Finding Treatment Effects in Alzheimer Trials in the Face of Disease Progression Heterogeneity

Allogeneic mesenchymal stem cell therapy with laromestrocel in mild Alzheimer's disease: a randomized controlled phase 2a trial

Alzheimer's disease (AD) is characterized by progressive cognitive decline, severe brain atrophy and neuroinflammation. We conducted a randomized, double-blind, placebo-controlled, parallel-group phase 2a clinical trial that tested the safety and efficacy of laromestrocel, a bone-marrow-derived, allogeneic mesenchymal stem-cell therapy, in slowing AD clinical progression, atrophy and neuroinflammation. Participants across ten centers in the United States were randomly assigned 1:1:1:1 to four infusion groups: group 1 (placebo; four monthly infusions, n = 12); group 2 (25 million cells, one infusion followed by three monthly infusions of placebo, n = 13); group 3 (25 million cells; four monthly doses, n = 13); and group 4 (100 million cells; four monthly doses, n = 11). The study met its primary end point of safety; the rate of treatment-emergent serious adverse events within 4 weeks of any infusion was similar in all four groups: group 1, 0% (95% CI 0-26.5%); group 2, 7.7% (95% CI 0.2-36%); group 3, 7.7% (95% CI 0.2-36%) and group 4, 9.1% (95% CI 0.2-41.3%). Additionally, there were no reported infusion-related reactions, hypersensitivities or amyloid-related imaging abnormalities. Laromestrocel improved clinical assessments at 39 weeks compared to placebo, as measured by a composite AD score (secondary end point was met: group 2 versus placebo change: 0.38; 95% CI -0.06-0.82), Montreal cognitive assessment and the Alzheimer's Disease Cooperative Study Activities of Daily Living. At 39 weeks, Laromestrocel slowed the decline of whole brain volume compared to placebo (n = 10) by 48.4% for all treatment groups combined (groups 2-4: P = 0.005; n = 32) and left hippocampal volume by 61.9% (groups 2-4, P = 0.021; n = 32), and reduced neuroinflammation as measured by diffusion tensor imaging. The change in bilateral hippocampal atrophy correlated with the change in mini-mental state exam scores (R = 0.41, P = 0.0075) in all study patients (N = 42). Collectively these results support safety of single and multiple doses of laromestrocel treatment for mild AD and provide indications of efficacy in combating decline of brain volume and potentially cognitive function. Larger-scale clinical trials of laromestrocel in AD are warranted. ClinicalTrials.gov registration: NCT05233774 .

Distinct medical and substance use histories associate with cognitive decline in Alzheimer's disease

INTRODUCTION

Phenotype clustering reduces patient heterogeneity and could be useful when designing precision clinical trials. We hypothesized that the onset of early cognitive decline in patients would exhibit variance predicated on the clinical history documented prior to an Alzheimer's disease (AD) diagnosis.

METHODS

Self-reported medical and substance use history (i.e., problem history) was used to cluster participants from the National Alzheimer's Coordinating Center (NACC) into distinct subtypes. Linear mixed effects modeling was used to determine the effect of problem history subtype on cognitive decline over 2 years.

RESULTS

Two thousand seven hundred fifty-four individuals were partitioned into three subtypes: minimal (n = 1380), substance use (n = 1038), and cardiovascular (n = 336). The cardiovascular problem history subtype had significantly worse cognitive decline over a 2 year follow-up period (p = 0.013).

DISCUSSION

Our study highlights the need to account for problem history to reduce heterogeneity of outcomes in AD clinical trials.

HIGHLIGHTS

Clinical data were used to identify subtypes of patients with Alzheimer's disease (AD) in the National Alzheimer's Coordinating Center dataset. Three problem history subtypes were found: minimal, substance use, and cardiovascular. The mean change in Clinical Dementia Rating Sum of Boxes (CDR-SB) was assessed over a 2 year follow-up. The cardiovascular subtype was associated with the worst cognitive decline. The magnitude of change in CDR-SB was similar to recent AD clinical trials.

The graded multidimensional geometry of phenotypic variation and progression in neurodegenerative syndromes

Clinical variants of Alzheimer's disease and frontotemporal lobar degeneration display a spectrum of cognitive-behavioural changes varying between individuals and over time. Understanding the landscape of these graded individual/group level longitudinal variations is critical for precise phenotyping; however, this remains challenging to model. Addressing this challenge, we leverage the National Alzheimer's Coordinating Center database to derive a unified geometric framework of graded longitudinal phenotypic variation in Alzheimer's disease and frontotemporal lobar degeneration. We included three time point, cognitive-behavioural and clinical data from 390 typical, atypical and intermediate Alzheimer's disease and frontotemporal lobar degeneration variants (114 typical Alzheimer's disease; 107 behavioural variant frontotemporal dementia; 42 motor variants of frontotemporal lobar degeneration; and 103 primary progressive aphasia patients). On these data, we applied advanced data-science approaches to derive low-dimensional geometric spaces capturing core features underpinning clinical progression of Alzheimer's disease and frontotemporal lobar degeneration syndromes. To do so, we first used principal component analysis to derive six axes of graded longitudinal phenotypic variation capturing patient-specific movement along and across these axes. Then, we distilled these axes into a visualizable 2D manifold of longitudinal phenotypic variation using Uniform Manifold Approximation and Projection. Both geometries together enabled the assimilation and interrelation of paradigmatic and mixed cases, capturing dynamic individual trajectories and linking syndromic variability to neuropathology and key clinical end points, such as survival. Through these low-dimensional geometries, we show that (i) specific syndromes (Alzheimer's disease and primary progressive aphasia) converge over time into a de-differentiated pooled phenotype, while others (frontotemporal dementia variants) diverge to look different from this generic phenotype; (ii) phenotypic diversification is predicted by simultaneous progression along multiple axes, varying in a graded manner between individuals and syndromes; and (iii) movement along specific principal axes predicts survival at 36 months in a syndrome-specific manner and in individual pathological groupings. The resultant mapping of dynamics underlying cognitive-behavioural evolution potentially holds paradigm-changing implications to predicting phenotypic diversification and phenotype-neurobiological mapping in Alzheimer's disease and frontotemporal lobar degeneration.

Studying Alzheimer's disease through an integrative serum metabolomic and lipoproteomic approach

BACKGROUND

Alzheimer's disease (AD) is the most frequent neurodegenerative disorder worldwide. The great variability in disease evolution and the incomplete understanding of the molecular mechanisms underlying AD make it difficult to predict when a patient will convert from prodromal stage to dementia. We hypothesize that metabolic alterations present at the level of the brain could be reflected at a systemic level in blood serum of patients, and that these alterations could be used as prognostic biomarkers.

METHODS

This pilot study proposes a serum investigation via nuclear magnetic resonance (NMR) spectroscopy in a consecutive series of AD patients including 57 patients affected by Alzheimer's disease at dementia stage (AD-dem) and 45 patients with mild cognitive impairment (MCI) due to AD (MCI-AD). As control group, we considered 31 subjects with mild cognitive impairment in whom AD and other neurodegenerative disorders were excluded (MCI). A panel of 26 metabolites and 112 lipoprotein-related parameters was quantified and the logistic LASSO regression algorithm was employed to identify the optimal combination of metabolites-lipoproteins and their ratios to discriminate the groups of interest.

RESULTS

In the training set, our model classified AD-dem and MCI with an accuracy of 81.7%. These results were reproduced in the validation set (accuracy 75.0%). Evolution of MCI-AD patients was evaluated over time. Patients who displayed a decrease in MMSE < 1.5 point per year were considered at lower progression rate: we obtained a division in 18 MCI-AD at lower progression rate (MCI-AD LR) and 27 at higher progression rate (MCI-AD HR). The model calculated using 4 metabolic features identified MCI-AD LR and MCI-AD HR with an accuracy of 73.3%.

CONCLUSIONS

The identification of potential novel peripheral biomarkers of Alzheimer's disease, as proposed in this study, opens a new prospect for an innovative and minimally invasive method to identify AD in its very early stages. We proposed a novel approach able to sub-stratify MCI-AD patients identifying those associated with a faster rate of clinical progression.

Updated Appropriate Use Criteria for Amyloid and Tau PET: A Report from the Alzheimer's Association and Society for Nuclear Medicine and Molecular Imaging Workgroup

The Alzheimer's Association and the Society of Nuclear Medicine and Molecular Imaging convened a multidisciplinary workgroup to update appropriate use criteria (AUC) for amyloid positron emission tomography (PET) and to develop AUC for tau PET. Methods: The workgroup identified key research questions that guided a systematic literature review on clinical amyloid/tau PET. Building on this review, the workgroup developed 17 clinical scenarios in which amyloid or tau PET may be considered. A modified Delphi approach was used to rate each scenario by consensus as "rarely appropriate," "uncertain," or "appropriate." Ratings were performed separately for amyloid and tau PET as stand-alone modalities. Results: For amyloid PET, 7 scenarios were rated as appropriate, 2 as uncertain, and 8 as rarely appropriate. For tau PET, 5 scenarios were rated as appropriate, 6 as uncertain, and 6 as rarely appropriate. Conclusion: AUC for amyloid and tau PET provide expert recommendations for clinical use of these technologies in the evolving landscape of diagnostics and therapeutics for Alzheimer's disease.

