Predicting progression to dementia in persons with mild cognitive impairment using cerebrospinal fluid markers

From understanding to action: Exploring molecular connections of Down syndrome to Alzheimer's disease for targeted therapeutic approach

Down syndrome (DS) is caused by a third copy of chromosome 21. Alzheimer's disease (AD) is a neurodegenerative condition characterized by the deposition of amyloid-beta (Aβ) plaques and neurofibrillary tangles in the brain. Both disorders have elevated Aβ, tau, dysregulated immune response, and inflammation. In people with DS, Hsa21 genes like APP and DYRK1A are overexpressed, causing an accumulation of amyloid and neurofibrillary tangles, and potentially contributing to an increased risk of AD. As a result, people with DS are a key demographic for research into AD therapeutics and prevention. The molecular links between DS and AD shed insights into the underlying causes of both diseases and highlight potential therapeutic targets. Also, using biomarkers for early diagnosis and treatment monitoring is an active area of research, and genetic screening for high-risk individuals may enable earlier intervention. Finally, the fundamental mechanistic parallels between DS and AD emphasize the necessity for continued research into effective treatments and prevention measures for DS patients at risk for AD. Genetic screening with customized therapy approaches may help the DS population in current clinical studies and future biomarkers.

Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression

Importance

Predictive models using machine learning techniques have potential to improve early detection and management of Alzheimer disease (AD). However, these models potentially have biases and may perpetuate or exacerbate existing disparities.

Objective

To characterize the algorithmic fairness of longitudinal prediction models for AD progression.

Design, Setting, and Participants

This prognostic study investigated the algorithmic fairness of logistic regression, support vector machines, and recurrent neural networks for predicting progression to mild cognitive impairment (MCI) and AD using data from participants in the Alzheimer Disease Neuroimaging Initiative evaluated at 57 sites in the US and Canada. Participants aged 54 to 91 years who contributed data on at least 2 visits between September 2005 and May 2017 were included. Data were analyzed in October 2022.

Exposures

Fairness was quantified across sex, ethnicity, and race groups. Neuropsychological test scores, anatomical features from T1 magnetic resonance imaging, measures extracted from positron emission tomography, and cerebrospinal fluid biomarkers were included as predictors.

Main Outcomes and Measures

Outcome measures quantified fairness of prediction models (logistic regression [LR], support vector machine [SVM], and recurrent neural network [RNN] models), including equal opportunity, equalized odds, and demographic parity. Specifically, if the model exhibited equal sensitivity for all groups, it aligned with the principle of equal opportunity, indicating fairness in predictive performance.

Results

A total of 1730 participants in the cohort (mean [SD] age, 73.81 [6.92] years; 776 females [44.9%]; 69 Hispanic [4.0%] and 1661 non-Hispanic [96.0%]; 29 Asian [1.7%], 77 Black [4.5%], 1599 White [92.4%], and 25 other race [1.4%]) were included. Sensitivity for predicting progression to MCI and AD was lower for Hispanic participants compared with non-Hispanic participants; the difference (SD) in true positive rate ranged from 20.9% (5.5%) for the RNN model to 27.8% (9.8%) for the SVM model in MCI and 24.1% (5.4%) for the RNN model to 48.2% (17.3%) for the LR model in AD. Sensitivity was similarly lower for Black and Asian participants compared with non-Hispanic White participants; for example, the difference (SD) in AD true positive rate was 14.5% (51.6%) in the LR model, 12.3% (35.1%) in the SVM model, and 28.4% (16.8%) in the RNN model for Black vs White participants, and the difference (SD) in MCI true positive rate was 25.6% (13.1%) in the LR model, 24.3% (13.1%) in the SVM model, and 6.8% (18.7%) in the RNN model for Asian vs White participants. Models generally satisfied metrics of fairness with respect to sex, with no significant differences by group, except for cognitively normal (CN)-MCI and MCI-AD transitions (eg, an absolute increase [SD] in the true positive rate of CN-MCI transitions of 10.3% [27.8%] for the LR model).

Conclusions and Relevance

In this study, models were accurate in aggregate but failed to satisfy fairness metrics. These findings suggest that fairness should be considered in the development and use of machine learning models for AD progression.

