripheral body fluids of nasal discharge, tear, saliva, and urine for AD.

The Clinical Course of Early and Late Mild Cognitive Impairment

Introduction

Amnestic mild cognitive impairment (MCI) can be classified as either early MCI (EMCI) or late MCI (LMCI) according to the severity of memory impairment. The aim of this study was to compare the prognosis and clinical course between EMCI and LMCI.

Methods

Between January 2009 and December 2017, a total of 418 patients with MCI and 146 subjects with normal cognition were recruited from a memory clinic. All the patients received at least two series of neuropsychological evaluations each year and were categorized as either EMCI or LMCI according to Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) criteria.

Results

In total, our study included 161 patients with EMCI, 258 with LMCI, and 146 subjects with normal cognition as controls (NCs). The mean follow-up duration was 3.55 ± 2.18 years (range: 1–9). In a first-year follow-up assessment, 54 cases (32.8%) of EMCI and 16 (5%) of LMCI showed a normal cognitive status. There was no significant difference between the first year EMCI reverter and NCs in terms of dementia-free survival and further cognitive decline. However, first-year LMCI reverters still had a higher risk of cognitive decline during the following evaluations. Until the last follow-up, annual dementia conversion rates were 1.74, 4.33, and 18.6% in the NC, EMCI, and LMCI groups, respectively. The EMCI and LMCI groups showed a higher rate of progression to dementia (log-rank test, p < 0.001) than normal subjects. Compared with NCs, patients in the LMCI group showed a significantly faster annual decline in global cognition [annual rate of change for the mini-mental status examination (MMSE) score: −1.035, p < 0.001]) and all cognitive domains, while those in the EMCI group showed a faster rate of decline in global cognitive function (annual rate of change for the MMSE score: −0.299, p = 0.001).

Conclusion

It is important to arrange follow-up visits for patients with MCI, even in the EMCI stage. One-year short-term follow-up may provide clues about the progression of cognitive function and help to identify relatively low-risk EMCI subjects.

[Diagnosing Alzheimer's dementia - a playground for academics or a sensible clinical measure?]

The number of dementia cases continues to increase with Alzheimer's disease as the leading cause. The diagnostic workup of Alzheimer's dementia is complex, and its clinical relevance debatable considering the current lack of disease-modifying treatments. From this perspective, a stepwise diagnostic approach is recommended. Whenever Alzheimer's dementia is suspected, a patient' history a physical and psychiatric examination, neuropsychological tests, routine blood tests and standard cerebral imaging should be conducted. This allows in many cases a diagnosis to be given. In cases remaining unclear, modern biomarker tests are proving increasingly useful. Knowledge of the diagnosis is pivotal for the patients to assess the prognosis, to enable them to make plans for their future and to get access to available treatment. The approval of aducanumab in the USA and other promising monoclonal antibodies currently in phase 3-trials as well as the development of blood biomarkers give us hope for the future.

Combining plasma phospho-tau and accessible measures to evaluate progression to Alzheimer's dementia in mild cognitive impairment patients

Background

Up to now, there are no clinically available minimally invasive biomarkers to accurately identify mild cognitive impairment (MCI) patients who are at greater risk to progress to Alzheimer’s disease (AD) dementia. The recent advent of blood-based markers opens the door for more accessible biomarkers. We aimed to identify which combinations of AD related plasma biomarkers and other easily accessible assessments best predict progression to AD dementia in patients with mild cognitive impairment (MCI).

Methods

We included patients with amnestic MCI (n = 110) followed prospectively over 3 years to assess clinical status. Baseline plasma biomarkers (amyloid-β 42/40, phosphorylated tau217 [p-tau217], neurofilament light and glial fibrillary acidic protein), hippocampal volume, APOE genotype, and cognitive tests were available. Logistic regressions with conversion to amyloid-positive AD dementia within 3 years as outcome was used to evaluate the performance of different biomarkers measured at baseline, used alone or in combination. The first analyses included only the plasma biomarkers to determine the ones most related to AD dementia conversion. Second, hippocampal volume, APOE genotype and a brief cognitive composite score (mPACC) were combined with the best plasma biomarker.

Results

Of all plasma biomarker combinations, p-tau217 alone had the best performance for discriminating progression to AD dementia vs all other combinations (AUC 0.84, 95% CI 0.75–0.93). Next, combining p-tau217 with hippocampal volume, cognition, and APOE genotype provided the best discrimination between MCI progressors vs. non-progressors (AUC 0.89, 0.82–0.95). Across the few best models combining different markers, p-tau217 and cognition were consistently the main contributors. The most parsimonious model including p-tau217 and cognition had a similar model fit, but a slightly lower AUC (0.87, 0.79–0.95, p = 0.07).

Conclusion

We identified that combining plasma p-tau217 and a brief cognitive composite score was strongly related to greater risk of progression to AD dementia in MCI patients, suggesting that these measures could be key components of future prognostic algorithms for early AD.

Trial registration

NCT01028053, registered December 9, 2009.

Exosomes in Age-Related Cognitive Decline: Mechanistic Insights and Improving Outcomes

Aging is the most prominent risk factor for cognitive decline, yet behavioral symptomology and underlying neurobiology can vary between individuals. Certain individuals exhibit significant age-related cognitive impairments, while others maintain intact cognitive functioning with only minimal decline. Recent developments in genomic, proteomic, and functional imaging approaches have provided insights into the molecular and cellular substrates of cognitive decline in age-related neuropathologies. Despite the emergence of novel tools, accurately and reliably predicting longitudinal cognitive trajectories and improving functional outcomes for the elderly remains a major challenge. One promising approach has been the use of exosomes, a subgroup of extracellular vesicles that regulate intercellular communication and are easily accessible compared to other approaches. In the current review, we highlight recent findings which illustrate how the analysis of exosomes can improve our understanding of the underlying neurobiological mechanisms that contribute to cognitive variation in aging. Specifically, we focus on exosome-mediated regulation of miRNAs, neuroinflammation, and aggregate-prone proteins. In addition, we discuss how exosomes might be used to enhance individual patient outcomes by serving as reliable biomarkers of cognitive decline and as nanocarriers to deliver therapeutic agents to the brain in neurodegenerative conditions.