Updated appropriate use criteria for amyloid and tau PET: A report from the Alzheimer's Association and Society for Nuclear Medicine and Molecular Imaging Workgroup

INTRODUCTION

The Alzheimer's Association and the Society of Nuclear Medicine and Molecular Imaging convened a multidisciplinary workgroup to update appropriate use criteria (AUC) for amyloid positron emission tomography (PET) and to develop AUC for tau PET.

METHODS

The workgroup identified key research questions that guided a systematic literature review on clinical amyloid/tau PET. Building on this review, the workgroup developed 17 clinical scenarios in which amyloid or tau PET may be considered. A modified Delphi approach was used to rate each scenario by consensus as "rarely appropriate," "uncertain," or "appropriate." Ratings were performed separately for amyloid and tau PET as stand-alone modalities.

RESULTS

For amyloid PET, seven scenarios were rated as appropriate, two as uncertain, and eight as rarely appropriate. For tau PET, five scenarios were rated as appropriate, six as uncertain, and six as rarely appropriate.

DISCUSSION

AUC for amyloid and tau PET provide expert recommendations for clinical use of these technologies in the evolving landscape of diagnostics and therapeutics for Alzheimer's disease.

HIGHLIGHTS

A multidisciplinary workgroup convened by the Alzheimer's Association and the Society of Nuclear Medicine and Molecular Imaging updated the appropriate use criteria (AUC) for amyloid positron emission tomography (PET) and to develop AUC for tau PET. The goal of these updated AUC is to assist clinicians in identifying clinical scenarios in which amyloid or tau PET may be useful for guiding the diagnosis and management of patients who have, or are at risk for, cognitive decline These updated AUC are intended for dementia specialists who spend a significant proportion of their clinical effort caring for patients with cognitive complaints, as well as serve as a general reference for a broader audience interested in implementation of amyloid and tau PET in clinical practice.

Post-Hoc Assessment of Cognitive Efficacy in Alzheimer's Disease Using a Latent Growth Mixture Model in AMBAR, a Phase 2B Randomized Controlled Trial

BACKGROUND

Disease progression in Alzheimer's Dementia (AD) is typically characterized by accelerated cognitive and functional decline, where heterogeneous trajectories can impact the observed treatment response.

METHODS

We hypothesized that unobserved heterogeneity could obscure treatment benefits in AD. The effect of unobserved heterogeneity was empirically quantified within the Alzheimer's Management By Albumin Replacement (AMBAR) phase 2b trial data. The ADAS-Cog 12 cognition endpoint was reanalyzed in a 2-class latent growth mixture model initially fit to the treatment arm. The model with the best fit was then applied across both treatment arms to a larger (n=1000) simulated dataset that was representative of AMBAR trial cognitive data.

RESULTS

Two classes of patients were observed: a stable cognitive trajectory class and a highly variable class. Removal of the latter (n=48, 22%) from the analysis and refitting efficacy models comparing the stable class to full placebo yielded significant treatment efficacy on cognition (p=0.007, Cohen's D=-0.4). Comparison of the stable class of each arm within the simulated dataset revealed a significant difference in treatment efficacy favoring the simulated stable treatment arm.

CONCLUSION

This post hoc exploratory analysis suggests that prespecified strategies for addressing unobserved heterogeneity may yield improved effect detection in AD trials. The generalizability of the analytic strategy is limited by latent stratification in only the treatment arm, a requirement given the small placebo arm in AMBAR. This limitation was partially addressed by the simulation modeling.

The higher benefit of lecanemab in males compared to females in CLARITY AD is probably due to a real sex effect

INTRODUCTION

The phase 3 trial CLARITY AD found lecanemab slowed cognitive decline by 27%. However, subgroup analyses indicated a significant 31% sex difference in the effect and suggested no or limited effectiveness in females. We used simulations constrained by the trial design to determine whether that difference reflects a pre-existing sex difference in Alzheimer's disease progression or was a random event.

METHODS

Simulations were generated using linear mixed models of cognitive decline fit to data from Alzheimer's Disease Neuroimaging Initiative participants satisfying CLARITY AD inclusion criteria.

RESULTS

The statistically non-significant 7.9% smaller cognitive decline rate in our cohort's males versus females does not explain CLARITY AD's 31% sex difference in lecanemab's effect. A ≥ 31% difference occurred randomly in only 12 of our 10,000 simulations (0.0012 probability).

DISCUSSION

CLARITY AD's sex difference was probably not random. Lecanemab is likely less effective in females than males, but we cannot conclude the drug is ineffective in females.

HIGHLIGHTS

Lecanemab is more clinically effective in males than in females. Forest plots should only report subgroup-specific effects in well-powered subgroups. Trial simulations based on real data enable investigation of subgroup drug effects. We cannot conclude that lecanemab is clinically ineffective in females. A sex difference in lecanemab's efficacy could be linked to its action mechanism.

Distinct medical and substance use histories associate with cognitive decline in Alzheimer's Disease

INTRODUCTION

Phenotype clustering reduces patient heterogeneity and could be useful when designing precision clinical trials. We hypothesized that the onset of early cognitive decline in patients would exhibit variance predicated on the clinical history documented prior to an Alzheimer's Disease (AD) diagnosis.

METHODS

Self-reported medical and substance use history (i.e., problem history) was used to cluster participants from the National Alzheimer's Coordinating Centers (NACC) into distinct subtypes. Linear mixed effects modeling was used to determine the effect of problem history subtype on cognitive decline over two years.

RESULTS

2754 individuals were partitioned into three subtypes: minimal (n = 1380), substance use (n = 1038), and cardiovascular (n = 336) subtypes. The cardiovascular problem history subtype had significantly worse cognitive decline over a two-year follow-up period (p = 0.013).

DISCUSSION

Our study highlights the need to account for problem history to reduce heterogeneity of outcomes in AD clinical trials.

Baseline multimodal imaging to predict longitudinal clinical decline in atypical Alzheimer's disease

There are recognized neuroimaging regions of interest in typical Alzheimer's disease which have been used to track disease progression and aid prognostication. However, there is a need for validated baseline imaging markers to predict clinical decline in atypical Alzheimer's Disease. We aimed to address this need by producing models from baseline imaging features using penalized regression and evaluating their predictive performance on various clinical measures. Baseline multimodal imaging data, in combination with clinical testing data at two time points from 46 atypical Alzheimer's Disease patients with a diagnosis of logopenic progressive aphasia (N = 24) or posterior cortical atrophy (N = 22), were used to generate our models. An additional 15 patients (logopenic progressive aphasia = 7, posterior cortical atrophy = 8), whose data were not used in our original analysis, were used to test our models. Patients underwent MRI, FDG-PET and Tau-PET imaging and a full neurologic battery at two time points. The Schaefer functional atlas was used to extract network-based and regional gray matter volume or PET SUVR values from baseline imaging. Penalized regression (Elastic Net) was used to create models to predict scores on testing at Time 2 while controlling for baseline performance, education, age, and sex. In addition, we created models using clinical or Meta Region of Interested (ROI) data to serve as comparisons. We found the degree of baseline involvement on neuroimaging was predictive of future performance on cognitive testing while controlling for the above measures on all three imaging modalities. In many cases, model predictability improved with the addition of network-based neuroimaging data to clinical data. We also found our network-based models performed superiorly to the comparison models comprised of only clinical or a Meta ROI score. Creating predictive models from imaging studies at a baseline time point that are agnostic to clinical diagnosis as we have described could prove invaluable in both the clinical and research setting, particularly in the development and implementation of future disease modifying therapies.

Fluid biomarkers in the context of amyloid-targeting disease-modifying treatments in Alzheimer's disease

Clinical management and therapeutics development for Alzheimer's disease (AD) have entered a new era, with recent approvals of monoclonal antibody therapies targeting the underlying pathophysiology of the disease and modifying its trajectory. Imaging and fluid biomarkers are becoming increasingly important in the clinical development of AD therapeutics. This review focuses on the evidence of fluid biomarkers from recent amyloid-β-targeting clinical trials, summarizing biomarker data across 12 trials. It further proposes a simple framework to put biomarker guidance in the context of amyloid-pathway-targeted disease modification, delineates factors that impact biomarker data in clinical trials, and highlights knowledge gaps and future directions. Increased knowledge and data on biomarkers in the context of disease progression and disease modification will help to better design future AD trials and guide the clinical management of patients on AD-modifying therapies, bringing us closer to the implementation of precision medicine in AD.

Alzheimer's disease clinical variants show distinct neuroinflammatory profiles with neuropathology

AIMS

Although the neuroanatomical distribution of tau and amyloid-β is well studied in Alzheimer's disease (AD) (non)-amnestic clinical variants, that of neuroinflammation remains unexplored. We investigate the neuroanatomical distribution of activated myeloid cells, astrocytes, and complement alongside amyloid-β and phosphorylated tau in a clinically well-defined prospectively collected AD cohort.

