Preclinical Alzheimer disease-the challenges ahead

Neurophysiological correlates of subjective cognitive decline in perimenopausal and postmenopausal midlife women at risk for Alzheimer's disease

OBJECTIVE

This study aimed to investigate neurophysiological correlates of subjective cognitive decline (SCD) among midlife women at risk for Alzheimer's disease (AD).

METHODS

We examined 156 cognitively normal perimenopausal and postmenopausal women aged 40 to 65 years, with an AD family history and/or apolipoprotein E epsilon 4 genotype, who were not on menopause hormone therapy. Participants underwent neuropsychological testing, health and menopausal symptom questionnaires, and brain volumetric magnetic resonance imaging, arterial spin labeling-magnetic resonance (MR) measuring cerebral blood flow, and 31 phosphorus magnetic resonance spectroscopy ( 31 P-MRS) measuring mitochondria high-energy phosphates (adenosine triphosphate [ATP], phosphocreatine [PCr], inorganic phosphate [Pi]). We used multivariable regressions to compare outcomes between participants with and without SCD and to identify the main correlates of SCD status.

RESULTS

The SCD group (n = 53) exhibited worse verbal memory and executive function test performance (multivariable adjusted P  = 0.029) compared to controls (n = 103). On brain imaging, the SCD group showed higher PCr/ATP in precuneus, posterior cingulate, and parietal regions compared to controls (multivariable adjusted P  < 0.05) and no overall differences in Pi/ATP, PCr/Pi, volume, or cerebral blood flow measures. Results were controlled for age, race, smoking status, hysterectomy status, presence of vasomotor symptoms, menopause symptom severity score, past menopause hormone therapy usage, history of depression, AD family history, and apolipoprotein E epsilon 4 status. The factors more strongly associated with SCD status were inferior parietal PCr/ATP, menopause symptom severity, and presence of vasomotor symptoms.

CONCLUSIONS

Among perimenopausal and postmenopausal midlife women, SCD was associated with altered brain mitochondria bioenergetics in some brain regions similarly affected by AD, warranting further investigation.

New Horizons in Menopause, Menopausal Hormone Therapy, and Alzheimer's Disease: Current Insights and Future Directions

Accumulating evidence suggests that the effects of menopausal hormone therapy (MHT) on risk of Alzheimer disease (AD) and all-cause dementia are influenced by timing of initiation relative to age, time-since-menopause, and the type of formulation. Randomized clinical trials (RCTs) of MHT conducted in postmenopausal women ages 65 and older indicated an increased risk of dementia. While RCTs conducted in midlife are lacking, observational research has provided evidence for associations between midlife estrogen-only therapy (ET) use and a reduced risk of AD and dementia, whereas estrogen-progestogen therapy (EPT) was associated with more variable outcomes. However, existing studies are heterogenous, and conventional endpoints might not adequately assess MHT's potential for AD prevention. Herein, several approaches are being discussed, and the case is being made for utilizing AD biomarkers for assessment of early, AD-specific outcomes in relation to MHT use. From a clinical standpoint, findings that MHT may lower dementia risk warrant consideration as existing therapies like acetylcholinesterase inhibitors and memantine lack preventative efficacy, and vaccines for primary or secondary prevention are not yet available. MHT-associated risks, including breast cancer, stroke and venous thromboembolism, are generally considered rare (<10 events/10 000 women). Overall, the literature supports renewed research interest in evaluating MHT as a female-specific, time-sensitive approach for AD risk reduction, which is key to applying cumulated data in clinical decision making concerning AD prevention.

Precision neurology

Over the past several decades, high-resolution brain imaging, blood and cerebrospinal fluid analyses, and other advanced technologies have changed diagnosis from an exercise depending primarily on the history and physical examination to a computer- and online resource-aided process that relies on larger and larger quantities of data. In addition, randomized controlled trials (RCT) at a population level have led to many new drugs and devices to treat neurological disease, including disease-modifying therapies. We are now at a crossroads. Combinatorially profound increases in data about individuals has led to an alternative to population-based RCTs. Genotyping and comprehensive "deep" phenotyping can sort individuals into smaller groups, enabling precise medical decisions at a personal level. In neurology, precision medicine that includes prediction, prevention and personalization requires that genomic and phenomic information further incorporate imaging and behavioral data. In this article, we review the genomic, phenomic, and computational aspects of precision medicine for neurology. After defining biological markers, we discuss some applications of these "-omic" and neuroimaging measures, and then outline the role of computation and ultimately brain simulation. We conclude the article with a discussion of the relation between precision medicine and value-based care.

Gut Microbiota and Alzheimer Disease

ABSTRACT

The hallmarks of Alzheimer's disease (AD) include brain dysfunction and the buildup of amyloid and tau proteins. The onset of dementia is one of the latter symptoms. Imaging diagnostics allowed for the detection of amyloid buildup in the brain 10-20 years before the emergence of overt signs of the disease. The application of imaging diagnostic techniques allowed for this identification. Within the next few decades, the incidence and frequency of this disease are expected to reach epidemic proportions unless measures are done to stop or slow its growth. However, unless action is taken to slow or stop the disease's progression, it will continue to threaten the health of the general public. Recently, there has been some speculation that the gut flora might contribute to the development of AD. Not only that, but the rapidly expanding ischemia etiology is another possible contributor to the issue. Rumor has it that there's a network connecting the brain and the stomach called the "gut-brain-microbiota axis." The hypothesis is based on this network. Furthermore, a large amount of evidence implies that the gut microbiota (GMB) could potentially contribute to the onset of AD. It has been suggested that the GMB could play a role in the onset of AD. This notion has been bolstered by new studies. It is quite probable that this review will address the prospect of a link between the microbiome and AD. This concept could be explored as a potential therapy or preventative measure. Some techniques that show promise as new treatments for AD include changes to the GMB, which can be achieved through dietary changes or positive microflora interventions, and changes to microbiological partners and their products, like amyloid protein.

APOE4 Increases Susceptibility to Amyloid, Accelerating Episodic Memory Decline

Apolipoprotein E4 (APOE4) is the strongest genetic risk factor for sporadic Alzheimer's disease (AD). Individuals with one copy of APOE4 exhibit greater amyloid-beta (Aβ) deposition compared to noncarriers, an effect that is even more pronounced in APOE4 homozygotes. Interestingly, APOE4 carriers not only show more AD pathology but also experience more rapid cognitive decline, particularly in episodic memory. The underlying mechanisms driving this domain-specific vulnerability, however, remain unclear. In this study, we examined whether the accelerated decline in episodic memory among APOE4 carriers is due to increased Aβ deposition or heightened susceptibility to Aβ-related effects. Using data from the Alzheimer's Disease Research Initiative, we modeled amyloid duration, the estimated number of years an individual has been amyloid-positive, and its impact on cognitive trajectories. Our findings reveal that APOE4 is associated with more rapid episodic memory decline as a function of amyloid duration. This decline was dose-dependent, with APOE4 homozygotes declining more rapidly than heterozygotes, and it was consistently observed across multiple episodic memory tasks and measures. Importantly, this pattern was not observed in other cognitive domains, such as processing speed, executive function, visuospatial skills, language, or crystallized intelligence. These results suggest that cognitive trajectories in AD differ by APOE genotype, with APOE4 conferring increased vulnerability to hippocampal dysfunction early in the disease course. Future research should investigate whether these cognitive differences stem from distinct pathological cascades in APOE4 carriers.

Association between loneliness and mild cognitive impairment in older adults: a meta-analysis of longitudinal studies

OBJECTIVES

Prior studies reporting the effects of loneliness on mild impairment cognitive (MCI) have generated inconsistent results. This meta-analysis aimed to investigate the longitudinal association between loneliness and risk of MCI among community-dwelling middle-aged and older adults.

METHOD

Five electronic databases were searched from inception to 9 May 2023. Eligible studies examined the longitudinal association between loneliness and cognitive outcomes, including incident MCI, cognitive impairment, and cognitive decline. Odds ratios (OR) and 95% confidence intervals (CIs) were calculated using random-effects or fixed-effects meta-analysis. Sensitivity analysis and subgroup analysis were conducted. Publication bias was examined using Egger's and Begg tests.

RESULTS

Eight studies were included. Among the 45,032 participants, 10,570 were diagnosed with MCI/cognitive decline. Loneliness was positively associated with an increased risk of MCI (overall OR = 1.14; 95% CI = 1.05, 1.23), with moderate heterogeneity (I2 = 44.2%). Sensitivity analysis have minimal influence on the aforementioned pooled effect. Subgroup analyses indicated stronger associations in studies which employed incident MCI as cognitive outcome (OR = 2.55, 95%CI = 1.31, 1.83), were conducted in non-Asia countries (OR = 1.52, 95%CI = 0.95, 1.20), and reported no depression adjustment (OR = 1.51, 95%CI = 1.04, 1.25). The association between loneliness and MCI was stronger among males compare to females. The Egger test and Begg test showed no evidence of significant publication bias (p = .493; p = .474).

CONCLUSION

The findings indicated that loneliness was associated with an increased risk of MCI. Future longitudinal studies should evaluate potential cases of MCI through comprehensive clinical assessments by practitioners to draw robust findings on the association of loneliness with MCI.

Exploration of demographic prevalence of mild cognitive impairment using Montreal cognitive assessment: A cross-sectional pilot study in the Cape Coast Metropolis, Ghana

Background

The Global prevalence of dementia is projected to rise, particularly in low and middle-income countries like Ghana. Mild cognitive impairment (MCI), an intermediate phase between normal cognitive aging and dementia, is characterized by an objective and subjective decline in cognitive abilities. Individuals with MCI have a greater likelihood of progression to dementia.