Don’t forget about tau: the effects of ApoE4 genotype on Alzheimer’s disease cerebrospinal fluid biomarkers in subjects with mild cognitive impairment—data from the Dementia Competence Network

ApoE4, the strongest genetic risk factor for Alzheimer’s disease (AD), has been shown to be associated with both beta-amyloid (Aβ) and tau pathology, with the strongest evidence for effects on Aβ, while the association between ApoE4 and tau pathology remains inconsistent. This study aimed to investigate the associations between ApoE4 with CSF Aβ42, total tau (t-tau), phospho-tau181 (p-tau), and with the progression of decline in a large cohort of MCI subjects, both progressors to AD and other dementias, as well as non-progressors. We analyzed associations of CSF Aβ42, p-tau and t-tau with ApoE4 allele frequency cross-sectionally and longitudinally over 3 years of follow-up in 195 individuals with a diagnosis of MCI-stable, MCI-AD converters and MCI progressing to other dementias from the German Dementia Competence Network. In the total sample, ApoE4 carriers had lower concentrations of CSF Aβ42, and increased concentrations of t-tau and p-tau compared to non-carriers in a gene dose-dependent manner. Comparisons of these associations stratified by MCI-progression groups showed a significant influence of ApoE4 carriership and diagnostic group on all CSF biomarker levels. The effect of ApoE4 was present in MCI-stable individuals but not in the other groups, with ApoE4 + carriers having decreased CSF Aβ 42 levels, and increased concentration of t-tau and p-tau. Longitudinally, individuals with abnormal t-tau and Aβ42 had a more rapid progression of cognitive and clinical decline, independently of ApoE4 genotype. Overall, our results contribute to an emerging framework in which ApoE4 involves mechanisms associated with both CSF amyloid-β burden and tau aggregation at specific time points in AD pathogenesis.

Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data

Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.

The clinical use of blood-test factors for Alzheimer's disease: improving the prediction of cerebral amyloid deposition by the QPLEXTM Alz plus assay kit

Alzheimer's disease (AD) is the leading cause of dementia, and many studies have focused on finding effective blood biomarkers for the accurate diagnosis of this disease. Predicting cerebral amyloid deposition is considered the key for AD diagnosis because a cerebral amyloid deposition is the hallmark of AD pathogenesis. Previously, blood biomarkers were discovered to predict cerebral amyloid deposition, and further efforts have been made to increase their sensitivity and specificity. In this study, we analyzed blood-test factors (BTFs) that can be commonly measured in medical health check-ups from 149 participants with cognitively normal, 87 patients with mild cognitive impairment, and 64 patients with clinically diagnosed AD dementia with brain amyloid imaging data available. We demonstrated that four factors among regular health check-up blood tests, cortisol, triglyceride/high-density lipoprotein cholesterol ratio, alanine aminotransferase, and free triiodothyronine, showed either a significant difference by or correlation with cerebral amyloid deposition. Furthermore, we made a prediction model for Pittsburgh compound B-positron emission tomography positivity, using BTFs and the previously discovered blood biomarkers, the QPLEXTM Alz plus assay kit biomarker panel, and the area under the curve was significantly increased up to 0.845% with 69.4% sensitivity and 90.6% specificity. These results show that BTFs could be used as co-biomarkers and that a highly advanced prediction model for amyloid plaque deposition could be achieved by the combinational use of diverse biomarkers.

A Deep Neural Network-Based Method for Prediction of Dementia Using Big Data

The rise in dementia among the aging Korean population will quickly create a financial burden on society, but timely recognition of early warning for dementia and proper responses to the occurrence of dementia can enhance medical treatment. Health behavior and medical service usage data are relatively more accessible than clinical data, and a prescreening tool with easily accessible data could be a good solution for dementia-related problems. In this paper, we apply a deep neural network (DNN) to prediction of dementia using health behavior and medical service usage data, using data from 7031 subjects aged over 65 collected from the Korea National Health and Nutrition Examination Survey (KNHANES) in 2001 and 2005. In the proposed model, principal component analysis (PCA) featuring and min/max scaling are used to preprocess and extract relevant background features. We compared our proposed methodology, a DNN/scaled PCA, with five well-known machine learning algorithms. The proposed methodology shows 85.5% of the area under the curve (AUC), a better result than that using other algorithms. The proposed early prescreening method for possible dementia can be used by both patients and doctors.

Forgetting Rates on the Recency Portion of a Word List Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease

Amnestic mild cognitive impairment has a greater risk of progressing to Alzheimer's disease (AD). Consistent with AD patients' distinctive deficit in consolidating new memory traces, in a recent study we demonstrated that the forgetting rate on the recency portion of a word list differentiates AD from other forms of dementia. In line with this finding, the aim of this study was to investigate whether increased recency forgetting could be a reliable index for predicting amnestic mild cognitive impairment (MCI) patients' conversion to AD. For this purpose, we compared accuracy in immediate and delayed recall from different portions of a word list in a group of patients with amnestic MCI who converted (C-MCI) or did not convert (S-MCI) to AD during a three-year follow-up period and in a group of normal controls. The results of the present study show that the forgetting from the recency portion of the list (operationalized as a ratio between immediate and delayed recall) was significantly larger in C-MCI than in S-MCI patients. Consistently, the hierarchical logistic regression analyses demonstrated that the recency ratio is a strong predictor of group membership. Similar to what occurs in full-blown AD patients, the results of our study suggest that the increased forgetting rate from the recency portion of the list in C-MCI patients is due to severely reduced efficiency in converting transitory short-term memory representations into stable long-term memory traces. This is consistent with prominent involvement of neuropathological changes in the cortical areas of the medial-temporal lobes and suggests that the recency ratio is a cognitive marker able to identify MCI patients who have a greater likelihood of progressing to AD.

Value of Neuropsychological Tests to Identify Patients with Depressive Symptoms on the Alzheimer's Disease Continuum

Background:

Depressive symptoms often co-occur with Alzheimer’s disease (AD) and can impact neuropsychological test results. In early stages of AD, disentangling cognitive impairments due to depression from those due to neurodegeneration often poses a challenge.

Objective:

We aimed to identify neuropsychological tests able to detect AD-typical pathology while taking into account varying degrees of depressive symptoms.

Methods:

A battery of neuropsychological tests (CERAD-NP) and the Geriatric Depression Scale (GDS) were assessed, and cerebrospinal fluid (CSF) biomarkers were obtained. After stratifying patients into CSF positive or negative and into low, moderate, or high GDS score groups, sensitivity and specificity and area under the curve (AUC) were calculated for each subtest.

Results:

497 participants were included in the analyses. In patients with low GDS scores (≤10), the highest AUC (0.72) was achieved by Mini-Mental State Examination, followed by Constructional Praxis Recall and Wordlist Total Recall (AUC = 0.714, both). In patients with moderate (11–20) and high (≥21) GDS scores, Trail Making Test-B (TMT-B) revealed the highest AUCs with 0.77 and 0.82, respectively.

Conclusion:

Neuropsychological tests showing AD-typical pathology in participants with low GDS scores are in-line with previous results. In patients with higher GDS scores, TMT-B showed the best discrimination. This indicates the need to focus on executive function rather than on memory task results in depressed patients to explore a risk for AD.