METHODS

Clinical variants were diagnosed antemortem, and brain tissue was collected post-mortem. Typical AD (n = 10), behavioural/dysexecutive AD (n = 6), posterior cortical atrophy (PCA) AD (n = 3), and controls (n = 10) were neuropathologically assessed for AD neuropathology, concurrent pathology including Lewy body disease, limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC), and vascular pathology. For quantitative assessment, we analysed the corticolimbic distribution of phosphorylated tau, amyloid-β, CD68, MHC-II, C4b, and glial fibrillary acidic protein (GFAP) using digital pathology.

RESULTS

Phosphorylated tau was distinctly distributed in each variant. In all variants, amyloid-β was neocortical-dominant, with a notable increase in the middle frontal cortex of behavioural/dysexecutive AD. Typical AD and PCA AD had no concurrent Lewy body disease, whereas three out of six cases with behavioural/dysexecutive AD did. LATE-NC stage >0 was observed in three AD cases, two typical AD (stage 1/3), and one behavioural/dysexecutive AD (stage 2/3). Vascular pathology was present in each variant. In typical AD, CD68 and MHC-II were hippocampal-dominant. In behavioural/dysexecutive AD, C4b was elevated in the middle frontal and inferior parietal cortex. In PCA AD, MHC-II was increased in the fusiform gyrus, and GFAP in parietal cortices. Correlations between AD neuropathology and neuroinflammation were distinct within variants.

CONCLUSIONS

Our data suggests that different involvement of neuroinflammation may add to clinical heterogeneity in AD, which has implications for neuroinflammation-based biomarkers and future therapeutics.

Personalizing progressive changes to brain structure in Alzheimer's disease using normative modeling

INTRODUCTION

Neuroanatomical normative modeling captures individual variability in Alzheimer's disease (AD). Here we used normative modeling to track individuals' disease progression in people with mild cognitive impairment (MCI) and patients with AD.

METHODS

Cortical and subcortical normative models were generated using healthy controls (n ≈ 58k). These models were used to calculate regional z scores in 3233 T1-weighted magnetic resonance imaging time-series scans from 1181 participants. Regions with z scores < -1.96 were classified as outliers mapped on the brain and summarized by total outlier count (tOC).

RESULTS

tOC increased in AD and in people with MCI who converted to AD and also correlated with multiple non-imaging markers. Moreover, a higher annual rate of change in tOC increased the risk of progression from MCI to AD. Brain outlier maps identified the hippocampus as having the highest rate of change.

DISCUSSION

Individual patients' atrophy rates can be tracked by using regional outlier maps and tOC.

HIGHLIGHTS

Neuroanatomical normative modeling was applied to serial Alzheimer's disease (AD) magnetic resonance imaging (MRI) data for the first time. Deviation from the norm (outliers) of cortical thickness or brain volume was computed in 3233 scans. The number of brain-structure outliers increased over time in people with AD. Patterns of change in outliers varied markedly between individual patients with AD. People with mild cognitive impairment whose outliers increased over time had a higher risk of progression from AD.

Individualized and Biomarker-Based Prognosis of Longitudinal Cognitive Decline in Early Symptomatic Alzheimer's Disease

Background

Although individualized models using demographic, MRI, and biological markers have recently been applied in mild cognitive impairment (MCI), a similar study is lacking for patients with early Alzheimer's disease (AD) with biomarker evidence of abnormal amyloid in the brain.

Objective

We aimed to develop prognostic models for individualized prediction of cognitive change in early AD.

Methods

A total of 421 individuals with early AD (MCI or mild dementia due to AD) having biomarker evidence of abnormal amyloid in the brain were included in the current study. The primary cognitive outcome was the slope of change in Alzheimer's Disease Assessment Scale-cognitive subscale-13 (ADAS-Cog-13) over a period of up to 5 years.

Results

A model combining demographics, baseline cognition, neurodegenerative markers, and CSF AD biomarkers provided the best predictive performance, achieving an overfitting-corrected R2 of 0.59 (bootstrapping validation). A nomogram was created to enable clinicians or trialists to easily and visually estimate the individualized magnitude of cognitive change in the context of patient characteristics. Simulated clinical trials suggested that the inclusion of our nomogram into the enrichment strategy would lead to a substantial reduction of sample size in a trial of early AD.

Conclusions

Our findings may be of great clinical relevance to identify individuals with early AD who are likely to experience fast cognitive deterioration in clinical practice and in clinical trials.

Characterizing the clinical heterogeneity of early symptomatic Alzheimer's disease: a data-driven machine learning approach

Introduction

Alzheimer's disease (AD) is highly heterogeneous, with substantial individual variabilities in clinical progression and neurobiology. Amyloid deposition has been thought to drive cognitive decline and thus a major contributor to the variations in cognitive deterioration in AD. However, the clinical heterogeneity of patients with early symptomatic AD (mild cognitive impairment or mild dementia due to AD) already with evidence of amyloid abnormality in the brain is still unknown.

Methods

Participants with a baseline diagnosis of mild cognitive impairment or mild dementia, a positive amyloid-PET scan, and more than one follow-up Alzheimer's Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) administration within a period of 5-year follow-up were selected from the Alzheimer's Disease Neuroimaging Initiative database (n = 421; age = 73±7; years of education = 16 ± 3; percentage of female gender = 43%; distribution of APOE4 carriers = 68%). A non-parametric k-means longitudinal clustering analysis in the context of the ADAS-Cog-13 data was performed to identify cognitive subtypes.

Results

We found a highly variable profile of cognitive decline among patients with early AD and identified 4 clusters characterized by distinct rates of cognitive progression. Among the groups there were significant differences in the magnitude of rates of changes in other cognitive and functional outcomes, clinical progression from mild cognitive impairment to dementia, and changes in markers presumed to reflect neurodegeneration and neuronal injury. A nomogram based on a simplified logistic regression model predicted steep cognitive trajectory with an AUC of 0.912 (95% CI: 0.88 - 0.94). Simulation of clinical trials suggested that the incorporation of the nomogram into enrichment strategies would reduce the required sample sizes from 926.8 (95% CI: 822.6 - 1057.5) to 400.9 (95% CI: 306.9 - 516.8).

Discussion

Our findings show usefulness in the stratification of patients in early AD and may thus increase the chances of finding a treatment for future AD clinical trials.

TMS-derived short afferent inhibition discriminates cognitive status in older adults without dementia

Aging is a complex and diverse biological process characterized by progressive molecular, cellular, and tissue damage, resulting in a loss of physiological integrity and heightened vulnerability to pathology. This biological diversity corresponds with highly variable cognitive trajectories, which are further confounded by genetic and environmental factors that influence the resilience of the aging brain. Given this complexity, there is a need for neurophysiological indicators that not only discern physiologic and pathologic aging but also closely align with cognitive trajectories. Transcranial Magnetic Stimulation (TMS) may have utility in this regard as a non-invasive brain stimulation tool that can characterize features of cortical excitability. Particularly, as a proxy for central cholinergic function, short-afferent inhibition (SAI) dysfunction is robustly associated with cognitive deficits in the latter stages of Alzheimer's Disease and Related Dementia (ADRD). In this study, we evaluated SAI in healthy young adults and older adults who, though absent clinical diagnoses, were algorithmically classified as cognitively normal (CN) or cognitively impaired (CI) according to the Jak/Bondi actuarial criteria. We report that SAI is preserved in the Old-CN cohort relative to the young adults, and SAI is significantly diminished in the Old-CI cohort relative to both young and CN older adults. Additionally, diminished SAI was significantly associated with impaired sustained attention and working memory. As a proxy measure for central cholinergic deficits, we discuss the potential value of SAI for discerning physiological and pathological aging.

A generalizable data-driven model of atrophy heterogeneity and progression in memory clinic settings

Memory clinic patients are a heterogeneous population representing various aetiologies of pathological ageing. It is not known whether divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer's disease patients, are prevalent and clinically meaningful in this group of older adults. To uncover distinct atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to baseline structural MRI data from 813 participants enrolled in the DELCODE cohort (mean ± standard deviation, age = 70.67 ± 6.07 years, 52% females). Participants were cognitively unimpaired (n = 285) or fulfilled diagnostic criteria for subjective cognitive decline (n = 342), mild cognitive impairment (n = 118) or dementia of the Alzheimer's type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid Alzheimer's disease biomarker levels, the Preclinical Alzheimer Cognitive Composite (PACC-5) as well as episodic memory and executive functioning. PACC-5 trajectories over up to 240 weeks were examined. To test whether baseline atrophy subtype and stage predicted clinical trajectories before manifest cognitive impairment, we analysed PACC-5 trajectories and mild cognitive impairment conversion rates of cognitively unimpaired participants and those with subjective cognitive decline. Limbic-predominant and hippocampal-sparing atrophy subtypes were identified. Limbic-predominant atrophy initially affected the medial temporal lobes, followed by further temporal regions and, finally, the remaining cortical regions. At baseline, this subtype was related to older age, more pathological Alzheimer's disease biomarker levels, APOE ε4 carriership and an amnestic cognitive impairment. Hippocampal-sparing atrophy initially occurred outside the temporal lobe, with the medial temporal lobe spared up to advanced atrophy stages. This atrophy pattern also affected individuals with positive Alzheimer's disease biomarkers and was associated with more generalized cognitive impairment. Limbic-predominant atrophy, in all participants and in only unimpaired participants, was linked to more negative longitudinal PACC-5 slopes than observed in participants without or with hippocampal-sparing atrophy and increased the risk of mild cognitive impairment conversion. SuStaIn modelling was repeated in a sample from the Swedish BioFINDER-2 cohort. Highly similar atrophy progression patterns and associated cognitive profiles were identified. Cross-cohort model generalizability, at both the subject and the group level, was excellent, indicating reliable performance in previously unseen data. The proposed model is a promising tool for capturing heterogeneity among older adults at early at-risk states for Alzheimer's disease in applied settings. The implementation of atrophy subtype- and stage-specific end points might increase the statistical power of pharmacological trials targeting early Alzheimer's disease.