Purpose

There is a paucity of studies focused on assessing the prevalence, risk factors and characteristics of mild cognitive impairment within the Ghanaian population. This study assessed the prevalence of mild cognitive impairment and explored its relationship with various sociodemographic factors.

Methods

A prospective cross-sectional analytical study within Cape Coast, Ghana, evaluating the cognition of 100 participants using the Montreal Cognitive Assessment (MoCA) tool. The prevalence of MCI was determined using simple descriptive measures. The two-way ANOVA was used to determine risk factors for developing MCI. The Pearson correlation coefficient was used to determine the relationship between educational level and MoCA score.

Results

A majority (65.4 %) of participants within the age group 40-49 years had mild cognitive impairment. 42.86 % of male and 40.54 % of female participants had MCI (MoCA score < 26). There was a significant correlation (r= 0.608, p= 0.0001) between the educational level of participants and the MoCA score. Participants classified as having MCI based on their MoCA score, performed significantly poorer in visuospatial, attention, language, abstraction and delayed recall domains compared to those with normal cognition.

Conclusion

The MoCA tool is a useful for detecting MCI, particularly among Ghanaians with at least 7 years of formal education. The prevalence of MCI among individuals aged 40-49 years in the Cape Coast Metropolis represents an important health burden.

Sleep discrepancy and brain glucose metabolism in community-dwelling older adults

Sleep discrepancy (negative discrepancy reflects worse self-reported sleep than objective measures, such as actigraphy, and positive discrepancy the opposite) has been linked to adverse health outcomes. This study is first to investigate the relationship between sleep discrepancy and brain glucose metabolism (assessed globally and regionally via positron emission tomography), and to evaluate the contribution of insomnia severity and depressive symptoms to any associations. Using data from cognitively unimpaired community-dwelling older adults (N = 68), cluster analysis was used to characterise sleep discrepancy (for total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE)), and logistic regression was used to explore sleep discrepancy's associations with brain glucose metabolism, while controlling for insomnia severity and depressive symptoms. Lower glucose metabolism across multiple brain regions was associated with negative discrepancy for WASO and SE, and positive discrepancy for WASO only (large effect sizes; β ≥ 0.5). Higher glucose metabolism in the superior parietal and posterior cingulate regions was associated with negative discrepancy for TST (large effect sizes; β ≥ 0.5). These associations remained when controlling for insomnia severity and depressive symptoms, suggesting a unique role of sleep discrepancy as a potential early behavioural marker of brain health.

Cognitive modeling of the Mnemonic Similarity Task as a digital biomarker for Alzheimer's disease

BACKGROUND

The Mnemonic Similarity Task (MST) is a popular memory task designed to assess hippocampal integrity. We assessed whether analyzing MST performance using a multinomial processing tree (MPT) cognitive model could detect individuals with elevated Alzheimer's disease (AD) biomarker status prior to cognitive decline.

METHOD

We analyzed MST data from >200 individuals (young, cognitively healthy older adults and individuals with mild cognitive impairment [MCI]), a subset of which also had existing cerebrospinal fluid (CSF) amyloid beta (Aβ) and phosphorylated tau (pTau) data using both traditional and model-derived approaches. We assessed how well each could predict age group, memory ability, MCI status, Aβ, and pTau status using receiver operating characteristic analyses.

RESULTS

Both approaches predicted age group membership equally, but MPT-derived metrics exceeded traditional metrics in all other comparisons.

DISCUSSION

A MPT model of the MST can detect individuals with AD prior to cognitive decline, making it a potentially useful tool for screening and monitoring older adults during the asymptomatic phase of AD.

HIGHLIGHTS

The MST, along with cognitive modeling, identifies individuals with memory deficits and cognitive impairment. Cognitive modeling of the MST identifies individuals with increased AD biomarkers prior to changes in cognitive function. The MST is a digital biomarker that identifies individuals at high risk of AD.

An interpretable deep learning framework identifies proteomic drivers of Alzheimer's disease

Alzheimer's disease (AD) is the leading neurodegenerative pathology in aged individuals, but many questions remain on its pathogenesis, and a cure is still not available. Recent research efforts have generated measurements of multiple omics in individuals that were healthy or diagnosed with AD. Although machine learning approaches are well-suited to handle the complexity of omics data, the models typically lack interpretability. Additionally, while the genetic landscape of AD is somewhat more established, the proteomic landscape of the diseased brain is less well-understood. Here, we establish a deep learning method that takes advantage of an ensemble of autoencoders (AEs) - EnsembleOmicsAE-to reduce the complexity of proteomics data into a reduced space containing a small number of latent features. We combine brain proteomic data from 559 individuals across three AD cohorts and demonstrate that the ensemble autoencoder models generate stable latent features which are well-suited for downstream biological interpretation. We present an algorithm to calculate feature importance scores based on the iterative scrambling of individual input features (i.e., proteins) and show that the algorithm identifies signaling modules (AE signaling modules) that are significantly enriched in protein-protein interactions. The molecular drivers of AD identified within the AE signaling modules derived with EnsembleOmicsAE were missed by linear methods, including integrin signaling and cell adhesion. Finally, we characterize the relationship between the AE signaling modules and the age of death of the patients and identify a differential regulation of vimentin and MAPK signaling in younger compared with older AD patients.

Reasons for undergoing amyloid imaging among diverse enrollees in the A4 study

INTRODUCTION

Understanding attitudes toward participation among diverse preclinical Alzheimer's disease (AD) trial participants could yield insights to instruct future recruitment.

METHODS

Using data from the Anti-Amyloid Treatment in Asymptomatic AD (A4) Study, we examined differences among mutually exclusive racial and ethnic groups in views and perceptions of amyloid imaging (VPAI), a measure of motivations to undergo amyloid biomarker testing in the setting of preclinical AD. We used linear regression to quantify differences at baseline.

RESULTS

Compared to non-Hispanic or Latino (NH) White participants, Hispanic or Latino (3.52 points, 95% confidence interval [CI]: [2.61, 4.42]); NH Asian (2.97 points, 95% CI: [1.71, 4.22]); and NH Black participants (2.79 points, 95% CI: [1.96, 3.63]) participants demonstrated higher levels of endorsement of the VPAI items at baseline.

DISCUSSION

Differences may exist among participants from differing ethnic and racial groups in motivations to undergo biomarker testing in the setting of a preclinical AD trial.

HIGHLIGHTS

Representative samples in AD clinical trials are vital to result in generalizability. We assessed motivations to undergo amyloid imaging in a preclinical AD trial. Racial and ethnic minority groups showed higher endorsement of VPAI items. Differences were driven by perceived risk, plan/prepare, and curiosity domains. Few observations among racial and ethnic groups changed after biomarker disclosure.

A transformer-based unified multimodal framework for Alzheimer's disease assessment

In Alzheimer's disease (AD) assessment, traditional deep learning approaches have often employed separate methodologies to handle the diverse modalities of input data. Recognizing the critical need for a cohesive and interconnected analytical framework, we propose the AD-Transformer, a novel transformer-based unified deep learning model. This innovative framework seamlessly integrates structural magnetic resonance imaging (sMRI), clinical, and genetic data from the extensive Alzheimer's Disease Neuroimaging Initiative (ADNI) database, encompassing 1651 subjects. By employing a Patch-CNN block, the AD-Transformer efficiently transforms image data into image tokens, while a linear projection layer adeptly converts non-image data into corresponding tokens. As the core, a transformer block learns comprehensive representations of the input data, capturing the intricate interplay between modalities. The AD-Transformer sets a new benchmark in AD diagnosis and Mild Cognitive Impairment (MCI) conversion prediction, achieving remarkable average area under curve (AUC) values of 0.993 and 0.845, respectively, surpassing those of traditional image-only models and non-unified multimodal models. Our experimental results confirmed the potential of the AD-Transformer as a potent tool in AD diagnosis and MCI conversion prediction. By providing a unified framework that jointly learns holistic representations of both image and non-image data, the AD-Transformer paves the way for more effective and precise clinical assessments, offering a clinically adaptable strategy for leveraging diverse data modalities in the battle against AD.

Sex and Gender Differences in Alzheimer's Disease: Genetic, Hormonal, and Inflammation Impacts

Two-thirds of Americans with Alzheimer's disease are women, indicating a profound variance between the sexes. Variances exist between the sexes in the age and intensity of the presentation, cognitive deficits, neuroinflammatory factors, structural and functional brain changes, as well as psychosocial and cultural circumstances. Herein, we summarize the existing evidence for sexual dimorphism and present the available evidence for these distinctions. Understanding these complexities is critical to developing personalized interventions for the prevention, care, and treatment of Alzheimer's disease.

Understanding machine learning applications in dementia research and clinical practice: a review for biomedical scientists and clinicians

Several (inter)national longitudinal dementia observational datasets encompassing demographic information, neuroimaging, biomarkers, neuropsychological evaluations, and muti-omics data, have ushered in a new era of potential for integrating machine learning (ML) into dementia research and clinical practice. ML, with its proficiency in handling multi-modal and high-dimensional data, has emerged as an innovative technique to facilitate early diagnosis, differential diagnosis, and to predict onset and progression of mild cognitive impairment and dementia. In this review, we evaluate current and potential applications of ML, including its history in dementia research, how it compares to traditional statistics, the types of datasets it uses and the general workflow. Moreover, we identify the technical barriers and challenges of ML implementations in clinical practice. Overall, this review provides a comprehensive understanding of ML with non-technical explanations for broader accessibility to biomedical scientists and clinicians.