The value of maintaining cognition in patients with mild cognitive impairment: The innovation headroom and potential cost-effectiveness of roflumilast

INTRODUCTION

Early health-technology assessment can support discussing scarce resource allocation among stakeholders. We explored the value of maintaining cognition in patients with mild cognitive impairment (MCI) by estimating: (1) the innovation headroom and (2) the potential cost effectiveness of roflumilast treatment in this population.

METHODS

The innovation headroom was operationalized by a fictive 100% efficacious treatment effect, and the roflumilast effect on memory word learning test was assumed to be associated with 7% relative risk reduction of dementia onset. Both were compared to Dutch setting usual care using the adapted International Pharmaco-Economic Collaboration on Alzheimer's Disease (IPECAD) open-source model.

RESULTS

The total innovation headroom expressed as net health benefit was 4.2 (95% bootstrap interval: 2.9-5.7) quality-adjusted life years (QALYs). The potential cost effectiveness of roflumilast was k€34 per QALY.

DISCUSSION

The innovation headroom in MCI is substantial. Although the potential cost effectiveness of roflumilast treatment is uncertain, further research on its effect on dementia onset is likely valuable.

Effects of Multidisciplinary Rehabilitation Enhanced with Neuropsychological Treatment on Post-Acute SARS-CoV-2 Cognitive Impairment (Brain Fog): An Observational Study

Concentration and memory impairment (named "brain fog") represents a frequent and disabling neuropsychological sequela in post-acute COVID-19 syndrome (PACS) patients. The aim of this study was to assess whether neurocognitive function could improve after a multidisciplinary rehabilitation program enhanced with individualized neuropsychological treatment. A prospective monocentric registry of PACS patients consecutively admitted to our Rehabilitation Unit was created. The Montreal Cognitive Assessment (MoCA) was used to assess cognitive impairment at admission and discharge. A total of sixty-four (64) PACS patients, fifty-six (56) of them with brain fog, were treated with a day-by-day individualized psychological intervention of cognitive stimulation (45 min) on top of a standard in-hospital rehabilitation program. The mean duration of the acute-phase hospitalization was 55.8 ± 25.8 days and the mean in-hospital rehabilitation duration was 30 ± 10 days. The mean age of the patients was 67.3 ± 10.4 years, 66% of them were male, none had a previous diagnosis of dementia, and 66% of the entire sample had experienced severe COVID-19. At admission, only 12% of the patients had normal cognitive function, while 57% showed mild, 28% moderate, and 3% severe cognitive impairment. After psychological treatment, a significant improvement in the MoCA score was found (20.4 ± 5 vs. 24.7 ± 3.7; p < 0.0001) as a result of significant amelioration in the following domains: attention task (p = 0.014), abstract reasoning (p = 0.003), language repetition (p = 0.002), memory recall (p < 0.0001), orientation (p < 0.0001), and visuospatial abilities (p < 0.0001). Moreover, the improvement remained significant after multivariate analysis adjusted for several confounding factors. Finally, at discharge, 43% of the patients with cognitive impairment normalized their cognitive function, while 4.7% were discharged with residual moderate cognitive impairment. In conclusion, our study provides evidence of the effects of multidisciplinary rehabilitation enhanced with neuropsychological treatment on improvement in the cognitive function of post-acute COVID-19 patients.

Sleep EEG-Based Approach to Detect Mild Cognitive Impairment

Mild Cognitive Impairment (MCI) is an early stage of dementia, which may lead to Alzheimer's disease (AD) in older adults. Therefore, early detection of MCI and implementation of treatment and intervention can effectively slow down or even inhibit the progression of the disease, thus minimizing the risk of AD. Currently, we know that published work relies on an analysis of awake EEG recordings. However, recent studies have suggested that changes in the structure of sleep may lead to cognitive decline. In this work, we propose a sleep EEG-based method for MCI detection, extracting specific features of sleep to characterize neuroregulatory deficit emergent with MCI. This study analyzed the EEGs of 40 subjects (20 MCI, 20 HC) with the developed algorithm. We extracted sleep slow waves and spindles features, combined with spectral and complexity features from sleep EEG, and used the SVM classifier and GRU network to identify MCI. In addition, the classification results of different feature sets (including with sleep features from sleep EEG and without sleep features from awake EEG) and different classification methods were evaluated. Finally, the MCI classification accuracy of the GRU network based on features extracted from sleep EEG was the highest, reaching 93.46%. Experimental results show that compared with the awake EEG, sleep EEG can provide more useful information to distinguish between MCI and HC. This method can not only improve the classification performance but also facilitate the early intervention of AD.

