What success can teach us about failure: the plasma metabolome of older adults with superior memory and lessons for Alzheimer's disease

Cognitive aging outcomes are related to both tau pathology and maintenance of cingulate cortex structure

INTRODUCTION

Successful cognitive aging is related to both maintaining brain structure and avoiding Alzheimer's disease (AD) pathology, but how these factors interplay is unclear.

METHODS

A total of 109 cognitively normal older adults (70+ years old) underwent amyloid beta (Aβ) and tau positron emission tomography (PET) imaging, structural magnetic resonance imaging (MRI), and cognitive testing. Cognitive aging was quantified using the cognitive age gap (CAG), subtracting chronological age from predicted cognitive age.

RESULTS

Lower CAG (younger cognitive age) was related to slower decline in episodic memory, multi-domain cognition, and atrophy of the midcingulate cortex (MCC). Lower entorhinal cortical tau was linked to slower decline in episodic memory, multi-domain cognition, and hippocampal atrophy.

DISCUSSION

These results suggest that aging outcomes may be influenced by two independent pathways: one associated with tau accumulation, affecting primarily memory and hippocampal atrophy, and another involving tau-independent structural preservation of the MCC, benefiting multi-domain cognition over time.

HIGHLIGHTS

Younger cognitive age (lower cognitive age gap [CAG]) is related to slower cognitive decline. Lower CAG is linked to slower midcingulate cortex (MCC) atrophy. Reduced tau in the entorhinal cortex is related to less hippocampal atrophy and cognitive decline. Structural preservation of the MCC benefits multi-domain cognition over time. Two independent pathways influence cognitive aging: tau accumulation and MCC preservation.

Evaluating the association of APOE genotype and cognitive resilience in SuperAgers

Importance

"SuperAgers" are oldest-old adults (ages 80+) whose memory performance resembles that of adults in their 50s to mid-60s. Factors underlying their exemplary memory are underexplored in large, racially diverse cohorts.

Objective

To determine the frequency of APOE genotypes in non-Hispanic Black and non-Hispanic White SuperAgers compared to middle-aged (ages 50-64), old (ages 65-79), and oldest-old (ages 80+) controls and Alzheimer's disease (AD) dementia cases.

Design

This multicohort study selected data from eight longitudinal cohort studies of normal aging and AD.

Setting

Variable recruitment criteria and follow-up intervals, including both population-based and clinical-based samples.

Participants

Inclusion in our analyses required APOE genotype, that participants be age 50+, and are identified as either non-Hispanic Black or non-Hispanic White. In total, 18,080 participants were included in the present study with a total of 78,549 datapoints.

Main Outcomes and Measures

Harmonized, longitudinal memory, executive function, and language scores were obtained from the Alzheimer's Disease Sequencing Project Phenotype Harmonization Consortium (ADSP-PHC). SuperAgers, controls, and AD dementia cases were identified by cognitive scores using a residual approach and clinical diagnoses across multiple timepoints when available. SuperAgers were compared to AD dementia cases and cognitively normal controls using age-defined bins (middle-aged, old, oldest-old).

Results

Across racialized groups, SuperAgers had significantly higher proportions of APOE-ε2 alleles and lower proportions of APOE-ε4 alleles compared to cases. Similar differences were observed between SuperAgers and middle-aged and old controls. Non-Hispanic White SuperAgers had significantly lower proportions of APOE-ε4 alleles and significantly higher proportions of APOE-ε2 alleles compared to all cases and controls, including oldest-old controls. In contrast, non-Hispanic Black SuperAgers had significantly lower proportions of APOE-ε4 alleles compared to cases and younger controls, and significantly higher proportions of APOE-ε2 alleles compared only to cases.

Conclusions and Relevance

In the largest study to date, we demonstrated strong evidence that the frequency of APOE-ε4 and -ε2 alleles differ between non-Hispanic White SuperAgers and AD dementia cases and cognitively normal controls. Differences in the role of APOE in SuperAging by race underlines distinctions in mechanisms conferring resilience across race groups given likely differences in genetic ancestry.