Alzheimer's disease pathophysiology in the Retina

The retina is an emerging CNS target for potential noninvasive diagnosis and tracking of Alzheimer's disease (AD). Studies have identified the pathological hallmarks of AD, including amyloid β-protein (Aβ) deposits and abnormal tau protein isoforms, in the retinas of AD patients and animal models. Moreover, structural and functional vascular abnormalities such as reduced blood flow, vascular Aβ deposition, and blood-retinal barrier damage, along with inflammation and neurodegeneration, have been described in retinas of patients with mild cognitive impairment and AD dementia. Histological, biochemical, and clinical studies have demonstrated that the nature and severity of AD pathologies in the retina and brain correspond. Proteomics analysis revealed a similar pattern of dysregulated proteins and biological pathways in the retina and brain of AD patients, with enhanced inflammatory and neurodegenerative processes, impaired oxidative-phosphorylation, and mitochondrial dysfunction. Notably, investigational imaging technologies can now detect AD-specific amyloid deposits, as well as vasculopathy and neurodegeneration in the retina of living AD patients, suggesting alterations at different disease stages and links to brain pathology. Current and exploratory ophthalmic imaging modalities, such as optical coherence tomography (OCT), OCT-angiography, confocal scanning laser ophthalmoscopy, and hyperspectral imaging, may offer promise in the clinical assessment of AD. However, further research is needed to deepen our understanding of AD's impact on the retina and its progression. To advance this field, future studies require replication in larger and diverse cohorts with confirmed AD biomarkers and standardized retinal imaging techniques. This will validate potential retinal biomarkers for AD, aiding in early screening and monitoring.

ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid-beta from amyloid PET images

Detection and measurement of amyloid-beta (Aβ) in the brain is a key factor for early identification and diagnosis of Alzheimer's disease (AD). We aimed to develop a deep learning model to predict Aβ cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 Aβ PET images and CSF measurements to train and validate a convolutional neural network ("ArcheD"). We evaluated the ArcheD performance in relation to episodic memory and the standardized uptake value ratio (SUVR) of cortical Aβ. We also compared the brain region's relevance for the model's CSF prediction within clinical-based and biological-based classifications. ArcheD-predicted Aβ CSF values correlated with measured Aβ CSF values (r = 0.92; q < 0.01), SUVR (rAV45 = -0.64, rFBB = -0.69; q < 0.01) and episodic memory measures (0.33 < r < 0.44; q < 0.01). For both classifications, cerebral white matter significantly contributed to CSF prediction (q < 0.01), specifically in non-symptomatic and early stages of AD. However, in late-stage disease, the brain stem, subcortical areas, cortical lobes, limbic lobe and basal forebrain made more significant contributions (q < 0.01). Considering cortical grey matter separately, the parietal lobe was the strongest predictor of CSF amyloid levels in those with prodromal or early AD, while the temporal lobe played a more crucial role for those with AD. In summary, ArcheD reliably predicted Aβ CSF concentration from Aβ PET scans, offering potential clinical utility for Aβ level determination.

Post-disclosure distress among racial and ethnic groups in a preclinical AD trial

INTRODUCTION

Trialists need a thorough understanding of whether reactions to Alzheimer's disease (AD) biomarker information differ among racial and ethnic groups in preclinical AD trials.

METHODS

We used data from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease Study to analyze cognitively unimpaired participants' responses on the Impact of Event Scale (IES) 24 to 72 hours after amyloid disclosure. We fit a linear regression model to test whether mean IES scores differed among participants from specific racial and ethnic groups. We considered potential effect modification by amyloid status.

RESULTS

Reactions to disclosure did not significantly differ among participant groups based on self-reported race and ethnicity. Although the results were not significant when stratified by amyloid status, all racial and ethnic groups except for participants self-reporting Hispanic/Latino ethnicity were observed to have higher mean IES in the elevated amyloid group.

DISCUSSION

These results support continued use of current disclosure methods in preclinical AD trials.

Light on Alzheimer's disease: from basic insights to preclinical studies

Alzheimer's disease (AD), referring to a gradual deterioration in cognitive function, including memory loss and impaired thinking skills, has emerged as a substantial worldwide challenge with profound social and economic implications. As the prevalence of AD continues to rise and the population ages, there is an imperative demand for innovative imaging techniques to help improve our understanding of these complex conditions. Photoacoustic (PA) imaging forms a hybrid imaging modality by integrating the high-contrast of optical imaging and deep-penetration of ultrasound imaging. PA imaging enables the visualization and characterization of tissue structures and multifunctional information at high resolution and, has demonstrated promising preliminary results in the study and diagnosis of AD. This review endeavors to offer a thorough overview of the current applications and potential of PA imaging on AD diagnosis and treatment. Firstly, the structural, functional, molecular parameter changes associated with AD-related brain imaging captured by PA imaging will be summarized, shaping the diagnostic standpoint of this review. Then, the therapeutic methods aimed at AD is discussed further. Lastly, the potential solutions and clinical applications to expand the extent of PA imaging into deeper AD scenarios is proposed. While certain aspects might not be fully covered, this mini-review provides valuable insights into AD diagnosis and treatment through the utilization of innovative tissue photothermal effects. We hope that it will spark further exploration in this field, fostering improved and earlier theranostics for AD.

Cognitive modeling of the Mnemonic Similarity Task as a digital biomarker for Alzheimer's Disease

AD related pathologies, such as beta-amyloid (Aβ) and phosphorylated tau (pTau), are evident decades before any noticeable decline in memory occurs. Identifying individuals during this asymptomatic phase is crucial for timely intervention. The Mnemonic Similarity Task (MST), a modified recognition memory task, is especially relevant for early AD screening, as it assesses hippocampal integrity, a region affected (both directly and indirectly) early in the progression of the disease. Further, strong inferences on the underlying cognitive mechanisms that support performance on this task can be made using Bayesian cognitive modeling. We assessed whether analyzing MST performance using a cognitive model could detect subtle changes in cognitive function and AD biomarker status prior to overt cognitive decline. We analyzed MST data from >200 individuals (young, cognitively healthy older adults, and individuals with MCI), a subset of which also had existing CSF Aβ and pTau data. Traditional performance scores and cognitive modeling using multinomial processing trees was applied to each participants MST data using Bayesian approaches. We assessed how well each could predict age group, memory ability, MCI status, Aβ/pTau status using ROC analyses. Both approaches predicted age group membership equally, but cognitive modeling approaches exceeded traditional metrics in all other comparisons. This work establishes that cognitive modeling of the MST can detect individuals with AD prior to cognitive decline, making it a potentially useful tool for both screening and monitoring older adults during the asymptomatic phase of AD.

Systematic review and meta-analysis of the effects of menopause hormone therapy on cognition

Introduction

Despite evidence from preclinical studies suggesting estrogen's neuroprotective effects, the use of menopausal hormone therapy (MHT) to support cognitive function remains controversial.

Methods

We used random-effect meta-analysis and multi-level meta-regression to derive pooled standardized mean difference (SMD) and 95% confidence intervals (C.I.) from 34 randomized controlled trials, including 14,914 treated and 12,679 placebo participants.

Results

Associations between MHT and cognitive function in some domains and tests of interest varied by formulation and treatment timing. While MHT had no overall effects on cognitive domain scores, treatment for surgical menopause, mostly estrogen-only therapy, improved global cognition (SMD=1.575, 95% CI 0.228, 2.921; P=0.043) compared to placebo. When initiated specifically in midlife or close to menopause onset, estrogen therapy was associated with improved verbal memory (SMD=0.394, 95% CI 0.014, 0.774; P=0.046), while late-life initiation had no effects. Overall, estrogen-progestogen therapy for spontaneous menopause was associated with a decline in Mini Mental State Exam (MMSE) scores as compared to placebo, with most studies administering treatment in a late-life population (SMD=-1.853, 95% CI -2.974, -0.733; P = 0.030). In analysis of timing of initiation, estrogen-progestogen therapy had no significant effects in midlife but was associated with improved verbal memory in late-life (P = 0.049). Duration of treatment >1 year was associated with worsening in visual memory as compared to shorter duration. Analysis of individual cognitive tests yielded more variable results of positive and negative effects associated with MHT.

Discussion

These findings suggest time-dependent effects of MHT on certain aspects of cognition, with variations based on formulation and timing of initiation, underscoring the need for further research with larger samples and more homogeneous study designs.

Dairy products and brain structure in French older adults

Among food groups with putative benefits for brain structures, dairy products (DP) have been poorly studied. The sample included participants without dementia from the ancillary brain imaging study of the Three-City cohort who were aged 65+ years, had their DP intake assessed with a FFQ at baseline and underwent an anatomical scan 3 years (n 343) or 9 years (n 195) after completing the dietary survey. The frequencies of consumption of total DP, milk and cheese were not associated with brain structure. Compared with the lowest frequency, the highest frequency of fresh DP (F-DP) consumption (< 0·5 v. > 1·5 times/d) was significantly associated with a lower medial temporal lobe volume (MTLV) (β = -1·09 cm3, 95 % CI - 1·83, -0·36) 9 years later. In this population-based study of older adults, the consumption of F-DP more than 1·5 times/d was associated with a lower MTLV, which is considered an early biomarker of Alzheimer's disease, 9 years later. This original study should be replicated in different settings before conclusions are drawn.

Unveiling the sound of the cognitive status: Machine Learning-based speech analysis in the Alzheimer's disease spectrum

BACKGROUND

Advancement in screening tools accessible to the general population for the early detection of Alzheimer's disease (AD) and prediction of its progression is essential for achieving timely therapeutic interventions and conducting decentralized clinical trials. This study delves into the application of Machine Learning (ML) techniques by leveraging paralinguistic features extracted directly from a brief spontaneous speech (SS) protocol. We aimed to explore the capability of ML techniques to discriminate between different degrees of cognitive impairment based on SS. Furthermore, for the first time, this study investigates the relationship between paralinguistic features from SS and cognitive function within the AD spectrum.