Prediction Models for Conversion From Mild Cognitive Impairment to Alzheimer’s Disease: A Systematic Review and Meta-Analysis

Background and Purpose

Alzheimer’s disease (AD) is a devastating neurodegenerative disorder with no cure, and available treatments are only able to postpone the progression of the disease. Mild cognitive impairment (MCI) is considered to be a transitional stage preceding AD. Therefore, prediction models for conversion from MCI to AD are desperately required. These will allow early treatment of patients with MCI before they develop AD. This study performed a systematic review and meta-analysis to summarize the reported risk prediction models and identify the most prevalent factors for conversion from MCI to AD.

Methods

We systematically reviewed the studies from the databases of PubMed, CINAHL Plus, Web of Science, Embase, and Cochrane Library, which were searched through September 2021. Two reviewers independently identified eligible articles and extracted the data. We used the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) checklist for the risk of bias assessment.

Results

In total, 18 articles describing the prediction models for conversion from MCI to AD were identified. The dementia conversion rate of elderly patients with MCI ranged from 14.49 to 87%. Models in 12 studies were developed using the data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). C-index/area under the receiver operating characteristic curve (AUC) of development models were 0.67–0.98, and the validation models were 0.62–0.96. MRI, apolipoprotein E genotype 4 (APOE4), older age, Mini-Mental State Examination (MMSE) score, and Alzheimer’s Disease Assessment Scale cognitive (ADAS-cog) score were the most common and strongest predictors included in the models.

Conclusion

In this systematic review, many prediction models have been developed and have good predictive performance, but the lack of external validation of models limited the extensive application in the general population. In clinical practice, it is recommended that medical professionals adopt a comprehensive forecasting method rather than a single predictive factor to screen patients with a high risk of MCI. Future research should pay attention to the improvement, calibration, and validation of existing models while considering new variables, new methods, and differences in risk profiles across populations.

[Predictive Diagnosis of Alzheimer's Dementia]

Expanding technologies of early disease detection allow to identify Alzheimer's disease (AD) long before symptom onset. Hence, patients are increasingly demanding for these diagnostic procedures. Biomarker-based early detection of AD is therefore increasingly important in the clinical work-up. This article gives an overview of predictive procedures in the field of Alzheimer's dementia.

Use of Alzheimer's Disease Cerebrospinal Fluid Biomarkers in A Tertiary Care Memory Clinic

INTRODUCTION

Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers are promising tools to help identify the underlying pathology of neurocognitive disorders. In this manuscript, we report our experience with AD CSF biomarkers in 262 consecutive patients in a tertiary care memory clinic.

METHODS

We retrospectively reviewed 262 consecutive patients who underwent lumbar puncture (LP) and CSF measurement of AD biomarkers (Aβ1-42, total tau or t-tau, and p-tau181). We studied the safety of the procedure and its impact on patient's diagnosis and management.

RESULTS

The LP allowed to identify underlying AD pathology in 72 of the 121 patients (59%) with early onset amnestic mild cognitive impairment (aMCI) with a high probability of progression to AD; to distinguish the behavioral/dysexecutive variant of AD from the behavioral variant of frontotemporal dementia (bvFTD) in 25 of the 45 patients (55%) with an atypical neurobehavioral profile; to identify AD as the underlying pathology in 15 of the 27 patients (55%) with atypical or unclassifiable primary progressive aphasia (PPA); and to distinguish AD from other disorders in 9 of the 29 patients (31%) with psychiatric differential diagnoses and 19 of the 40 patients (47%) with lesional differential diagnoses (normal pressure hydrocephalus, encephalitis, prion disease, etc.). No major complications occurred following the LP.

INTERPRETATION

Our results suggest that CSF analysis is a safe and effective diagnostic tool in select patients with neurocognitive disorders. We advocate for a wider use of this biomarker in tertiary care memory clinics in Canada.

A Pilot Study on the Cutoff Value of Related Brain Metabolite in Chinese Elderly Patients With Mild Cognitive Impairment Using MRS

Objective: This cross-sectional study aimed to distinguish patients with mild cognitive impairment (MCI) from patients with normal controls (NCs) by measuring the levels of N-acetyl aspartate (NAA), total creatinine (tCr), and choline (Cho) in their hippocampus (HIP) and their posterior cingulate gyrus (PCG) by using proton magnetic resonance spectroscopy (MRS) and to predict the cutoff value on the ratios of metabolites. We further aimed to provide a reference for the diagnosis of MCI in elderly patients in China.