The path to next-generation disease-modifying immunomodulatory combination therapies in Alzheimer's disease

The cautious optimism following recent anti-amyloid therapeutic trials for Alzheimer's disease (AD) provides a glimmer of hope after years of disappointment. Although these encouraging results represent discernible progress, they also highlight the need to enhance further the still modest clinical efficacy of current disease-modifying immunotherapies. Here, we highlight crucial milestones essential for advancing precision medicine in AD. These include reevaluating the choice of therapeutic targets by considering the key role of both central neuroinflammation and peripheral immunity in disease pathogenesis, refining patient stratification by further defining the inflammatory component within the forthcoming ATN(I) (amyloid, tau and neurodegeneration (and inflammation)) classification of AD biomarkers and defining more accurate clinical outcomes and prognostic biomarkers that better reflect disease heterogeneity. Next-generation immunotherapies will need to go beyond the current antibody-only approach by simultaneously targeting pathological proteins together with innate neuroinflammation and/or peripheral-central immune crosstalk. Such innovative immunomodulatory combination therapy approaches should be evaluated in appropriately redesigned clinical therapeutic trials, which must carefully integrate the neuroimmune component.

Deep learning to predict rapid progression of Alzheimer's disease from pooled clinical trials: A retrospective study

The rate of progression of Alzheimer's disease (AD) differs dramatically between patients. Identifying the most is critical because when their numbers differ between treated and control groups, it distorts the outcome, making it impossible to tell whether the treatment was beneficial. Much recent effort, then, has gone into identifying RPs. We pooled de-identified placebo-arm data of three randomized controlled trials (RCTs), EXPEDITION, EXPEDITION 2, and EXPEDITION 3, provided by Eli Lilly and Company. After processing, the data included 1603 mild-to-moderate AD patients with 80 weeks of longitudinal observations on neurocognitive health, brain volumes, and amyloid-beta (Aβ) levels. RPs were defined by changes in four neurocognitive/functional health measures. We built deep learning models using recurrent neural networks with attention mechanisms to predict RPs by week 80 based on varying observation periods from baseline (e.g., 12, 28 weeks). Feature importance scores for RP prediction were computed and temporal feature trajectories were compared between RPs and non-RPs. Our evaluation and analysis focused on models trained with 28 weeks of observation. The models achieved robust internal validation area under the receiver operating characteristic (AUROCs) ranging from 0.80 (95% CI 0.79-0.82) to 0.82 (0.81-0.83), and the area under the precision-recall curve (AUPRCs) from 0.34 (0.32-0.36) to 0.46 (0.44-0.49). External validation AUROCs ranged from 0.75 (0.70-0.81) to 0.83 (0.82-0.84) and AUPRCs from 0.27 (0.25-0.29) to 0.45 (0.43-0.48). Aβ plasma levels, regional brain volumetry, and neurocognitive health emerged as important factors for the model prediction. In addition, the trajectories were stratified between predicted RPs and non-RPs based on factors such as ventricular volumes and neurocognitive domains. Our findings will greatly aid clinical trialists in designing tests for new medications, representing a key step toward identifying effective new AD therapies.

Predicting clinical progression trajectories of early Alzheimer's disease patients

BACKGROUND

Models for forecasting individual clinical progression trajectories in early Alzheimer's disease (AD) are needed for optimizing clinical studies and patient monitoring.

METHODS

Prediction models were constructed using a clinical trial training cohort (TC; n = 934) via a gradient boosting algorithm and then evaluated in two validation cohorts (VC 1, n = 235; VC 2, n = 421). Model inputs included baseline clinical features (cognitive function assessments, APOE ε4 status, and demographics) and brain magnetic resonance imaging (MRI) measures.

RESULTS

The model using clinical features achieved R2 of 0.21 and 0.31 for predicting 2-year cognitive decline in VC 1 and VC 2, respectively. Adding MRI features improved the R2 to 0.29 in VC 1, which employed the same preprocessing pipeline as the TC. Utilizing these model-based predictions for clinical trial enrichment reduced the required sample size by 20% to 49%.

DISCUSSION

Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications.

A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease

Alzheimer's disease is one of the most important health-care challenges in the world. For decades, numerous efforts have been made to develop therapeutics for Alzheimer's disease, but most clinical trials have failed to show significant treatment effects on slowing or halting cognitive decline. Among several challenges in such trials, one recently noticed but unsolved is biased allocation of fast and slow cognitive decliners to treatment and placebo groups during randomization caused by the large individual variation in the speed of cognitive decline. This allocation bias directly results in either over- or underestimation of the treatment effect from the outcome of the trial. In this study, we propose a stratified randomization method using the degree of cognitive decline predicted by an artificial intelligence model as a stratification index to suppress the allocation bias in randomization and evaluate its effectiveness by simulation using ADNI data set.

Prognostic and Predictive Factors in Early Alzheimer's Disease: A Systematic Review

Background

Alzheimer's disease (AD) causes progressive decline of cognition and function. There is a lack of systematic literature reviews on prognostic and predictive factors in its early clinical stages (eAD), i.e., mild cognitive impairment due to AD and mild AD dementia.

Objective

To identify prognostic factors affecting eAD progression and predictive factors for treatment efficacy and safety of approved and/or under late-stage development disease-modifying treatments.

Methods

Databases were searched (August 2022) for studies reporting prognostic factors associated with eAD progression and predictive factors for treatment response. The Quality in Prognostic Factor Studies tool or the Cochrane risk of bias tool were used to assess risk of bias. Two reviewers independently screened the records. A single reviewer performed data extraction and quality assessment. A second performed a 20% check. Content experts reviewed and interpreted the data collected.

Results

Sixty-one studies were included. Self-reporting, diagnosis definition, and missing data led to high risk of bias. Population size ranged from 110 to 11,451. Analyses found data indicating that older age was and depression may be associated with progression. Greater baseline cognitive impairment was associated with progression. APOE4 may be a prognostic factor, a predictive factor for treatment efficacy and predicts an adverse response (ARIA). Elevated biomarkers (CSF/plasma p-tau, CSF t-tau, and plasma neurofilament light) were associated with disease progression.

Conclusions

Age was the strongest risk factor for progression. Biomarkers were associated with progression, supporting their use in trial selection and aiding diagnosis. Baseline cognitive impairment was a prognostic factor. APOE4 predicted ARIA, aligning with emerging evidence and relevant to treatment initiation/monitoring.

Cognitive and Functional Change Over Time in Cognitively Healthy Individuals According to Alzheimer Disease Biomarker-Defined Subgroups

BACKGROUND AND OBJECTIVES

It is unclear to what extent cognitive outcome measures are sensitive to capture decline in Alzheimer disease (AD) prevention trials. We aimed to analyze the sensitivity to changes over time of a range of neuropsychological tests in several cognitively unimpaired, biomarker-defined patient groups.

METHODS

Cognitively unimpaired individuals from the Amsterdam Dementia Cohort and the SCIENCe project with available AD biomarkers, obtained from CSF, PET scans, and plasma at baseline, were followed over time (4.5 ± 3.1 years, range 0.6-18.9 years). Based on common inclusion criteria for clinical trials, we defined groups (amyloid, phosphorylated tau [p-tau], APOE ε4). Linear mixed models, adjusted for age, sex, and education, were used to estimate change over time in neuropsychological tests, a functional outcome, and 2 cognitive composite measures. Standardized regression coefficients of time in years (βtime) were reported as outcome of interest. We analyzed change over time with full follow-up, as well as with follow-up limited to 1.5 and 3 years.

RESULTS

We included 387 individuals (aged 61.7 ± 8.6 years; 44% female) in the following (partly overlapping) biomarker groups: APOE ε4 carriers (n = 212), amyloid-positive individuals (n = 109), amyloid-positive APOE ε4 carriers (n = 66), CSF p-tau-positive individuals (n = 127), plasma p-tau-positive individuals (n = 71), and amyloid and CSF p-tau-positive individuals (n = 50), or in a control group (normal biomarkers; n = 65). An executive functioning task showed most decline in all biomarker groups (βtime range -0.30 to -0.71), followed by delayed word list recognition (βtime range -0.18 to -0.50). Functional decline (βtime range -0.17 to -0.63) was observed in all, except the CSF and plasma tau-positive groups. Both composites showed comparable amounts of change (βtime range -0.12 to -0.62) in all groups, except plasma p-tau-positive individuals. When limiting original follow-up duration, many effects disappeared or even flipped direction.