A machine learning approach to screen for preclinical Alzheimer's disease

Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on ¹⁸F-florbetapir and ¹⁸F-fluorodeoxyglucose PET, respectively. We used a nested cross-validation approach with non-invasive features (electroencephalography [EEG], APOE4 genotype, demographic, neuropsychological and MRI data) to predict: 1/ amyloid status; 2/ neurodegeneration status; 3/ decline to prodromal AD at 5-year follow-up. Importantly, EEG was most strongly predictive of neurodegeneration, even when reducing the number of channels from 224 down to 4, as 4-channel EEG best predicted neurodegeneration (negative predictive value [NPV] = 82%, positive predictive value [PPV] = 38%, 77% specificity, 45% sensitivity). The combination of demographic, neuropsychological data, APOE4 and hippocampal volumetry most strongly predicted amyloid (80% NPV, 41% PPV, 70% specificity, 58% sensitivity) and most strongly predicted decline to prodromal AD at 5 years (97% NPV, 14% PPV, 83% specificity, 50% sensitivity). Thus, machine learning can help to screen patients at high risk of preclinical AD using non-invasive and affordable biomarkers.

Relation of retinal and hippocampal thickness in patients with amnestic mild cognitive impairment and healthy controls

OBJECTIVE

Investigating retinal thickness may complement existing biological markers for dementia and other neurodegenerative diseases. Although retinal thinning is predictive for cognitive decline, it remains to be investigated if and how this feature aligns with neurodegeneration elsewhere in the brain, specifically in early disease stages.

METHODS

Using optical coherence tomography and magnetic resonance imaging, we examined retinal thickness as well as hippocampal structure in patients with amnestic mild cognitive impairment and healthy controls.

RESULTS

The groups did not differ in hippocampal and retinal thickness measures. However, we detected a correlation of peripapillary retinal nerve fiber layer thickness and hippocampal thickness in healthy people but not in cognitively impaired patients. The ratio of hippocampus to retina thickness was significantly smaller in patients with mild cognitive impairment and correlated positively with cognitive performance.

CONCLUSIONS

Different temporal trajectories of neurodegeneration may disrupt transregional brain structure associations in patients with amnestic mild cognitive impairment.

CDKN2A Gene Expression as a Potential Aging Biomarker in Dogs

Describing evolutionary conserved physiological or molecular patterns, which can reliably mark the age of both model organisms and humans or predict the onset of age-related pathologies has become a priority in aging research. The age-related gene-expression changes of the Cyclin Dependent Kinase Inhibitor 2A (CDKN2A) gene have been well-documented in humans and rodents. However, data is lacking from other relevant species, including dogs. Therefore, we quantified the CDKN2A mRNA abundance in dogs of different ages, in four tissue types: the frontal cortex of the brain, temporal muscle, skin, and blood. We found a significant, positive correlation between CDKN2A relative expression values and age in the brain, muscle, and blood; however, no correlation was detected in the skin. The strongest correlation was detected in the brain tissue (CDKN2A/GAPDH: r = 0.757, p < 0.001), similarly to human findings, while the muscle and blood showed weaker, but significant correlation. Our results suggest that CDKN2A might be a potential blood-borne biomarker of aging in dogs, although the validation and optimization will require further, more focused research. Our current results also clearly demonstrate that the role of CDKN2A in aging is conserved in dogs, regarding both tissue specificity and a pivotal role of CDKN2A in brain aging.

Association between CSF Aβ42 and amyloid negativity in patients with different stage mild cognitive impairment

Whether the cerebrospinal fluid (CSF) biomarkers of amyloid-positive and amyloid-negative patients with mild cognitive impairment (MCI) or Alzheimer's disease (AD) are significantly different is still unknown. The purpose of this study is to compare the differences in CSF total tau, P-tau and Aβ42 in patients with amyloid-positive positron emission tomography (PET) and amyloid-negative PET, and to explore related risk factors in cognitive normal (CN), early MCI (EMCI), late MCI (LMCI) and AD. 558 participants (140 CN; 233 EMCI; 125 LMCI; 60 AD) were recruited in this study from the AD Neuroimaging Initiative (ADNI) database. The associations between CSF biomarkers were assessed by partial correlation analysis. The relations between significant variables were determined by multinomial logistic regression. Compared with amyloid-positive PET patients, patients with amyloid-negative PET had higher CSF Aβ42 and lower P-tau in the whole samples. The concentration of Aβ42 in the positive amyloid PET was significantly different in different groups, but not the negative amyloid PET (CN vs. LMCI; CN vs. AD; EMCI vs. AD, all P < 0.05). When amyloid PET was positive, a weak correlation was found between the levels of Aβ42 and P-tau only in CN group. However, a moderate degree of correlation between Aβ42 and P-tau was found in EMCI and LMCI when amyloid PET was negative. After covariates adjustment, CSF Aβ42 was significantly associated with EMCI [adjusted odds ratio (OR) = 0.99, 95 % confidence interval (CI) = 0.99-1.00, P = 0.02) and LMCI (adjusted OR = 0.99, 95 % CI = 0.99-1.00, P = 0.007)] in patients with negative amyloid PET, not in patients with positive amyloid PET. Our findings highlight that Aβ42 had strong correlations with other biomarkers and might help reduce risk of EMCI or LMCI in patients with amyloid negativity.

Diagnose it yourself: will there be a home test kit for Alzheimer's disease?

Alzheimer's disease is the most common neurodegenerative process leading to dementia. To date, there is no curative approach; thus, establishing a diagnosis as early as possible is necessary to implement preventive measures. However, today's gold standard for diagnosing Alzheimer's disease is high in both cost and effort and is not readily available. This defines the need for low-effort and economic alternatives that give patients low-threshold access to testing systems at their general practitioners or even at home for an independent retrieval of a biologic specimen. This perspective gives an overview of established and novel approaches in the field and speculates on the future of test strategies eventually technically implementable at home.

Diagnostic accuracy of the Clinical Dementia Rating Scale for detecting mild cognitive impairment and dementia: A bivariate meta-analysis

OBJECTIVE

The Clinical Dementia Rating (CDR) Scale comprising global score (CDR-GS) and sum of boxes scores (CDR-SB) is commonly used in staging cognitive impairment; however, its diagnostic accuracy is not well clarified. The meta-analysis aimed to investigate the diagnostic accuracy of the CDR for mild cognitive impairment (MCI) and dementia in older populations.

METHODS

Studies examining the diagnostic accuracy of the CDR for MCI or dementia against reference standards were included from seven electronic databases. The bivariate analysis with a random-effects model was adopted to calculate the pooled sensitivity and specificity of the CDR for MCI and dementia.

RESULTS

Fifteen studies investigating the diagnostic accuracy of the CDR-GS (n = 13) or CDR-SB (n = 5) for MCI or dementia were included. The pooled sensitivity and specificity of the CDR-GS for MCI were 93% and 97%, respectively. With respect to dementia, the CDR-GS had superior pooled specificity compared to the CDR-SB (99% vs. 94%), while similar sensitivities were found between the CDR-GS and CDR-SB (both 87%). Significant moderators of an old age, a high educational level, a high prevalence of MCI or dementia, being in a developing country, and a lack of informants' observations may affect the estimation of the sensitivity or specificity of the CDR.

CONCLUSIONS

Evidence supports the CDR being useful for detecting MCI and dementia; applying the CDR for staging cognitive impairment in at risk populations should be considered. Furthermore, including objective observations from relevant informants or proxies to increase the accuracy of the CDR for dementia is suggested.

