The clinical practice of risk reduction for Alzheimer's disease: A precision medicine approach

The impact of rare genetic variants on Alzheimer disease

Alzheimer disease (AD) is a progressive neurodegenerative disease with a strong genetic component. Although autosomal dominant mutations and common risk variants in AD risk have been extensively studied, the genetic underpinning of polygenic AD remains incompletely understood. Rare variants could elucidate part of the missing heritability in AD. Rare variant research gained momentum with the discovery of a rare variant in TREM2, along with loss-of-function variants in ABCA7 and SORL1, and has come into full bloom in recent years. Not only has the number of rare variant discoveries increased through large-scale whole-exome and genome sequencing studies, improved imputation in genome-wide association studies and increased focus on understudied populations, the number of studies mapping the functional effects of several of these rare variants has also significantly increased, leading to insights in the pathogenesis of AD and drug development. Here we provide a comprehensive overview of the known and novel rare variants implicated in AD risk, highlighting how they shine new light on AD pathophysiology and provide new inroads for drug development. We will review their impact on individual, familial and population levels, and discuss the potential and challenges of rare variants in genetic risk prediction.

Novel techniques for early diagnosis and monitoring of Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD) is the most common neurodegenerative disorder, which is characterized by a progressive loss of cognitive functions. The high prevalence, chronicity, and multimorbidity are very common in AD, which significantly impair the quality of life and functioning of patients. Early detection and accurate diagnosis of Alzheimer's disease (AD) can stop the illness from progressing thereby postponing its symptoms. Therefore, for the early diagnosis and monitoring of AD, more sensitive, noninvasive, straightforward, and affordable screening tools are needed.

AREAS COVERED

This review summarizes the importance of early detection methods and novel techniques for Alzheimer's disease diagnosis that can be used by healthcare professionals.

EXPERT OPINION

Early diagnosis assists the patient and caregivers to understand the problem establishing reasonable goals and making future plans together. Early diagnosis techniques not only help in monitoring disease progression but also provide crucial information for the development of novel therapeutic targets. Researchers can plan to potentially alleviate symptoms or slow down the progression of Alzheimer's disease by identifying early molecular changes and targeting altered pathways.

The double life of glucose metabolism: brain health, glycemic homeostasis, and your patients with type 2 diabetes

The maintenance of cognitive function is essential for quality of life and health outcomes in later years. Cognitive impairment, however, remains an undervalued long-term complication of type 2 diabetes by patients and providers alike. The burden of sustained hyperglycemia includes not only cognitive deficits but also the onset and progression of dementia-related conditions, including Alzheimer's disease (AD). Recent research has shown that the brain maintains an independent glucose "microsystem"-evolved to ensure the availability of fuel for brain neurons without interruption by transient hypoglycemia. When this milieu is perturbed, brain hyperglycemia, brain glucotoxicity, and brain insulin resistance can ensue and interfere with insulin signaling, a key pathway to cognitive function and neuronal integrity. This newly understood brain homeostatic system operates semi-autonomously from the systemic glucoregulatory apparatus. Large-scale clinical studies have shown that systemic dysglycemia is also strongly associated with poorer cognitive outcomes, which can be mitigated through appropriate clinical management of plasma glucose levels. Moreover, these studies demonstrated that glucose-lowering agents are not equally effective at preventing cognitive dysfunction. Glucagon-like peptide-1 (GLP-1) receptor analogs and sodium glucose cotransporter 2 inhibitors (SGLT2is) appear to afford the greatest protection; metformin and dipeptidyl peptidase 4 inhibitors (DPP-4is) also significantly improved cognitive outcomes. Sulfonylureas (SUs) and exogenous insulin, on the other hand, do not provide the same protection and may actually worsen cognitive outcomes. In the creation of a treatment plan, comorbid cognitive conditions should be considered. These efficacious treatments create a new gold standard of managing hyperglycemia-one which is consistent with the "complication-centric prescribing" mandates issued in type 2 diabetes treatment guidelines. The increasing longevity enjoyed by our populace places the onus on clinical care to play the "long game" in using targeted treatments for glucose control in patients with, or at risk for, cognitive decline to maintain cognitive wellness later in life. This article reviews critical emerging data for scientists and trialists and translates new enhancements in patient care for practitioners.

The therapeutic potential of glycyrrhizic acid and its metabolites in neurodegenerative diseases: Evidence from animal models

Neurodegenerative diseases, mostly occurring in the elderly population, are the significant cause of disability and death worldwide. The pathogenesis of neurodegenerative diseases is still largely unknown yet, although they have been continuously explored. Thus, there is still a lack of safe, effective, and low side effect drugs in clinical practice for the treatment of neurodegenerative diseases. Pieces of accumulating evidence have demonstrated that licorice played neuroprotective roles in various neurodegenerative diseases. In the past two decades, increasing studies have indicated that glycyrrhizic acid (GL), the main active ingredient from traditional Chinese medicine licorice (widely used in the food industry) and a triterpenoid saponin with multiple pharmacological effects (such as anti-oxidant, anti-inflammatory, and immune regulation), and its metabolites (glycyrrhetinic acid and carbenoxolone) play a neuroprotective role in a range of neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease and epilepsy. This review will elaborate on the multiple neuroprotective mechanisms of GL and its metabolites in this series of diseases, aiming to provide a basis for further research on these protective drugs for neurodegenerative diseases and their clinical application. In summary, GL may be a promising candidate drug for the therapy of neurodegenerative diseases.

A Peptide-Drug Conjugate-Based Nanoplatform for Immunometabolic Activation and In Situ Nerve Regeneration in Advanced-Stage Alzheimer's Disease

The formidable protection of physiological barriers and unclear pathogenic mechanisms impede drug development for Alzheimer's disease (AD). As defenders of the central nervous system, immune-metabolism function, and stemness of glial cells remain dormant during degeneration, representing a significant challenge for simultaneously targeting and modulating. Here, a modular nanoplatform is presented composed of peptide-drug conjugates and an inflammation-responsive core. The nanoplatform is transported through the blood-brain barrier via transcytosis and disassembles in the oxidative stress microenvironment upon intravenous administration. The released drug-conjugated modules can specifically target and deliver hydroxychloroquine (HCQ) and all-trans retinoic acid (ATRA) to microglia and astrocytes, respectively. The immune function of chronic tolerant microglia is activated by metabolic modulation, and reactive astrocytes trans-differentiate into functional neurons. In a transgenic mouse model, nanoplatform reduces levels of toxic proteins and inflammation while increasing neuronal density. This results in the amelioration of learning and memory decline. The modular nanoplatform provides design principles for multi-cellular targeting and combination nano-therapy for inflammation-related diseases.

Alzheimer's Disease Risk Reduction Health Coaching: Comparative Analysis

BACKGROUND AND OBJECTIVES

Recent guidelines point to lifestyle as a tool for decreasing Alzheimer's disease (AD) risk. To address the limited practice and availability of AD risk reduction interventions, this study aimed to explore the feasibility of a community-level lifestyle intervention targeting high-risk groups.

RESEARCH DESIGN AND METHODS

Diverse older adults (60+) living in the Richmond, VA, local area, with the following risk factors, incomes below $12,000/year and managing diabetes or cardiovascular disease, were offered weekly lifestyle telephone health coaching for 12 weeks in 2019-2020 (intervention group). The health coaching sessions provided Alzheimer's disease (AD) lifestyle risk reduction education and goal setting/planning. The intervention sample (n = 40, mean age 68 years (range: 60-76 years), was 90% African American/Black (n = 36) and 45% male (n = 18). Thereafter, in 2021-2022, n = 37 individuals in the same area were recruited as a comparison group and not given health coaching (control group), mean age of 65.5 years (range: 57-83 years), 92% African American/Black (n = 34), and 50% male (n = 18).

RESULTS

Repeated-measures intervention effects were seen for cognitive ability, indicating greater improvement in the intervention group (p < .01). Significant difference scores indicated greater cognitive ability (p < .01) and physical activity (p < .001) gains in the intervention group, with intervention subjects with reported memory problems showed relatively less physical activity gains (p < .05).

DISCUSSION AND IMPLICATIONS

This work creates the impetus for future large-scale AD risk reduction investigations to mitigate and improve modifiable risk among diverse older adults. Our positive trends in AD risk reduction support telephone-based health coaching as a feasible AD risk reduction intervention.

Toolkit to Examine Lifelike Language v.2.0: Optimizing Speech Biomarkers of Neurodegeneration

INTRODUCTION

The Toolkit to Examine Lifelike Language (TELL) is a web-based application providing speech biomarkers of neurodegeneration. After deployment of TELL v.1.0 in over 20 sites, we now introduce TELL v.2.0.