METHODS

Physical-acoustic features were extracted from voice recordings of patients evaluated in a memory unit who underwent a SS protocol. We implemented several ML models evaluated via cross-validation to identify individuals without cognitive impairment (subjective cognitive decline, SCD), with mild cognitive impairment (MCI), and with dementia due to AD (ADD). In addition, we established models capable of predicting cognitive domain performance based on a comprehensive neuropsychological battery from Fundació Ace (NBACE) using SS-derived information.

RESULTS

The results of this study showed that, based on a paralinguistic analysis of sound, it is possible to identify individuals with ADD (F1 = 0.92) and MCI (F1 = 0.84). Furthermore, our models, based on physical acoustic information, exhibited correlations greater than 0.5 for predicting the cognitive domains of attention, memory, executive functions, language, and visuospatial ability.

CONCLUSIONS

In this study, we show the potential of a brief and cost-effective SS protocol in distinguishing between different degrees of cognitive impairment and forecasting performance in cognitive domains commonly affected within the AD spectrum. Our results demonstrate a high correspondence with protocols traditionally used to assess cognitive function. Overall, it opens up novel prospects for developing screening tools and remote disease monitoring.

Design and Feasibility Analysis of a Smartphone-Based Digital Cognitive Assessment Study in the Framingham Heart Study

BACKGROUND

Smartphone-based digital technology is increasingly being recognized as a cost-effective, scalable, and noninvasive method of collecting longitudinal cognitive and behavioral data. Accordingly, a state-of-the-art 3-year longitudinal project focused on collecting multimodal digital data for early detection of cognitive impairment was developed.

METHODS AND RESULTS

A smartphone application collected 2 modalities of cognitive data, digital voice and screen-based behaviors, from the FHS (Framingham Heart Study) multigenerational Generation 2 (Gen 2) and Generation 3 (Gen 3) cohorts. To understand the feasibility of conducting a smartphone-based study, participants completed a series of questions about their smartphone and app use, as well as sensory and environmental factors that they encountered while completing the tasks on the app. Baseline data collected to date were from 537 participants (mean age=66.6 years, SD=7.0; 58.47% female). Across the younger participants from the Gen 3 cohort (n=455; mean age=60.8 years, SD=8.2; 59.12% female) and older participants from the Gen 2 cohort (n=82; mean age=74.2 years, SD=5.8; 54.88% female), an average of 76% participants agreed or strongly agreed that they felt confident about using the app, 77% on average agreed or strongly agreed that they were able to use the app on their own, and 81% on average rated the app as easy to use.

CONCLUSIONS

Based on participant ratings, the study findings are promising. At baseline, the majority of participants are able to complete the app-related tasks, follow the instructions, and encounter minimal barriers to completing the tasks independently. These data provide evidence that designing and collecting smartphone application data in an unsupervised, remote, and naturalistic setting in a large, community-based population is feasible.

Pre-Randomization Predictors of Study Discontinuation in a Preclinical Alzheimer's Disease Randomized Controlled Trial

BACKGROUND

Participant discontinuation from study treatment in a clinical trial can leave a trial underpowered, produce bias in statistical analysis, and limit interpretability of study results. Retaining participants in clinical trials for the full study duration is therefore as important as participant recruitment.

OBJECTIVE

This analysis aims to identify associations of pre-randomization characteristics of participants with premature discontinuation during the blinded phase of the Anti-Amyloid treatment in Asymptomatic AD (A4) Study.

DESIGN

All A4 trial randomized participants were classified as having prematurely discontinued study during the blinded period of the study for any reason (dropouts) or completed the blinded phase of the study on treatment (completers).

SETTING

The trial was conducted across 67 study sites in the United States, Canada, Japan and Australia through the global COVID-19 pandemic.

PARTICIPANTS

The sample consisted of all 1169 A4 trial randomized participants.

MEASUREMENTS

Pre-randomization demographic, clinical, amyloid PET and genetic predictors of study discontinuation were evaluated using a univariate generalized linear mixed model (GLMM), with discontinuation status as the binary outcome, each predictor as a fixed effect, and site as a random effect to account for differences among study sites in the trial. Characteristics significant at p<0.10 were then included in a multivariable GLMM.

RESULTS

Among randomized participants, 339 (29%) discontinued the study during the blinded period (median follow-up time in trial: 759 days). From the multivariable analysis, the two main predictors of study discontinuation were screening State-Trait Anxiety Inventory (STAI) scores (OR = 1.07 [95%CI = 1.02; 1.12]; p=0.002) and age (OR = 1.06 [95%CI = 1.03; 1.09]; p<0.001). Participants with a family history of dementia (OR = 0.75 [95%CI = 0.55; 1.01]; p=0.063) and APOE ε4 carriers (OR = 0.79 [95%CI = 0.6; 1.04]; p=0.094) were less likely to discontinue from the study, with the association being marginally significant. In these analyses, sex, race and ethnicity, cognitive scores and amyloid/tau PET scores were not associated with study dropout.

CONCLUSIONS

In the A4 trial, older participants and those with higher levels of anxiety at baseline as measured by the STAI were more likely to discontinue while those who had a family history of dementia or were APOE ε4 carriers were less likely to drop out. These findings have direct implications for future preclinical trial design and selection processes to identify those individuals at greatest risk of dropout and provide information to the study team to develop effective selection and retention strategies in AD prevention studies.

Elevated gonadotropin levels are associated with increased biomarker risk of Alzheimer's disease in midlife women

Introduction

In preclinical studies, menopausal elevations in pituitary gonadotropins, follicle-stimulating hormone (FSH) and luteinizing hormone (LH), trigger Alzheimer's disease (AD) pathology and synaptic loss in female animals. Herein, we took a translational approach to test whether gonadotropin elevations are linked to AD pathophysiology in women.

Methods

We examined 191 women ages 40-65 years, carrying risk factors for late-onset AD, including 45 premenopausal, 67 perimenopausal, and 79 postmenopausal participants with clinical, laboratory, cognitive exams, and volumetric MRI scans. Half of the cohort completed 11C-Pittsburgh Compound B (PiB) amyloid-β (Aβ) PET scans. Associations between serum FSH, LH and biomarkers were examined using voxel-based analysis, overall and stratified by menopause status. Associations with region-of-interest (ROI) hippocampal volume, plasma estradiol levels, APOE-4 status, and cognition were assessed in sensitivity analyses.

Results

FSH levels were positively associated with Aβ load in frontal cortex (multivariable adjusted P≤0.05, corrected for family wise type error, FWE), an effect that was driven by the postmenopausal group (multivariable adjusted PFWE ≤ 0.044). LH levels were also associated with Aβ load in frontal cortex, which did not survive multivariable adjustment. FSH and LH were negatively associated with gray matter volume (GMV) in frontal cortex, overall and in each menopausal group (multivariable adjusted PFWE ≤ 0.040), and FSH was marginally associated with ROI hippocampal volume (multivariable adjusted P = 0.058). Associations were independent of age, clinical confounders, menopause type, hormone therapy status, history of depression, APOE-4 status, and regional effects of estradiol. There were no significant associations with cognitive scores.

Discussion

Increasing serum gonadotropin levels, especially FSH, are associated with higher Aβ load and lower GMV in some AD-vulnerable regions of midlife women at risk for AD. These findings are consistent with preclinical work and provide exploratory hormonal targets for precision medicine strategies for AD risk reduction.

ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid-beta from amyloid PET images

Detection and measurement of amyloid-beta (Aβ) aggregation in the brain is a key factor for early identification and diagnosis of Alzheimer's disease (AD). We aimed to develop a deep learning model to predict Aβ cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 Aβ PET images and CSF measurements to train and validate a convolutional neural network ("ArcheD"). We evaluated the ArcheD performance in relation to episodic memory and the standardized uptake value ratio (SUVR) of cortical Aβ. We also compared the brain region's relevance for the model's CSF prediction within clinical-based and biological-based classifications. ArcheD-predicted Aβ CSF values correlated strongly with measured Aβ CSF values ( r =0.81; p <0.001) and showed correlations with SUVR and episodic memory measures in all participants except in those with AD. For both clinical and biological classifications, cerebral white matter significantly contributed to CSF prediction ( q <0.01), specifically in non-symptomatic and early stages of AD. However, in late-stage disease, brain stem, subcortical areas, cortical lobes, limbic lobe, and basal forebrain made more significant contributions (q<0.01). Considering cortical gray matter separately, the parietal lobe was the strongest predictor of CSF amyloid levels in those with prodromal or early AD, while the temporal lobe played a more crucial role for those with AD. In summary, ArcheD reliably predicted Aβ CSF concentration from Aβ PET scans, offering potential clinical utility for Aβ level determination and early AD detection.

Systematic review and meta-analysis of the effects of menopause hormone therapy on risk of Alzheimer's disease and dementia

Introduction

Despite a large preclinical literature demonstrating neuroprotective effects of estrogen, use of menopausal hormone therapy (HT) for Alzheimer's disease (AD) risk reduction has been controversial. Herein, we conducted a systematic review and meta-analysis of HT effects on AD and dementia risk.

Methods

Our systematic search yielded 6 RCT reports (21,065 treated and 20,997 placebo participants) and 45 observational reports (768,866 patient cases and 5.5 million controls). We used fixed and random effect meta-analysis to derive pooled relative risk (RR) and 95% confidence intervals (C.I.) from these studies.