The role of peripheral fatty acids as biomarkers for Alzheimer's disease and brain inflammation

Alzheimer's disease (AD) is a complex and heterogeneous neurodegenerative disease. A wide range of techniques have been proposed to facilitate early diagnosis of AD, including biomarkers from the cerebrospinal fluid and blood. Although phosphorylated tau and amyloid beta are amongst the most promising biomarkers of AD, other peripheral biomarkers have been identified and in this review we synthesize the current knowledge on circulating fatty acids. Fatty acids are involved in different biological process including neurotransmission and inflammation. Interestingly, some fatty acids appear to be modulated during disease progression, including long chain saturated fatty acids, and polyunsaturated fatty acids, such as docosahexaenoic acid . However, discrepant results have been reported in the literature and there is the need for further validation and method standardization. Nonetheless, our literature review suggests that fatty acid analyses could potentially provide a valuable source of data to further inform the pathology and progression of AD.

Reciprocal Incremental Value of 18F-FDG-PET and Cerebrospinal Fluid Biomarkers in Mild Cognitive Impairment Patients Suspected for Alzheimer's Disease and Inconclusive First Biomarker

BACKGROUND

In Alzheimer's disease (AD) diagnosis, both cerebrospinal fluid (CSF) biomarkers and FDG-PET sometimes give inconclusive results.

OBJECTIVE

To evaluate the incremental diagnostic value of FDG-PET over CSF biomarkers, and vice versa, in patients with mild cognitive impairment (MCI) and suspected AD, in which the first biomarker resulted inconclusive.

METHODS

A consecutive series of MCI patients was retrospectively selected from two Memory Clinics where, as per clinical routine, either the first biomarker choice is FDG-PET and CSF biomarkers are only used in patients with uninformative FDG-PET, or vice versa. We defined criteria of uncertainty in interpretation of FDG-PET and CSF biomarkers, according to current evidence. The final diagnosis was established according to clinical-neuropsychological follow-up of at least one year (mean 4.4±2.2).

RESULTS

When CSF was used as second biomarker after FDG-PET, 14 out of 36 (39%) received informative results. Among these 14 patients, 11 (79%) were correctly classified with respect to final diagnosis, thus with a relative incremental value of CSF over FDG-PET of 30.6%. When FDG-PET was used as second biomarker, 26 out of 39 (67%) received informative results. Among these 26 patients, 15 (58%) were correctly classified by FDG-PET with respect to final diagnosis, thus with a relative incremental value over CSF of 38.5%.

CONCLUSION

Our real-world data confirm the added values of FDG-PET (or CSF) in a diagnostic pathway where CSF (or FDG-PET) was used as first biomarkers in suspected AD. These findings should be replicated in larger studies with prospective enrolment according to a Phase III design.

Blood TDP-43 Combined with Demographics Information Predicts Dementia Occurrence in Community Non-Dementia Elderly

BACKGROUND

TAR DNA-binding protein-43 (TDP-43) and neurofilament light chain (NfL) are promising fluid biomarkers of disease progression for various dementia.

OBJECTIVE

We would explore whether blood levels of NfL and TDP-43 could predict the long-term progression to dementia, and the relationship of TDP-43 levels between cerebrospinal fluid (CSF) and blood.

METHODS

A total of 86 non-dementia elderly received 7-year follow-up, and were divided into 49 stable normal control (NC)/mild cognitive impairment (MCI) subjects, 19 subjects progressing from NC to MCI, and 18 subjects progressing from NC/MCI to dementia. Blood TDP-43 and NfL levels, and cognitive functions were measured in all subjects. Furthermore, another cohort of 23 dementia patients, including 13 AD and 10 non-AD patients received blood and CSF measurements of TDP-43.

RESULTS

In cohort 1, compared to stable NC/MCI group, there were higher levels of blood TDP-43 at baseline in subjects progressing from NC/MCI to dementia. The combination of baseline blood TDP-43 levels with demographics including age, education, and diabetes had the detection for dementia occurrence. Baseline blood levels of NfL are negatively associated with cognitive function at 7-year follow-up. In cohort 2, we found there were no relationship between CSF and blood levels of TDP-43. Moreover, the levels of TDP-43 in CSF was positively associated with the age of patients, especially in AD group.

CONCLUSION

Single blood TDP-43 could not estimate dementia occurrence; however, TDP-43 combined with demographics has the predictive effect for dementia occurrence and NfL level is associated with a decrease of cognitive function.

Predementia constructs: Mild cognitive impairment or mild neurocognitive disorder? A narrative review

BACKGROUND

Predementia is a heuristic umbrella concept to classify older adults with cognitive impairment who do not suffer dementia. Many diagnostic entities have been proposed to address this concept, but most of them have not had widespread acceptance.

AIMS

To review clinical definitions, epidemiologic data (prevalence, incidence) and rate of conversion to dementia of the main predementia constructs, with special interest in the two most frequently used: mild cognitive impairment (MCI) and minor neurocognitive disorder (miNCD).

METHODS

We have selected in three databases (MEDLINE, Web of Science and Google scholar) the references from inception to 31 December 2019 of relevant reviews, population and community-based surveys, and clinical series with >500 participants and >3 years follow-up as the best source of evidence.

MAIN RESULTS

The history of predementia constructs shows that MCI is the most referred entity. It is widely recognized as a clinical syndrome harbinger of dementia of several etiologies, mainly MCI due to Alzheimer's disease. The operational definition of MCI has shortcomings: vagueness of its requirement of "preserved independence in functional abilities" and others. The recent miNCD construct presents analogous difficulties. Current data indicate that it is a stricter predementia condition, with lower prevalence than MCI, less sensitivity to cognitive decline and, possibly, higher conversion rate to dementia.

CONCLUSIONS

MCI is a widely employed research and clinical entity. Preliminary data indicate that the clinical use of miNCD instead of MCI requires more scientific evidence. Both approaches have common limitations that need to be addressed.

White matter pathways underlying Chinese semantic and phonological fluency in mild cognitive impairment

Neuroimaging evidence has suggested that Chinese-language processing differs from that of its alphabetic-language counterparts. However, the underlying white matter pathway correlations between semantic and phonological fluency in Chinese-language processing remain unknown. Thus, we investigated the differences between two verbal fluency tests on 50 participants with amnestic mild cognitive impairment (aMCI) and 36 healthy controls (HC) with respect to five groups (ventral and dorsal stream fibers, frontal-striatal fibers, hippocampal-related fibers, and the corpus callosum) of white matter microstructural integrity. Diffusion spectrum imaging was used. The results revealed a progressive reduction in advantage in semantic fluency relative to phonological fluency from HC to single-domain aMCI to multidomain aMCI. Common and dissociative white matter correlations between tests of the two types of fluency were identified. Both types of fluency relied on the corpus callosum and ventral stream fibers, semantic fluency relied on the hippocampal-related fibers, and phonological fluency relied on the dorsal stream and frontal-striatal fibers. The involvement of bilateral tracts of interest as well as the association with the corpus callosum indicate the uniqueness of Chinese-language fluency processing. Dynamic associations were noted between white matter tract involvement and performance on the two fluency tests in four time blocks. Overall, our findings suggest the clinical utility of verbal fluency tests in geriatric populations, and they elucidate both task-specific and language-specific brain-behavior associations.