Profiles of brain topology for dual-functional stability in old age

Dual-functional stability (DFS) in cognitive and physical abilities is important for successful aging. This study examines the brain topology profiles that underpin high DFS in older adults by testing two hypotheses: (1) older adults with high DFS would exhibit a unique brain organization that preserves their physical and cognitive functions across various tasks, and (2) any individuals with this distinct brain topology would consistently show high DFS. We analyzed two cohorts of cognitively and physically healthy older adults from the UK (Cam-CAN, n = 79) and the US (CF, n = 48) using neuroimaging data and a combination of cognitive and physical tasks. Variability in DFS was characterized using k-mean clustering for intra-individual variability (IIV) in cognitive and physical tasks. Graph theory analyses of diffusion tensor imaging connectomes were used to assess brain network segregation and integration through clustering coefficients (CCs) and shortest path lengths (PLs). Using support vector machine and regression, brain topology features, derived from PLs + CCs, differentiated the high DFS subgroup from low and mix DFS subgroups with accuracies of 65.82% and 84.78% in Cam-CAN and CF samples, respectively, which predicted cross-task DFS score in CF samples at 58.06% and 70.53% for cognitive and physical stability, respectively. Results showed distinctive neural correlates associated with high DFS, notably varying regional brain segregation and integration within critical areas such as the insula, frontal pole, and temporal pole. The identified brain topology profiles suggest a distinctive neural basis for DFS, a trait indicative of successful aging. These insights offer a foundation for future research to explore targeted interventions that could enhance cognitive and physical resilience in older adults, promoting a healthier and more functional lifespan.

Frequency of cognitive "super-aging" in three Australian samples using different diagnostic criteria

OBJECTIVES

To investigate the frequency of exceptional cognition (cognitive super-aging) in Australian older adults using different published definitions, agreement between definitions, and the relationship of super-aging status with function, brain imaging markers, and incident dementia.

DESIGN

Three longitudinal cohort studies.

SETTING

Participants recruited from the electoral roll, Australian Twins Registry, and community advertisements.

PARTICIPANTS

Older adults (aged 65-106) without dementia from the Sydney Memory and Ageing Study (n = 1037; median age 78), Older Australian Twins Study (n = 361; median age 68), and Sydney Centenarian Study (n = 217; median age 97).

MEASUREMENTS

Frequency of super-aging was assessed using nine super-aging definitions based on performance on neuropsychological testing. Levels of agreement between definitions were calculated, and associations between super-aging status for each definition and functioning (Bayer ADL score), structural brain imaging measures, and incident dementia were explored.

RESULTS

Frequency of super-aging varied between 2.9 and 43.4 percent with more stringent definitions associated with lower frequency. Agreement between different criteria varied from poor (K = 0.04, AC1 = .24) to very good (K = 0.83, AC1 = .91) with better agreement between definitions using similar tests and cutoffs. Super-aging was associated with better functional performance (4.7-11%) and lower rates of incident dementia (hazard ratios 0.08-0.48) for most definitions. Super-aging status was associated with a lower burden of white matter hyperintensities (3.8-33.2%) for all definitions.

CONCLUSIONS

The frequency of super-aging is strongly affected by the demographic and neuropsychological testing parameters used. Greater consistency in defining super-aging would enable better characterization of this exceptional minority.

The Potential of Metabolomics to Find Proper Biomarkers for Addressing the Neuroprotective Efficacy of Drugs Aimed at Delaying Parkinson's and Alzheimer's Disease Progression

The first objective is to highlight the lack of tools to measure whether a given intervention affords neuroprotection in patients with Alzheimer's or Parkinson's diseases. A second aim is to present the primary outcome measures used in clinical trials in cohorts of patients with neurodegenerative diseases. The final aim is to discuss whether metabolomics using body fluids may lead to the discovery of biomarkers of neuroprotection. Information on the primary outcome measures in clinical trials related to Alzheimer's and Parkinson's disease registered since 2018 was collected. We analysed the type of measures selected to assess efficacy, not in terms of neuroprotection since, as stated in the aims, there is not yet any marker of neuroprotection. Proteomic approaches using plasma or CSF have been proposed. PET could estimate the extent of lesions, but disease progression does not necessarily correlate with a change in tracer uptake. We propose some alternatives based on considering the metabolome. A new opportunity opens with metabolomics because there have been impressive technological advances that allow the detection, among others, of metabolites related to mitochondrial function and mitochondrial structure in serum and/or cerebrospinal fluid; some of the differentially concentrated metabolites can become reliable biomarkers of neuroprotection.