Results

Randomized controlled trials conducted in postmenopausal women ages 65 and older show an increased risk of dementia with HT use compared with placebo [RR = 1.38, 95% C.I. 1.16-1.64, p < 0.001], driven by estrogen-plus-progestogen therapy (EPT) [RR = 1.64, 95% C.I. 1.20-2.25, p = 0.002] and no significant effects of estrogen-only therapy (ET) [RR = 1.19, 95% C.I. 0.92-1.54, p = 0.18]. Conversely, observational studies indicate a reduced risk of AD [RR = 0.78, 95% C.I. 0.64-0.95, p = 0.013] and all-cause dementia [RR = .81, 95% C.I. 0.70-0.94, p = 0.007] with HT use, with protective effects noted with ET [RR = 0.86, 95% C.I. 0.77-0.95, p = 0.002] but not with EPT [RR = 0.910, 95% C.I. 0.775-1.069, p = 0.251]. Stratified analysis of pooled estimates indicates a 32% reduced risk of dementia with midlife ET [RR = 0.685, 95% C.I. 0.513-0.915, p = 0.010] and non-significant reductions with midlife EPT [RR = 0.775, 95% C.I. 0.474-1.266, p = 0.309]. Late-life HT use was associated with increased risk, albeit not significant [EPT: RR = 1.323, 95% C.I. 0.979-1.789, p = 0.069; ET: RR = 1.066, 95% C.I. 0.996-1.140, p = 0.066].

Discussion

These findings support renewed research interest in evaluating midlife estrogen therapy for AD risk reduction.

The mismatch negativity as an index of cognitive abilities in adults with Down syndrome

Down syndrome (DS) is associated with an ultra-high risk of developing Alzheimer's disease (AD). Understanding variability in pre-AD cognitive abilities may help understand cognitive decline in this population. The mismatch negativity (MMN) is an event-related potential component reflecting the detection of deviant stimuli that is thought to represent underlying memory processes, with reduced MMN amplitudes being associated with cognitive decline. To further understand the MMN in adults with DS without AD, we explored the relationships between MMN, age, and cognitive abilities (memory, language, and attention) in 27 individuals (aged 17-51) using a passive auditory oddball task. Statistically significant MMN was present only in 18 individuals up to 41 years of age and the latency were longer than canonical parameters reported in the literature. Reduced MMN amplitude was associated with lower memory scores, while longer MMN latencies were associated with poorer memory, verbal abilities, and attention. Therefore, the MMN may represent a valuable index of cognitive abilities in DS. In combination with previous findings, we hypothesize that while MMN response and amplitude may be associated with AD-related memory loss, MMN latency may be associated with speech signal processing. Future studies may explore the potential impact of AD on MMN in people with DS.

Awareness of diagnosis predicts changes in quality of life in individuals with mild cognitive impairment and mild stage dementia

BACKGROUND AND OBJECTIVE

This observational study examined how awareness of diagnosis predicted changes in cognition and quality of life (QOL) 1 year later in older adults with normal cognition and dementia diagnoses.

RESEARCH DESIGN AND METHODS

Older adults (n = 259) with normal cognition, mild cognitive impairment (MCI), or mild stage Alzheimer's disease (AD) completed measures of diagnostic awareness, cognition, and multiple domains of QOL. We compared 1-year change in cognition and QOL by diagnostic group and diagnostic awareness.

RESULTS

Patients who were unaware of their diagnosis at baseline showed average decreases in both satisfaction with daily life (QOL-AD; paired mean difference (PMD) = -0.9, p < 0.05) and physical functioning (SF-12 PCS; PMD = -2.5, p < 0.05). In contrast, patients aware of their diagnosis at baseline showed no statistically discernable changes in most QOL domains (all p > 0.05). Of patients aware of their diagnosis at baseline (n = 111), those who were still aware (n = 84) showed a decrease in mental functioning at follow up (n = 27; SF-12 MCS). Change in MoCA scores in patients unaware of their diagnosis was similar to that in patients aware of their diagnosis, -1.4 points (95% CI -2.6 to -0.6) and -1.7 points (95% CI -2.4 to -1.1) respectively.

DISCUSSION AND IMPLICATIONS

Awareness of one's diagnosis of MCI or AD, not the severity of cognitive impairment, may predict changes in patients' mental functioning, expectations of their memory, satisfaction with daily life, and physical functioning. The findings may help clinicians anticipate the types of threats to wellbeing that a patient might encounter and identify key domains for monitoring.

Systematic review of 31P-magnetic resonance spectroscopy studies of brain high energy phosphates and membrane phospholipids in aging and Alzheimer's disease

Many lines of evidence suggest that mitochondria have a central role in aging-related neurodegenerative diseases, such as Alzheimer's disease (AD). Mitochondrial dysfunction, cerebral energy dysmetabolism and oxidative damage increase with age, and are early event in AD pathophysiology and may precede amyloid beta (Aβ) plaques. In vivo probes of mitochondrial function and energy metabolism are therefore crucial to characterize the bioenergetic abnormalities underlying AD risk, and their relationship to pathophysiology and cognition. A majority of the research conducted in humans have used 18F-fluoro-deoxygluose (FDG) PET to image cerebral glucose metabolism (CMRglc), but key information regarding oxidative phosphorylation (OXPHOS), the process which generates 90% of the energy for the brain, cannot be assessed with this method. Thus, there is a crucial need for imaging tools to measure mitochondrial processes and OXPHOS in vivo in the human brain. 31Phosphorus-magnetic resonance spectroscopy (31P-MRS) is a non-invasive method which allows for the measurement of OXPHOS-related high-energy phosphates (HEP), including phosphocreatine (PCr), adenosine triphosphate (ATP), and inorganic phosphate (Pi), in addition to potential of hydrogen (pH), as well as components of phospholipid metabolism, such as phosphomonoesters (PMEs) and phosphodiesters (PDEs). Herein, we provide a systematic review of the existing literature utilizing the 31P-MRS methodology during the normal aging process and in patients with mild cognitive impairment (MCI) and AD, with an additional focus on individuals at risk for AD. We discuss the strengths and limitations of the technique, in addition to considering future directions toward validating the use of 31P-MRS measures as biomarkers for the early detection of AD.

Centralizing prescreening data collection to inform data-driven approaches to clinical trial recruitment

BACKGROUND

Recruiting to multi-site trials is challenging, particularly when striving to ensure the randomized sample is demographically representative of the larger disease-suffering population. While previous studies have reported disparities by race and ethnicity in enrollment and randomization, they have not typically investigated whether disparities exist in the recruitment process prior to consent. To identify participants most likely to be eligible for a trial, study sites frequently include a prescreening process, generally conducted by telephone, to conserve resources. Collection and analysis of such prescreening data across sites could provide valuable information to improve understanding of recruitment intervention effectiveness, including whether traditionally underrepresented participants are lost prior to screening.

METHODS

We developed an infrastructure within the National Institute on Aging (NIA) Alzheimer's Clinical Trials Consortium (ACTC) to centrally collect a subset of prescreening variables. Prior to study-wide implementation in the AHEAD 3-45 study (NCT NCT04468659), an ongoing ACTC trial recruiting older cognitively unimpaired participants, we completed a vanguard phase with seven study sites. Variables collected included age, self-reported sex, self-reported race, self-reported ethnicity, self-reported education, self-reported occupation, zip code, recruitment source, prescreening eligibility status, reason for prescreen ineligibility, and the AHEAD 3-45 participant ID for those who continued to an in-person screening visit after study enrollment.

RESULTS

Each of the sites was able to submit prescreening data. Vanguard sites provided prescreening data on a total of 1029 participants. The total number of prescreened participants varied widely among sites (range 3-611), with the differences driven mainly by the time to receive site approval for the main study. Key learnings instructed design/informatic/procedural changes prior to study-wide launch.

CONCLUSION

Centralized capture of prescreening data in multi-site clinical trials is feasible. Identifying and quantifying the impact of central and site recruitment activities, prior to participants signing consent, has the potential to identify and address selection bias, instruct resource use, contribute to effective trial design, and accelerate trial enrollment timelines.

The effects of herbal medicine (Jujadokseo-hwan) on quality of life in patients with mild cognitive impairment: Cost-effectiveness analysis alongside randomized controlled trial

Background

Mild cognitive impairment (MCI), the early stage of dementia, requires effective intervention for symptom management and improving patients' quality of life (QoL). Jujadokseo-hwan (JDH) is a Korean herbal medicine prescription used to improve MCI symptoms, such as memory deficit. This study evaluates the improvement in QoL through JDH. Alongside a clinical trial, it estimates the cost-effectiveness of JDH, compared to placebo, for MCI over 24 weeks.

Methods

Changes in QoL were measured using the EuroQol-5 Dimensions (EQ-5D) and Korean version QoL-Alzheimer's Disease (KQOL-AD). Direct medical and non-medical costs were surveyed and incremental cost-effectiveness ratios (ICER) per QALY for JDH were produced.

Results

In total, 64 patients were included in the economic evaluation (n = 35 in JDH, n = 29 in placebo). In the JDH group, EQ-5D and KQOL-AD improved by 0.020 (p = .318) and 3.40 (p = .011) over 24 weeks, respectively. In the placebo group, they increased by 0.001 (p=.920) and 1.07 (p=.130), respectively. The ICER was KRW 76,400,000 per QALY and KRW 108,000 per KQOL-AD for JDH, compared to the placebo group.

Conclusion

JDH is not considered a cost-effective treatment option compared with placebo; however, it positively affects QoL improvement in patients with MCI.