A Fragment of S38AA is a Novel Plasma Biomarker of Alzheimer's Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disease without a cure. The pathological process starts decades before clinical onset, and thus clinical trials of drugs aimed at treating AD should start at a presymptomatic stage. Therefore, it is critical to diagnose AD at an early stage. Tau, phosphorylated tau, and amyloid-β peptide (Aβ) in cerebrospinal fluid (CSF), and positron emission tomography (PET) imaging of Aβ or tau accumulation are supportive biomarkers for AD diagnosis, but there is no reliable presymptomatic diagnostic marker. Since CSF sampling is invasive, and PET imaging is expensive and available only at specialized centers, a reliable blood biomarker has long been sought for. Here we describe a novel extramembrane fragment from solute carrier family 38 member 10 (SLC38A10, S38AA) that we found to be decreased in pyramidal neurons in AD cases by proteomics and immunohistochemical analysis. We detected a S38AA fragment in CSF and found the levels to correlate with severity of AD and APOE genotype. Importantly, the plasma levels of the fragment also showed a significant correlation with Mini-Mental State Examination scores in AD. Moreover, plasma from other neurodegenerative disease was analyzed and the fragment was found to be increased specifically in AD. Interestingly, the fragment is detected in mouse, rat, and monkey, and increases in amyloid precursor protein transgenic mice as the AD-like pathology progresses. We propose that the S38AA fragment in plasma could be a novel quantitative diagnostic marker for AD and potentially a marker of disease progression in AD.

Use of radiomic features and support vector machine to discriminate subjective cognitive decline and healthy controls

Subjective cognitive decline (SCD) is a high-risk preclinical stage in the progress of Alzheimer's disease (AD). Its timely diagnosis is of great significance for older adults. Though multi-parameter magnetic resonance imaging (MPMRI) is a noninvasive neuroimaging technique to detect SCD, the lack of biomarkers and computed aided diagnosis (CAD) tools is a major concern for its application. Radiomics, a high-dimensional imaging feature extraction method, has been widely used for identifying biomarkers and developing CAD tools in oncological studies. Therefore, in this study, we aimed to investigate whether the radiomic approach could be used for the diagnosis of SCD. In the proposed radiomic approach, we mainly performed four steps: image preprocessing, feature extraction and screening, and classification. The dataset from Xuanwu Hospital, Beijing, China, was used in this study, including 105 healthy controls (HC) and 130 SCD subjects. All subjects were divided into one training & validation group and one test group. We extracted 30128 radiomic features from MPMRI of each subject. The t-test, autocorrelation, and Fisher score were performed for feature selection, and we deployed the support vector machine (SVM) for classification. The above process was performed 100 times with 5-fold cross-validation. The results showed that the accuracy, sensitivity, and specificity of classification was 89.03%±5.37%, 85.44%±9.28% and 91.97%±6.38% in the validation set and 84.70%±4.68%, 86.98%±10.49% and 82.59%±7.07% in the test set. In conclusion, this study has shown that the radiomic approach could be used to discriminate SCD and HC with high accuracy and sensitivity effectively.

Significance of Blood and Cerebrospinal Fluid Biomarkers for Alzheimer's Disease: Sensitivity, Specificity and Potential for Clinical Use

Alzheimer's disease (AD) is the most common type of dementia, affecting more than 5 million Americans, with steadily increasing mortality and incredible socio-economic burden. Not only have therapeutic efforts so far failed to reach significant efficacy, but the real pathogenesis of the disease is still obscure. The current theories are based on pathological findings of amyloid plaques and tau neurofibrillary tangles that accumulate in the brain parenchyma of affected patients. These findings have defined, together with the extensive neurodegeneration, the diagnostic criteria of the disease. The ability to detect changes in the levels of amyloid and tau in cerebrospinal fluid (CSF) first, and more recently in blood, has allowed us to use these biomarkers for the specific in-vivo diagnosis of AD in humans. Furthermore, other pathological elements of AD, such as the loss of neurons, inflammation and metabolic derangement, have translated to the definition of other CSF and blood biomarkers, which are not specific of the disease but, when combined with amyloid and tau, correlate with the progression from mild cognitive impairment to AD dementia, or identify patients who will develop AD pathology. In this review, we discuss the role of current and hypothetical biomarkers of Alzheimer's disease, their specificity, and the caveats of current high-sensitivity platforms for their peripheral detection.

Bridging the Gap Between Fluid Biomarkers for Alzheimer's Disease, Model Systems, and Patients

Alzheimer’s disease (AD) is a debilitating neurodegenerative disease characterized by the accumulation of two proteins in fibrillar form: amyloid-β (Aβ) and tau. Despite decades of intensive research, we cannot yet pinpoint the exact cause of the disease or unequivocally determine the exact mechanism(s) underlying its progression. This confounds early diagnosis and treatment of the disease. Cerebrospinal fluid (CSF) biomarkers, which can reveal ongoing biochemical changes in the brain, can help monitor developing AD pathology prior to clinical diagnosis. Here we review preclinical and clinical investigations of commonly used biomarkers in animals and patients with AD, which can bridge translation from model systems into the clinic. The core AD biomarkers have been found to translate well across species, whereas biomarkers of neuroinflammation translate to a lesser extent. Nevertheless, there is no absolute equivalence between biomarkers in human AD patients and those examined in preclinical models in terms of revealing key pathological hallmarks of the disease. In this review, we provide an overview of current but also novel AD biomarkers and how they relate to key constituents of the pathological cascade, highlighting confounding factors and pitfalls in interpretation, and also provide recommendations for standardized procedures during sample collection to enhance the translational validity of preclinical AD models.

Digital biomarker-based individualized prognosis for people at risk of dementia

Background

Research investigating treatments and interventions for cognitive decline fail due to difficulties in accurately recognizing behavioral signatures in the presymptomatic stages of the disease. For this validation study, we took our previously constructed digital biomarker‐based prognostic models and focused on generalizability and robustness of the models.

Method

We validated prognostic models characterizing subjects using digital biomarkers in a longitudinal, multi‐site, 40‐month prospective study collecting data in memory clinics, general practitioner offices, and home environments.

Results

Our models were able to accurately discriminate between healthy subjects and individuals at risk to progress to dementia within 3 years. The model was also able to differentiate between people with or without amyloid neuropathology and classify fast and slow cognitive decliners with a very good diagnostic performance.

Conclusion

Digital biomarker prognostic models can be a useful tool to assist large‐scale population screening for the early detection of cognitive impairment and patient monitoring over time.

