A Case of Minimally Progressive Prodromal Alzheimer's Disease

Prodromal Alzheimer's disease (AD) is a neurodegenerative condition typically progressing to dementia within 3 years. We describe a case of a mild cognitive impairment (MCI) patient with biomarker evidence for amyloidosis, tau, and neurodegeneration who had minimal changes in clinical phenotype during an 11-year period. AD biomarkers were obtained with cerebrospinal fluid analysis and amyloid PET imaging, both of which supported a biological diagnosis of AD. However, the patient's neuropsychological profile remained stable over 11 years except for mild memory-retrieval changes. This case provides evidence that MCI with supportive AD biomarkers may have an atypically minimal progression.

Methodological Issues in Randomized Clinical Trials for Prodromal Alzheimer's and Parkinson's Disease

Alzheimer's disease (AD) and Parkinson's disease (PD) are the first and second most common neurodegenerative disorders, respectively. Both are proteinopathies with inexorable courses and no approved disease-modifying therapies. A substantial effort has been made to identify interventions that could slow down the progression of AD and PD; to date, with no success. The advances in biomarker research improved the identification of individuals at risk for these disorders before symptom onset, recognizing the pre-clinical stage, in which there is abnormal protein accumulation but no clinical symptoms of the disease, and the prodromal stage, in which mild symptoms are present but the clinical diagnostic criteria for disease cannot be fulfilled. The ability to detect pre-clinical and prodromal stages of these diseases has encouraged clinical trials for disease-modification at earlier phases, seeking to slow or prevent phenoconversion into clinical disease. Clinical trials at these stages have several challenges, such as the identification of the eligible population, the appropriate choice of biomarkers, the definition of clinical endpoints, the duration of follow-up, and the statistical analysis. This article aims to discuss some of the methodological challenges in the design of trials for pre-clinical and prodromal phases of AD and PD, to critically review the recent studies, and to discuss methodological approaches to mitigate these challenges in trial design.

Neuropsychological Contribution to Predict Conversion to Dementia in Patients with Mild Cognitive Impairment Due to Alzheimer's Disease

BACKGROUND

Diagnosis of Alzheimer's disease (AD) confirmed by biomarkers allows the patient to make important life decisions. However, doubt about the fleetness of symptoms progression and future cognitive decline remains. Neuropsychological measures were extensively studied in prediction of time to conversion to dementia for mild cognitive impairment (MCI) patients in the absence of biomarker information. Similar neuropsychological measures might also be useful to predict the progression to dementia in patients with MCI due to AD.

OBJECTIVE

To study the contribution of neuropsychological measures to predict time to conversion to dementia in patients with MCI due to AD.

METHODS

Patients with MCI due to AD were enrolled from a clinical cohort and the effect of neuropsychological performance on time to conversion to dementia was analyzed.

RESULTS

At baseline, converters scored lower than non-converters at measures of verbal initiative, non-verbal reasoning, and episodic memory. The test of non-verbal reasoning was the only statistically significant predictor in a multivariate Cox regression model. A decrease of one standard deviation was associated with 29% of increase in the risk of conversion to dementia. Approximately 50% of patients with more than one standard deviation below the mean in the z score of that test had converted to dementia after 3 years of follow-up.

CONCLUSION

In MCI due to AD, lower performance in a test of non-verbal reasoning was associated with time to conversion to dementia. This test, that reveals little decline in the earlier phases of AD, appears to convey important information concerning conversion to dementia.

Machine Learning Algorithm Helps Identify Non-Diagnosed Prodromal Alzheimer's Disease Patients in the General Population

BACKGROUND

Recruiting patients for clinical trials of potential therapies for Alzheimer's disease (AD) remains a major challenge, with demand for trial participants at an all-time high. The AD treatment R and D pipeline includes around 112 agents. In the United States alone, 150 clinical trials are seeking 70,000 participants. Most people with early cognitive impairment consult primary care providers, who may lack time, diagnostic skills and awareness of local clinical trials. Machine learning and predictive analytics offer promise to boost enrollment by predicting which patients have prodromal AD, and which will go on to develop AD.

OBJECTIVES

The authors set out to develop a machine learning predictive model that identifies prodromal AD patients in the general population, to aid early AD detection by primary care physicians and timely referral to expert sites for biomarker confirmation of diagnosis and clinical trial enrollment.