METHODS

First, we describe the app's usability features, including functions for collecting and processing data onsite, offline, and via videoconference. Second, we summarize its clinical survey, tapping on relevant habits (e.g., smoking, sleep) alongside linguistic predictors of performance (language history, use, proficiency, and difficulties). Third, we detail TELL's speech-based assessments, each combining strategic tasks and features capturing diagnostically relevant domains (motor function, semantic memory, episodic memory, and emotional processing). Fourth, we specify the app's new data analysis, visualization, and download options. Finally, we list core challenges and opportunities for development.

RESULTS

Overall, TELL v.2.0 offers scalable, objective, and multidimensional insights for the field.

CONCLUSION

Through its technical and scientific breakthroughs, this tool can enhance disease detection, phenotyping, and monitoring.

Gantenerumab for early Alzheimer's disease: a systematic review and meta-analysis

INTRODUCTION

Gantenerumab is a monoclonal antibody targeting amyloid β protein (Aβ) in early Alzheimer's disease (AD). The authors sought to evaluate gantenerumab safety and efficacy in early AD patients.

METHODS

MEDLINE, Embase, and Cochrane databases were systematically searched until 2 December 2023. Data were examined using the Mantel-Haenszel method and 95% confidence intervals (CIs). Meta-regression analysis was conducted to evaluate a possible link between baseline Clinical Dementia Rating Scale - Sum of Boxes (CDR-SB) and amyloid-related imaging abnormalities (ARIA) at follow-up. R, version 4.2.3, was used for statistical analysis.

RESULTS

A total of 4 RCTs and 2848 patients were included, of whom 1580 (55%) received subcutaneous gantenerumab. Concerning clinical scores, the placebo group achieved better rates of change in the Disease Assessment Scale (ADAS-Cog13) (SMD -0.11; 95% CI -0.19- -0.03; p = 0.008569; I2 = 0%). Gantenerumab was strongly associated with the occurrence of ARIA-E and ARIA-H: (19.67% vs. 2.31%; RR 9.46; 95% CI 5.55-16.11; p = <0.000001; I2 = 10%) and (21.95% vs. 12.38%; RR 1.79; 95% CI 1.50-2.13; p = <0.000001; I2 = 0%), respectively.

DISCUSSION

In this meta-analysis, consistent results suggest that gantenerumab is not safe and efficient for early AD, showing no improvement in clinical scores for AD and being associated with the occurrence of ARIA-E and ARIA-H.

Transcranial direct current stimulation and neuronal functional connectivity in MCI: role of individual factors associated to AD

Background

Alzheimer's disease (AD) encompasses a spectrum that may progress from mild cognitive impairment (MCI) to full dementia, characterized by amyloid-beta and tau accumulation. Transcranial direct current stimulation (tDCS) is being investigated as a therapeutic option, but its efficacy in relation to individual genetic and biological risk factors remains underexplored.

Objective

To evaluate the effects of a two-week anodal tDCS regimen on the left dorsolateral prefrontal cortex, focusing on functional connectivity changes in neural networks in MCI patients resulting from various possible underlying disorders, considering individual factors associated to AD such as amyloid-beta deposition, APOE ϵ4 allele, BDNF Val66Met polymorphism, and sex.

Methods

In a single-arm prospective study, 63 patients with MCI, including both amyloid-PET positive and negative cases, received 10 sessions of tDCS. We assessed intra- and inter-network functional connectivity (FC) using fMRI and analyzed interactions between tDCS effects and individual factors associated to AD.

Results

tDCS significantly enhanced intra-network FC within the Salience Network (SN) and inter-network FC between the Central Executive Network and SN, predominantly in APOE ϵ4 carriers. We also observed significant sex*tDCS interactions that benefited inter-network FC among females. Furthermore, the effects of multiple modifiers, particularly the interaction of the BDNF Val66Met polymorphism and sex, were evident, as demonstrated by increased intra-network FC of the SN in female Met non-carriers. Lastly, the effects of tDCS on FC did not differ between the group of 26 MCI patients with cerebral amyloid-beta deposition detected by flutemetamol PET and the group of 37 MCI patients without cerebral amyloid-beta deposition.

Conclusions

The study highlights the importance of precision medicine in tDCS applications for MCI, suggesting that individual genetic and biological profiles significantly influence therapeutic outcomes. Tailoring interventions based on these profiles may optimize treatment efficacy in early stages of AD.

Effects of transcranial direct current stimulation on cognition in MCI with Alzheimer's disease risk factors using Bayesian analysis

Despite the growing interest in precision medicine-based therapies for Alzheimer's disease (AD), little research has been conducted on how individual AD risk factors influence changes in cognitive function following transcranial direct current stimulation (tDCS). This study evaluates the cognitive effects of sequential tDCS on 63 mild cognitive impairment (MCI) patients, considering AD risk factors such as amyloid-beta deposition, APOE ε4, BDNF polymorphism, and sex. Using both frequentist and Bayesian methods, we assessed the interaction of tDCS with these risk factors on cognitive performance. Notably, we found that amyloid-beta deposition significantly interacted with tDCS in improving executive function, specifically Stroop Word-Color scores, with strong Bayesian support for this finding. Memory enhancements were differentially influenced by BDNF Met carrier status. However, sex and APOE ε4 status did not show significant effects. Our results highlight the importance of individual AD risk factors in modulating cognitive outcomes from tDCS, suggesting that precision medicine may offer more effective tDCS treatments tailored to individual risk profiles in early AD stages.

Autoencoder to Identify Sex-Specific Sub-phenotypes in Alzheimer's Disease Progression Using Longitudinal Electronic Health Records

Alzheimer's Disease (AD) is a complex neurodegenerative disorder significantly influenced by sex differences, with approximately two-thirds of AD patients being women. Characterizing the sex-specific AD progression and identifying its progression trajectory is a crucial step to developing effective risk stratification and prevention strategies. In this study, we developed an autoencoder to uncover sex-specific sub-phenotypes in AD progression leveraging longitudinal electronic health record (EHR) data from OneFlorida+ Clinical Research Consortium. Specifically, we first constructed temporal patient representation using longitudinal EHRs from a sex-stratified AD cohort. We used a long short-term memory (LSTM)-based autoencoder to extract and generate latent representation embeddings from sequential clinical records of patients. We then applied hierarchical agglomerative clustering to the learned representations, grouping patients based on their progression sub-phenotypes. The experimental results show we successfully identified five primary sex-based AD sub-phenotypes with corresponding progression pathways with high confidence. These sex-specific sub-phenotypes not only illustrated distinct AD progression patterns but also revealed differences in clinical characteristics and comorbidities between females and males in AD development. These findings could provide valuable insights for advancing personalized AD intervention and treatment strategies.

Clinical implications of brain asymmetries

No two human brains are alike, and with the rise of precision medicine in neurology, we are seeing an increased emphasis on understanding the individual variability in brain structure and function that renders every brain unique. Functional and structural brain asymmetries are a fundamental principle of brain organization, and recent research suggests substantial individual variability in these asymmetries that needs to be considered in clinical practice. In this Review, we provide an overview of brain asymmetries, variations in such asymmetries and their relevance in the clinical context. We review recent findings on brain asymmetries in neuropsychiatric and neurodevelopmental disorders, as well as in specific learning disabilities, with an emphasis on large-scale database studies and meta-analyses. We also highlight the relevance of asymmetries for disease symptom onset in neurodegenerative diseases and their implications for lateralized treatments, including brain stimulation. We conclude that alterations in brain asymmetry are not sufficiently specific to act as diagnostic biomarkers but can serve as meaningful symptom or treatment response biomarkers in certain contexts. On the basis of these insights, we provide several recommendations for neurological clinical practice.

Exploring symptom clusters in mild cognitive impairment and dementia with the NIH Toolbox

OBJECTIVE

Symptom clustering research provides a unique opportunity for understanding complex medical conditions. The objective of this study was to apply a variable-centered analytic approach to understand how symptoms may cluster together, within and across domains of functioning in mild cognitive impairment (MCI) and dementia, to better understand these conditions and potential etiological, prevention, and intervention considerations.

METHOD

Cognitive, motor, sensory, emotional, and social measures from the NIH Toolbox were analyzed using exploratory factor analysis (EFA) from a dataset of 165 individuals with a research diagnosis of either amnestic MCI or dementia of the Alzheimer's type.

