Prominent Non-Memory Deficits in Alzheimer's Disease Are Associated with Faster Disease Progression

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.

Heterogeneity of Alzheimer's disease identified by neuropsychological test profiling

Alzheimer's disease (AD) is a highly heterogeneous disorder. Untangling this variability could lead to personalized treatments and improve participant recruitment for clinical trials. We investigated the cognitive subgroups by using a data-driven clustering technique in an AD cohort. People with mild-moderate probable AD from Taiwan was included. Neuropsychological test results from the Cognitive Abilities Screening Instrument were clustered using nonnegative matrix factorization. We identified two clusters in 112 patients with predominant deficits in memory (62.5%) and non-memory (37.5%) cognitive domains, respectively. The memory group performed worse in short-term memory and orientation and better in attention than the non-memory group. At baseline, patients in the memory group had worse global cognitive status and dementia severity. Linear mixed effect model did not reveal difference in disease trajectory within 3 years of follow-up between the two clusters. Our results provide insights into the cognitive heterogeneity in probable AD in an Asian population.

Nondeclarative associative learning in Alzheimer's disease: An overview of eyeblink, fear, and other emotion-based conditioning

Alzheimer's disease is a neurodegenerative disorder that is characterized by progressive cognitive decline, particularly in declarative memory, and the presence of β-amyloid plaques, neurofibrillary tangles, and cortical atrophy (especially in the temporal lobe). Unlike the relationship between the temporal cortex and declarative memory, nondeclarative memories (e.g., motor, fear, and other emotion-based memories) involve distinct neural structures. The present review investigates nondeclarative associative learning ability in Alzheimer's disease. We discuss eyeblink conditioning, fear conditioning, and other emotion-based learning and present the functions and brain areas that are involved in each type of learning. Evidence suggests that nondeclarative learning is also affected by Alzheimer's disease, although some forms of learning may be relatively preserved. Details about each nondeclarative associative learning process and the implications of these findings are presented.

Cognitive differences across ethnoracial category, socioeconomic status across the Alzheimer's disease spectrum: Can an ability discrepancy score level the playing field?

An ability discrepancy (crystallized minus fluid abilities) might be a personally relevant cognitive marker of risk for Alzheimer's disease (AD) and might help reduce measurement bias often present in traditional measures of cognition. In a large national sample of adults aged 60-104 years (N = 14,257), we investigated whether the intersectionality of group characteristics previously shown to pose a risk for AD including ethnoracial category, socioeconomic status, and sex (a) differed in ability discrepancy compared to traditional neuropsychological tests and (b) moderated the relationship between an ability discrepancy and AD symptom severity. In cognitively normal older adults, results indicated that across each decade, fluid and memory composite scores generally exhibited large group differences with sex, education, and ethnoracial category. In contrast, the ability discrepancy score showed much smaller group differences, thus removing much of the biases inherent in the tests. Women with higher education differed in discrepancy performance from other groups, suggesting a subgroup in which this score might reduce bias to a lesser extent. Importantly, a greater ability discrepancy was associated with greater AD symptom severity across the AD continuum. Subgroup analyses suggest that this relationship holds for all groups except for some subgroups of Hispanic Americans. These findings suggest that an ability discrepancy measure might be a better indicator of baseline cognition than traditional measures that show more egregious measurement bias across diverse groups of people.

Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020.

METHODS

We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion.

RESULTS

Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity.

DISCUSSION

ADNI has had a profound impact in improving clinical trials for AD.

Functional Integrity of Executive Control Network Contributed to Retained Executive Abilities in Mild Cognitive Impairment

Background: Mild cognitive impairment (MCI) is considered to be a transitional state between normal aging and Alzheimer's dementia (AD). Recent studies have indicated that executive function (EF) declines during MCI. However, only a limited number of studies have investigated the neural basis of EF deficits in MCI. Herein, we investigate the changes of regional brain spontaneous activity and functional connectivity (FC) of the executive control network (ECN) between high EF and low EF groups. Methods: According to EF composite score (ADNI-EF) from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we divided MCI into two groups, including the MCI-highEF group and MCI-lowEF group. Resting-state functional MRI was utilized to investigate the fractional amplitude of low-frequency fluctuation (fALFF) and ECN functional connectivity across 23 healthy controls (HC), 11 MCI-highEF, and 14 MCI-lowEF participants. Moreover, a partial correlation analysis was carried out to examine the relationship between altered fALFF or connectivity of the ECN and the ADNI-EF. Results: Compared to HC, the MCI-highEF participants demonstrated increased fALFF in the left superior temporal gyrus (STG), as well as decreased fALFF in the right precentral gyrus, right postcentral gyrus, and left middle frontal gyrus (MFG). The MCI-lowEF participants demonstrated increased fALFF in the cerebellar vermis and decreased fALFF in the left MFG. Additionally, compared to HC, the MCI-highEF participants indicated no significant difference in connectivity of the ECN. Furthermore, the MCI-lowEF participants showed increased ECN FC in the left cuneus and left MFG, as well as decreased ECN functional connectivity in the right parahippocampal gyrus (PHG). Notably, the altered fALFF in the left MFG was positively correlated to ADNI-EF, while the altered fALFF in cerebellar vermis is negatively correlated with ADNI-EF across the two MCI groups and the HC group. Altered ECN functional connectivity in the right PHG is negatively correlated to ADNI-EF, while altered ECN functional connectivity in the left cuneus is negatively correlated to ADNI-EF across the three groups. Conclusions: Our current study demonstrates the presence of different patterns of regional brain spontaneous activity and ECN FC in the MCI-highEF group and MCI-lowEF group. Furthermore, the ECN FC of the MCI-highEF group was not disrupted, which may contribute to retained EF in MCI.