Methods: About 69 patients who underwent a clinical diagnosis of the MCI group and 67 patients with NCs, the Mini-Mental Status Examination (MMSE) score, the Montreal Cognitive Assessment (MoCA) score, and MRS of the bilateral HIP and bilateral PCG were considered. The ratio of NAA/tCr and Cho/tCr in the bilateral HIP and bilateral PCG was calculated. The relationship between the ratios of metabolites and the scores of MMSE and MoCA was analyzed, and the possible brain metabolite cutoff point for the diagnosis of MCI was evaluated.

Results: Compared with the NC group, the scores of MMSE and MoCA in the MCI group decreased significantly (p < 0.05); the ratio of NAA/tCr in the bilateral HIP and bilateral PCG and the ratio of Cho/tCr at the right HIP in the MCI group decreased significantly (p < 0.05); however, there was no significant difference in the ratio of Cho/tCr in the left HIP and bilateral PCG between the two groups (p > 0.05). The correlation coefficient between MMSE/MoCA and the ratio of NAA/tCr was 0.49–0.56 in the bilateral HIP (p < 0.01). The best cutoff value of NAA/creatine (Cr) in the left HIP and the right HIP was 1.195 and 1.19. Sensitivity, specificity, and the Youden index (YDI) in the left HIP and the right HIP were (0.725, 0.803, 0.528) and (0.754, 0.803, 0.557), respectively.

Conclusion: The level of metabolites in the HIP and the PCG of patients with MCI and of those with normal subjects has a certain correlation with the score of their MMSE and MoCA. When the value of NAA/tCr in the left HIP and right HIP is <1.19, it suggests that MCI may have occurred. According to this cutoff point, elderly patients with MCI in China could be screened.

[Early detection of Alzheimer's disease and dementia prediction in patients with mild cognitive impairment : Summary of current recommendations]

Mild cognitive impairment (MCI) is characterized by cognitive deficits but essentially preserved competence in activities of daily living. It is a risk factor for the development of dementia and can reflect a prodromal predementia state of Alzheimer's disease (AD). The pathology of AD is defined by cerebral deposition of amyloid-beta-1-42 protein and aggregation of phosphorylated tau protein, which can be identified in vivo by biomarkers for these alterations. As a result of advances in the field of biomarker-based early detection of AD, it is possible to differentiate between MCI patients with and without a pathological AD condition and therefore, between patients with a low and those with a high risk for the development of dementia. At present there are no specific guideline recommendations in Germany for the diagnostic use of biomarkers in predementia detection of AD and for dementia risk assessment in patients with MCI. This article summarizes the current recommendations of a European expert consensus publication and a multidisciplinary working group of the Alzheimer's Association on the clinical application of cerebrospinal fluid (CSF) biomarkers for the diagnostics of AD in patients with MCI. If the clinical diagnostic criteria for MCI are fulfilled according to the medical history and neuropsychological testing, it is recommended to carry out further diagnostics (blood test, brain imaging) in order to more precisely define the differential diagnostic classification. Counseling on the potential benefits, limits and risks of biomarker testing for early AD detection and dementia risk prediction should always precede assessment of CSF biomarkers. Information about the individual risk of developing dementia has potential consequences for the psychological well-being and life planning; therefore, clinical follow-up visits are recommended.

Dynamics of plasma biomarkers in Down syndrome: the relative levels of Aβ42 decrease with age, whereas NT1 tau and NfL increase

Background

Down syndrome (DS) is the most common genetic cause of Alzheimer’s disease (AD), but diagnosis of AD in DS is challenging due to the intellectual disability which accompanies DS. When disease-modifying agents for AD are approved, reliable biomarkers will be required to identify when and how long people with DS should undergo treatment. Three cardinal neuropathological features characterize AD, and AD in DS—Aβ amyloid plaques, tau neurofibrillary tangles, and neuronal loss. Here, we quantified plasma biomarkers of all 3 neuropathological features in a large cohort of people with DS aged from 3 months to 68 years. Our primary aims were (1) to assess changes in the selected plasma biomarkers in DS across age, and (2) to compare biomarkers measured in DS plasma versus age- and sex-matched controls.