RESULTS

The six-factor EFA solution described here primarily replicated the intended structure of the NIH Toolbox with a few deviations, notably sensory and motor scores loading onto factors with measures of cognition, emotional, and social health. These findings suggest the presence of cross-domain symptom clusters in these populations. In particular, negative affect, stress, loneliness, and pain formed one unique symptom cluster that bridged the NIH Toolbox domains of physical, social, and emotional health. Olfaction and dexterity formed a second unique cluster with measures of executive functioning, working memory, episodic memory, and processing speed. A third novel cluster was detected for mobility, strength, and vision, which was considered to reflect a physical functioning factor. Somewhat unexpectedly, the hearing test included did not load strongly onto any factor.

CONCLUSION

This research presents a preliminary effort to detect symptom clusters in amnestic MCI and dementia using an existing dataset of outcome measures from the NIH Toolbox.

Toolkit to Examine Lifelike Language (TELL): An app to capture speech and language markers of neurodegeneration

Automated speech and language analysis (ASLA) is a promising approach for capturing early markers of neurodegenerative diseases. However, its potential remains underexploited in research and translational settings, partly due to the lack of a unified tool for data collection, encryption, processing, download, and visualization. Here we introduce the Toolkit to Examine Lifelike Language (TELL) v.1.0.0, a web-based app designed to bridge such a gap. First, we outline general aspects of its development. Second, we list the steps to access and use the app. Third, we specify its data collection protocol, including a linguistic profile survey and 11 audio recording tasks. Fourth, we describe the outputs the app generates for researchers (downloadable files) and for clinicians (real-time metrics). Fifth, we survey published findings obtained through its tasks and metrics. Sixth, we refer to TELL's current limitations and prospects for expansion. Overall, with its current and planned features, TELL aims to facilitate ASLA for research and clinical aims in the neurodegeneration arena. A demo version can be accessed here:  https://demo.sci.tellapp.org/ .

Mindfulness-Based Interventions Targeting Modifiable Lifestyle Behaviors Associated With Brain Health: A Systematic Review and Meta-Analysis

A systematic review and meta-analysis investigated randomized clinical trials (RCTs) of mindfulness-based interventions (MBIs) targeting lifestyle behaviors commonly associated with brain health in adults. Data sources included Ovid Medline, Ovid PsycINFO, CINAHL [EBSCO], Embase, Cochrane Library [Ovid], Web of Science, and https://ClinicalTrials.gov. Studies were screened using Covidence 2.0. A total of 79 published RCTs of MBIs for adults (18+, patient and non-patient populations) targeting one or more lifestyle behavior (physical activity, sleep, diet, alcohol use, tobacco cessation, and social and mental activities) met eligibility criteria. MBIs were associated with reduced sleep disturbance (40/54 RCTs; 3537 participants; SMD = -.53; 95% CI = -.74 to -.32; I 2 = 78%), increased physical activity (9/17 RCTs analyzed; 685 participants; SMD = .72; 95% CI = .04 to 1.40; I 2 = 89%), improved tobacco cessation (8/12 RCTs; 1234 participants; OR = 2.11; 95% CI = 1.12 to 3.97; I 2 = 55%), and lowered alcohol use (4/6 RCTs; 261 participants; SMD = -.39; 95% CI = -.45 to -.32; I 2 = 0%). This review found moderate to high-quality evidence for MBIs targeting sleep, physical activity, alcohol use, and tobacco cessation. Heterogeneity for these outcomes and insufficient data to analyze diet, mental activities, and cognitive functioning limit our ability to draw definitive conclusions about the effects of MBIs on brain health.

Effect of Multimodal Intervention in Individuals with Mild Cognitive Impairment: A Randomized Clinical Trial in Shanghai

Background

Previous trials have indicated that multimodal training could improve cognitive functions and moods in individuals with mild cognitive impairment (MCI). However, evidence was mainly obtained from studies in high-income countries.

Objective

This trial aims to investigate the efficacy, safety, and potential mechanism of a multimodal intervention on cognitive function in individuals with MCI living in a community.

Methods

In this single-blind, randomized controlled trial, 120 participants with MCI were randomly assigned to either the intervention group or the control group. The intervention group received the multimodal intervention, while the control group received regular health education. Neuropsychological tests and magnetic resonance imaging (MRI) were conducted at baseline and after the 12-week intervention.

Results

Fifty-nine and fifty-seven participants respectively in the intervention and control groups completed the trial. The intervention group shown improvements in primary outcome, Mini-Mental State Exam (MMSE) total score (mean difference -0.96, 95% CI [-1.58, -0.34], p = 0.003), and secondary outcomes: MMSE recall (-0.39, 95% CI [-0.71, -0.07], p = 0.019), MMSE language (-0.26, 95% CI [-0.44, -0.07], p = 0.007), Auditory Verbal Learning Test instantaneous memory (-3.30, 95% CI [-5.70, -0.89], p = 0.008), Digit Symbol Substitution Test total score (-2.91, 95% CI [-5.67, -0.15], p = 0.039), digit span forwards (-1.25, 95% CI [-1.93, -0.56], p < 0.001), and Digit Span Test (-1.33, 95% CI [-2.33, -0.34], p = 0.009) compared to the control group. Improvements were observed in structural and functional connectivity related to language, concentration, executive function, memory, and recall functioning via MRI in the intervention group.

Conclusions

The multimodal intervention improved cognitive function in individuals with MCI in cognitive performance and neuroimaging.

An Evolutionary Concept Analysis of Precision Medicine, and Its Contribution to a Precision Health Model for Nursing Practice

Precision medicine is a new concept that has been routinely encountered in the literature for little more than a decade. With increasing use, it becomes crucial to understand the meaning of this concept as it is applied in various settings. An evolutionary concept analysis was conducted to develop an understanding of the essential features of precision medicine and its use. The analysis led to a comprehensive list of the antecedents, attributes, and consequences of precision medicine in multiple settings. With this understanding, precision medicine becomes part of the broader practice of precision health, an important process proposed by nursing scholars to provide complete, holistic care to our patients. A model for precision health is presented as a framework for care.

sTREM2 Mediates the Correlation Between BIN1 Gene Polymorphism and Tau Pathology in Alzheimer's Disease

Background

Bridging integrator 1 (BIN1) gene polymorphism has been reported to play a role in the pathological processes of Alzheimer's disease (AD).

Objective

To explore the association of BIN1 loci with neuroinflammation and AD pathology.

Methods

Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 495) was the discovery cohort, and Chinese Alzheimer's Biomarker and LifestylE (CABLE, N = 619) study was used to replicate the results. Two BIN1 gene polymorphism (rs7561528 and rs744373) were included in the analysis. Multiple linear regression model and causal mediation analysis conducted through 10,000 bootstrapped iterations were used to examine the BIN1 loci relationship with cerebrospinal fluid (CSF) AD biomarkers and alternative biomarker of microglial activation microglia-soluble triggering receptor expressed on myeloid cells 2 (sTREM2).

Results

In ADNI database, we found a significant association between BIN1 loci (rs7561528 and rs744373) and levels of CSF phosphorylated-tau (P-tau) (pc = 0.017; 0.010, respectively) and total-tau (T-tau) (pc = 0.011; 0.013, respectively). The BIN1 loci were also correlated with CSF sTREM2 levels (pc = 0.010; 0.008, respectively). Mediation analysis demonstrated that CSF sTREM2 partially mediated the association of BIN1 loci with P-tau (Proportion of rs7561528 : 20.8%; Proportion of rs744373 : 24.8%) and T-tau (Proportion of rs7561528 : 36.5%; Proportion of rs744373 : 43.9%). The analysis in CABLE study replicated the mediation role of rs7561528.

Conclusions

This study demonstrated the correlation between BIN1 loci and CSF AD biomarkers as well as microglia biomarkers. Additionally, the link between BIN1 loci and tau pathology was partially mediated by CSF sTREM2.

Cognitive Performance following Single- or Multi-Session Exercise Intervention in Middle Age: A Systematic Review

BACKGROUND

Research in modifiable behaviors, like exercise, on risk for dementia is increasing. Although many studies focus on older adults, brain pathology for Alzheimer's Disease can begin in middle age, suggesting an ideal target for intervention.

METHODS

We conducted a systematic review from exercise intervention studies on cognitive function among healthy, middle-aged participants (45-65). We searched multiple databases (PubMed, PsycINFO, MEDLINE, Cochrane Central Register of Controlled Trials, Google Scholar) for studies using standard, validated, neuropsychological measures following either single- or multi-session interventions in cognitively-unimpaired, middle-aged adults.