Dysfunction of the Blood-Brain Barrier-A Key Step in Neurodegeneration and Dementia

The vascular endothelium in the brain is an essential part of the blood-brain-barrier (BBB) because of its very tight structure to secure a functional and molecular separation of the brain from the rest of the body and to protect neurons from pathogens and toxins. Impaired transport of metabolites across the BBB due to its increasing dysfunction affects brain health and cognitive functioning, thus providing a starting point of neurodegenerative diseases. The term “cerebral metabolic syndrome” is proposed to highlight the importance of lifestyle factors in neurodegeneration and to describe the impact of increasing BBB dysfunction on neurodegeneration and dementia, especially in elderly patients. If untreated, the cerebral metabolic syndrome may evolve into dementia. Due to the high energy demand of the brain, impaired glucose transport across the BBB via glucose transporters as GLUT1 renders the brain increasingly susceptible to neurodegeneration. Apoptotic processes are further supported by the lack of essential metabolites of the phosphocholine synthesis. In Alzheimer’s disease (AD), inflammatory and infectious processes at the BBB increase the dysfunction and might be pace-making events. At this point, the potentially highly relevant role of the thrombocytic amyloid precursor protein (APP) in endothelial inflammation of the BBB is discussed. Chronic inflammatory processes of the BBB transmitted to an increasing number of brain areas might cause a lasting build-up of spreading, pore-forming β-amyloid fragments explaining the dramatic progression of the disease. In the view of the essential requirement of an early diagnosis to investigate and implement causal therapeutic strategies against dementia, brain imaging methods are of great importance. Therefore, status and opportunities in the field of diagnostic imaging of the living human brain will be portrayed, comprising diverse techniques such as positron emissions tomography (PET) and functional magnetic resonance imaging (fMRI) to uncover the patterns of atrophy, protein deposits, hypometabolism, and molecular as well as functional alterations in AD.

Identification of a Simple and Novel Cut-Point Based Cerebrospinal Fluid and MRI Signature for Predicting Alzheimer's Disease Progression that Reinforces the 2018 NIA-AA Research Framework

The 2018 NIA-AA research framework proposes a classification system with Amyloid-β deposition, pathologic Tau, and Neurodegeneration (ATN) for diagnosis and staging of Alzheimer's disease (AD). Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database can be utilized to identify diagnostic signatures for predicting AD progression, and to determine the utility of this NIA-AA research framework. Profiles of 320 peptides from baseline cerebrospinal fluid (CSF) samples of 287 normal, mild cognitive impairment (MCI), and AD subjects followed over a 3-10-year period were measured via multiple reaction monitoring mass spectrometry. CSF Aβ42, total-Tau (tTau), phosphorylated-Tau (pTau-181), and hippocampal volume were also measured. From these candidate markers, optimal signatures with decision thresholds to separate AD and normal subjects were first identified via unbiased regression and tree-based algorithms. The best performing signature determined via cross-validation was then tested in an independent group of MCI subjects to predict future progression. This multivariate analysis yielded a simple diagnostic signature comprising CSF pTau-181 to Aβ42 ratio, MRI hippocampal volume, and low CSF levels of a novel PTPRN peptide, with a decision threshold on each marker. When applied to a separate MCI group at baseline, subjects meeting these signature criteria experience 4.3-fold faster progression to AD compared to a 2.2-fold faster progression using only conventional markers. This novel 4-marker signature represents an advance over the current diagnostics based on widely used markers, and is easier to use in practice than recently published complex signatures. This signature also reinforces the ATN construct from the 2018 NIA-AA research framework.

Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment

BACKGROUND

Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year.

OBJECTIVES

To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI.

SEARCH METHODS

On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches.

SELECTION CRITERIA

We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter.

DATA COLLECTION AND ANALYSIS

Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available.

MAIN RESULTS

We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate.

AUTHORS' CONCLUSIONS

The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.

Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning

Introduction:

Developing cross-validated multi-biomarker models for the prediction of the rate of cognitive decline in Alzheimer’s disease (AD) is a critical yet unmet clinical challenge.

Methods:

We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid-PET and fluorodeoxyglucose positron-emission tomography (FDG-PET) to predict rates of cognitive decline. Prediction models were trained in autosomal-dominant Alzheimer’s disease (ADAD, n = 121) and subsequently cross-validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model-based risk enrichment was estimated.

Results:

A model combining all biomarker modalities and established in ADAD predicted the 4-year rate of decline in global cognition (R² = 24%) and memory (R² =25%) in sporadic AD. Model-based risk-enrichment reduced the sample size required for detecting simulated intervention effects by 50%–75%.

Discussion:

Our independently validated machine-learning model predicted cognitive decline in sporadic prodromal AD and may substantially reduce sample size needed in clinical trials in AD.

Hippocampal Volume Loss, Brain Amyloid Accumulation, and APOE Status in Cognitively Intact Elderly Subjects

BACKGROUND

Hippocampal volume loss (HVL), PET-documented brain amyloid accumulation, and APOE-ε4 status are predictive biomarkers of the transition from mild cognitive impairment to Alzheimer disease (AD). In asymptomatic cases, the role of these biomarkers remains ambiguous. In contrast to the idea that HVL occurs in late phases of neurodegeneration, recent contributions indicate that it might occur before abnormal amyloid PET occurrence in elderly subjects and that its severity could be only marginally related to APOE variants. Using a longitudinal design, we examined the determinants of HVL in our sample, i.e., brain amyloid burden and the presence of APOE-ε4, and made a longitudinal assessment of cognitive functions.

METHODS

We performed a 4.5-year longitudinal study on 81 elderly community dwellers (all right-handed;, 48 (59.3%) women; mean age 73.7 ± 3.7 years) including MRI at baseline and follow-up, PET amyloid during follow-up, neuropsychological assessment at 18 and 54 months, and APOE genotyping. All cases were assessed using a continuous cognitive score (CCS) that took into account the global evolution of neuropsychological performance. Linear regression models were used to identify predictors of HVL.

RESULTS

There was a negative association between the CCS and HVL bilaterally. In multivariate models adjusting for demographic variables, the presence of APOE-ε4 was related to increased HVL bilaterally. A trend of significance was observed with respect to the impact of amyloid positivity on HVL in the left hemisphere. No significant interaction was found between amyloid positivity and the APOE-ε4 allele.

CONCLUSION

The progressive decrement of neuropsychological performance is associated with HVL long before the emergence of clinically overt symptoms. In this cohort of healthy individuals, the presence of the APOE-ε4 allele was shown to be an independent predictor of worst hippocampal integrity in asymptomatic cases independently of amyloid positivity.