Methods

Using ultra-sensitive single molecule array (Simoa) assays, we measured 3 analytes (Aβ42, NfL, and tau) in plasmas of 100 individuals with DS and 100 age- and sex-matched controls. Tau was measured using an assay (NT1) which detects forms of tau containing at least residues 6–198. The stability of the 3 analytes was established using plasma from ten healthy volunteers collected at 6 intervals over a 5-day period.

Results

High Aβ42 and NT1 tau and low NfL were observed in infants. Across all ages, Aβ42 levels were higher in DS than controls. Levels of Aβ42 decreased with age in both DS and controls, but this decrease was greater in DS than controls and became prominent in the third decade of life. NT1 tau fell in adolescents and young adults, but increased in older individuals with DS. NfL levels were low in infants, children, adolescents, and young adults, but thereafter increased in DS compared to controls.

Conclusions

High levels of Aβ42 and tau in both young controls and DS suggest these proteins are produced by normal physiological processes, whereas the changes seen in later life are consistent with emergence of pathological alterations. These plasma biomarker results are in good agreement with prior neuropathology studies and indicate that the third and fourth decades (i.e., 20 to 40 years of age) of life are pivotal periods during which AD processes manifest in DS. Application of the assays used here to longitudinal studies of individuals with DS aged 20 to 50 years of age should further validate the use of these biomarkers, and in time may allow identification and monitoring of people with DS best suited for treatment with AD therapies.

Progression to dementia in memory clinic patients with mild cognitive impairment and normal β-amyloid

Background

Determination of β-amyloid (Aβ) positivity and likelihood of underlying Alzheimer’s disease (AD) relies on dichotomous biomarker cut-off values. Individuals with mild cognitive impairment (MCI) and Aβ within the normal range may still have a substantial risk of developing dementia, primarily of Alzheimer type. Their prognosis, as well as predictors of clinical progression, are not fully understood. The aim of this study was to explore the associations of cerebrospinal fluid (CSF) biomarkers (Aβ42, total tau, phosphorylated tau) and other characteristics, including modifiable vascular factors, with the risk of progression to dementia among patients with MCI and normal CSF Aβ42.

Methods

Three hundred eighteen memory clinic patients with CSF and clinical data, and at least 1-year follow-up, were included. Patients had normal CSF Aβ42 levels based on clinical cut-offs. Cox proportional hazard models with age as time scale and adjusted for sex, education, and cognition (Mini-Mental State Examination) were used to investigate predictors of progression to dementia and Alzheimer-type dementia. Potential predictors included CSF biomarkers, cognitive performance (verbal learning and memory), apolipoprotein E (APOE) ε4 genotype, medial temporal lobe atrophy, family history of dementia, depressive symptoms, and vascular factors, including the Cardiovascular Risk Factors, Aging and Dementia (CAIDE) risk score. Predictive performance of patient characteristics was further explored with Harrell C statistic.

Results

Lower normal Aβ42 and higher total tau and phosphorylated tau were associated with higher dementia risk, and the association was not driven by Aβ42 values close to cut-off. Additional predictors included poorer cognition, APOE ε4 genotype, higher systolic blood pressure, and lower body mass index, but not the CAIDE dementia risk score. Aβ42 individually and in combination with other CSF biomarkers improved the risk prediction compared to age and cognition alone. Medial temporal lobe atrophy or vascular factors did not increase the predictive performance.

Conclusions

Possibility of underlying AD pathology and increased dementia risk should not be ruled out among MCI patients with CSF Aβ42 within the normal range. While cut-offs may be useful in clinical practice to identify high-risk individuals, personalized risk prediction tools incorporating continuous biomarkers may be preferable among individuals with intermediate risk. The role of modifiable vascular factors could be explored in this context.

Assessing cost-effectiveness of early intervention in Alzheimer's disease: An open-source modeling framework

INTRODUCTION

We develop a framework to model disease progression across Alzheimer's disease (AD) and to assess the cost-effectiveness of future disease-modifying therapies (DMTs) for people with mild cognitive impairment (MCI) due to AD.

METHODS

Using data from the US National Alzheimer's Coordinating Center, we apply survival analysis to estimate transition from predementia to AD dementia and ordered probit regression to estimate transitions across AD dementia stages. We investigate the cost-effectiveness of a hypothetical treatment scenario for people in MCI due to AD.