RESULTS

We identified 13 eligible studies. There was notable heterogeneity across studies, with varying design, measures, interventions, and results. Results from single-session studies showed improvement in response inhibition, while results for cognitive flexibility were mixed. No significant changes were found on measures of attention, working memory, or processing speed. Results from multi-session studies were more varied. Verbal memory was found to improve while performance on tests of attention and working memory, processing speed, and executive function were mixed.

CONCLUSION

Importantly, for both single-session and multi-session studies, there was no standard set of neuropsychological tests administered, making it more difficult to synthesize the findings into a single narrative. We end with a discussion on future directions and implementation.

Moving Towards Ethical-Practical Recommendations for Alzheimer's Disease Prediction: Addressing Interindividual, Interprofessional, and Societal Aspects

Biomarkers for predicting Alzheimer's disease (AD) are advancing and their implementation in various healthcare systems is imminent. There is a need for ethical standards addressing information needs, socio-ethical concerns, and expectations of healthy and at-risk persons. We present an ethical approach that integrates different existing ethical frameworks and discussion of our empirical, cross-cultural findings in a multi-layered perspective by addressing three levels. The micro-level focuses on the communication between counseling professionals, persons at risk or in an early stage of dementia, and family members. The meso-level addresses interprofessional cooperation and exchange as a key element for best person-centered care. The macro-level considers public health promotion, the media, and public-funded research. This approach allows to address key ethical concepts including beneficence, non-maleficence, autonomy, informational self-determination, empowerment, and justice. Our contribution specifically examines the ethical challenges associated with AD prediction by means of biomarkers, based on insights from a German-Israeli comparison, and promotes a transdisciplinary discussion across different healthcare contexts. We propose a reflection on three levels to go beyond the clinical counseling context and to consider the rapidly evolving field of biomarkers in the coming years. Our ethical-practical recommendations should not be considered final, but rather procedural and will require continuous adaptation regarding culturally varying practices, new algorithms, meta-analyses, and re-evaluation of established recommendations.

Exploring the potential impact of multi-factor precision interventions in Alzheimer's disease with system dynamics

Numerous clinical trials based on a single-cause paradigm have not resulted in efficacious treatments for Alzheimer's disease (AD). Recently, prevention trials that simultaneously intervened on multiple risk factors have shown mixed results, suggesting that careful design is necessary. Moreover, intensive pilot precision medicine (PM) trial results have been promising but may not generalize to a broader population. These observations suggest that a model-based approach to multi-factor precision medicine (PM) is warranted. We systematically developed a system dynamics model (SDM) of AD for PM using data from two longitudinal studies (N=3660). This method involved a model selection procedure in identifying interaction terms between the SDM components and estimating individualized parameters. We used the SDM to explore simulated single- and double-factor interventions on 14 modifiable risk factors. We quantified the potential impact of double-factor interventions over single-factor interventions as 1.5 [95% CI: 1.5-2.6] and of SDM-based PM over a one-size-fits-all approach as 3.5 [3.1, 3.8] ADAS-cog-13 points in 12 years. Although the model remains to be validated, we tentatively conclude that multi-factor PM could come to play an important role in AD prevention.

Impact of transcranial direct current stimulation on white matter microstructure integrity in mild cognitive impairment patients according to effect modifiers as risk factors for Alzheimer's disease

Background

Little research exists on how individual risk factors for Alzheimer's disease (AD) affect the intermediate phenotype after transcranial direct current stimulation (tDCS), despite the importance of precision medicine-based therapeutic approaches.

Objective

To determine how an application of sequential tDCS (2 mA/day, left dorsolateral prefrontal cortex, 10 sessions) affects changes in white matter (WM) microstructure integrity in 63 mild cognitive impairment (MCI) patients with effect modifiers such as Aβ deposition, APOE ε4 carrier status, BDNF Val66Met polymorphism status, and sex.

Methods

We examined individual effect modifier-by-tDCS interactions and multiple effect modifiers-by-tDCS interactions for diffusion metrics. We also evaluated the association between baseline Aβ deposition and changes in WM microstructure integrity following tDCS.

Results

We found that APOE ε4 carrier status and sex had a significant interaction with tDCS, resulting in increased fractional anisotropy (FA) in the right uncinate fasciculus (UF) after stimulation. Additionally, we observed multiple effect modifiers-by-tDCS interactions on WM integrity of the right UF, leading to a more pronounced increase in FA values in APOE ε4 carriers and females with Val66 homozygotes. Finally, baseline Aβ deposition was positively associated with a difference in FA of the left cingulum in the hippocampal area, which showed a positive association with the changes in the score for delayed memory.

Conclusion

Our study shows the differential impact of individual AD risk factors on changes in the early intermediate phenotype after sequential tDCS in MCI patients. This research emphasizes the importance of precision medicine approaches in tDCS for the prodromal stages of AD.

Sex-based differences in effector cells of the adaptive immune system during Alzheimer's disease and related dementias

Neurological conditions such as Alzheimer's disease (AD) and related dementias (ADRD) present with many challenges due to the heterogeneity of the related disease(s), making it difficult to develop effective treatments. Additionally, the progression of ADRD-related pathologies presents differently between men and women. With two-thirds of the population affected with ADRD being women, ADRD has presented itself with a bias toward the female population. However, studies of ADRD generally do not incorporate sex-based differences in investigating the development and progression of the disease, which is detrimental to understanding and treating dementia. Additionally, recent implications for the adaptive immune system in the development of ADRD bring in new factors to be considered as part of the disease, including sex-based differences in immune response(s) during ADRD development. Here, we review the sex-based differences of pathological hallmarks of ADRD presentation and progression, sex-based differences in the adaptive immune system and how it changes with ADRD, and the importance of precision medicine in the development of a more targeted and personalized treatment for this devastating and prevalent neurodegenerative condition.

The Population Attributable Fraction of Dementia From Audiometric Hearing Loss Among a Nationally Representative Sample of Community-Dwelling Older Adults

BACKGROUND

The population attributable fraction (PAF) of dementia from hearing loss (HL) in the United States is ~2% when incorporating self-reported HL measures. However, self-report might underestimate clinically significant audiometric HL among older adults. Here, we quantified PAFs of dementia from audiometric HL overall and by age, sex, and race/ethnicity groups among a nationally representative sample of community-dwelling older adults in the United States.

METHODS

We used cross-sectional data from Round 11 (2021) of the National Health and Aging Trends Study, a prospective cohort study representing the U.S. Medicare population aged 65+ years (N = 2 470). We estimated model-adjusted PAFs of prevalent dementia by audiometric HL (pure-tone averages: normal hearing, <26 dB HL; mild HL, 26-40 dB HL; moderate or greater HL, ≥41 dB HL).

RESULTS

Among eligible participants (34.8% aged ≥80 years; 55.3% female; 82.4% non-Hispanic White), 37.5% had mild, and 28.8% had moderate or greater HL. Dementia prevalence overall was 10.6%, with the PAF predominately driven by moderate or greater HL (PAF = 16.9%; 95% confidence interval [CI]: 4.1-28.7%). The PAF from any degree of HL was larger but with a wider CI (PAF = 18.7%, 95% CI: -5.3% to 40.1%). There was evidence associations differed by sex but not age or race/ethnicity; moderate or greater HL exhibited stronger associations among males (PAF = 40.5%; 95% CI: 19.5% to 57.2%) than females (PAF = 3.2%; 95% CI: -12.7% to 17.9%).

CONCLUSIONS

In a nationally representative sample of community-dwelling older adults in the United States, 17% of dementia cases were attributable to moderate or greater audiometric HL, an estimate that is eightfold higher relative to studies relying on self-reported hearing measures only.

Sex Differences in the Relationship between Perceived Stress and Cognitive Trajectories

OBJECTIVE

Chronic stress adversely affects cognition, in part due to stress-induced inflammation. Rodent models suggest females are more resilient against stress-related cognitive dysfunction than males; however, few studies have examined this in humans. We examined sex differences in the relationship between perceived stress, cognitive functioning, and peripheral inflammation over time among cognitively normal older adults.

DESIGN

Longitudinal observational study.

SETTING

University research center.

PARTICIPANTS

274 community-dwelling older adults (baseline age: M=70.7, SD=7.2; 58% women; Clinical Dementia Rating=0) who completed at least two study visits.

MEASUREMENTS

Neurocognitive functioning and perceived stress (Perceived Stress Scale [PSS]) were assessed at each visit. Plasma was analyzed for interleukin 6 (IL-6) and tumor necrosis factor alpha (TNF-α) in a subset of 147 participants. Linear mixed effects models examined the interaction between average PSS (i.e., averaged within persons across visits), sex, and time on cognitive domains and on inflammatory markers.