Successful cognitive aging is associated with thicker anterior cingulate cortex and lower tau deposition compared to typical aging

INTRODUCTION

There is no consensus on either the definition of successful cognitive aging (SA) or the underlying neural mechanisms.

METHODS

We examined the agreement between new and existing definitions using: (1) a novel measure, the cognitive age gap (SA-CAG, cognitive-predicted age minus chronological age), (2) composite scores for episodic memory (SA-EM), (3) non-memory cognition (SA-NM), and (4) the California Verbal Learning Test (SA-CVLT).

RESULTS

Fair to moderate strength of agreement was found between the four definitions. Most SA groups showed greater cortical thickness compared to typical aging (TA), especially in the anterior cingulate and midcingulate cortices and medial temporal lobes. Greater hippocampal volume was found in all SA groups except SA-NM. Lower entorhinal 18 F-Flortaucipir (FTP) uptake was found in all SA groups.

DISCUSSION

These findings suggest that a feature of SA, regardless of its exact definition, is resistance to tau pathology and preserved cortical integrity, especially in the anterior cingulate and midcingulate cortices.

HIGHLIGHTS

Different approaches have been used to define successful cognitive aging (SA). Regardless of definition, different SA groups have similar brain features. SA individuals have greater anterior cingulate thickness and hippocampal volume. Lower entorhinal tau deposition, but not amyloid beta is related to SA. A combination of cortical integrity and resistance to tau may be features of SA.

Mendelian Randomization of Blood Metabolites Suggests Circulating Glutamine Protects Against Late-Onset Alzheimer's Disease

Background

Late-onset Alzheimer's disease (LOAD) represents a growing health burden. Previous studies suggest that blood metabolite levels influence risk of LOAD.

Objective

We used a genetics-based study design which may overcome limitations of other epidemiological studies to assess the influence of metabolite levels on LOAD risk.

Methods

We applied Mendelian randomization (MR) to evaluate bi-directional causal effects using summary statistics from the largest genome-wide association studies (GWAS) of 249 blood metabolites (n = 115,082) and GWAS of LOAD (ncase = 21,982, ncontrol = 41,944).

Results

MR analysis of metabolites as exposures revealed a negative association of genetically-predicted glutamine levels with LOAD (Odds Ratio (OR) = 0.83, 95% CI = 0.73, 0.92) that was consistent in multiple sensitivity analyses. We also identified a positive association of genetically-predicted free cholesterol levels in small LDL (OR = 1.79, 95% CI = 1.36, 2.22) on LOAD. Using genetically-predicted LOAD as the exposure, we identified associations with phospholipids to total lipids ratio in large LDL (OR = 0.96, 95% CI = 0.94, 0.98), but not with glutamine, suggesting that the relationship between glutamine and LOAD is unidirectional.

Conclusions

Our findings support previous evidence that higher circulating levels of glutamine may be a target for protection against LOAD.

Defining exceptional cognition in older adults: A systematic review of cognitive super-ageing

OBJECTIVE

A consistent approach to defining cognitive super-ageing is needed to increase the value of research insights that may be gained from studying this population including ageing well and preventing and treating neurodegenerative conditions. This review aims to evaluate the existing definitions of 'super-ageing' with a focus on cognition.

METHODS

A systematic literature search was conducted across PubMed, Embase, Web of Science, Scopus, PsycINFO and Google Scholar from inception to 24 July 2023.

RESULTS

Of 44 English language studies that defined super-ageing from a cognitive perspective in older adults (60-97 years), most (n = 33) were based on preserved verbal episodic memory performance comparable to that of younger adult in age range 16-65 years. Eleven studies defined super-agers as the top cognitive performers for their age group based upon standard deviations or percentiles above the population mean. Only nine studies included longitudinal cognitive performance in their definitions.