RESULTS

We present an open-access model-based decision-analytic framework. Assuming a modest DMT treatment effect in MCI, we predict extended life expectancy and a reduction in time with AD dementia.

DISCUSSION

Any future DMT for AD is expected to pose significant economic challenges across all health-care systems, and decision-analytic modeling will be required to assess costs and outcomes. Further developments are needed to inform these health policy considerations.

A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R ² = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.

Biomarker-based prognosis for people with mild cognitive impairment (ABIDE): a modelling study

BACKGROUND

Biomarker-based risk predictions of dementia in people with mild cognitive impairment are highly relevant for care planning and to select patients for treatment when disease-modifying drugs become available. We aimed to establish robust prediction models of disease progression in people at risk of dementia.

METHODS

In this modelling study, we included people with mild cognitive impairment (MCI) from single-centre and multicentre cohorts in Europe and North America: the European Medical Information Framework for Alzheimer's Disease (EMIF-AD; n=883), Alzheimer's Disease Neuroimaging Initiative (ADNI; n=829), Amsterdam Dementia Cohort (ADC; n=666), and the Swedish BioFINDER study (n=233). Inclusion criteria were a baseline diagnosis of MCI, at least 6 months of follow-up, and availability of a baseline Mini-Mental State Examination (MMSE) and MRI or CSF biomarker assessment. The primary endpoint was clinical progression to any type of dementia. We evaluated performance of previously developed risk prediction models-a demographics model, a hippocampal volume model, and a CSF biomarkers model-by evaluating them across cohorts, incorporating different biomarker measurement methods, and determining prognostic performance with Harrell's C statistic. We then updated the models by re-estimating parameters with and without centre-specific effects and evaluated model calibration by comparing observed and expected survival. Finally, we constructed a model combining markers for amyloid deposition, tauopathy, and neurodegeneration (ATN), in accordance with the National Institute on Aging and Alzheimer's Association research framework.

FINDINGS

We included all 2611 individuals with MCI in the four cohorts, 1007 (39%) of whom progressed to dementia. The validated demographics model (Harrell's C 0·62, 95% CI 0·59-0·65), validated hippocampal volume model (0·67, 0·62-0·72), and updated CSF biomarkers model (0·72, 0·68-0·74) had adequate prognostic performance across cohorts and were well calibrated. The newly constructed ATN model had the highest performance (0·74, 0·71-0·76).

INTERPRETATION

We generated risk models that are robust across cohorts, which adds to their potential clinical applicability. The models could aid clinicians in the interpretation of CSF biomarker and hippocampal volume results in individuals with MCI, and help research and clinical settings to prepare for a future of precision medicine in Alzheimer's disease. Future research should focus on the clinical utility of the models, particularly if their use affects participants' understanding, emotional wellbeing, and behaviour.

FUNDING

ZonMW-Memorabel.

Altered bile acid profile in mild cognitive impairment and Alzheimer's disease: Relationship to neuroimaging and CSF biomarkers

INTRODUCTION

Bile acids (BAs) are the end products of cholesterol metabolism produced by human and gut microbiome co-metabolism. Recent evidence suggests gut microbiota influence pathological features of Alzheimer's disease (AD) including neuroinflammation and amyloid-β deposition.

METHOD

Serum levels of 20 primary and secondary BA metabolites from the AD Neuroimaging Initiative (n = 1562) were measured using targeted metabolomic profiling. We assessed the association of BAs with the "A/T/N" (amyloid, tau, and neurodegeneration) biomarkers for AD: cerebrospinal fluid (CSF) biomarkers, atrophy (magnetic resonance imaging), and brain glucose metabolism ([18F]FDG PET).

RESULTS

Of 23 BAs and relevant calculated ratios after quality control procedures, three BA signatures were associated with CSF Aβ1-42 ("A") and three with CSF p-tau181 ("T") (corrected P < .05). Furthermore, three, twelve, and fourteen BA signatures were associated with CSF t-tau, glucose metabolism, and atrophy ("N"), respectively (corrected P < .05).

DISCUSSION

This is the first study to show serum-based BA metabolites are associated with "A/T/N" AD biomarkers, providing further support for a role of BA pathways in AD pathophysiology. Prospective clinical observations and validation in model systems are needed to assess causality and specific mechanisms underlying this association.