RESULTS

The interaction between stress, sex, and time predicted executive functioning (β = 0.26, SE = 0.10, p = 0.01) such that higher average PSS related to steeper declines in men, but not in women. Among the 147 participants with inflammatory data, higher average PSS was associated with steeper increases in IL-6 over time in men, but not in women.

CONCLUSION

Consistent with animal models, results showed older men were more vulnerable to negative effects of stress on cognitive aging, with domain-specific declines in executive function. Findings also suggest systemic immunological mechanisms may underlie increased risk for cognitive decline in men with higher levels of stress. Future work is needed to examine the potential efficacy of person-specific stress interventions.

Association of rs2062323 in the TREM1 gene with Alzheimer's disease and cerebrospinal fluid-soluble TREM2

INTRODUCTION AND AIMS

Genetic variations play a significant role in determining an individual's AD susceptibility. Research on the connection between AD and TREM1 gene polymorphisms (SNPs) remained lacking. We sought to examine the associations between TREM1 SNPs and AD.

METHODS

Based on the 1000 Genomes Project data, linkage disequilibrium (LD) analyses were utilized to screen for candidate SNPs in the TREM1 gene. AD cases (1081) and healthy control subjects (870) were collected and genotyped, and the associations between candidate SNPs and AD risk were analyzed. We explored the associations between target SNP and AD biomarkers. Moreover, 842 individuals from ADNI were selected to verify these results. Linear mixed models were used to estimate associations between the target SNP and longitudinal cognitive changes.

RESULTS

The rs2062323 was identified to be associated with AD risk in the Han population, and rs2062323T carriers had a lower AD risk (co-dominant model: OR, 0.67, 95% CI, 0.51-0.88, p = 0.0037; additive model: OR, 0.82, 95% CI, 0.72-0.94, p = 0.0032). Cerebrospinal fluid (CSF) sTREM2 levels were significantly increased in middle-aged rs2062323T carriers (additive model: β = 0.18, p = 0.0348). We also found significantly elevated levels of CSF sTREM2 in the ADNI. The rate of cognitive decline slowed down in rs2062323T carriers.

CONCLUSIONS

This study is the first to identify significant associations between TREM1 rs2062323 and AD risk. The rs2062323T may be involved in AD by regulating the expression of TREM1, TREML1, TREM2, and sTREM2. The TREM family is expected to be a potential therapeutic target for AD.

[Brain health services: individual dementia risk profiling at the Cologne Alzheimer Prevention Center]

BACKGROUND

According to current knowledge approximately 30-40% of all cases of dementia can be attributed to modifiable risk factors. As a result, dementia prevention and the concept of brain health are becoming increasingly relevant.

RESEARCH QUESTION

The requirements for brain health services and their implementation are discussed and the Cologne Alzheimer Prevention Center (KAP) of the University Hospital Cologne is described as an example.

MATERIAL AND METHODS

In addition to a report on international brain health initiatives, the main activities of the KAP are presented. A program for individual risk profiling and risk communication is provided, which was piloted in the KAP in the context of the "Individual risk profiling for Alzheimer's disease and dementia prevention (INSPIRATION)" study. The prevalence of risk factors in a cognitively healthy sample aged 50-86 years (n = 162) with interest in dementia prevention is presented.

RESULTS

The most common risk factors were non-Mediterranean diet, obesity, subjective poor sleep quality and increased stress. Based on these results, preventive interventions can be developed that are adapted to the individual risk profile as a personalized medicine approach.

DISCUSSION

Structures such as the KAP can provide individual risk factor assessment and personalized dementia prevention. The efficacy of this approach on dementia risk reduction needs to be evaluated.

A Review on Phyto-Therapeutic Approaches in Alzheimer's Disease

Neurodegenerative diseases occur due to progressive and sometimes irreversible loss of function and death of nerve cells. A great deal of effort is being made to understand the pathogenesis of neurodegenerative diseases. In particular, the prevalence of Alzheimer's disease (AD) is quite high, and only symptomatic therapy is available due to the absence of radical treatment. The aim of this review is to try to elucidate the general pathogenesis of AD, to provide information about the limit points of symptomatic treatment approaches, and to emphasize the potential neurologic effects of phytocompounds as new tools as therapeutic agents for disease prevention, retardation, and therapy. This survey also covers the notable properties of herbal compounds such as their effects on the inhibition of an enzyme called acetylcholinesterase, which has significant value in the treatment of AD. It has been proven that phytopharmaceuticals have long-term effects that could protect nervous system health, eliminate inflammatory responses, improve cognitive damage, provide anti-aging effects in the natural aging process, and alleviate dementia sequelae. Herbal-based therapeutic agents can afford many advantages and can be used as potentially as new-generation therapeutics or complementary agents with high compliance, fewer adverse effects, and lower cost in comparison to the traditional pharmaceutical agents in the fight against AD.

Systems Medicine as a Strategy to Deal with Alzheimer's Disease

The traits of Alzheimer's disease (AD) include amyloid plaques made of Aβ1-40 and Aβ1-42, and neurofibrillary tangles by the hyperphosphorylation of tau protein. AD is a complex disorder that is heterogenous in genetical, neuropathological, and clinical contexts. Current available therapeutics are unable to cure AD. Systems medicine is a strategy by viewing the body as a whole system, taking into account each individual's unique health profile, provide treatment and associated nursing care clinically for the patient, aiming for precision. Since the onset of AD can lead towards cognitive impairment, it is vital to intervene and diagnose early and prevent further progressive loss of neurons. Moreover, as the individual's brain functions are impaired due to neurodegeneration in AD, it is essential to reconstruct the neurons or brain cells to enable normal brain functions. Although there are different subtypes of AD due to varied pathological lesions, in the majority cases of AD, neurodegeneration and severe brain atrophy develop at the chronic stage. Novel approaches including RNA based gene therapy, stem cell based technology, bioprinting technology, synthetic biology for brain tissue reconstruction are researched in recent decades in the hope to decrease neuroinflammation and restore normal brain function in individuals of AD. Systems medicine include the prevention of disease, diagnosis and treatment by viewing the individual's body as a whole system, along with systems medicine based nursing as a strategy against AD that should be researched further.

A Remotely Coached Multimodal Lifestyle Intervention for Alzheimer's Disease Ameliorates Functional and Cognitive Outcomes

BACKGROUND

Comprehensive treatment of Alzheimer's disease and related dementias (ADRD) requires not only pharmacologic treatment but also management of existing medical conditions and lifestyle modifications including diet, cognitive training, and exercise. Personalized, multimodal therapies are needed to best prevent and treat Alzheimer's disease (AD).

OBJECTIVE

The Coaching for Cognition in Alzheimer's (COCOA) trial was a prospective randomized controlled trial to test the hypothesis that a remotely coached multimodal lifestyle intervention would improve early-stage AD.

METHODS

Participants with early-stage AD were randomized into two arms. Arm 1 (N = 24) received standard of care. Arm 2 (N = 31) additionally received telephonic personalized coaching for multiple lifestyle interventions. The primary outcome was a test of the hypothesis that the Memory Performance Index (MPI) change over time would be better in the intervention arm than in the control arm. The Functional Assessment Staging Test was assessed for a secondary outcome. COCOA collected psychometric, clinical, lifestyle, genomic, proteomic, metabolomic, and microbiome data at multiple timepoints (dynamic dense data) across two years for each participant.

RESULTS

The intervention arm ameliorated 2.1 [1.0] MPI points (mean [SD], p = 0.016) compared to the control over the two-year intervention. No important adverse events or side effects were observed.

CONCLUSION

Multimodal lifestyle interventions are effective for ameliorating cognitive decline and have a larger effect size than pharmacological interventions. Dietary changes and exercise are likely to be beneficial components of multimodal interventions in many individuals. Remote coaching is an effective intervention for early stage ADRD. Remote interventions were effective during the COVID pandemic.

Disclosure of Genetic Risk Factors for Alzheimer's Disease to Cognitively Healthy Individuals-From Current Practice towards a Personalised Medicine Scenario

Alzheimer's disease (AD) is a genetically complex disorder. In addition to the relatively small number of pathogenic variants causing autosomal dominant AD, many others have been associated with the much more common sporadic form. The E4 allele of the Apolipoprotein E (APOE) is the first discovered genetic risk factor for AD. In addition, more than 70 genetic risk loci contributing to AD have been identified. Current guidelines do not recommend AD susceptibility genetic testing in cognitively healthy adults because the implications for clinical care are limited. However, secondary prevention clinical trials of disease-modifying therapies enrol individuals based on genetic criteria, and participants are often informed of APOE testing results. Moreover, the availability of direct-to-consumer genetic testing allows individuals to learn their own AD genetic risk profile without medical supervision. A number of research protocols for AD susceptibility genetic testing have been proposed. In Italy, disclosure processes and protocols beyond those developed for inherited dementia have not been established yet. We reviewed the literature on the current practice and clinical issues related to disclosing AD genetic risk to cognitively healthy individuals and provide suggestions that may help to develop specific guidelines at the national level.