CONCLUSIONS

Equivalent cognitive abilities to younger adults, exceptional cognition for age and a lack of cognitive deterioration over time are all meaningful constructs and may provide different insights into cognitive ageing. Using these criteria in combination or individually to define super-agers, with a clear rationale for which elements have been selected, could be fit for purpose depending on the research question. However, major discrepancies including the age range of super-agers and comparator groups and the choice of cognitive domains assessed should be addressed to reach some consensus in the field.

Exploring Successful Cognitive Aging: Insights Regarding Brain Structure, Function, and Demographics

In the realm of cognitive science, the phenomenon of "successful cognitive aging" stands as a hallmark of individuals who exhibit cognitive abilities surpassing those of their age-matched counterparts. However, it is paramount to underscore a significant gap in the current research, which is marked by a paucity of comprehensive inquiries that deploy substantial sample sizes to methodically investigate the cerebral biomarkers and contributory elements underpinning this cognitive success. It is within this context that our present study emerges, harnessing data derived from the UK Biobank. In this study, a highly selective cohort of 1060 individuals aged 65 and above was meticulously curated from a larger pool of 17,072 subjects. The selection process was guided by their striking cognitive resilience, ascertained via rigorous evaluation encompassing both generic and specific cognitive assessments, compared to their peers within the same age stratum. Notably, the cognitive abilities of the chosen participants closely aligned with the cognitive acumen commonly observed in middle-aged individuals. Our study leveraged a comprehensive array of neuroimaging-derived metrics, obtained from three Tesla MRI scans (T1-weighted images, dMRI, and resting-state fMRI). The metrics included image-derived phenotypes (IDPs) that addressed grey matter morphology, the strength of brain network connectivity, and the microstructural attributes of white matter. Statistical analyses were performed employing ANOVA, Mann-Whitney U tests, and chi-square tests to evaluate the distinctive aspects of IDPs pertinent to the domain of successful cognitive aging. Furthermore, these analyses aimed to elucidate lifestyle practices that potentially underpin the maintenance of cognitive acumen throughout the aging process. Our findings unveiled a robust and compelling association between heightened cognitive aptitude and the integrity of white matter structures within the brain. Furthermore, individuals who exhibited successful cognitive aging demonstrated markedly enhanced activity in the cerebral regions responsible for auditory perception, voluntary motor control, memory retention, and emotional regulation. These advantageous cognitive attributes were mirrored in the health-related lifestyle choices of the surveyed cohort, characterized by elevated educational attainment, a lower incidence of smoking, and a penchant for moderate alcohol consumption. Moreover, they displayed superior grip strength and enhanced walking speeds. Collectively, these findings furnish valuable insights into the multifaceted determinants of successful cognitive aging, encompassing both neurobiological constituents and lifestyle practices. Such comprehensive comprehension significantly contributes to the broader discourse on aging, thereby establishing a solid foundation for the formulation of targeted interventions aimed at fostering cognitive well-being among aging populations.

Changes in levels of the antioxidant glutathione in brain and blood across the age span of healthy adults: A systematic review

Aging is characterized by a gradual decline of the body's biological functions, which can lead to increased production of reactive oxygen species (ROS). Antioxidants neutralize ROS and maintain balance between oxidation and reduction. If ROS production exceeds the ability of antioxidant systems to neutralize, a damaging state of oxidative stress (OS) may exist. The reduced form of glutathione (GSH) is the most abundant antioxidant, and decline of GSH is considered a marker of OS. Our review summarizes the literature on GSH variations with age in healthy adults in brain (in vivo, ex vivo) and blood (plasma, serum), and reliability of in vivo magnetic resonance spectroscopy (MRS) measurement of GSH. A systematic PubMed search identified 35 studies. All in vivo MRS studies (N = 13) reported good to excellent reproducibility of GSH measures. In brain, 3 out of 4 MRS studies reported decreased GSH with age, measured in precuneus, cingulate, and occipital regions, while 1 study reported increased GSH with age in frontal and sensorimotor regions. In post-mortem brain, out of 3 studies, 2 reported decreased GSH with age in hippocampal and frontal regions, while 1 study reported increased GSH with age in a frontal region. Oxidized glutathione disulfide (GSSG) was reported to be increased in caudate with age in 1 study, suggesting OS. Although findings in the brain lacked a clear consensus, the majority of studies suggested a decline of GSH with age. The low number of studies (particularly ex vivo) and potential regional differences may have contributed to variability in the findings in brain. In blood, in contrast, GSH levels predominately were reported to decrease with advancing age (except in the oldest-old, who may represent a select group of particularly successful agers), while GSSG findings lacked consensus. The larger number of studies assessing age-specific GSH level changes in blood (N = 16) allowed for more robust consensus across studies than in brain. Overall, the literature suggests that aging is associated with increased OS in brain and body, but the timing and regional distribution of changes in the brain require further study. The contribution of brain OS to brain aging, and the effect of interventions to raise brain GSH levels on decline of brain function, remain understudied. Given that reliable tools to measure brain GSH exist, we hope this paper will serve as a catalyst to stimulate more work in this field.