DISCUSSION

In conclusion, functional, composite, and neuropsychological outcome measures across all cognitive domains detect changes over time in various biomarker-defined groups, with changes being most evident among individuals with more AD pathology. AD prevention trials should use sufficiently long follow-up duration and/or more sensitive outcome measures to optimally capture subtle cognitive changes over time.

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.

Serial Cerebrospinal Fluid Sampling Reveals Trajectories of Potential Synaptic Biomarkers in Early Stages of Alzheimer's Disease

Background

Synaptic dysfunction is closely associated with cognitive function in Alzheimer's disease (AD), and is present already in an early stage of the disease.

Objective

Using serial cerebrospinal fluid (CSF) sampling, we aimed to investigate slopes of CSF synaptic proteins, and their relation with cognition along the AD continuum.

Methods

We included subjects with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) (n = 50 amyloid-β+ [A +], n = 50 A-) and 50 patients with AD dementia from the Amsterdam dementia cohort, with CSF at two time points (median[IQR] 2.1[1.4-2.7] years). We analyzed 17 synaptic proteins and neurofilament light (NfL). Using linear mixed models we assessed trajectories of protein levels, and associations with cognitive decline (repeated Mini-Mental State Examination). We used Cox regression models to assess predictive value of protein levels for progression to AD dementia.

Results

At baseline most proteins showed increased levels in AD dementia compared to the other groups. In contrast NPTX2 levels were lower in AD dementia. Higher baseline levels of SNAP25, β-syn, and 14-3-3 proteins were associated with faster cognitive decline (St.B[SE] -0.27[0.12] to -0.61[0.12]). Longitudinal analyses showed that SYT1 and NPTX levels decreased over time in AD dementia (st.B[SE] -0.10[0.04] to -0.15[0.05]) and SCD/MCI-A+ (St.B[SE] -0.07[0.03] to -0.12[0.03]), but not in SCD/MCI-A- (pinteraction < 0.05). Increase over time in NfL levels was associated with faster cognitive decline in AD dementia (St.B[SE] -1.75[0.58]), but not in the other groups (pinteraction < 0.05).

Conclusions

CSF synaptic proteins showed different slopes over time, suggesting complex synaptic dynamics. High levels of especially SNAP-25 may have value for prediction of cognitive decline in early AD stages, while increase in NfL over time correlates better with cognitive decline in later stages.

Genome-wide Survival Study Identifies PARL as a Novel Locus for Clinical Progression and Neurodegeneration in Alzheimer's Disease

BACKGROUND

Variability exists in the trajectories of Alzheimer's disease (AD). We aimed to identify genetic modulators of clinical progression in AD.

METHODS

We conducted the first genome-wide survival study on AD using a two-stage approach. The discovery and replication stage separately included 1158 and 211,817 individuals without dementia from the Alzheimer's Disease Neuroimaging Initiative and the UK Biobank, respectively (325 and 1103 progressed in average follow-up of 4.33 and 8.63 years, respectively). Cox proportional hazards models were applied with time to AD dementia as the phenotype of clinical progression. A series of bioinformatic analyses and functional experiments was performed to validate the novel findings.

RESULTS

We found that APOE and PARL, a novel locus tagged by rs6795172 (hazard ratio = 1.66, p = 1.45 × 10-9), were significantly associated with AD clinical progression and were successfully replicated. The novel locus was linked to accelerated cognitive changes, higher tau levels, and faster atrophy of AD-specific brain structures, which were also verified in UK Biobank neuroimaging follow-up. Gene analysis and summary data-based Mendelian randomization indicated PARL as the most functionally relevant gene in the locus. Expression quantitative trait locus analyses and dual-luciferase reporter assays confirmed that PARL expression could be regulated by rs6795172. Three different AD mouse models consistently showed decreased PARL expression accompanied by elevated tau levels, and in vitro experiments revealed that knockdown/overexpression of PARL inversely changed tau levels.

CONCLUSIONS

Collectively, genetic, bioinformatic, and functional evidence suggests that PARL modulates clinical progression and neurodegeneration in AD. Targeting PARL may potentially modify AD progression and have implications for disease-modifying therapies.

Implications of the Approval of Lecanemab for Alzheimer Disease Patient Care: Incremental Step or Paradigm Shift?

The amyloid cascade model of the pathogenesis of Alzheimer disease (AD) is well supported in observational studies. Its therapeutic corollary asserts that removal of amyloid-β peptide ("amyloid") would provide clinical benefits. After 2 decades of pursuing the strategy of amyloid removal without success, clinical trials of the antiamyloid monoclonal antibody (AAMA) donanemab and a phase 3 clinical trial of lecanemab have reported clinical benefits linked to amyloid removal. Lecanemab (trade name, Leqembi) is the first with published phase 3 trial results. When administered through IV every 2 weeks to patients with elevated brain amyloid and mild cognitive impairment or mild dementia, lecanemab delayed cognitive and functional worsening by approximately 5 months in an 18-month double-blind, placebo-controlled trial. The trial was well conducted, and the results favoring lecanemab were internally consistent. The demonstration that lecanemab treatment delayed clinical progression in persons with mild symptoms due to AD is a major conceptual achievement, but a better appreciation of the magnitude and durability of benefits for individual patients will require extended observations from clinical practice settings. Amyloid-related imaging abnormalities (ARIA) that were largely asymptomatic occurred in approximately 20%, slightly more than half of which were attributable to treatment and the rest to underlying AD-related amyloid angiopathy. Persons who were homozygous for the APOE ε4 allele had greater ARIA risks. Hemorrhagic complications with longer-term lecanemab use need to be better understood. Administration of lecanemab will place unprecedented pressures on dementia care personnel and infrastructure, both of which need to grow exponentially to meet the challenge.

Longitudinal rates of atrophy and tau accumulation differ between the visual and language variants of atypical Alzheimer's disease

INTRODUCTION

Atypical variants of Alzheimer's disease (AD) include the visual variant, known as posterior cortical atrophy (PCA), and the language variant, known as logopenic progressive aphasia (LPA). Clinically, rates of disease progression differ between them.

METHODS

We evaluated 34 PCA and 29 LPA participants. Structural magnetic resonance imaging and 18 F-flortaucipir positron emission tomography were performed at baseline and at 1-year follow-up. Rates of change in tau uptake and grey matter volumes were compared between PCA and LPA with linear mixed-effects models and voxel-based analyses.

RESULTS

PCA had faster rates of occipital atrophy. LPA had faster rates of left temporal atrophy and faster rates of tau accumulation in the parietal, right temporal, and occipital lobes. Age was negatively associated with rates of atrophy and tau accumulation.

DISCUSSION

Longitudinal patterns of neuroimaging abnormalities differed between PCA and LPA, although with divergent results for tau accumulation and atrophy.

HIGHLIGHTS

The language variant of Alzheimer's disease accumulates tau faster than the visual variant. Each variant shows faster rates of atrophy than the other in its signature regions. Age negatively influences rates of atrophy and tau accumulation in both variants.

Location of pathogenic variants in PSEN1 impacts progression of cognitive, clinical, and neurodegenerative measures in autosomal-dominant Alzheimer's disease

Although pathogenic variants in PSEN1 leading to autosomal-dominant Alzheimer disease (ADAD) are highly penetrant, substantial interindividual variability in the rates of cognitive decline and biomarker change are observed in ADAD. We hypothesized that this interindividual variability may be associated with the location of the pathogenic variant within PSEN1. PSEN1 pathogenic variant carriers participating in the Dominantly Inherited Alzheimer Network (DIAN) observational study were grouped based on whether the underlying variant affects a transmembrane (TM) or cytoplasmic (CY) protein domain within PSEN1. CY and TM carriers and variant non-carriers (NC) who completed clinical evaluation, multimodal neuroimaging, and lumbar puncture for collection of cerebrospinal fluid (CSF) as part of their participation in DIAN were included in this study. Linear mixed effects models were used to determine differences in clinical, cognitive, and biomarker measures between the NC, TM, and CY groups. While both the CY and TM groups were found to have similarly elevated Aβ compared to NC, TM carriers had greater cognitive impairment, smaller hippocampal volume, and elevated phosphorylated tau levels across the spectrum of pre-symptomatic and symptomatic phases of disease as compared to CY, using both cross-sectional and longitudinal data. As distinct portions of PSEN1 are differentially involved in APP processing by γ-secretase and the generation of toxic β-amyloid species, these results have important implications for understanding the pathobiology of ADAD and accounting for a substantial portion of the interindividual heterogeneity in ongoing ADAD clinical trials.

Quantification of race/ethnicity representation in Alzheimer's disease neuroimaging research in the USA: a systematic review

BACKGROUND

Racial and ethnic minoritized groups are disproportionately at risk for Alzheimer's Disease (AD), but are not sufficiently recruited in AD neuroimaging research in the United States. This is important as sample composition impacts generalizability of findings, biomarker cutoffs, and treatment effects. No studies have quantified the breadth of race/ethnicity representation in the AD literature.