Effects of sex and APOE ε4 genotype on brain mitochondrial high-energy phosphates in midlife individuals at risk for Alzheimer's disease: A 31Phosphorus MR spectroscopy study

Age, female sex, and APOE epsilon 4 (APOE4) genotype are the three greatest risk factors for late-onset Alzheimer's disease (AD). The convergence of these risks creates a hypometabolic AD-risk profile unique to women, which may help explain their higher lifetime risk of AD. Less is known about APOE4 effects in men, although APOE4 positive men also experience an increased AD risk. This study uses 31Phosphorus Magnetic Resonance Spectroscopy (31P-MRS) to examine effects of sex and APOE4 status on brain high-energy phosphates [adenosine triphosphate (ATP), phosphocreatine (PCr), inorganic phosphate (Pi)] and membrane phospholipids [phosphomonoesters (PME), phosphodiesters (PDE)] in 209 cognitively normal individuals at risk for AD, ages 40-65, 80% female, 46% APOE4 carriers (APOE4+). Women exhibited lower PCr/ATP and PCr/Pi levels than men in AD-vulnerable regions, including frontal, posterior cingulate, lateral and medial temporal cortex (multi-variable adjusted p≤0.037). The APOE4+ group exhibited lower PCr/ATP and PCr/Pi in frontal regions as compared to non-carriers (APOE4-) (multi-variable adjusted p≤0.005). Sex by APOE4 status interactions were observed in frontal regions (multi-variable adjusted p≤0.046), where both female groups and APOE4+ men exhibited lower PCr/ATP and PCr/Pi than APOE4- men. Among men, APOE4 homozygotes exhibited lower frontal PCr/ATP than heterozygotes and non-carriers. There were no significant effects of sex or APOE4 status on Pi/ATP and PME/PDE measures. Among midlife individuals at risk for AD, women exhibit lower PCr/ATP (e.g. higher ATP utilization) and lower PCr/Pi (e.g. higher energy demand) than age-controlled men, independent of APOE4 status. However, a double dose of APOE4 allele shifted men's brains to a similar metabolic range as women's brains. Examination of brain metabolic heterogeneity can support identification of AD-specific pathways within at-risk subgroups, further advancing both preventive and precision medicine for AD.

Whole-brain microscopy reveals distinct temporal and spatial efficacy of anti-Aβ therapies

Many efforts targeting amyloid-β (Aβ) plaques for the treatment of Alzheimer's Disease thus far have resulted in failures during clinical trials. Regional and temporal heterogeneity of efficacy and dependence on plaque maturity may have contributed to these disappointing outcomes. In this study, we mapped the regional and temporal specificity of various anti-Aβ treatments through high-resolution light-sheet imaging of electrophoretically cleared brains. We assessed the effect on amyloid plaque formation and growth in Thy1-APP/PS1 mice subjected to β-secretase inhibitors, polythiophenes, or anti-Aβ antibodies. Each treatment showed unique spatiotemporal Aβ clearance, with polythiophenes emerging as a potent anti-Aβ compound. Furthermore, aligning with a spatial-transcriptomic atlas revealed transcripts that correlate with the efficacy of each Aβ therapy. As observed in this study, there is a striking dependence of specific treatments on the location and maturity of Aβ plaques. This may also contribute to the clinical trial failures of Aβ-therapies, suggesting that combinatorial regimens may be significantly more effective in clearing amyloid deposition.

Association between dietary inflammatory index and cognitive impairment: A meta-analysis

Aims

Cognitive impairment is an increasingly urgent global public health challenge. Dietary Inflammatory Index (DII) is a literature-derived score that links diet to inflammation. The relationship between DII and cognitive impairment remains controversial. Therefore, our study aimed to analysis the role of DII on the risk of cognitive impairment by meta-analysis.

Methods

PubMed, Cochrane Library, MEDLINE, Web of Science and EMBASE databases were searched up to July 2022. Newcastle-Ottawa scale (NOS) and Joanna Briggs Institute (JBI) Checklist were performed to estimate the quality of studies.

Results

Nine observational studies with 19,379 subjects were included. Our study found that higher DII could elevate the risk of cognitive impairment (OR = 1.46, 95%CI = 1.26, 1.69). Meanwhile, the OR of cognitive impairment was 1.49 (95%CI = 1.21, 1.83) for cross-sectional studies and 1.42 (95%CI = 1.12, 1.79) for cohort studies, respectively.

Conclusion

Our meta-analysis indicated that higher DII (indicating a more pro-inflammatory diet) is related to increased risk of cognitive impairment.

Evolution of Detecting Early Onset of Alzheimer's Disease: From Neuroimaging to Optical Immunoassays

Alzheimer's disease (AD) is a pathological disorder defined by the symptoms of memory loss and deterioration of cognitive abilities over time. Although the etiology is complex, it is mainly associated with the accumulation of toxic amyloid-β peptide (Aβ) aggregates and tau protein-induced neurofibrillary tangles (NFTs). Even now, creating non-invasive, sensitive, specific, and cost-effective diagnostic methods for AD remains challenging. Over the past few decades, polymers, and nanomaterials (e.g., nanodiamonds, nanogold, quantum dots) have become attractive and practical tools in nanomedicine for diagnosis and treatment. This review focuses on current developments in sensing methods such as enzyme-linked immunosorbent assay (ELISA) and surface-enhanced Raman scattering (SERS) to boost the sensitivity in detecting related biomarkers for AD. In addition, optical analysis platforms such as ELISA and SERS have found increasing popularity among researchers due to their excellent sensitivity and specificity, which may go as low as the femtomolar range. While ELISA offers easy technological usage and high throughput, SERS has the advantages of improved mobility, simple electrical equipment integration, and lower cost. Both portable optical sensing techniques are highly superior in terms of sensitivity, specificity, human application, and practicality, enabling the early identification of AD biomarkers.

PET molecular imaging for pathophysiological visualization in Alzheimer's disease

Alzheimer's disease (AD) is the most common dementia worldwide. The exact etiology of AD is unclear as yet, and no effective treatments are currently available, making AD a tremendous burden posed on the whole society. As AD is a multifaceted and heterogeneous disease, and most biomarkers are dynamic in the course of AD, a range of biomarkers should be established to evaluate the severity and prognosis. Positron emission tomography (PET) offers a great opportunity to visualize AD from diverse perspectives by using radiolabeled agents involved in various pathophysiological processes; PET imaging technique helps to explore the pathomechanisms of AD comprehensively and find out the most appropriate biomarker in each AD phase, leading to a better evaluation of the disease. In this review, we discuss the application of PET in the course of AD and summarized radiolabeled compounds with favorable imaging characteristics.

Memory-driven eye movements prospectively predict dementia in people at risk of Alzheimer's disease

Introduction

Oculomotor behaviors linked to cognitive performance revealed neurocognitive features of Alzheimer's disease (AD) that can enhance the accuracy of its assessment and diagnosis.

Methods

A sample of 107 participants (i.e., 65 mild cognitive impairment [MCI] and 42 controls) were recruited and followed up for 40 months. At baseline, they underwent assessment with the ViewMind digital biomarker, which draws cognitive-related patterns of eye movement while people perform the visual short-term memory binding task.

Results

Baseline data predicted that 36 patients with MCI would progress to the AD clinical syndrome (ADS Progressing). The remaining 29 MCI patients were predicted to remain as MCI or progress to other forms of dementia. After 40 months of follow-up, 94% of ADS Progressing patients had received a diagnosis of dementia, whereas none of the non-ADS Progressing had.

Discussion

The analysis of eye movement behavior combined with cognitive markers for AD can effectively predict progression to ADS among patients with MCI.

Sex and menopause impact 31P-Magnetic Resonance Spectroscopy brain mitochondrial function in association with 11C-PiB PET amyloid-beta load

Increasing evidence implicates sex and endocrine aging effects on brain bioenergetic aging in the greater lifetime risk of Alzheimer's disease (AD) in women. We conducted 31Phosphorus Magnetic Resonance Spectroscopy (31P-MRS) to assess the impact of sex and menopause on brain high-energy phosphates [adenosine triphosphate (ATP), phosphocreatine (PCr), inorganic phosphate (Pi)] and membrane phospholipids [phosphomonoesters/phosphodiesters (PME/PDE)] in 216 midlife cognitively normal individuals at risk for AD, 80% female. Ninety-seven participants completed amyloid-beta (Aβ) 11C-PiB PET. Women exhibited higher ATP utilization than men in AD-vulnerable frontal, posterior cingulate, fusiform, medial and lateral temporal regions (p < 0.001). This profile was evident in frontal cortex at the pre-menopausal and peri-menopausal stage and extended to the other regions at the post-menopausal stage (p = 0.001). Results were significant after multi-variable adjustment for age, APOE-4 status, midlife health indicators, history of hysterectomy/oophorectomy, use of menopause hormonal therapy, and total intracranial volume. While associations between ATP/PCr and Aβ load were not significant, individuals with the highest Aβ load were post-menopausal and peri-menopausal women with ATP/PCr ratios in the higher end of the distribution. No differences in Pi/PCr, Pi/ATP or PME/PDE were detected. Outcomes are consistent with dynamic bioenergetic brain adaptations that are associated with female sex and endocrine aging.

Association of MRI Indices of Glymphatic System With Amyloid Deposition and Cognition in Mild Cognitive Impairment and Alzheimer Disease

BACKGROUND AND OBJECTIVES

The glymphatic system is a whole-brain perivascular network, which promotes CSF/interstitial fluid exchange. Alterations to this system may play a pivotal role in amyloid β (Aβ) accumulation. However, its involvement in Alzheimer disease (AD) pathogenesis is not fully understood. Here, we investigated the changes in noninvasive MRI measurements related to the perivascular network in patients with mild cognitive impairment (MCI) and AD. Additionally, we explored the associations of MRI measures with neuropsychological score, PET standardized uptake value ratio (SUVR), and Aβ deposition.