[Status Quo and Prospects of Research on Precision Nursing of Life-Cycle Health and Disease]

With the changing lifestyle and spectrum of diseases among Chinese people, the life-cycle approach to health has been given national strategic importance. Over the past decade, global nursing researchers have gradually started to pay more attention to the research related to precision nursing at different stages of the life cycle. Researchers have applied multi-omics to explore the pathogenesis and novel biomarkers of relevant symptoms in tumor patients or patients with chronic diseases in order to manage symptoms with better precision. However, systematic theories of precision nursing of life-cycle health and disease have not yet been developed, and the research field and its implications still need to be continuously expanded and innovated. In the nursing discipline, the advantages of interdisciplinary integration should be given full play and the precise and effective resolution of life-cycle health problems should be taken as its goal. Through accurately defining key quantitative objective indicators of nursing care, the nursing discipline will be able to achieve early identification of life-cycle health problems, clarify the occurrence and patterns of change in life-cycle health problems, and gain a better understanding of the regulatory mechanisms. Precise and effective nursing-related technologies and products of non-medication and non-surgery nature should be developed to achieve better precision in nursing interventions, thereby effectively promoting recovery from diseases and improving the overall health of the people.

Enhanced putamen functional connectivity underlies altered risky decision-making in age-related cognitive decline

Risky decision-making is critical to survival and development, which has been compromised in elderly populations. However, the neural substrates of altered financial risk-taking behavior in aging are still under-investigated. Here we examined the intrinsic putamen network in modulating risk-taking behaviors of Balloon Analogue Risk Task in healthy young and older adults using resting-state fMRI. Compared with the young group, the elderly group showed significantly different task performance. Based on the task performance, older adults were further subdivided into two subgroups, showing young-like and over-conservative risk behaviors, regardless of cognitive decline. Compared with young adults, the intrinsic pattern of putamen connectivity was significantly different in over-conservative older adults, but not in young-like older adults. Notably, age-effects on risk behaviors were mediated via the putamen functional connectivity. In addition, the putamen gray matter volume showed significantly different relationships with risk behaviors and functional connectivity in over-conservative older adults. Our findings suggest that reward-based risky behaviors might be a sensitive indicator of brain aging, highlighting the critical role of the putamen network in maintaining optimal risky decision-making in age-related cognitive decline.

The identification of a potential plasma metabolite marker for Alzheimer’s disease by LC-MS untargeted metabolomics

BACKGROUND AND AIMS

Alzheimer's disease (AD), the most common type of dementia, is hard to recognize early, resulting in delayed treatment and poor outcome. At present, there is neither reliable, non-invasive methods to diagnose it accurately and nor effective drugs to recover it. Discovery and quantification of novel metabolite markers in plasma of AD patients and investigation of the correlation between the markers and AD assessment scores.

MATERIALS AND METHODS

Untargeted liquid chromatography-mass spectrometry (LC-MS)-based metabolomics with LC-quadrupole- time-of-flight (Q-TOF) was performed in plasma samples of age-matched AD patients and healthy controls. The potential markers were further quantified with targeted multiple reaction monitoring (MRM) approach.

RESULTS

Among the candidates, progesterone, and 3-indoleacetic acid (3-IAA) were successfully identified and then validated in 50 plasma samples from 25 AD patients and 25 matched normal controls with MRM approach. As a result, 3-IAA was significantly altered in AD patients and correlated with some AD assessment scores.