METHODS

This review identified median race/ethnicity composition of AD neuroimaging US-based research samples available as free full-text articles on PubMed. Two types of published studies were analyzed: studies that directly report race/ethnicity data (i.e., direct studies), and studies that do not report race/ethnicity but used data from a cohort study/database that does report this information (i.e., indirect studies).

RESULTS

Direct studies (n = 719) have median representation of 88.9% white or 87.4% Non-Hispanic white, 7.3% Black/African American, and 3.4% Hispanic/Latino ethnicity, with 0% Asian American, Native Hawaiian/Pacific Islander, and American Indian/Alaska Native, Multiracial, and Other Race participants. Cohort studies/databases (n = 44) from which indirect studies (n = 1745) derived are more diverse, with median representation of 84.2% white, 83.7% Non-Hispanic white, 11.6% Black/African American, 4.7% Hispanic/Latino, and 1.75% Asian American participants. Notably, 94% of indirect studies derive from just 10 cohort studies/databases. Comparisons of two time periods using a median split for publication year, 1994-2017 and 2018-2022, indicate that sample diversity has improved recently, particularly for Black/African American participants (3.39% from 1994-2017 and 8.29% from 2018-2022).

CONCLUSIONS

There is still underrepresentation of all minoritized groups relative to Census data, especially for Hispanic/Latino and Asian American individuals. The AD neuroimaging literature will benefit from increased representative recruitment of ethnic/racial minorities. More transparent reporting of race/ethnicity data is needed.

Aberrant palmitoylation caused by a ZDHHC21 mutation contributes to pathophysiology of Alzheimer's disease

BACKGROUND

The identification of pathogenic mutations in Alzheimer's disease (AD) causal genes led to a better understanding of the pathobiology of AD. Familial Alzheimer's disease (FAD) is known to be associated with mutations in the APP, PSEN1, and PSEN2 genes involved in Aβ production; however, these genetic defects occur in only about 10-20% of FAD cases, and more genes and new mechanism causing FAD remain largely obscure.

METHODS

We performed exome sequencing on family members with a FAD pedigree and identified gene variant ZDHHC21 p.T209S. A ZDHHC21^T209S/T209S knock-in mouse model was then generated using CRISPR/Cas9. The Morris water navigation task was then used to examine spatial learning and memory. The involvement of aberrant palmitoylation of FYN tyrosine kinase and APP in AD pathology was evaluated using biochemical methods and immunostaining. Aβ and tau pathophysiology was evaluated using ELISA, biochemical methods, and immunostaining. Field recordings of synaptic long-term potentiation were obtained to examine synaptic plasticity. The density of synapses and dendritic branches was quantified using electron microscopy and Golgi staining.

RESULTS

We identified a variant (c.999A > T, p.T209S) of ZDHHC21 gene in a Han Chinese family. The proband presented marked cognitive impairment at 55 years of age (Mini-Mental State Examination score = 5, Clinical Dementia Rating = 3). Considerable Aβ retention was observed in the bilateral frontal, parietal, and lateral temporal cortices. The novel heterozygous missense mutation (p.T209S) was detected in all family members with AD and was not present in those unaffected, indicating cosegregation. ZDHHC21^T209S/T209S mice exhibited cognitive impairment and synaptic dysfunction, suggesting the strong pathogenicity of the mutation. The ZDHHC21 p.T209S mutation significantly enhanced FYN palmitoylation, causing overactivation of NMDAR2B, inducing increased neuronal sensitivity to excitotoxicity leading to further synaptic dysfunction and neuronal loss. The palmitoylation of APP was also increased in ZDHHC21^T209S/T209S mice, possibly contributing to Aβ production. Palmitoyltransferase inhibitors reversed synaptic function impairment.

CONCLUSIONS

ZDHHC21 p.T209S is a novel, candidate causal gene mutation in a Chinese FAD pedigree. Our discoveries strongly suggest that aberrant protein palmitoylation mediated by ZDHHC21 mutations is a new pathogenic mechanism of AD, warranting further investigations for the development of therapeutic interventions.

Personalising Alzheimer's Disease progression using brain atrophy markers

INTRODUCTION

Neuroanatomical normative modelling can capture individual variability in Alzheimer's Disease (AD). We used neuroanatomical normative modelling to track individuals' disease progression in people with mild cognitive impairment (MCI) and patients with AD.

METHODS

Cortical thickness and subcortical volume neuroanatomical normative models were generated using healthy controls (n~58k). These models were used to calculate regional Z-scores in 4361 T1-weighted MRI time-series scans. Regions with Z-scores <-1.96 were classified as outliers and mapped on the brain, and also summarised by total outlier count (tOC).

RESULTS

Rate of change in tOC increased in AD and in people with MCI who converted to AD and correlated with multiple non-imaging markers. Moreover, a higher annual rate of change in tOC increased the risk of MCI progression to AD. Brain Z-score maps showed that the hippocampus had the highest rate of atrophy change.

CONCLUSIONS

Individual-level atrophy rates can be tracked by using regional outlier maps and tOC.

Intensive assessment of executive functions derived from performance in cognitive training games

Traditional neuropsychological tests accurately describe the current cognitive state but fall short to characterize cognitive change over multiple short time periods. We present an innovative approach to remote monitoring of executive functions on a monthly basis, which leverages the performance indicators from self-administered computerized cognitive training games (NUP-EXE). We evaluated the measurement properties of NUP-EXE in N = 56 individuals (59% women, 60-80 years) at increased risk of Alzheimer's disease (APOE-ϵ4 carriers with subjective cognitive decline) who completed a 12-month multimodal intervention for preventing cognitive decline. NUP-EXE presented good psychometric properties and greater sensitivity to change than traditional tests. Improvements in NUP-EXE correlated with improvements in functionality and were affected by participants' age and gender. This novel data collection methodology is expected to allow a more accurate characterization of an individual's response to a cognitive decline preventive intervention and to inform development of outcome measures for a new generation of intervention trials.

Key questions for the evaluation of anti-amyloid immunotherapies for Alzheimer's disease

The clinical benefit associated with anti-amyloid immunotherapies, a new class of drugs for the treatment of Alzheimer's disease, is predicated on their ability to modify disease course by lowering brain amyloid levels. At the time of writing, two amyloid-lowering antibodies, aducanumab and lecanemab, have obtained United States Food and Drug Administration accelerated approval, with further agents of this class in the Alzheimer's disease treatment pipeline. Based on limited published clinical trial data to date, regulators, payors and physicians will need to assess their efficacy, clinical effectiveness and safety, as well as cost and accessibility. We propose that attention to three important questions related to treatment efficacy, clinical effectiveness and safety should guide evidence-based consideration of this important class of drugs. These are: (1) Were trial statistical analyses appropriate and did they convincingly support claims of efficacy? (2) Do reported treatment effects outweigh safety concerns and are they generalizable to a representative clinical population of people with Alzheimer's disease? and (3) Do the data convincingly demonstrate disease course modification, suggesting that increasing clinical benefits beyond the duration of the trials are likely? We suggest specific approaches to interpreting trial results for these drugs and highlight important areas of uncertainty where additional data and a cautious interpretation of existing results is warranted. Safe, effective and accessible treatments for Alzheimer's disease are eagerly awaited by millions of patients and their caregivers worldwide. While amyloid-targeting immunotherapies may be promising disease-modifying Alzheimer's disease treatments, rigorous and unbiased assessment of clinical trial data is critical to regulatory decision-making and subsequently determining their provision and utility in routine clinical practice. Our recommendations provide a framework for evidence-based appraisal of these drugs by regulators, payors, physicians and patients.

Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach

BACKGROUND

Given the complex and progressive nature of mild cognitive impairment (MCI), the ability to delineate and understand the heterogeneous cognitive trajectories is crucial for developing personalized medicine and informing trial design. The primary goals of this study were to examine whether different cognitive trajectories can be identified within subjects with MCI and, if present, to characterize each trajectory in relation to changes in all major Alzheimer's disease (AD) biomarkers over time.

METHODS

Individuals with a diagnosis of MCI at the first visit and ≥ 1 follow-up cognitive assessment were selected from the Alzheimer's Disease Neuroimaging Initiative database (n = 936; age 73 ± 8; 40% female; 16 ± 3 years of education; 50% APOE4 carriers). Based on the Alzheimer's Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) total scores from baseline up to 5 years follow-up, a non-parametric k-means longitudinal clustering method was performed to obtain clusters of individuals with similar patterns of cognitive decline. We further conducted a series of linear mixed-effects models to study the associations of cluster membership with longitudinal changes in other cognitive measures, neurodegeneration, and in vivo AD pathologies.

RESULTS

Four distinct cognitive trajectories emerged. Cluster 1 consisted of 255 individuals (27%) with a nearly non-existent rate of change in the ADAS-Cog-13 over 5 years of follow-up and a healthy-looking biomarker profile. Individuals in the cluster 2 (n = 336, 35%) and 3 (n = 240, 26%) groups showed relatively mild and moderate cognitive decline trajectories, respectively. Cluster 4, comprising about 11% of our study sample (n = 105), exhibited an aggressive cognitive decline trajectory and was characterized by a pronouncedly abnormal biomarker profile.