METHODS

MRI measures, including perivascular space (PVS) volume fraction (PVSVF), fractional volume of free water in white matter (FW-WM), and index of diffusivity along the perivascular space (ALPS index) of patients with MCI, those with AD, and healthy controls from the Alzheimer's Disease Neuroimaging Initiative database were compared. MRI measures were also correlated with the levels of CSF biomarkers, PET SUVR, and cognitive score in the combined subcohort of patients with MCI and AD. Statistical analyses were performed with age, sex, years of education, and APOE status as confounding factors.

RESULTS

In total, 36 patients with AD, 44 patients with MCI, and 31 healthy controls were analyzed. Patients with AD had significantly higher total, WM, and basal ganglia PVSVF (Cohen d = 1.15-1.48; p < 0.001) and FW-WM (Cohen d = 0.73; p < 0.05) and a lower ALPS index (Cohen d = 0.63; p < 0.05) than healthy controls. Meanwhile, the MCI group only showed significantly higher total (Cohen d = 0.99; p < 0.05) and WM (Cohen d = 0.91; p < 0.05) PVSVF. Low ALPS index was associated with lower CSF Aβ42 (r s = 0.41, p fdr = 0.026), FDG-PET uptake (r s = 0.54, p fdr < 0.001), and worse multiple cognitive domain deficits. High FW-WM was also associated with lower CSF Aβ42 (r s = -0.47, p fdr = 0.021) and worse cognitive performances.

DISCUSSION

Our study indicates that changes in PVS-related MRI parameters occur in MCI and AD, possibly due to impairment of the glymphatic system. We also report the associations between MRI parameters and Aβ deposition, neuronal change, and cognitive impairment in AD.

Preclinical Alzheimer Disease and the Electronic Health Record: Balancing Confidentiality and Care

Because information technologies are increasingly used to improve clinical research and care, personal health information (PHI) has wider dissemination than ever before. The 21st Century Cures Act in the United States now requires patient access to many components of the electronic health record (EHR). Although these changes promise to enhance communication and information sharing, they also bring higher risks of unwanted disclosure, both within and outside of health systems. Having preclinical Alzheimer disease (AD), where biological markers of AD are identified before the onset of any symptoms, is sensitive PHI. Because of the melding of ideas between preclinical and "clinical" (symptomatic) AD, unwanted disclosure of preclinical AD status can lead to personal harms of stigma, discrimination, and changes to insurability. At present, preclinical AD is identified mainly in research settings, although the consensus criteria for a clinical diagnosis may soon be established. There is not yet adequate legal protection for the growing number of individuals with preclinical AD. Some PHI generated in preclinical AD trials has clinical significance, necessitating urgent evaluations and longitudinal monitoring in care settings. AD researchers are obligated to both respect the confidentiality of participants' sensitive PHI and facilitate providers' access to necessary information, often requiring disclosure of preclinical AD status. The AD research community must continue to develop ethical, participant-centered practices related to confidentiality and disclosure, with attention to sensitive information in the EHR. These practices will be essential for translation into the clinic and across health systems and society at large.

Effects of preventive interventions on neuroimaging biomarkers in subjects at-risk to develop Alzheimer's disease: A systematic review

Alzheimer's Disease (AD) is a multifactorial and complex neurodegenerative disorder. Some modifiable risk factors have been associated with an increased risk of appearance of the disease and/or cognitive decline. Preventive clinical trials aiming at reducing one or combined risk factors have been implemented and their potential effects assessed on cognitive trajectories and on AD biomarkers. However, the effect of interventions on surrogate markers, in particular imaging biomarkers, remains poorly understood. We conducted a review of the literature and analyzed 43 interventional studies that included physical exercise, nutrition, cognitive training or multidomain interventions, and assessed various brain imaging biomarkers, to determine the effects of preventive interventions on imaging biomarkers for subjects at-risk to develop AD. Deciphering the global and regional brain effect of each and combined interventions will help to better understand the interplay relationship between multimodal interventions, cognition, surrogate brain markers, and to better design primary and secondary outcomes for future preventive clinical trials. Those studies were pondered using generally-admitted quality criteria to reveal that interventions may affect the brain of patients with cognitive impairment rather than those without cognitive impairment thus indicating that particular care should be taken when selecting individuals for interventions. Additionally, a majority of the studies concurred on the effect of the interventions and particularly onto the frontal brain areas.

Dual-targeted repetitive transcranial magnetic stimulation modulates brain functional network connectivity to improve cognition in mild cognitive impairment patients

Background: Mild cognitive impairment (MCI) is a condition between normal aging and dementia; nearly 10-15% of MCI patients develop dementia annually. There are no effective interventions for MCI progression. Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique that has attempted to improve the overall cognitive function of MCI patients. However, it does not affect episodic memory improvement. Methods: In this study, we engaged 15 clinically diagnosed MCI patients and normal controls to explore the effect of dual-targeted rTMS on progressing cognitive function, particularly episodic memory in MCI patients. Resting-state EEG recordings and neuropsychological assessments were conducted before and after the intervention. EEG features were extracted using an adaptive algorithm to calculate functional connectivity alterations in relevant brain regions and the mechanisms of altered brain functional networks in response to dual-target rTMS. Results: The study revealed that the functional brain connectivity between the right posterior cingulate gyrus (PCC) and the right dorsal caudate nucleus (DC) was significantly reduced in MCI patients compared to normal controls (p < 0.001). Dual-target rTMS increased the strength of the reduced functional connectivity (p < 0.001), which was related to cognitive enhancement (p < 0.05). Conclusion: This study provides a new stimulation protocol for rTMS intervention. Improving the functional connectivity of the right PCC to the right DC is a possible mechanism by which rTMS improves overall cognitive and memory function in MCI patients.

In Vivo Preclinical Assessment of β-Amyloid-Affine [11C]C-PIB Accumulation in Aluminium-Induced Alzheimer's Disease-Resembling Hypercholesterinaemic Rat Model

Aluminum (Al) excess and hypercholesterinaemia are established risks of Alzheimer's disease (AD). The aim of this study was to establish an AD-resembling hypercholesterinaemic animal model-with the involvement of 8 week and 48 week-old Fischer-344 rats-by Al administration for the safe and rapid verification of β-amyloid-targeted positron emission tomography (PET) radiopharmaceuticals. Measurement of lipid parameters and β-amyloid-affine [¹¹C]C-Pittsburgh Compound B ([¹¹C]C-PIB) PET examinations were performed. Compared with the control, the significantly elevated cholesterol and LDL levels of the rats receiving the cholesterol-rich diet support the development of hypercholesterinaemia (p ≤ 0.01). In the older cohort, a notably increased age-related radiopharmaceutical accumulation was registered compared to in the young (p ≤ 0.05; p ≤ 0.01). A monotherapy-induced slight elevation of mean standardised uptake values (SUV_mean) was statistically not significant; however, adult rats administered a combined diet expressed remarkable SUV_mean increment compared to the adult control (SUV_mean: from 0.78 ± 0.16 to 1.99 ± 0.28). One and two months after restoration to normal diet, the cerebral [¹¹C]C-PIB accumulation of AD-mimicking animals decreased by half and a third, respectively, to the baseline value. The proposed in vivo Al-induced AD-resembling animal system seems to be adequate for the understanding of AD neuropathology and future drug testing and radiopharmaceutical development.

The mean diffusivity of forceps minor is useful to distinguish amnestic mild cognitive impairment from mild cognitive impairment caused by cerebral small vessel disease

Objectives

In recent years, the desire to make a more fine-grained identification on mild cognitive impairment (MCI) has become apparent, the etiological diagnosis of MCI in particular. Nevertheless, new methods for the etiological diagnosis of MCI are currently insufficient. The objective of this study was to establish discriminative measures for amnestic mild cognitive impairment (a-MCI) and MCI caused by cerebral small vessel disease (CSVD).

Materials and methods

In total, 20 normal controls (NCs), 33 a-MCI patients, and 25 CSVD-MCI patients performed comprehensive neuropsychological assessments concerning global cognitive function and five cognitive domains as well as magnetic resonance imaging scan with diffusion tensor imaging (DTI). Diffusion parameters including fractional anisotropy and mean diffusivity of 20 major white matter metrics were obtained by ROI-based analyses. The neuropsychological tests and diffusion measurements were compared and binary logistic regression was used to identify the best differential indicator for the two MCI subgroups. The discriminating power was calculated by receiver operating characteristic analysis.

Results

Amnestic mild cognitive impairment group showed significant impairment in memory and language function, while CSVD-MCI group revealed more deficits in multi-cognitive domains of memory, language, attention and executive function than controls. Compared to the a-MCI, CSVD-MCI was significantly dysfunctional in the executive function. The CSVD-MCI group had decreased fractional anisotropy and increased mean diffusivity values throughout widespread white matter areas. CSVD-MCI presented more severe damage in the anterior thalamic radiation, forceps major, forceps minor and right inferior longitudinal fasciculus compared with a-MCI group. No significant neuropsychological tests were found in the binary logistic regression model, yet the DTI markers showed a higher discriminative power than the neuropsychological tests. The Stroop test errors had moderate potential (AUC = 0.747; sensitivity = 76.0%; specificity = 63.6%; P = 0.001; 95% CI: 0.617–0.877), and the mean diffusivity value of forceps minor demonstrated the highest predictive power to discriminate each MCI subtype (AUC = 0.815; sensitivity = 88.0%; specificity = 72.7%; P &lt; 0.001; 95% CI: 0.698–0.932).