Predicting clinical decline and conversion to Alzheimer's disease or dementia using novel Elecsys Aβ(1-42), pTau and tTau CSF immunoassays

We evaluated the performance of CSF biomarkers for predicting risk of clinical decline and conversion to dementia in non-demented patients with cognitive symptoms. CSF samples from patients in two multicentre longitudinal studies (ADNI, n = 619; BioFINDER, n = 431) were analysed. Aβ(1-42), tTau and pTau CSF concentrations were measured using Elecsys CSF immunoassays, and tTau/Aβ(1-42) and pTau/Aβ(1-42) ratios calculated. Patients were classified as biomarker (BM)-positive or BM-negative at baseline. Ability of biomarkers to predict risk of clinical decline and conversion to AD/dementia was assessed using pre-established cut-offs for Aβ(1-42) and ratios; tTau and pTau cut-offs were determined. BM-positive patients showed greater clinical decline than BM-negative patients, demonstrated by greater decreases in MMSE scores (all biomarkers: -2.10 to -0.70). Risk of conversion to AD/dementia was higher in BM-positive patients (HR: 1.67 to 11.48). Performance of Tau/Aβ(1-42) ratios was superior to single biomarkers, and consistent even when using cut-offs derived in a different cohort. Optimal pTau and tTau cut-offs were approximately 27 pg/mL and 300 pg/mL in both BioFINDER and ADNI. Elecsys pTau/Aβ(1-42) and tTau/Aβ(1-42) are robust biomarkers for predicting risk of clinical decline and conversion to dementia in non-demented patients, and may support AD diagnosis in clinical practice.

The accuracy of hippocampal volumetry and glucose metabolism for the diagnosis of patients with suspected Alzheimer's disease, using automatic quantitative clinical tools

The hippocampus is one of the earliest sites involved in the pathology of Alzheimer's disease (AD). Therefore, we specifically investigated the sensitivity and specificity of hippocampal volume and glucose metabolism in patients being evaluated for AD, using automated quantitative tools (NeuroQuant - magnetic resonance imaging [MRI] and Scenium - positron emission tomography [PET]) and clinical evaluation.This retrospective study included adult patients over the age of 45 years with suspected AD, who had undergone fluorodeoxyglucose positron emission tomography-computed tomography (FDG-PET-CT) and MRI. FDG-PET-CT images were analyzed both qualitatively and quantitatively. In quantitative volumetric MRI analysis, the percentage of the total intracranial volume of each brain region, as well as the total hippocampal volume, were considered in comparison to an age-adjusted percentile. The remaining brain regions were compared between groups according to the final diagnosis.Thirty-eight patients were included in this study. After a mean follow-up period of 23 ± 11 months, the final diagnosis for 16 patients was AD or high-risk mild cognitive impairment (MCI). Out of the 16 patients, 8 patients were women, and the average age of all patients was 69.38 ± 10.98 years. Among the remaining 22 patients enrolled in the study, 14 were women, and the average age was 67.50 ± 11.60 years; a diagnosis of AD was initially excluded, but the patients may have low-risk MCI. Qualitative FDG-PET-CT analysis showed greater accuracy (0.87), sensitivity (0.76), and negative predictive value (0.77), when compared to quantitative PET analysis, hippocampal MRI volumetry, and specificity. The positive predictive value of FDG-PET-CT was similar to the MRI value.The performance of FDG-PET-CT qualitative analysis was significantly more effective compared to MRI volumetry. At least in part, this observation could corroborate the sequential hypothesis of AD pathophysiology, which posits that functional changes (synaptic dysfunction) precede structural changes (atrophy).

Prediction and Classification of Alzheimer's Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers

Alzheimer's disease (AD), including its mild cognitive impairment (MCI) phase that may or may not progress into the AD, is the most ordinary form of dementia. It is extremely important to correctly identify patients during the MCI stage because this is the phase where AD may or may not develop. Thus, it is crucial to predict outcomes during this phase. Thus far, many researchers have worked on only using a single modality of a biomarker for the diagnosis of AD or MCI. Although recent studies show that a combination of one or more different biomarkers may provide complementary information for the diagnosis, it also increases the classification accuracy distinguishing between different groups. In this paper, we propose a novel machine learning-based framework to discriminate subjects with AD or MCI utilizing a combination of four different biomarkers: fluorodeoxyglucose positron emission tomography (FDG-PET), structural magnetic resonance imaging (sMRI), cerebrospinal fluid (CSF) protein levels, and Apolipoprotein-E (APOE) genotype. The Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset was used in this study. In total, there were 158 subjects for whom all four modalities of biomarker were available. Of the 158 subjects, 38 subjects were in the AD group, 82 subjects were in MCI groups (including 46 in MCIc [MCI converted; conversion to AD within 24 months of time period], and 36 in MCIs [MCI stable; no conversion to AD within 24 months of time period]), and the remaining 38 subjects were in the healthy control (HC) group. For each image, we extracted 246 regions of interest (as features) using the Brainnetome template image and NiftyReg toolbox, and later we combined these features with three CSF and two APOE genotype features obtained from the ADNI website for each subject using early fusion technique. Here, a different kernel-based multiclass support vector machine (SVM) classifier with a grid-search method was applied. Before passing the obtained features to the classifier, we have used truncated singular value decomposition (Truncated SVD) dimensionality reduction technique to reduce high dimensional features into a lower-dimensional feature. As a result, our combined method achieved an area under the receiver operating characteristic (AU-ROC) curve of 98.33, 93.59, 96.83, 94.64, 96.43, and 95.24% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIs, AD vs. MCIc, HC vs. MCIc, and HC vs. MCIs subjects which are high relative to single modality results and other state-of-the-art approaches. Moreover, combined multimodal methods have improved the classification performance over the unimodal classification.

Radiomics features as predictors to distinguish fast and slow progression of Mild Cognitive Impairment to Alzheimer's disease

Prediction of Alzheimer's disease (AD) from Mild Cognitive Impairment (MCI) by analyzing Magnetic Resonance Imaging (MRI) image features has become popular in recent years. However, defining effective predictive biomarkers is still challengeable. The 'radiomics' is an established method to identify advanced and high order quantitative imaging features for computer-aided diagnosis and has been applied into oncology study. However, it has not been applied into brain disorder disease study. Therefore, the purpose of this study is to identify whether the features from radiomics could be the predictors of the conversion from MCI to AD. We analyzed 197 samples with MRI scans from the ADNI database, which contained 32 healthy subjects and 165 MCI patients. Firstly, we extracted 215 radiomics features from hippocampus. Then we used Cronbach's alpha coefficient, the intra-class correlation coefficient, Kaplan-Meier model and cox regression to select 44 radiomics features as effective features. Finally, we used SVM classification to validate these features. The results showed that the classification accuracy using linear, polynomial and sigmoid kernel could achieve 80.0%, 93.3% and 86.6% to distinguish MCI-to-AD fast and slow converter. As a result, this study indicated that the radiomics features are potential to be applied into predicting AD from MCI.

Multivariate Platelet Analysis Differentiates Between Patients with Alzheimer's Disease and Healthy Controls at First Clinical Diagnosis

BACKGROUND

Early diagnosis of Alzheimer's disease (AD) is challenging, and easily accessible biomarkers are an unmet need. Blood platelets frequently serve as peripheral model for studying AD pathogenesis and might represent a reasonable biomarker source.

OBJECTIVE

In the present study, we investigated the potential to differentiate AD patients from healthy controls (HC) based on blood count, platelet morphology, and function as well as molecular markers at the time of first clinical diagnosis.