CONCLUSIONS

Individuals with MCI show substantial heterogeneity in cognitive decline. Our findings may potentially contribute to improved trial design and patient stratification.

Heterogeneity in Preclinical Alzheimer's Disease Trial Cohort Identified by Image-based Data-Driven Disease Progression Modelling

Importance

Undetected biological heterogeneity adversely impacts trials in Alzheimer's disease because rate of cognitive decline - and perhaps response to treatment - differs in subgroups. Recent results show that data-driven approaches can unravel the heterogeneity of Alzheimer's disease progression. The resulting stratification is yet to be leveraged in clinical trials.

Objective

Investigate whether image-based data-driven disease progression modelling could identify baseline biological heterogeneity in a clinical trial, and whether these subgroups have prognostic or predictive value.

Design

Screening data from the Anti-Amyloid Treatment in Asymptomatic Alzheimer Disease (A4) Study collected between April 2014 and December 2017, and longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) observational study downloaded in February 2022 were used.

Setting

The A4 Study is an interventional trial involving 67 sites in the US, Canada, Australia, and Japan. ADNI is a multi-center observational study in North America.

Participants

Cognitively unimpaired amyloid-positive participants with a 3-Tesla T1-weighted MRI scan. Amyloid positivity was determined using florbetapir PET imaging (in A4) and CSF Aβ(1-42) (in ADNI).

Main Outcomes and Measures

Regional volumes estimated from MRI scans were used as input to the Subtype and Stage Inference (SuStaIn) algorithm. Outcomes included cognitive test scores and SUVr values from florbetapir and flortaucipir PET.

Results

We included 1,240 Aβ+ participants (and 407 Aβ- controls) from the A4 Study, and 731 A4-eligible ADNI participants. SuStaIn identified three neurodegeneration subtypes - Typical, Cortical, Subcortical - comprising 523 (42%) individuals. The remainder are designated subtype zero (insufficient atrophy). Baseline PACC scores (A4 primary outcome) were significantly worse in the Cortical subtype (median = -1.27, IQR=[-3.34,0.83]) relative to both subtype zero (median=-0.013, IQR=[-1.85,1.67], P<.0001) and the Subcortical subtype (median=0.03, IQR=[-1.78,1.61], P=.0006). In ADNI, over a four-year period (comparable to A4), greater cognitive decline in the mPACC was observed in both the Typical (-0.23/yr; 95% CI, [-0.41,-0.05]; P=.01) and Cortical (-0.24/yr; [-0.42,-0.06]; P=.009) subtypes, as well as the CDR-SB (Typical: +0.09/yr, [0.06,0.12], P<.0001; and Cortical: +0.07/yr, [0.04,0.10], P<.0001).

Conclusions and Relevance

In a large secondary prevention trial, our image-based model detected neurodegenerative heterogeneity predictive of cognitive heterogeneity. We argue that such a model is a valuable tool to be considered in future trial design to control for previously undetected variance.

Forecasting individual progression trajectories in Alzheimer's disease

The anticipation of progression of Alzheimer's disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of more than 4,600 patients from four continents. We measured the accuracy of the method for selecting participants displaying a progression of clinical endpoints during a hypothetical trial. We show that enriching the population with the predicted progressors decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease and is therefore highly suitable for use in prevention trials.

Why a clinical trial is as good as its outcome measure: A framework for the selection and use of cognitive outcome measures for clinical trials of Alzheimer's disease

A crucial aspect of any clinical trial is using the right outcome measure to assess treatment efficacy. Compared to the rapidly evolved understanding and measurement of pathophysiology in preclinical and early symptomatic stages of Alzheimer's disease (AD), relatively less progress has been made in the evolution of clinical outcome assessments (COAs) for those stages. The current paper aims to provide a benchmark for the design and evaluation of COAs for use in early AD trials. We discuss lessons learned on capturing cognitive changes in predementia stages of AD, including challenges when validating novel COAs for those early stages and necessary evidence for their implementation in clinical trials. Moving forward, we propose a multi-step framework to advance the use of more effective COAs to assess clinically meaningful changes in early AD, which will hopefully contribute to the much-needed consensus around more appropriate outcome measures to assess clinical efficacy of putative treatments. HIGHLIGHTS: We discuss lessons learned on capturing cognitive changes in predementia stages of AD. We propose a framework for the design and evaluation of performance based cognitive tests for use in early AD trials. We provide recommendations to facilitate the implementation of more effective cognitive outcome measures in AD trials.

Neuropathology-Independent Association Between APOE Genotype and Cognitive Decline Rate in the Normal Aging-Early Alzheimer Continuum

Background and Objectives

We previously found that the APOE genotype affects the rate of cognitive decline in mild-to-moderate Alzheimer disease (AD) dementia independently of its effects on AD neuropathologic changes (ADNC) and copathologies. In this study, we tested the hypothesis that the APOE alleles differentially affect the rate of cognitive decline at the normal aging-early AD continuum and that this association is independent of their effects on classical ADNC and copathologies.

Methods

We analyzed APOE associations with the cognitive trajectories (Clinical Dementia Rating scale Sum of Boxes [CDR-SOB] and Mini-Mental State Examination [MMSE]) of more than 1,000 individuals from a national clinicopathologic sample who had either no, mild (sparse neuritic plaques and the Braak neurofibrillary tangle [NFT] stage I/II), or intermediate (moderate neuritic plaques and the Braak NFT stage III/IV) ADNC levels at autopsy via 2 latent classes reverse-time longitudinal modeling.

Results

Carrying the APOEε4 allele was associated with a faster rate of cognitive decline by both CDR-SOB and MMSE relative to APOEε3 homozygotes. This association remained statistically significant after adjusting for ADNC severity, comorbid pathologies, and the effects of ADNC on the slope of cognitive decline. Our modeling strategy identified 2 latent classes in which APOEε4 carriers declined faster than APOEε3 homozygotes, with latent class 1 members representing slow decliners (CDR-SOB: 76.7% of individuals, 0.195 vs 0.146 points/y in APOEε4 vs APOEε3/ε3; MMSE: 88.6% of individuals, -0.303 vs -0.153 points/y in APOEε4 vs APOEε3/ε3), whereas latent class 2 members were fast decliners (CDR-SOB: 23.3% of participants, 1.536 vs 1.487 points/y in APOEε4 vs APOEε3/ε3; MMSE: 11.4% of participants, -2.538 vs -2.387 points/y in APOEε4 vs APOEε3/ε3). Compared with slow decliners, fast decliners were more likely to carry the APOEε4 allele, younger at initial visit and death, more impaired at initial and last visits, and more likely to have intermediate (vs none or mild) ADNC levels, as well as concurrent Lewy bodies and hippocampal sclerosis at autopsy.

Discussion

In a large national sample selected to represent the normal aging-early AD continuum, the APOEε4 allele is associated with a modest but statistically significant acceleration of the cognitive decline rate even after controlling for its effects on ADNC and comorbid pathologies.

High-clearance anti-amyloid immunotherapies in Alzheimer's disease. Part 1: Meta-analysis and review of efficacy and safety data, and medico-economical aspects

In 2021, aducanumab, an immunotherapy targeting amyloid-β, was approved for Alzheimer's disease (AD) by the US Food and Drug Administration thanks to positive results on a putative biological surrogate marker. This approval has raised an unprecedented controversy. It was followed by a refusal of the European Medicine Agency, which does not allow the marketing of drugs solely on biological arguments and raised safety issues, and important US coverage limitations by the Centers for Medicare & Medicaid Services. Two other anti-amyloid immunotherapies showed significant results regarding a clinical outcome in phase 2 trials, and five drugs are being studied in phase 3 trials. Compared to those tested in previous trials of the 2010s, the common feature and novelty of these anti-amyloid immunotherapies is their ability to induce a high clearance of amyloid load, as measured with positron emission tomography, in the brain of early-stage biomarker-proven AD patients. Here, we review the available evidence regarding efficacy and safety data and medico-economical aspects for high-clearance anti-amyloid immunotherapies. We also perform frequentist and Bayesian meta-analyses of the clinical efficacy and safety of the highest dose groups from the two aducanumab phase 3 trials and the donanemab and lecanemab phase 2 trials. When pooled together, the data from high-clearance anti-amyloid immunotherapies trials confirm a statistically significant clinical effect of these drugs on cognitive decline after 18 months (difference in cognitive decline measured with CDR-SB after 18 months between the high dose immunotherapy groups vs. placebo = -0.24 points; P=0.04, frequentist random-effect model), with results on ADAS-Cog being the most statistically robust. However, this effect remains below the previously established minimal clinically relevant values. In parallel, the drugs significantly increased the occurrence of amyloid-related imaging abnormalities-edema (ARIA-E: risk ratio=13.39; P<0.0001), ARIA-hemorrhage (risk ratio=2.78; P=0.0002), and symptomatic and serious ARIA (7/1321=0.53% in the high dose groups versus 0/1446 in the placebo groups; risk ratio=6.44; P=0.04). The risk/benefit ratio of high-clearance immunotherapies in early AD is so far questionable after 18 months. Identifying subgroups of better responders, the perspective of combination therapies, and a longer follow-up may help improve their clinical relevance. Finally, the preliminary evidence from medico-economical analyses seems to indicate that the current cost of aducanumab in the US is not in reasonable alignment with its clinical benefits.