DESIGN

The authors use a classification machine learning algorithm to extract patterns within healthcare claims and prescription data three years prior to AD diagnosis/AD drug initiation.

SETTING

The study focused on subjects included within proprietary IQVIA US data assets (claims and prescription databases). Patient information was extracted from January 2010 to July 2018, for cohorts aged between 50 and 85 years.

PARTICIPANTS

A total of 88,298,289 subjects aged between 50 and 85 years were identified. For the positive cohort, 667,288 subjects were identified who had 24 months of medical history and at least one record with AD or AD treatment. For the negative cohort, 3,670,254 patients were selected who had a similar length of medical history and who were matched to positive cohort subjects based on the prevalence rate. The scoring cohort was selected based on availability of recent medical data of 2-5 years and included 72,670,283 subjects between the ages of 50 and 85 years. Intervention (if any): None.

MEASUREMENTS

A list of clinically-relevant and interpretable predictors was generated and extracted from the data sets for each subject, including pharmacological treatments (NDC/product), office/specialist visits (specialty), tests and procedures (HCPCS and CPT), and diagnosis (ICD). The positive cohort was defined as patients who have AD diagnosis/AD treatment with a 3 years offset as an estimate for prodromal AD diagnosis. Supervised ML techniques were used to develop algorithms to predict the occurrence of prodromal AD cases. The sample dataset was divided randomly into a training dataset and a test dataset. The classification models were trained and executed in the PySpark framework. Training and evaluation of LogisticRegression, DecisionTreeClassifier, RandomForestClassifier, and GBTClassifier were executed using PySpark's mllib module. The area under the precision-recall curve (AUCPR) was used to compare the results of the various models.

RESULTS

The AUCPRs are 0.426, 0.157, 0.436, and 0.440 for LogisticRegression, DecisionTreeClassifier, RandomForestClassifier, and GBTClassifier, respectively, meaning that GBTClassifier (Gradient Boosted Tree) outperforms the other three classifiers. The GBT model identified 222,721 subjects in the prodromal AD stage with 80% precision. Some 76% of identified prodromal AD patients were in the primary care setting.

CONCLUSIONS

Applying the developed predictive model to 72,670,283 U.S. residents, 222,721 prodromal AD patients were identified, the majority of whom were in the primary care setting. This could drive major advances in AD research by enabling more accurate and earlier prodromal AD diagnosis at the primary care physician level , which would facilitate timely referral to expert sites for in-depth assessment and potential enrolment in clinical trials.

On the horizon-the value and promise of the global pipeline of Alzheimer's disease therapeutics

INTRODUCTION

The recent failure of several late-stage Alzheimer's disease (AD) clinical trials focused on amyloid beta (Aβ) highlights the challenges of finding effective disease-modifying therapeutics. Despite major advances in our understanding of the genetic risk factors of disease and the development of clinical biomarkers, and that not all Aβ-based approaches are equivalent, these failures may engender skepticism regarding the value of the AD pipeline.

METHODS

To investigate these concerns, we compiled a database of current Phase 2 and 3 trials based on disease-modifying targets through a query of the National Institutes of Health's ClinicalTrials.gov. We then assessed the financial value of the pipeline. Financial modeling utilized risk-adjusted net present value (rNPV) measurements and included sensitivity analyses to help inform the drug development process.

RESULTS

Results indicate that the preponderance of current Phase 3 trials were indeed targeting Aβ, with only 15% addressing other targets. In contrast, the pipeline of Phase 2 trials was more diverse. The estimated rNPV of Phase 2 and 3 therapeutics was estimated to be $338 billion over 10 years. This figure increased to a theoretical cumulative value of $788 billion when incorporating the assumption that diagnostics will be developed to identify individuals at high risk for developing AD. Results from model sensitivity analyses showed that speed of market penetration and patient access contributed the most weight to financial value. In contrast, decreasing drug development costs had minimal impact on rNPV.

DISCUSSION

These findings argue in favor of conducting thorough biomarker-driven Phase 2 proof of concept studies to avoid prematurely advancing assets into Phase 3. Insights from these analyses are also discussed in the context of the financial ecosystem needed to maintain a healthy AD pipeline.