METHODS

Blood samples from 40 AD patients and 29 age-matched HC were included for determination of 78 parameter by blood counting, platelet morphometry, aggregometry, flow cytometry (CD62P, CD63, activated fibrinogen receptor), protein quantification of nicotinic acetylcholine receptor α7 (nAChRα7) and caveolin-1 (CAV-1), and miRNA quantification (miR-26b, miR-199a, miR-335). Group comparison between patients and controls was performed in univariate and multivariate statistical analyses.

RESULTS

AD patients showed significantly lower aggregation response to ADP and arachidonic acid and significantly decreased CD62P and CD63 surface expression induced by ADP and U46619 compared to HC. Relative nAChRα7 and CAV-1 expression was significantly higher AD platelets than in HC. Multivariate analysis of 63 parameter revealed significant differences between AD patients and healthy controls. The best performing feature model revealed a sensitivity of 96.6%, a specificity of 80.0%, and a positive predictive value of 89.3%. No grouping could be achieved by using single parameter groups.

CONCLUSION

Significant differences between platelet characteristics from AD patients and HC at the time of first clinical diagnosis were observed. The best performing parameter can be used as a blood-based biomarker for AD diagnosis in a multivariate model in addition to the standardized mental tests.

Advantages and disadvantages of the use of the CSF Amyloid β (Aβ) 42/40 ratio in the diagnosis of Alzheimer's Disease

The cerebrospinal fluid (CSF) biochemical markers (biomarkers) Amyloidβ 42 (Aβ₄₂), total Tau (T-tau) and Tau phosphorylated at threonine 181 (P-tau₁₈₁) have proven diagnostic accuracy for mild cognitive impairment and dementia due to Alzheimer’s Disease (AD). In an effort to improve the accuracy of an AD diagnosis, it is important to be able to distinguish between AD and other types of dementia (non-AD). The concentration ratio of Aβ₄₂ to Aβ₄₀ (Aβ_42/40 Ratio) has been suggested to be superior to the concentration of Aβ₄₂ alone when identifying patients with AD. This article reviews the available evidence on the use of the CSF Aβ_42/40 ratio in the diagnosis of AD. Based on the body of evidence presented herein, it is the conclusion of the current working group that the CSF Aβ_42/40 ratio, rather than the absolute value of CSF Aβ₄₂, should be used when analysing CSF AD biomarkers to improve the percentage of appropriately diagnosed patients.

Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer's Disease

Predicting progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and ¹⁸F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell’s C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.

An Algorithm for Preclinical Diagnosis of Alzheimer's Disease

Almost all Alzheimer's disease (AD) therapeutic trials have failed in recent years. One of the main reasons for failure is due to designing the disease-modifying clinical trials at the advanced stage of the disease when irreversible brain damage has already occurred. Diagnosis of the preclinical stage of AD and therapeutic intervention at this phase, with a perfect target, are key points to slowing the progression of the disease. Various AD biomarkers hold enormous promise for identifying individuals with preclinical AD and predicting the development of AD dementia in the future, but no single AD biomarker has the capability to distinguish the AD preclinical stage. A combination of complimentary AD biomarkers in cerebrospinal fluid (Aβ₄₂, tau, and phosphor-tau), non-invasive neuroimaging, and genetic evidence of AD can detect preclinical AD in the in-vivo ante mortem brain. Neuroimaging studies have examined region-specific cerebral blood flow (CBF) and microstructural changes in the preclinical AD brain. Functional MRI (fMRI), diffusion tensor imaging (DTI) MRI, arterial spin labeling (ASL) MRI, and advanced PET have potential application in preclinical AD diagnosis. A well-validated simple framework for diagnosis of preclinical AD is urgently needed. This article proposes a comprehensive preclinical AD diagnostic algorithm based on neuroimaging, CSF biomarkers, and genetic markers.

Classification of Alzheimer's Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling

Alzheimer's disease (AD) is a progressive brain disease. The goal of this study is to provide a new computer-vision based technique to detect it in an efficient way. The brain-imaging data of 98 AD patients and 98 healthy controls was collected using data augmentation method. Then, convolutional neural network (CNN) was used, CNN is the most successful tool in deep learning. An 8-layer CNN was created with optimal structure obtained by experiences. Three activation functions (AFs): sigmoid, rectified linear unit (ReLU), and leaky ReLU. The three pooling-functions were also tested: average pooling, max pooling, and stochastic pooling. The numerical experiments demonstrated that leaky ReLU and max pooling gave the greatest result in terms of performance. It achieved a sensitivity of 97.96%, a specificity of 97.35%, and an accuracy of 97.65%, respectively. In addition, the proposed approach was compared with eight state-of-the-art approaches. The method increased the classification accuracy by approximately 5% compared to state-of-the-art methods.

Addition of the Aβ42/40 ratio to the cerebrospinal fluid biomarker profile increases the predictive value for underlying Alzheimer's disease dementia in mild cognitive impairment

BACKGROUND

Cerebrospinal fluid (CSF) biomarkers have been used to increase the evidence of underlying Alzheimer's disease (AD) pathology in mild cognitive impairment (MCI). However, CSF biomarker-based classification often results in conflicting profiles with controversial prognostic value. Normalization of the CSF Aβ42 concentration to the level of total amyloid beta (Aβ), using the Aβ42/40 ratio, has been shown to improve the distinction between AD and non-AD dementia. Therefore, we evaluated whether the Aβ42/40 ratio would improve MCI categorization and more accurately predict progression to AD.

METHODS

Our baseline population consisted of 197 MCI patients, of which 144 had a follow-up ≥ 2 years, and comprised the longitudinal study group. To establish our own CSF Aβ42/40 ratio reference value, a group of 168 AD-dementia patients and 66 neurological controls was also included. CSF biomarker-based classification was operationalized according to the framework of the National Institute of Aging-Alzheimer Association criteria for MCI.

RESULTS

When using the core CSF biomarkers (Aβ42, total Tau and phosphorylated Tau), 30% of the patients fell into the high-AD-likelihood (HL) group (both amyloid and neurodegeneration markers positive), 30% into the low-AD-likelihood group (all biomarkers negative), 28% into the suspected non-Alzheimer pathophysiology (SNAP) group (only neurodegeneration markers positive) and 12% into the isolated amyloid pathology group (only amyloid-positive). Replacing Aβ42 by the Aβ42/40 ratio resulted in a significant increase in the percentage of patients with amyloidosis (42-59%) and in the proportion of interpretable biological profiles (61-75%), due to a reduction by half in the number of SNAP cases and an increase in the proportion of the HL subgroup. Survival analysis showed that risk of progression to AD was highest in the HL group, and increased when the Aβ42/40 ratio, instead of Aβ42, combined with total Tau and phosphorylated Tau was used for biomarker-based categorization.

CONCLUSIONS

Our results confirm the usefulness of the CSF Aβ42/40 ratio in the interpretation of CSF biomarker profiles in MCI patients, by increasing the proportion of conclusive profiles and enhancing their predictive value for underlying AD.