Cognitive Assessment Tools for Screening Older Adults With Low Levels of Education: A Critical Review

Introduction: Cognitive assessment of older adults who are either illiterate or with low levels of education is particularly challenging because several battery tasks require a certain educational background. Early detection of mild cognitive impairment (MCI) in the elderly using validated screening tools is of great importance since this population group could benefit from new drugs that are being investigated for the treatment of dementias. Cutoff scores for psychometric properties of cognitive tests are not well established among adults with low levels of education. The present study aimed to critically review the literature on cognitive assessment tools for screening cognitive syndromes including MCI and Alzheimer's disease (AD) in older adults with low levels of education. Methods: We conducted a systematic search of MEDLINE, LILACS, Cochrane, and SCOPUS electronic databases of cross-sectional and prospective studies with adults over 55 years of age. Results: We found a significant number of assessment tools available (n = 44), but only a few of them showed diagnostic accuracy for the diagnosis of MCI and AD in older adults with low levels of education: the Mini-Mental State Exam; the Montreal Cognitive Assessment; the Persian Test of Elderly for Assessment of Cognition and Executive Function; the Six-Item Screener; and the Memory Alteration Test. Few studies evaluated individuals with low levels of education, with a wide range of cutoff scores and cognitive test batteries. Conclusion: We found that a small number of studies evaluated adults with 4 years of formal education or less. Our findings further support the importance of developing specific tools for the assessment of older adults with low levels of education.

Appropriate use criteria for lumbar puncture and cerebrospinal fluid testing in the diagnosis of Alzheimer's disease

INTRODUCTION

The Alzheimer's Association convened a multidisciplinary workgroup to develop appropriate use criteria to guide the safe and optimal use of the lumbar puncture procedure and cerebrospinal fluid (CSF) testing for Alzheimer's disease pathology detection in the diagnostic process.

METHODS

The workgroup, experienced in the ethical use of lumbar puncture and CSF analysis, developed key research questions to guide the systematic review of the evidence and developed clinical indications commonly encountered in clinical practice based on key patient groups in whom the use of lumbar puncture and CSF may be considered as part of the diagnostic process. Based on their expertise and interpretation of the evidence from systematic review, members rated each indication as appropriate or inappropriate.

RESULTS

The workgroup finalized 14 indications, rating 6 appropriate and 8 inappropriate.

DISCUSSION

In anticipation of the emergence of more reliable CSF analysis platforms, the manuscript offers important guidance to health-care practitioners and suggestions for implementation and future research.

Somatostatin and Neuropeptide Y in Cerebrospinal Fluid: Correlations With Amyloid Peptides Aβ1-42 and Tau Proteins in Elderly Patients With Mild Cognitive Impairment

A combination of low cerebrospinal fluid (CSF) Amyloid β_1–42 (Aβ_1–42) and high Total-Tau (T-Tau) and Phosphorylated-Tau (P-Tau) occurs at a prodromal stage of Alzheimer’s disease (AD) and recent findings suggest that network abnormalities and interneurons dysfunction contribute to cognitive deficits. Somatostatin (SOM) and Neuropeptide Y (NPY) are two neuropeptides which are expressed in GABAergic interneurons with different fates in AD the former only being markedly affected. The aim of this study was to analyze CSF SOM, NPY and CSF Aβ_1–42; T-Tau, P-Tau relationships in 43 elderly mild cognitively impairment (MCI) participants from the Biomarker of AmyLoïd pepTide and AlZheimer’s disease Risk (BALTAZAR) cohort. In these samples, CSF SOM and CSF Aβ_1–42 on the one hand, and CSF NPY and CSF T-Tau and P-Tau on the other hand are positively correlated. CSF SOM and NPY concentrations should be further investigated to determine if they can stand for early AD biomarkers.

Clinical Trial Registration: www.ClinicalTrials.gov, identifier #NCT01315639.

Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinson's Disease

In last years, several approaches to develop an effective Computer-Aided-Diagnosis (CAD) system for Parkinson's Disease (PD) have been proposed. Most of these methods have focused almost exclusively on brain images through the use of Machine-Learning algorithms suitable to characterize structural or functional patterns. Those patterns provide enough information about the status and/or the progression at intermediate and advanced stages of Parkinson's Disease. Nevertheless this information could be insufficient at early stages of the pathology. The Parkinson's Progression Markers Initiative (PPMI) database includes neurological images along with multiple biomedical tests. This information opens up the possibility of comparing different biomarker classification results. As data come from heterogeneous sources, it is expected that we could include some of these biomarkers in order to obtain new information about the pathology. Based on that idea, this work presents an Ensemble Classification model with Performance Weighting. This proposal has been tested comparing Healthy Control subjects (HC) vs. patients with PD (considering both PD and SWEDD labeled subjects as the same class). This model combines several Support-Vector-Machine (SVM) with linear kernel classifiers for different biomedical group of tests—including CerebroSpinal Fluid (CSF), RNA, and Serum tests—and pre-processed neuroimages features (Voxels-As-Features and a list of defined Morphological Features) from PPMI database subjects. The proposed methodology makes use of all data sources and selects the most discriminant features (mainly from neuroimages). Using this performance-weighted ensemble classification model, classification results up to 96% were obtained.

Cost-Utility of Using Alzheimer's Disease Biomarkers in Cerebrospinal Fluid to Predict Progression from Mild Cognitive Impairment to Dementia

BACKGROUND

Diagnostic research criteria for Alzheimer's disease support the use of biomarkers in the cerebrospinal fluid (CSF) to improve the accuracy of the prognosis regarding progression to dementia for people with mild cognitive impairment (MCI).

OBJECTIVE

The aim of this study was to estimate the potential incremental cost-effectiveness ratio of adding CSF biomarker testing to the standard diagnostic workup to determine the prognosis for patients with MCI.

METHODS

In an early technology assessment, a mathematical simulation model was built, using available evidence on added prognostic value as well as expert opinion to estimate the incremental costs and quality-adjusted life years (QALYs) of 20,000 virtual MCI patients with (intervention strategy) and without (control strategy) relying on CSF, from a health-care sector perspective and with a 5-year time horizon.

RESULTS

Adding the CSF test improved the accuracy of prognosis by 11%. This resulted in an average QALY gain of 0.046 and € 432 additional costs per patient, representing an incremental cost-effectiveness ratio of € 9,416.

CONCLUSION

The results show the potential of CSF biomarkers in current practice from a health-economics perspective. This result was, however, marked by a high degree of uncertainty, and empirical research is required into the impact of a prognosis on worrying, false-positive/negative prognosis, and stigmatization.

Advance directives for future dementia can be modified by a brief video presentation on dementia care: An experimental study

OBJECTIVES

To investigate whether life-sustaining measures in medical emergency situations are less accepted for an anticipated own future of living with dementia, and to test whether a resource-oriented, in contrast to a deficit-oriented video about the same demented person, would increase the acceptance of such life-saving measures.

DESIGN

Experimental study conducted between September 2012 and February 2013.

SETTING

Community dwelling female volunteers living in the region of Bonn, Germany.

PARTICIPANTS

278 women aged 19 to 89 (mean age 53.4 years).

INTERVENTION

Presentation of a video on dementia care focusing either on the deficits of a demented woman (negative framing), or focusing on the remaining resources (positive framing) of the same patient.

MAIN OUTCOME MEASURES

Approval of life-sustaining treatments in five critical medical scenarios under the assumption of having comorbid dementia, before and after the presentation of the brief videos on care.

RESULTS

At baseline, the acceptance of life-sustaining measures in critical medical situations was significantly lower in subjects anticipating their own future life with dementia. Participants watching the resource-oriented film on living with dementia had significantly higher post-film acceptance rates compared to those watching the deficit-oriented negatively framed film. This effect particularly emerges if brief and efficient life-saving interventions with a high likelihood of physical recovery are available (eg, antibiotic treatment for pneumonia).

CONCLUSIONS

Anticipated decisions regarding life-sustaining measures are negatively influenced by the subjective imagination of living with dementia, which might be shaped by common, unquestioned stereotypes. This bias can be reduced by providing audio-visual information on living with dementia which is not only centred around cognitive and functional losses but also focuses on remaining resources and the apparent quality of life. This is particularly true if the medical threat can be treated efficiently. These findings have implications for the practice of formulating, revising, and supporting advance directives.

Progress in brain barriers and brain fluid research in 2017

The past year, 2017, has seen many important papers published in the fields covered by Fluids and Barriers of the CNS. This article from the Editors highlights some.