Trajectories and risk factors of dementia progression: a memory clinic cohort followed up to 3 years from diagnosis

OBJECTIVES

Patients with dementia follow different trajectories of progression. We aimed to investigate which factors at the time of diagnosis could predict trajectory group membership.

DESIGN

Longitudinal observational study.

SETTING

Specialized memory clinic, Oslo University Hospital in Norway.

PARTICIPANTS

Patients assessed at the memory clinic, between 12 January 2009 and 31 July 2016, who were registered in the Norwegian Registry of persons assessed for cognitive symptoms (NorCog) and diagnosed with dementia after the baseline examination period (n = 442). The patients were followed up to 3 years, with an average of 3.5 examinations.

MEASUREMENTS

Clinical Dementia Rating Scale Sum of Boxes (CDR-SB), Mini-Mental State Examination (MMSE), the Consortium to Establish a Registry of Alzheimer's disease (CERAD) 10-item word list delayed recall, the Clock Drawing Test, (CDT) Trail Making Test A (TMT-A), and Neuropsychiatric Inventory Questionnaire (NPI-Q). Based on changes in scores on the CDR-SB, we used group-based trajectory modeling (GBTM) to explore the presence of trajectory groups. Multinomial logistic regression was used to explore whether a set of baseline variables could predict trajectory group membership.

RESULTS

Three trajectory groups were identified, one with a slow progression rate and two with more-rapid progression. Rapid progression was associated with older age, lower cognitive function (MMSE and TMT-A), and more-pronounced neuropsychiatric symptoms (NPI-Q) at the time of diagnosis.

CONCLUSIONS

Our findings demonstrate the heterogeneity of dementia progression and describe risk factors for rapid progression, emphasizing the need for individual follow-up regimes. For future intervention studies, our results may guide the selection of patients.

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

OBJECTIVE

To investigate the influence of heterogeneity in disease progression for detecting treatment effects in Alzheimer disease (AD) trials, using a simulation study.

METHODS

Individuals with an abnormal amyloid PET scan, diagnosis of mild cognitive impairment or dementia, baseline Mini-Mental State Examination (MMSE) score ≥24, global Clinical Dementia Rating (CDR) score of 0.5, and ≥1 follow-up cognitive assessment were selected from the Alzheimer's Disease Neuroimaging Initiative database (n = 302, age 73 ± 6.7; 44% female; 16.1 ± 2.7 years of education; 69% APOE ε4 carrier). We simulated a clinical trial by randomly assigning individuals to a "placebo" and "treatment" group and subsequently computed group differences on the CDR-sum of boxes (CDR-SB), Alzheimer's Disease Assessment Scale-cognitive subscale-13 and MMSE after 18 months follow-up. We repeated this simulation 10,000 times to determine the 95% range of effect sizes. We further studied the influence of known AD risk factors (age, sex, education, APOE ε4 status, CSF total tau levels) on the variability in effect sizes.

RESULTS

Individual trajectories on all cognitive outcomes were highly variable, and the 95% ranges of possible effect sizes at 18 months were broad (e.g., ranging from 0.35 improvement to 0.35 decline on the CDR-SB). Results of recent anti-amyloid trials mostly fell within these 95% ranges of effect sizes. APOE ε4 carriers and individuals with abnormal baseline tau levels showed faster decline at group level, but also greater within-group variability, as illustrated by broader 95% effect size ranges (e.g., ±0.70 points for the CDR-SB).

CONCLUSIONS

Individuals with early AD show heterogeneity in disease progression, which increases when stratifying on risk factors associated with progression. We provide guidance for a priori effect sizes on cognitive outcomes for detecting true change, which is crucial for future AD trials.