Conclusion

The mean diffusivity of forceps minor may serve as an optimal indicator to differentiate between a-MCI and CSVD-MCI.

Exploring electrophysiological markers of auditory predictive processes and pathological ageing in adults with Down's syndrome

Down's syndrome is associated with pathological ageing and a propensity for early-onset Alzheimer's disease. The early symptoms of dementia in people with Down's syndrome may reflect frontal lobe vulnerability to amyloid deposition. Auditory predictive processes rely on the bilateral auditory cortices with the recruitment of frontal cortices and appear to be impaired in pathologies characterized by compromised frontal lobe. Hence, auditory predictive processes were investigated to assess Down's syndrome pathology and its relationship with pathological ageing. An auditory electroencephalography (EEG) global-local paradigm was presented to the participants, in which oddball stimuli could either violate local or higher level global rules. We characterised predictive processes in individuals with Down's syndrome and their relationship with pathological ageing, with a focus on the EEG event-related potential called Mismatch Negativity (MMN) and the P300. In Down's syndrome, we also evaluated the EEG components as predictor of cognitive decline 1 year later. We found that predictive processes of detection of auditory violations are overall preserved in Down's syndrome but also that the amplitude of the MMN to local deviancies decreases with age. However, the 1-year follow-up of Down's syndrome found that none of the ERPs measures predicted subsequent cognitive decline. The present study provides a novel characterization of electrophysiological markers of local and global predictive processes in Down's syndrome.

Memory markers in the continuum of the Alzheimer's clinical syndrome

BACKGROUND

The individual and complementary value of the Visual Short-Term Memory Binding Test (VSTMBT) and the Free and Cued Selective Reminding Test (FCSRT) as markers to trace the AD continuum was investigated. It was hypothesised that the VSTMBT would be an early indicator while the FCSRT would inform on imminent progression.

METHODS

Healthy older adults (n=70) and patients with mild cognitive impairment (MCI) (n=80) were recruited and followed up between 2012 and 2017. Participants with at least two assessment points entered the study. Using baseline and follow-up assessments four groups were defined: Older adults who were healthy (HOA), with very mild cognitive but not functional impairment (eMCI), and with MCI who did and did not convert to dementia (MCI converters and non-converters).

RESULTS

Only the VSTMBT predicted group membership in the very early stages (HOA vs eMCI). As the disease progressed, the FCSRT became a strong predictor excluding the VSTMB from the models. Their complementary value was high during the mid-prodromal stages and decreased in stages closer to dementia.

DISCUSSION

The study supports the notion that neuropsychological assessment for AD needs to abandon the notion of one-size-fits-all. A memory toolkit for AD needs to consider tools that are early indicators and tools that suggest imminent progression. The VSTMBT and the FSCRT are such tools.

Alzheimer's disease amyloid-β pathology in the lens of the eye

Neuropathological hallmarks of Alzheimer's disease (AD) include pathogenic accumulation of amyloid-β (Aβ) peptides and age-dependent formation of amyloid plaques in the brain. AD-associated Aβ neuropathology begins decades before onset of cognitive symptoms and slowly progresses over the course of the disease. We previously reported discovery of Aβ deposition, β-amyloidopathy, and co-localizing supranuclear cataracts (SNC) in lenses from people with AD, but not other neurodegenerative disorders or normal aging. We confirmed AD-associated Aβ molecular pathology in the lens by immunohistopathology, amyloid histochemistry, immunoblot analysis, epitope mapping, immunogold electron microscopy, quantitative immunoassays, and tryptic digest mass spectrometry peptide sequencing. Ultrastructural analysis revealed that AD-associated Aβ deposits in AD lenses localize as electron-dense microaggregates in the cytoplasm of supranuclear (deep cortex) fiber cells. These Aβ microaggregates also contain αB-crystallin and scatter light, thus linking Aβ pathology and SNC phenotype expression in the lenses of people with AD. Subsequent research identified Aβ lens pathology as the molecular origin of the distinctive cataracts associated with Down syndrome (DS, trisomy 21), a chromosomal disorder invariantly associated with early-onset Aβ accumulation and Aβ amyloidopathy in the brain. Investigation of 1249 participants in the Framingham Eye Study found that AD-associated quantitative traits in brain and lens are co-heritable. Moreover, AD-associated lens traits preceded MRI brain traits and cognitive deficits by a decade or more and predicted future AD. A genome-wide association study of bivariate outcomes in the same subjects identified a new AD risk factor locus in the CTNND2 gene encoding δ-catenin, a protein that modulates Aβ production in brain and lens. Here we report identification of AD-related human Aβ (hAβ) lens pathology and age-dependent SNC phenotype expression in the Tg2576 transgenic mouse model of AD. Tg2576 mice express Swedish mutant human amyloid precursor protein (APP-Swe), accumulate hAβ peptides and amyloid pathology in the brain, and exhibit cognitive deficits that slowly progress with increasing age. We found that Tg2576 trangenic (Tg⁺) mice, but not non-transgenic (Tg^-) control mice, also express human APP, accumulate hAβ peptides, and develop hAβ molecular and ultrastructural pathologies in the lens. Tg2576 Tg⁺ mice exhibit age-dependent Aβ supranuclear lens opacification that recapitulates lens pathology and SNC phenotype expression in human AD. In addition, we detected hAβ in conditioned medium from lens explant cultures prepared from Tg⁺ mice, but not Tg^- control mice, a finding consistent with constitutive hAβ generation in the lens. In vitro studies showed that hAβ promoted mouse lens protein aggregation detected by quasi-elastic light scattering (QLS) spectroscopy. These results support mechanistic (genotype-phenotype) linkage between Aβ pathology and AD-related phenotypes in lens and brain. Collectively, our findings identify Aβ pathology as the shared molecular etiology of two age-dependent AD-related cataracts associated with two human diseases (AD, DS) and homologous murine cataracts in the Tg2576 transgenic mouse model of AD. These results represent the first evidence of AD-related Aβ pathology outside the brain and point to lens Aβ as an optically-accessible AD biomarker for early detection and longitudinal monitoring of this devastating neurodegenerative disease.

Ovarian steroid hormones: A long overlooked but critical contributor to brain aging and Alzheimer's disease

Ovarian hormones, particularly 17β-estradiol, are involved in numerous neurophysiological and neurochemical processes, including those subserving cognitive function. Estradiol plays a key role in the neurobiology of aging, in part due to extensive interconnectivity of the neural and endocrine system. This aspect of aging is fundamental for women's brains as all women experience a drop in circulating estradiol levels in midlife, after menopause. Given the importance of estradiol for brain function, it is not surprising that up to 80% of peri-menopausal and post-menopausal women report neurological symptoms including changes in thermoregulation (vasomotor symptoms), mood, sleep, and cognitive performance. Preclinical evidence for neuroprotective effects of 17β-estradiol also indicate associations between menopause, cognitive aging, and Alzheimer's disease (AD), the most common cause of dementia affecting nearly twice more women than men. Brain imaging studies demonstrated that middle-aged women exhibit increased indicators of AD endophenotype as compared to men of the same age, with onset in perimenopause. Herein, we take a translational approach to illustrate the contribution of ovarian hormones in maintaining cognition in women, with evidence implicating menopause-related declines in 17β-estradiol in cognitive aging and AD risk. We will review research focused on the role of endogenous and exogenous estrogen exposure as a key underlying mechanism to neuropathological aging in women, with a focus on whether brain structure, function and neurochemistry respond to hormone treatment. While still in development, this research area offers a new sex-based perspective on brain aging and risk of AD, while also highlighting an urgent need for better integration between neurology, psychiatry, and women's health practices.

A Systematic Review and Meta-Analysis of Cerebrospinal Fluid Amyloid and Tau Levels Identifies Mild Cognitive Impairment Patients Progressing to Alzheimer's Disease

Reported levels of amyloid-beta and tau in human cerebrospinal fluid (CSF) were evaluated to discover if these biochemical markers can predict the transition from Mild Cognitive Impairment (MCI) to Alzheimer’s disease (AD). A systematic review of the literature in PubMed and Web of Science (April 2021) was performed by a single researcher to identify studies reporting immunologically-based (xMAP or ELISA) measures of CSF analytes Aβ(1-42) and/or P-tau and/or T-tau in clinical studies with at least two timepoints and a statement of diagnostic criteria. Of 1137 screened publications, 22 met the inclusion criteria for CSF Aβ(1-42) measures, 20 studies included T-tau, and 17 included P-tau. Six meta-analyses were conducted to compare the analytes for healthy controls (HC) versus progressive MCI (MCI_AD) and for non-progressive MCI (Stable_MCI) versus MCI_AD; effect sizes were determined using random effects models. The heterogeneity of effect sizes across studies was confirmed with very high significance (p < 0.0001) for all meta-analyses except HC versus MCI_AD T-tau (p < 0.05) and P-tau (non-significant). Standard mean difference (SMD) was highly significant (p < 0.0001) for all comparisons (Stable_MCI versus MCI_AD: SMD [95%-CI] Aβ(1-42) = 1.19 [0.96,1.42]; T-tau = −1.03 [−1.24,−0.82]; P-tau = −1.03 [−1.47,−0.59]; HC versus MCI_AD: SMD Aβ(1-42) = 1.73 [1.39,2.07]; T-tau = −1.13 [−1.33,−0.93]; P-tau = −1.10 [−1.23,−0.96]). The follow-up interval in longitudinal evaluations was a critical factor in clinical study design, and the Aβ(1−42)/P-tau ratio most robustly differentiated progressive from non-progressive MCI. The value of amyloid-beta and tau as markers of patient outcome are supported by these findings.