Recognizing novel drugs against Keap1 in Alzheimer's disease using machine learning grounded computational studies

Alzheimer's disease (AD) is the most common neurodegenerative disorder in the world, affecting an estimated 50 million individuals. The nerve cells become impaired and die due to the formation of amyloid-beta (Aβ) plaques and neurofibrillary tangles (NFTs). Dementia is one of the most common symptoms seen in people with AD. Genes, lifestyle, mitochondrial dysfunction, oxidative stress, obesity, infections, and head injuries are some of the factors that can contribute to the development and progression of AD. There are just a few FDA-approved treatments without side effects in the market, and their efficacy is restricted due to their narrow target in the etiology of AD. Therefore, our aim is to identify a safe and potent treatment for Alzheimer's disease. We chose the ursolic acid (UA) and its similar compounds as a compounds' library. And the ChEMBL database was adopted to obtain the active and inactive chemicals against Keap1. The best Quantitative structure-activity relationship (QSAR) model was created by evaluating standard machine learning techniques, and the best model has the lowest RMSE and greatest R2 (Random Forest Regressor). We chose pIC50 of 6.5 as threshold, where the top five potent medicines (DB06841, DB04310, DB11784, DB12730, and DB12677) with the highest predicted pIC50 (7.091184, 6.900866, 6.800155, 6.768965, and 6.756439) based on QSAR analysis. Furthermore, the top five medicines utilize as ligand molecules were docked in Keap1's binding region. The structural stability of the nominated medications was then evaluated using molecular dynamics simulations, RMSD, RMSF, Rg, and hydrogen bonding. All models are stable at 20 ns during simulation, with no major fluctuations observed. Finally, the top five medications are shown as prospective inhibitors of Keap1 and are the most promising to battle AD.

Dementia Prevention in Clinical Practice

Over 55 million people globally are living with dementia and, by 2050, this number is projected to increase to 131 million. This poses immeasurable challenges for patients and their families and a significant threat to domestic and global economies. Given this public health crisis and disappointing results from disease-modifying trials, there has been a recent shift in focus toward primary and secondary prevention strategies. Approximately 40% of Alzheimer's disease (AD) cases, which is the most common form of dementia, may be prevented or at least delayed. Success of risk reduction studies through addressing modifiable risk factors, in addition to the failure of most drug trials, lends support for personalized multidomain interventions rather than a "one-size-fits-all" approach. Evolving evidence supports early intervention in at-risk patients using individualized interventions directed at modifiable risk factors. Comprehensive risk stratification can be informed by emerging principals of precision medicine, and include expanded clinical and family history, anthropometric measurements, blood biomarkers, neurocognitive evaluation, and genetic information. Risk stratification is key in differentiating subtypes of dementia and identifies targetable areas for intervention. This article reviews a clinical approach toward dementia risk stratification and evidence-based prevention strategies, with a primary focus on AD.

A multi-expert ensemble system for predicting Alzheimer transition using clinical features

Alzheimer’s disease (AD) diagnosis often requires invasive examinations (e.g., liquor analyses), expensive tools (e.g., brain imaging) and highly specialized personnel. The diagnosis commonly is established when the disorder has already caused severe brain damage, and the clinical signs begin to be apparent. Instead, accessible and low-cost approaches for early identification of subjects at high risk for developing AD years before they show overt symptoms are fundamental to provide a critical time window for more effective clinical management, treatment, and care planning. This article proposes an ensemble-based machine learning algorithm for predicting AD development within 9 years from first overt signs and using just five clinical features that are easily detectable with neuropsychological tests. The validation of the system involved both healthy individuals and mild cognitive impairment (MCI) patients drawn from the ADNI open dataset, at variance with previous studies that considered only MCI. The system shows higher levels of balanced accuracy, negative predictive value, and specificity than other similar solutions. These results represent a further important step to build a preventive fast-screening machine-learning-based tool to be used as a part of routine healthcare screenings.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

The Coaching for Cognition in Alzheimer's (COCOA) trial: Study design

Comprehensive treatment of Alzheimer's disease (AD) requires not only pharmacologic treatment but also management of existing medical conditions and lifestyle modifications including diet, cognitive training, and exercise. We present the design and methodology for the Coaching for Cognition in Alzheimer's (COCOA) trial. AD and other dementias result from the interplay of multiple interacting dysfunctional biological systems. Monotherapies have had limited success. More interventional studies are needed to test the effectiveness of multimodal multi-domain therapies for dementia prevention and treatment. Multimodal therapies use multiple interventions to address multiple systemic causes and potentiators of cognitive decline and functional loss; they can be personalized, as different sets of etiologies and systems responsive to therapy may be present in different individuals. COCOA is designed to test the hypothesis that coached multimodal interventions beneficially alter the trajectory of cognitive decline for individuals on the spectrum of AD and related dementias (ADRD). COCOA is a two-arm prospective randomized controlled trial (RCT). COCOA collects psychometric, clinical, lifestyle, genomic, proteomic, metabolomic, and microbiome data at multiple timepoints across 2 years for each participant. These data enable systems biology analyses. One arm receives standard of care and generic healthy aging recommendations. The other arm receives standard of care and personalized data-driven remote coaching. The primary outcome measure is the Memory Performance Index (MPI), a measure of cognition. The MPI is a summary statistic of the MCI Screen (MCIS). Secondary outcome measures include the Functional Assessment Staging Test (FAST), a measure of function. COCOA began enrollment in January 2018. We hypothesize that multimodal interventions will ameliorate cognitive decline and that data-driven health coaching will increase compliance, assist in personalizing multimodal interventions, and improve outcomes for patients, particularly for those in the early stages of the AD spectrum.

Highlights

The Coaching for Cognition in Alzheimer's (COCOA) trial tests personalized multimodal lifestyle interventions for Alzheimer's disease and related dementias.Dense longitudinal molecular data will be useful for future studies.Increased use of Hill's criteria in analyses may advance knowledge generation.Remote coaching may be an effective intervention.Because lifestyle interventions are inexpensive, they may be particularly valuable in reducing global socioeconomic disparities in dementia care.

Stratification of the Gut Microbiota Composition Landscape across the Alzheimer's Disease Continuum in a Turkish Cohort

Alzheimer's disease (AD) is a heterogeneous disorder that spans a continuum with multiple phases, including preclinical, mild cognitive impairment, and dementia. Unlike for most other chronic diseases, human studies reporting on AD gut microbiota in the literature are very limited. With the scarcity of approved drugs for AD therapies, the rational and precise modulation of gut microbiota composition using diet and other tools is a promising approach to the management of AD. Such an approach could be personalized if an AD continuum can first be deconstructed into multiple strata based on specific microbiota features by using single or multiomics techniques. However, stratification of AD gut microbiota has not been systematically investigated before, leaving an important research gap for gut microbiota-based therapeutic approaches. Here, we analyze 16S rRNA amplicon sequencing of stool samples from 27 patients with mild cognitive impairment, 47 patients with AD, and 51 nondemented control subjects by using tools compatible with the compositional nature of microbiota. To stratify the AD gut microbiota community, we applied four machine learning techniques, including partitioning around the medoid clustering and fitting a probabilistic Dirichlet mixture model, the latent Dirichlet allocation model, and we performed topological data analysis for population-scale microbiome stratification based on the Mapper algorithm. These four distinct techniques all converge on Prevotella and Bacteroides stratification of the gut microbiota across the AD continuum, while some methods provided fine-scale resolution in stratifying the community landscape. Finally, we demonstrate that the signature taxa and neuropsychometric parameters together robustly classify the groups. Our results provide a framework for precision nutrition approaches aiming to modulate the AD gut microbiota. IMPORTANCE The prevalence of AD worldwide is estimated to reach 131 million by 2050. Most disease-modifying treatments and drug trials have failed, due partly to the heterogeneous and complex nature of the disease. Recent studies demonstrated that gut dybiosis can influence normal brain function through the so-called "gut-brain axis." Modulation of the gut microbiota, therefore, has drawn strong interest in the clinic in the management of the disease. However, there is unmet need for microbiota-informed stratification of AD clinical cohorts for intervention studies aiming to modulate the gut microbiota. Our study fills in this gap and draws attention to the need for microbiota stratification as the first step for microbiota-based therapy. We demonstrate that while Prevotella and Bacteroides clusters are the consensus partitions, the newly developed probabilistic methods can provide fine-scale resolution in partitioning the AD gut microbiome landscape.