Using machine intelligence to uncover Alzheimer’s disease progression heterogeneity

Aim: Research suggests that Alzheimer’s disease (AD) is heterogeneous with numerous subtypes. Through a proprietary interactive ML system, several underlying biological mechanisms associated with AD pathology were uncovered. This paper is an introduction to emerging analytic efforts that can more precisely elucidate the heterogeneity of AD. Methods: A public AD data set (GSE84422) consisting of transcriptomic data of postmortem brain samples from healthy controls (n = 121) and AD (n = 380) subjects was analyzed. Data were processed by an artificial intelligence platform designed to discover potential drug repurposing candidates, followed by an interactive augmented intelligence program. Results: Using perspective analytics, six perspective classes were identified: Class I is defined by TUBB1, ASB4, and PDE5A; Class II by NRG2 and ZNF3; Class III by IGF1, ASB4, and GTSE1; Class IV is defined by cDNA FLJ39269, ITGA1, and CPM; Class V is defined by PDE5A, PSEN1, and NDUFS8; and Class VI is defined by DCAF17, cDNA FLJ75819, and SLC33A1. It is hypothesized that these classes represent biological mechanisms that may act alone or in any combination to manifest an Alzheimer’s pathology. Conclusions: Using a limited transcriptomic public database, six different classes that drive AD were uncovered, supporting the premise that AD is a heterogeneously complex disorder. The perspective classes highlighted genetic pathways associated with vasculogenesis, cellular signaling and differentiation, metabolic function, mitochondrial function, nitric oxide, and metal ion metabolism. The interplay among these genetic factors reveals a more profound underlying complexity of AD that may be responsible for the confluence of several biological factors. These results are not exhaustive; instead, they demonstrate that even within a relatively small study sample, next-generation machine intelligence can uncover multiple genetically driven subtypes. The models and the underlying hypotheses generated using novel analytic methods may translate into potential treatment pathways.

Perioperative probiotic treatment decreased the incidence of postoperative cognitive impairment in elderly patients following non-cardiac surgery: A randomised double-blind and placebo-controlled trial

BACKGROUND

Given that population aging is a global health challenge, the high prevalence of postoperative neurocognitive impairment in elderly patients necessitates the development of novel simple and effective prevention methods.

OBJECTIVE

To evaluate the effects of perioperative application of oral probiotic as a prophylaxis for cognitive impairment in elderly patients following non-cardiac surgery.

METHODS

This randomised double-blind and placebo-controlled trial included 120 elderly patients (in a modified intention-to-treat analysis) scheduled for elective orthopaedic or colorectal surgery. Patients were assigned to receive either probiotics or a placebo from hospital admission until discharge. The primary endpoint was the incidence of postoperative cognitive impairment, defined as a decrease of ≥3 points in the Mini-Mental State Examination (MMSE) scores from admission baseline to the 7th day post-surgery. Secondary endpoints included changes in plasma interleukin-6 (IL-6) and cortisol, postoperative pain intensity, postoperative sleep quality, gastrointestinal function recovery, and faecal microbiota composition.

RESULTS

The incidence of postoperative cognitive impairment in the probiotic group was significantly lower than in the control group (3 of 59 patients [5.1%] vs. 10 of 61 patients [16.4%], P = 0.046). In addition, compared to pre-surgery, the levels of plasma IL-6 and cortisol in the probiotic group decreased more than in the control group 5-7 days after surgery (IL-6: -117.90 ± 49.15 vs. -14.93 ± 15.21, P = 0.044; cortisol: -158.70 ± 53.52 vs. 40.98 ± 72.48, P = 0.010). Relative abundance at the genus level in the faeces of the probiotic group also changed more than in that of the control group during the perioperative period. In contrast, postoperative pain intensity, sleep quality, and gastrointestinal function recovery did not differ significantly between the two groups.

CONCLUSION

Perioperative application of oral probiotic prevents postoperative cognitive impairment in elderly patients following non-cardiac surgery, possibly via the limitation of peripheral inflammation and the stress response.

Understanding and predicting the longitudinal course of dementia

PURPOSE OF REVIEW

To date, most research in dementia has focused either on the identification of dementia risk prediction or on understanding changes and predictors experienced by individuals before diagnosis. Despite little is known about how individuals change after dementia diagnosis, there is agreement that changes occur over different time scales and are multidomain. In this study, we present an overview of the literature regarding the longitudinal course of dementia.

RECENT FINDINGS

Our review suggests the evidence is scarce and findings reported are often inconsistent. We identified large heterogeneity in dementia trajectories, risk factors considered and modelling approaches employed. The heterogeneity of dementia trajectories also varies across outcomes and domains investigated.

SUMMARY

It became clear that dementia progresses very differently, both between and within individuals. This implies an average trajectory is not informative to individual persons and this needs to be taken into account when communicating prognosis in clinical care. As persons with dementia change in many more ways during their patient journey, heterogeneous disease progressions are the result of disease and patient characteristics. Prognostic models would benefit from including variables across a number of domains. International coordination of replication and standardization of the research approach is recommended.