The effect of ApoE ε 4 on clinical and structural MRI markers in prodromal Alzheimer's disease

BACKGROUND

Apolipoprotein E (ApoE) ε 4 has been identified as the strongest genetic risk factor for Alzheimer's disease (AD). However, the importance of ApoE ε 4 on clinical and biological heterogeneity of AD is still to be determined, particularly at the prodromal stage. Here, we evaluate the association of ApoE ε 4 with clinical cognition and neuroimaging regions in mild cognitive impairment (MCI) participants based on the AT (N) system, which is increasingly essential for developing a precise assessment of AD.

METHODS

We stratified 178 A+T+MCI participants (prodromal AD) into ApoE ε 4 (+) and ApoE ε 4 (-) according to ApoE genotype from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We determined Aβ-positivity (A+) by the standardized uptake values ratios (SUVR) means of florbetapir-PET-AV45 (the cut-off value of 1.1) and fibrillar tau-positivity (T+) by cerebrospinal fluid (CSF) phosphorylated-tau at threonine 181 position (p-Tau) (cut-off value of 23 pg/mL). We evaluated the effect of ApoE ε 4 status on cognitive conditions and brain atrophy from structural magnetic resonance imaging (MRI) scans. A multivariate analysis of variance was used to compare the differences of cognitive scores and brain atrophy from structural MRI regions of interest (ROIs) between both groups. Furthermore, we performed a linear regression model to assess the correlation between signature ROIs of structural MRI and cognitive scores in the prodromal AD participants.

RESULTS

ApoE ε 4 (+) prodromal AD participants had lower levels of CSF Aβ 1-42, higher levels of t-Tau, more memory and global cognitive impairment, and faster decline of global cognition, compared to ApoE ε 4 (-) prodromal AD. ApoE ε 4 (+) prodromal AD participants had a thinner cortical thickness of bilateral entorhinal, smaller subcortical volume of the left amygdala, bilateral hippocampus, and left ventral diencephalon (DC) relative to ApoE ε 4 (-) prodromal AD. Furthermore, the cortical thickness average of bilateral entorhinal was highly correlated with memory and global cognition.

CONCLUSIONS

ApoE ε 4 status in prodromal AD participants has an important effect on clinical cognitive domains. After ascertaining the ApoE ε 4 status, specific MRI regions can be correlated to the cognitive domain and will be helpful for precise assessment in prodromal AD.

The structural MRI markers and cognitive decline in prodromal Alzheimer's disease: a 2-year longitudinal study

Background

Being clinically diagnosed with a mild cognitive impairment (MCI) due to Alzheimer's disease (AD) is widely studied. Yet, the clinical and structural neuroimaging characteristics for prodromal AD, which are defined as A+T+MCI based on the AT (N) system are still highly desirable. This study evaluates the differences of the cognitive assessments and structural magnetic resonance imaging (MRI) between the early MCI (EMCI) and late MCI (LMCI) participants based on the AT (N) system. The potential clinical value of the structural MRI as a predictor of cognitive decline during follow-up in prodromal AD is further investigated.

Methods

A total of 406 MCI participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were chosen and dichotomized into EMCI and LMCI groups according to the Second Edition (Logical Memory II) Wechsler Memory Scale. Multiple markers' data was collected, including age, sex, years of education, ApoE4 status, cerebrospinal fluid (CSF) biomarkers, standardized uptake values ratios (SUVR) means of florbetapir-PET-AV45, cognitive measures, and structural MRI. We chose 197 A+T+MCI participants (prodromal AD) with positive biomarkers of Aβ plaques (labeled "A") and fibrillar tau (labeled "T"). We diagnosed Aβ plaques positive by the SUVR means of florbetapir-PET-AV45 (cut-off >1.1) and fibrillar tau positive by CSF phosphorylated-tau at threonine 181 (p-tau) (cut-off >23 pg/mL). The differences of cognitive assessments and regions of interest (ROIs) defined on the MRI template between EMCI and LMCI were compared. Furthermore, the potential clinical utility of the MRI as the predictor of cognitive decline in prodromal AD was evaluated by investigating the relationship between baseline MRI markers and cognition decline at the follow-up period, through a linear regression model.