Combining tau-PET and fMRI meta-analyses for patient-centered prediction of cognitive decline in Alzheimer’s disease

Background

Tau-PET is a prognostic marker for cognitive decline in Alzheimer’s disease, and the heterogeneity of tau-PET patterns matches cognitive symptom heterogeneity. Thus, tau-PET may allow precision-medicine prediction of individual tau-related cognitive trajectories, which can be important for determining patient-specific cognitive endpoints in clinical trials. Here, we aimed to examine whether tau-PET in cognitive-domain-specific brain regions, identified via fMRI meta-analyses, allows the prediction of domain-specific cognitive decline. Further, we aimed to determine whether tau-PET-informed personalized cognitive composites capture patient-specific cognitive trajectories more sensitively than conventional cognitive measures.

Methods

We included Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants classified as controls (i.e., amyloid-negative, cognitively normal, n = 121) or Alzheimer’s disease-spectrum (i.e., amyloid-positive, cognitively normal to dementia, n = 140), plus 111 AVID-1451-A05 participants for independent validation (controls/Alzheimer’s disease-spectrum=46/65). All participants underwent baseline ¹⁸F-flortaucipir tau-PET, amyloid-PET, and longitudinal cognitive testing to assess annual cognitive changes (i.e., episodic memory, language, executive functioning, visuospatial). Cognitive changes were calculated using linear mixed models. Independent meta-analytical task-fMRI activation maps for each included cognitive domain were obtained from the Neurosynth database and applied to tau-PET to determine tau-PET signal in cognitive-domain-specific brain regions. In bootstrapped linear regression, we assessed the strength of the relationship (i.e., partial R²) between cognitive-domain-specific tau-PET vs. global or temporal-lobe tau-PET and cognitive changes. Further, we used tau-PET-based prediction of domain-specific decline to compose personalized cognitive composites that were tailored to capture patient-specific cognitive decline.

Results

In both amyloid-positive cohorts (ADNI [age = 75.99±7.69] and A05 [age = 74.03±9.03]), cognitive-domain-specific tau-PET outperformed global and temporal-lobe tau-PET for predicting future cognitive decline in episodic memory, language, executive functioning, and visuospatial abilities. Further, a tau-PET-informed personalized cognitive composite across cognitive domains enhanced the sensitivity to assess cognitive decline in amyloid-positive subjects, yielding lower sample sizes required for detecting simulated intervention effects compared to conventional cognitive endpoints (i.e., memory composite, global cognitive composite). However, the latter effect was less strong in A05 compared to the ADNI cohort.

Conclusion

Combining tau-PET with task-fMRI-derived maps of major cognitive domains facilitates the prediction of domain-specific cognitive decline. This approach may help to increase the sensitivity to detect Alzheimer’s disease-related cognitive decline and to determine personalized cognitive endpoints in clinical trials.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-022-01105-5.

Long-term Cognitive Trajectory After Total Joint Arthroplasty

Importance

Individuals with total joint arthroplasty (TJA) have long-term exposure to metal-containing implants; however, whether long-term exposure to artificial implants is associated with cognitive function is unknown.

Objective

To compare long-term cognitive trajectories in individuals with and without TJA.

Design, Setting, and Participants

This population-based cohort study assessed serial cognitive evaluations of 5550 participants (≥50 years of age) from the Mayo Clinic Study of Aging between November 1, 2004, and December 31, 2020.

Exposures

Total joint arthroplasty of the hip or the knee.

Main Outcomes and Measures

Linear mixed-effects models were used to compare the annualized rate of change in global and domain-specific cognitive scores in participants with and without TJA, adjusting for age, sex, educational level, apolipoprotein E ε4 carrier status, and cognitive test practice effects.

Results

A total of 5550 participants (mean [SD] age at baseline, 73.04 [10.02] years; 2830 [51.0%] male) were evaluated. A total of 952 participants had undergone at least 1 TJA of the hip (THA, n = 430) or the knee (TKA, n = 626) before or after entry into the cohort. Participants with TJA were older, more likely to be female, and had a higher body mass index than participants without TJA. No difference was observed in the rate of cognitive decline in participants with and without TJA until 80 years of age. A slightly faster cognitive decline at 80 years or older and more than 8 years from surgery was observed (b = -0.03; 95% CI, -0.04 to -0.02). In stratified analyses by surgery type, the faster decline was observed primarily among older participants with TKA (b = -0.04; 95% CI, -0.06 to -0.02).

Conclusions and Relevance

In this cohort study, long-term cognitive trajectories in individuals with and without TJA were largely similar except for a slightly faster decline among the oldest patients with TKA; however, the magnitude of difference was small and of unknown clinical significance.

A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation

The early stages of Alzheimer's disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future pathological tau accumulation. In particular, we use machine learning to quantify interactions between key pathological markers (β-amyloid, medial temporal lobe  atrophy, tau and APOE 4) at mildly impaired and asymptomatic stages of AD. Using baseline non-tau markers we derive a prognostic index that: (a) stratifies patients based on future pathological tau accumulation, (b) predicts individualised regional future rate of tau accumulation, and (c) translates predictions from deep phenotyping patient cohorts to cognitively normal individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design targeting the earliest stages of AD.

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.

Grey matter network markers identify individuals with prodromal Alzheimer's disease who will show rapid clinical decline

Individuals with prodromal Alzheimer's disease show considerable variability in rates of cognitive decline, which hampers the ability to detect potential treatment effects in clinical trials. Prognostic markers to select those individuals who will decline rapidly within a trial time frame are needed. Brain network measures based on grey matter covariance patterns have been associated with future cognitive decline in Alzheimer's disease. In this longitudinal cohort study, we investigated whether cut-offs for grey matter networks could be derived to detect fast disease progression at an individual level. We further tested whether detection was improved by adding other biomarkers known to be associated with future cognitive decline [i.e. CSF tau phosphorylated at threonine 181 (p-tau181) levels and hippocampal volume]. We selected individuals with mild cognitive impairment and abnormal CSF amyloid β1-42 levels from the Amsterdam Dementia Cohort and the Alzheimer's Disease Neuroimaging Initiative, when they had available baseline structural MRI and clinical follow-up. The outcome was progression to dementia within 2 years. We determined prognostic cut-offs for grey matter network properties (gamma, lambda and small-world coefficient) using time-dependent receiver operating characteristic analysis in the Amsterdam Dementia Cohort. We tested the generalization of cut-offs in the Alzheimer's Disease Neuroimaging Initiative, using logistic regression analysis and classification statistics. We further tested whether combining these with CSF p-tau181 and hippocampal volume improved the detection of fast decliners. We observed that within 2 years, 24.6% (Amsterdam Dementia Cohort, n = 244) and 34.0% (Alzheimer's Disease Neuroimaging Initiative, n = 247) of prodromal Alzheimer's disease patients progressed to dementia. Using the grey matter network cut-offs for progression, we could detect fast progressors with 65% accuracy in the Alzheimer's Disease Neuroimaging Initiative. Combining grey matter network measures with CSF p-tau and hippocampal volume resulted in the best model fit for classification of rapid decliners, increasing detecting accuracy to 72%. These data suggest that single-subject grey matter connectivity networks indicative of a more random network organization can contribute to identifying prodromal Alzheimer's disease individuals who will show rapid disease progression. Moreover, we found that combined with p-tau and hippocampal volume this resulted in the highest accuracy. This could facilitate clinical trials by increasing chances to detect effects on clinical outcome measures.

Heterogeneity in Alzheimer's Disease Diagnosis and Progression Rates: Implications for Therapeutic Trials

The clinical presentation and the pathological processes underlying Alzheimer's disease (AD) can be very heterogeneous in severity, location, and composition including the amount and distribution of AB deposition and spread of neurofibrillary tangles in different brain regions resulting in atypical clinical patterns and the existence of distinct AD variants. Heterogeneity in AD may be related to demographic factors (such as age, sex, educational and socioeconomic level) and genetic factors, which influence underlying pathology, the cognitive and behavioral phenotype, rate of progression, the occurrence of neuropsychiatric features, and the presence of comorbidities (e.g., vascular disease, neuroinflammation). Heterogeneity is also manifest in the individual resilience to the development of neuropathology (brain reserve) and the ability to compensate for its cognitive and functional impact (cognitive and functional reserve). The variability in specific cognitive profiles and types of functional impairment may be associated with different progression rates, and standard measures assessing progression may not be equivalent for individual cognitive and functional profiles. Other factors, which may govern the presence, rate, and type of progression of AD, include the individuals' general medical health, the presence of specific systemic conditions, and lifestyle factors, including physical exercise, cognitive and social stimulation, amount of leisure activities, environmental stressors, such as toxins and pollution, and the effects of medications used to treat medical and behavioral conditions. These factors that affect progression are important to consider while designing a clinical trial to ensure, as far as possible, well-balanced treatment and control groups.