Using the Biopsychosocial Model to Guide Patient-Centered Neurological Treatments

The biopsychosocial model was defined by George L. Engel to propose a holistic approach to patient care. Through this model, physicians can understand patients in their context to aid the development of tailored, individualized treatment plans that consider relevant biological, psychological, and social-cultural-spiritual factors impacting health and longitudinal care. In this article, we advocate for the use of the biopsychosocial model in neurology practice across outpatient and inpatient clinical settings. To do so, we first present the history of the biopsychosocial model, and its relationships to precision medicine and deep phenotyping. Then, we bring the neurologist up-to-date information on the components of the biopsychosocial clinical formulation, including predisposing, precipitating, perpetuating, and protective factors. We conclude by detailing illustrative neurological case examples using the biopsychosocial model, emphasizing the importance of considering relevant psychological and social factors to aid the delivery of patient-centered clinical care in neurology.

Subjective Cognitive Decline: Level of Risk for Future Dementia and Mild Cognitive Impairment, a Meta-Analysis of Longitudinal Studies

Subjective Cognitive Decline (SCD) in older adults has been identified as a risk factor for dementia, although the literature is inconsistent, and it is unclear which factors moderate progression from SCD to dementia. Through separate meta-analyses, we aimed to determine if SCD increased the risk of developing dementia or mild cognitive impairment (MCI). Furthermore, we examined several possible moderators. Longitudinal studies of participants with SCD at baseline, with data regarding incident dementia or MCI, were extracted from MEDLINE and PsycINFO. Articles were excluded if SCD occurred solely in the context of dementia, MCI, or as part of a specific disease. Pooled estimates were calculated using a random-effects model, with moderator analyses examining whether risk varied according to SCD definition, demographics, genetics, recruitment source, and follow-up duration. Risk of study bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool. 46 studies with more than 74,000 unique participants were included. SCD was associated with increased risk of developing dementia (HR = 1.90, 95% CI 1.52-2.36; OR = 2.48, 95% CI 1.97-3.14) and MCI (HR = 1.73, 95% CI 1.18-2.52; OR = 1.83, 95% CI 1.56-2.16). None of the potential moderating factors examined influenced the HR or OR of developing dementia. In contrast, including worry in the definition of SCD, younger age, and recruitment source impacted the OR of developing MCI, with clinic samples demonstrating highest risk. SCD thus represents an at-risk phase, ideal for early intervention, with further research required to identify effective interventions for risk reduction, and cognitive-behavioural interventions for cognitive management. PROSPERO, protocol number: CRD42016037993.

Brain Health Services: organization, structure, and challenges for implementation. A user manual for Brain Health Services-part 1 of 6

Dementia has a devastating impact on the quality of life of patients and families and comes with a huge cost to society. Dementia prevention is considered a public health priority by the World Health Organization. Delaying the onset of dementia by treating associated risk factors will bring huge individual and societal benefit. Empirical evidence suggests that, in higher-income countries, dementia incidence is decreasing as a result of healthier lifestyles. This observation supports the notion that preventing dementia is possible and that a certain degree of prevention is already in action. Further reduction of dementia incidence through deliberate prevention plans is needed to counteract its growing prevalence due to increasing life expectancy.An increasing number of individuals with normal cognitive performance seek help in the current memory clinics asking an evaluation of their dementia risk, preventive interventions, or interventions to ameliorate their cognitive performance. Consistent evidence suggests that some of these individuals are indeed at increased risk of dementia. This new health demand asks for a shift of target population, from patients with cognitive impairment to worried but cognitively unimpaired individuals. However, current memory clinics do not have the programs and protocols in place to deal with this new population.We envision the development of new services, henceforth called Brain Health Services, devoted to respond to demands from cognitively unimpaired individuals concerned about their risk of dementia. The missions of Brain Health Services will be (i) dementia risk profiling, (ii) dementia risk communication, (iii) dementia risk reduction, and (iv) cognitive enhancement. In this paper, we present the organizational and structural challenges associated with the set-up of Brain Health Services.

The Apathy Evaluation Scale (AES-C): Psychometric Properties and Invariance of Italian Version in Mild Cognitive Impairment and Alzheimer's Disease

Apathy is a neuropsychiatric symptom observed in different neurological and psychiatric disorders. Although apathy is considered a symptom, it has been recently reconsidered as a syndrome characterised by three dimensions: cognitive symptoms, affective symptoms and behavioural symptoms. Recent studies have shown that apathy can be considered as a prodromal symptom of Alzheimer's disease (AD), but also an indicator of the transition from mild cognitive impairment to AD. According to this scenario, an early detection of apathy in subjects with Mild Cognitive Impairment (MCI) and Mild AD can be a valid psychometric strategy to improve an early diagnosis and promote a prompt intervention. The Apathy Evaluation Scale is a validated tool composed of 18 items that assess and quantify emotional, behavioural and cognitive aspects of apathy. The aim of this study is to assess the specific reliability and validity of the Italian version of the Apathy Evaluation Scale-Clinician Version (AES-C) to detect apathy both in amnestic MCI and mild AD patients. In the present paper, we therefore examined the psychometric properties and the invariance of the Italian Version of the AES-C conducted on a sample composed of an experimental group of amnestic MCI and AD patients (N = 107) and a control group (N = 107) constituted by Age- and Sex-matched healthy controls. Results confirm the goodness of the scale. Confirmatory factory analysis confirmed that the AES-C Italian Version presents the same stability of one second-order factor and three first-order factors identified in the original version, and all items are predicted by a single general factor. Moreover, the scale was found to be invariant across both populations. Moreover, reliability and discriminant analysis showed good values. We found in the experimental group a negative correlation between the AES-C and Frontal Assessment Battery (FAB) (rs = -0.21, p < 0.001) and Mini Mental State Examination (MMSE) (rs = -0.04, p < 0.001), while a positive correlation was found between the AES-C and Hamilton psychiatric Rating scale for Depression (HAM-D) scores (rs = 0.58, p < 0.001) Overall, our data demonstrated the validity of the Italian version of the AES-C for the assessment of apathy both in MCI and in AD patients.

Late-life depression, subjective cognitive decline, and their additive risk in incidence of dementia: A nationwide longitudinal study

OBJECTIVE

Late-life depression and subjective cognitive decline (SCD) are significant risk factors for dementia. However, studies with a large sample size are needed to clarify their independent and combined risks for subsequent dementia.

METHODS

This nationwide population-based cohort study included all individuals aged 66 years who participated in the National Screening Program between 2009 and 2013 (N = 939,099). Subjects were followed from the day they underwent screening to the diagnosis of dementia, death, or the last follow-up day (December 31, 2017).

RESULTS

Depressive symptom presentation, recent depressive disorder, and SCD independently increased dementia incidence with adjusted hazard ratio (aHR) of 1.286 (95% CI:1.255-1.318), 1.697 (95% CI:1.621-1.776), and 1.748 (95% CI: 689-1.808) respectively. Subjects having both SCD and depression had a higher risk (aHR = 2.466, 95% CI:2.383-2.551) of dementia than having depression (aHR = 1.402, 95% CI:1.364-1.441) or SCD (aHR = 1.748, 95% CI:1.689-1.808) alone.

CONCLUSIONS

Depressive symptoms, depressive disorder, and SCD are independent risk factors for dementia. Co-occurring depression and SCD have an additive effect on the risk of dementia; thus, early intervention and close follow up are necessary for patients with co-occurring SCD and depression.

Disease-specific interactome alterations via epichaperomics: the case for Alzheimer's disease

The increasingly appreciated prevalence of complicated stressor-to-phenotype associations in human disease requires a greater understanding of how specific stressors affect systems or interactome properties. Many currently untreatable diseases arise due to variations in, and through a combination of, multiple stressors of genetic, epigenetic, and environmental nature. Unfortunately, how such stressors lead to a specific disease phenotype or inflict a vulnerability to some cells and tissues but not others remains largely unknown and unsatisfactorily addressed. Analysis of cell- and tissue-specific interactome networks may shed light on organization of biological systems and subsequently to disease vulnerabilities. However, deriving human interactomes across different cell and disease contexts remains a challenge. To this end, this opinion article links stressor-induced protein interactome network perturbations to the formation of pathologic scaffolds termed epichaperomes, revealing a viable and reproducible experimental solution to obtaining rigorous context-dependent interactomes. This article presents our views on how a specialized 'omics platform called epichaperomics may complement and enhance the currently available conventional approaches and aid the scientific community in defining, understanding, and ultimately controlling interactome networks of complex diseases such as Alzheimer's disease. Ultimately, this approach may aid the transition from a limited single-alteration perspective in disease to a comprehensive network-based mindset, which we posit will result in precision medicine paradigms for disease diagnosis and treatment.