Results

The LMCI participants had a significantly more amyloid burden and CSF levels of total t-tau than the EMCI participants. The LMCI participants scored a lower result than the EMCI group in the global cognition scales and subscales which included tests for memory, delayed recall memory, executive function, language, attention and visuospatial skills. The cognition levels declined faster in the LMCI participants during the 12- and 24-month follow-up. There were significant differences in ROIs on the structural MRI between the two groups, including a bilateral entorhinal, a bilateral hippocampus, a bilateral amygdala, a bilateral lateral ventricle and cingulate, a corpus callosum, and a left temporal. The thickness average of the left entorhinal, the left middle temporal, the left superior temporal, and the right isthmus cingulate was a main contributor to the decreased global cognition levels. The thickness average of the left superior temporal and bilateral entorhinal played a key role in the memory domain decline. The thickness average of the left middle temporal, and the right isthmus cingulate was significantly associated with an executive function decline.

Conclusions

Based on the AT (N) system, surely, both the EMCI and LMCI diagnoses presented significant differences in multiple cognition domains. Signature ROIs from the structural MRI tests had correlated a cognitive decline, and could act as one potential predictive marker.

Electroencephalography-driven approach to prodromal Alzheimer's disease diagnosis: from biomarker integration to network-level comprehension

Decay of the temporoparietal cortex is associated with prodromal Alzheimer's disease (AD). Additionally, shrinkage of the temporoparietal cerebral area has been connected with an increase in α3/α2 electroencephalogram (EEG) power ratio in prodromal AD. Furthermore, a lower regional blood perfusion has been exhibited in patients with a higher α3/α2 proportion when contrasted with low α3/α2 proportion. Furthermore, a lower regional blood perfusion and reduced hippocampal volume has been exhibited in patients with higher α3/α2 when contrasted with lower α3/α2 EEG power ratio. Neuropsychological evaluation, EEG recording, and magnetic resonance imaging were conducted in 74 patients with mild cognitive impairment (MCI). Estimation of cortical thickness and α3/α2 frequency power ratio was conducted for each patient. A subgroup of 27 patients also underwent single-photon emission computed tomography evaluation. In view of α3/α2 power ratio, the patients were divided into three groups. The connections among cortical decay, cerebral perfusion, and memory loss were evaluated by Pearson's r coefficient. Results demonstrated that higher α3/α2 frequency power ratio group was identified with brain shrinkage and cutdown perfusion inside the temporoparietal projections. In addition, decay and cutdown perfusion rate were connected with memory shortfalls in patients with MCI. MCI subgroup with higher α3/α2 EEG power ratio are at a greater risk to develop AD dementia.

Clinical and biomarker profiling of prodromal Alzheimer's disease in workpackage 5 of the Innovative Medicines Initiative PharmaCog project: a 'European ADNI study'

BACKGROUND

In the field of Alzheimer's disease (AD), the validation of biomarkers for early AD diagnosis and for use as a surrogate outcome in AD clinical trials is of considerable research interest.

OBJECTIVE

To characterize the clinical profile and genetic, neuroimaging and neurophysiological biomarkers of prodromal AD in amnestic mild cognitive impairment (aMCI) patients enrolled in the IMI WP5 PharmaCog (also referred to as the European ADNI study).

METHODS

A total of 147 aMCI patients were enrolled in 13 European memory clinics. Patients underwent clinical and neuropsychological evaluation, magnetic resonance imaging (MRI), electroencephalography (EEG) and lumbar puncture to assess the levels of amyloid β peptide 1-42 (Aβ42), tau and p-tau, and blood samples were collected. Genetic (APOE), neuroimaging (3T morphometry and diffusion MRI) and EEG (with resting-state and auditory oddball event-related potential (AO-ERP) paradigm) biomarkers were evaluated.

RESULTS

Prodromal AD was found in 55 aMCI patients defined by low Aβ42 in the cerebrospinal fluid (Aβ positive). Compared to the aMCI group with high Aβ42 levels (Aβ negative), Aβ positive patients showed poorer visual (P = 0.001), spatial recognition (P < 0.0005) and working (P = 0.024) memory, as well as a higher frequency of APOE4 (P < 0.0005), lower hippocampal volume (P = 0.04), reduced thickness of the parietal cortex (P < 0.009) and structural connectivity of the corpus callosum (P < 0.05), higher amplitude of delta rhythms at rest (P = 0.03) and lower amplitude of posterior cingulate sources of AO-ERP (P = 0.03).

CONCLUSION

These results suggest that, in aMCI patients, prodromal AD is characterized by a distinctive cognitive profile and genetic, neuroimaging and neurophysiological biomarkers. Longitudinal assessment will help to identify the role of these biomarkers in AD progression.

Prevalence and prognosis of prodromal Alzheimer's disease as assessed by magnetic resonance imaging and 18F-fluorodeoxyglucose-positron emission tomography in a community: reanalysis from the Osaki-Tajiri Project

BACKGROUND

Dubois et al. proposed the criteria for prodromal Alzheimer's disease (AD) to detect dementia in its very early stage. Because detection requires magnetic resonance imaging and (18) F-fluorodeoxyglucose-positron emission tomography (PET), the prevalence and prognosis have not been fully investigated.

METHODS

Our database included 346 healthy participants (Clinical Dementia Rating (CDR) 0), 119 with questionable dementia (CDR 0.5), and 32 dementia participants (CDR 1+) and was applied to investigate the prevalence of prodromal AD. Forty-four CDR 0.5 participants (37%) were randomly selected to undergo (18) F-fluorodeoxyglucose-PET. The same percentage was applied to select 128 CDR 0 and 12 CDR 1 + participants (total: n = 184) to calculate the prevalence. A neuroradiologist classified the PET images in a blinded manner based on the criteria of Silverman et al. Participants were considered to have prodromal AD if they exhibited 'parietal/temporal +/- frontal hypometabolism' (PET) with hippocampal atrophy (magnetic resonance imaging).

RESULTS

Eighteen CDR 0.5 participants (40.9%) met the criteria for prodromal AD, which was a prevalence rate of 9.8% among older adults aged ≥ 65 years. Thirteen prodromal AD participants (72%) converted to AD during the 5-year follow-up period.

DISCUSSION

The concept and criteria for prodromal AD are useful for predicting which subjects in a community will convert to AD.

A Novel Study Paradigm for Long-term Prevention Trials in Alzheimer Disease: The Placebo Group Simulation Approach (PGSA): Application to MCI data from the NACC database

INTRODUCTION

The PGSA (Placebo Group Simulation Approach) aims at avoiding problems of sample representativeness and ethical issues typical of placebo-controlled secondary prevention trials with MCI patients. The PGSA uses mathematical modeling to forecast the distribution of quantified outcomes of MCI patient groups based on their own baseline data established at the outset of clinical trials. These forecasted distributions are then compared with the distribution of actual outcomes observed on candidate treatments, thus substituting for a concomitant placebo group. Here we investigate whether a PGSA algorithm that was developed from the MCI population of ADNI 1*, can reliably simulate the distribution of composite neuropsychological outcomes from a larger, independently selected MCI subject sample.

METHODS

Data available from the National Alzheimer's Coordinating Center (NACC) were used. We included 1523 patients with single or multiple domain amnestic mild cognitive impairment (aMCI) and at least two follow-ups after baseline. In order to strengthen the analysis and to verify whether there was a drift over time in the neuropsychological outcomes, the NACC subject sample was split into 3 subsamples of similar size. The previously described PGSA algorithm for the trajectory of a composite neuropsychological test battery (NTB) score was adapted to the test battery used in NACC. Nine demographic, clinical, biological and neuropsychological candidate predictors were included in a mixed model; this model and its error terms were used to simulate trajectories of the adapted NTB.

RESULTS

The distributions of empirically observed and simulated data after 1, 2 and 3 years were very similar, with some over-estimation of decline in all 3 subgroups. The by far most important predictor of the NTB trajectories is the baseline NTB score. Other significant predictors are the MMSE baseline score and the interactions of time with ApoE4 and FAQ (functional abilities). These are essentially the same predictors as determined for the original NTB score.

CONCLUSION

An algorithm comprising a small number of baseline variables, notably cognitive performance at baseline, forecasts the group trajectory of cognitive decline in subsequent years with high accuracy. The current analysis of 3 independent subgroups of aMCI patients from the NACC database supports the validity of the PGSA longitudinal algorithm for a NTB. Use of the PGSA in long-term secondary AD prevention trials deserves consideration.