A Deep Neural Network-Based Method for Prediction of Dementia Using Big Data

The rise in dementia among the aging Korean population will quickly create a financial burden on society, but timely recognition of early warning for dementia and proper responses to the occurrence of dementia can enhance medical treatment. Health behavior and medical service usage data are relatively more accessible than clinical data, and a prescreening tool with easily accessible data could be a good solution for dementia-related problems. In this paper, we apply a deep neural network (DNN) to prediction of dementia using health behavior and medical service usage data, using data from 7031 subjects aged over 65 collected from the Korea National Health and Nutrition Examination Survey (KNHANES) in 2001 and 2005. In the proposed model, principal component analysis (PCA) featuring and min/max scaling are used to preprocess and extract relevant background features. We compared our proposed methodology, a DNN/scaled PCA, with five well-known machine learning algorithms. The proposed methodology shows 85.5% of the area under the curve (AUC), a better result than that using other algorithms. The proposed early prescreening method for possible dementia can be used by both patients and doctors.

The Role of Nutrition in Individualized Alzheimer's Risk Reduction

PURPOSE OF REVIEW

Decades of research suggests nutritional interventions can be an effective tool for reducing risk of Alzheimer's disease (AD), especially as part of an individualized clinical management plan. This review aims to emphasize new findings examining how specific dietary changes may delay or possibly prevent AD onset, and highlight how interventions can be adopted in clinical practice based on emerging principles of precision medicine.

RECENT FINDINGS

Specific dietary patterns and varied nutrient combinations can have a protective effect on brain health, promote cognitive function, and mediate the comorbidity of chronic conditions associated with increased AD risk. Individuals at risk for AD may see a greater impact of evidence-based dietary changes when initiated earlier in the AD spectrum. Depending on individual clinical profiles, incorporation of nutrition strategies is an essential component of an AD risk reduction plan in clinical practice.

Precision Nutrition for Alzheimer's Prevention in ApoE4 Carriers

The ApoE4 allele is the most well-studied genetic risk factor for Alzheimer’s disease, a condition that is increasing in prevalence and remains without a cure. Precision nutrition targeting metabolic pathways altered by ApoE4 provides a tool for the potential prevention of disease. However, no long-term human studies have been conducted to determine effective nutritional protocols for the prevention of Alzheimer’s disease in ApoE4 carriers. This may be because relatively little is yet known about the precise mechanisms by which the genetic variant confers an increased risk of dementia. Fortunately, recent research is beginning to shine a spotlight on these mechanisms. These new data open up the opportunity for speculation as to how carriers might ameliorate risk through lifestyle and nutrition. Herein, we review recent discoveries about how ApoE4 differentially impacts microglia and inflammatory pathways, astrocytes and lipid metabolism, pericytes and blood–brain barrier integrity, and insulin resistance and glucose metabolism. We use these data as a basis to speculate a precision nutrition approach for ApoE4 carriers, including a low-glycemic index diet with a ketogenic option, specific Mediterranean-style food choices, and a panel of seven nutritional supplements. Where possible, we integrate basic scientific mechanisms with human observational studies to create a more complete and convincing rationale for this precision nutrition approach. Until recent research discoveries can be translated into long-term human studies, a mechanism-informed practical clinical approach may be useful for clinicians and patients with ApoE4 to adopt a lifestyle and nutrition plan geared towards Alzheimer’s risk reduction.

Determining Appropriate Screening Tools and Cutoffs for Cognitive Impairment in the Chinese Elderly

Background: The Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) are the most commonly used tools for cognitive impairment screening. The present study aimed to investigate the ability of the MOCA and MMSE to differentiate between cognitively normal elderly individuals, MCI patients and dementia patients at different ages and education levels and to establish the optimal cutoff scores of the MoCA and MMSE for MCI and dementia in the Chinese elderly. Methods: A total of 2,954 Chinese elderly individuals, including 1,746 normal controls, 599 MCI patients and 249 dementia patients, were consecutively recruited in the study. The optimal cutoffs for MoCA and MMSE were determined using receiver operating characteristic (ROC) analysis among the different age and education levels in the three groups. Furthermore, comparison of ROC curves were made to evaluate the performances of the two tests. Results: The area under the curve(AUC) of the MoCA (0.82) for detecting MCI was significantly higher than that of the MMSE (0.75) (P < 0.001). When the sample was divided according to age and education level, the AUC of the MoCA (0.84) was higher than those of the MMSE (0.71) for MCI (P < 0.001) in the younger and more highly-educated groups. The optimal cutoff scores of the MoCA for the groups aged ≤ 75 years old and education ≤ 6 years, aged > 75 years old and education ≤ 6 years, aged ≤ 75 years old and education > 6 years, aged > 75 years old and education > 6 years in screening for MCI were identified as 19.5, 15.5, 24.5 and 24.5, respectively, and the optimal cutoff scores for dementia were 18.5, 10.5, 18.5 and 20.5, respectively. For MMSE in the above four groups, the cutoff scores to detect MCI were 26.5, 22.5, 28.5 and 26.5, respectively, and the optimal cutoff scores for dementia were 23.5, 19.5, 23.5 and 23.5, respectively. Conclusion: Compared to MMSE, the MoCA is more suitable for discriminating MCI in younger and more highly educated elderly Chinese individuals. However, the MMSE has advantage over MoCA in screening MCI in individuals with lower education levels and the older groups of Chinese elderly.

Spiritual Fitness: A New Dimension in Alzheimer's Disease Prevention

BACKGROUND

Religious and spiritual interventions may have an effect on Alzheimer's disease prevention. Kirtan Kriya meditation has been shown to mitigate the deleterious effects of chronic stress on cognition, reverse memory loss, and create psychological and spiritual wellbeing, which may reduce multiple drivers of Alzheimer's disease risk.

OBJECTIVE

To detail a new concept in medicine called Spiritual Fitness, a merging of stress reduction, basic wellbeing, and psycho/spiritual wellbeing to prevent Alzheimer's disease.

METHODS

The literature on the topics mentioned above is described, including an in-depth discussion on why and how each are critical to advancing the future of Alzheimer's disease prevention. The many negative effects of chronic stress, and the benefits of Kirtan Kriya, are reviewed. The four pillars of basic wellbeing, six practical aspects of psychological wellbeing, and the four new non-sectarian features of spiritual fitness are then disclosed. Moreover, instructions on practicing Kirtan Kriya are offered in the Supplementary Material.

CONCLUSION

Religious and spiritual practices, including Kirtan Kriya, are crucial components in the development of enhanced cognition and well-being, which may help prevent and, in some cases, reverse cognitive decline. The key point of this review is that making a commitment to live a brain longevity lifestyle including spiritual fitness is a critically important way for aging Alzheimer's disease free. We hope that this article will inspire scientists, clinicians, and patients to embrace this new concept of spiritual fitness and make it a part of every multidomain program for the prevention of cognitive disability.

Diet, Microbiota and Brain Health: Unraveling the Network Intersecting Metabolism and Neurodegeneration

Increasing evidence gives support for the idea that extra-neuronal factors may affect brain physiology and its predisposition to neurodegenerative diseases. Epidemiological and experimental studies show that nutrition and metabolic disorders such as obesity and type 2 diabetes increase the risk of Alzheimer's and Parkinson's diseases after midlife, while the relationship with amyotrophic lateral sclerosis is uncertain, but suggests a protective effect of features of metabolic syndrome. The microbiota has recently emerged as a novel factor engaging strong interactions with neurons and glia, deeply affecting their function and behavior in these diseases. In particular, recent evidence suggested that gut microbes are involved in the seeding of prion-like proteins and their spreading to the central nervous system. Here, we present a comprehensive review of the impact of metabolism, diet and microbiota in neurodegeneration, by affecting simultaneously several aspects of health regarding energy metabolism, immune system and neuronal function. Advancing technologies may allow researchers in the future to improve investigations in these fields, allowing the buildup of population-based preventive interventions and development of targeted therapeutics to halt progressive neurologic disability.