CONCLUSION

By using untargeted LC-MS metabolomic and LC-MRM approaches to analyze plasma metabolites of AD patients and normal subjects, 3-IAA was discovered and quantified to be significantly altered in AD patients and correlated with several AD assessment scores.

Clear-headed into old age: Resilience and resistance against brain aging-A PET imaging perspective

With the advances in modern medicine and the adaptation towards healthier lifestyles, the average life expectancy has doubled since the 1930s, with individuals born in the millennium years now carrying an estimated life expectancy of around 100 years. And even though many individuals around the globe manage to age successfully, the prevalence of aging-associated neurodegenerative diseases such as sporadic Alzheimer's disease has never been as high as nowadays. The prevalence of Alzheimer's disease is anticipated to triple by 2050, increasing the societal and economic burden tremendously. Despite all efforts, there is still no available treatment defeating the accelerated aging process as seen in this disease. Yet, given the advances in neuroimaging techniques that are discussed in the current Review article, such as in positron emission tomography (PET) or magnetic resonance imaging (MRI), pivotal insights into the heterogenous effects of aging-associated processes and the contribution of distinct lifestyle and risk factors already have and are still being gathered. In particular, the concepts of resilience (i.e. coping with brain pathology) and resistance (i.e. avoiding brain pathology) have more recently been discussed as they relate to mechanisms that are associated with the prolongation and/or even stop of the progressive brain aging process. Better understanding of the underlying mechanisms of resilience and resistance may one day, hopefully, support the identification of defeating mechanism against accelerating aging.

APOE ε4 and Alzheimer's disease diagnosis associated differences in L-carnitine, GBB, TMAO, and acylcarnitines in blood and brain

BACKGROUND

The apolipoprotein E (APOE) ε4 allele, involved in fatty acid (FA) metabolism, is a major genetic risk factor for Alzheimer's disease (AD). This study examined the influence of APOE genotypes on blood and brain markers of the L-carnitine system, necessary for fatty acid oxidation (FAO), and their collective influence on the clinical and pathological outcomes of AD.

METHODS

L-carnitine, its metabolites γ-butyrobetaine (GBB) and trimethylamine-n-oxide (TMAO), and its esters (acylcarnitines) were analyzed in blood from predominantly White community/clinic-based individuals (n = 372) and in plasma and brain from the Religious Order Study (ROS) (n = 79) using liquid chromatography tandem mass spectrometry (LC-MS/MS).

FINDINGS

Relative to total blood acylcarnitines, levels of short chain acylcarnitines (SCAs) were higher whereas long chain acylcarnitines (LCAs) were lower in AD, which was observed pre-clinically in APOE ε4s. Plasma medium chain acylcarnitines (MCAs) were higher amongst cognitively healthy APOE ε2 carriers relative to other genotypes. Compared to their respective controls, elevated TMAO and lower L-carnitine and GBB were associated with AD clinical diagnosis and these differences were detected preclinically among APOE ε4 carriers. Plasma and brain GBB, TMAO, and acylcarnitines were also associated with post-mortem brain amyloid, tau, and cerebrovascular pathologies.

INTERPRETATION

Alterations in blood L-carnitine, GBB, TMAO, and acylcarnitines occur early in clinical AD progression and are influenced by APOE genotype. These changes correlate with post-mortem brain AD and cerebrovascular pathologies. Additional studies are required to better understand the role of the FAO disturbances in AD.

Longitudinal Analysis of the Microbiome and Metabolome in the 5xfAD Mouse Model of Alzheimer's Disease

Recent reports implicate gut microbiome dysbiosis in the onset and progression of Alzheimer's disease (AD), yet studies involving model animals overwhelmingly omit the microbial perspective. Here, we evaluate longitudinal microbiomes and metabolomes from a popular transgenic mouse model for familial AD (5xfAD). Cecal and fecal samples from 5xfAD and wild-type B6J (WT) mice from 4 to 18 months of age were subjected to shotgun Illumina sequencing. Metabolomics was performed on plasma and feces from a subset of the same animals. Significant genotype, sex, age, and cage-specific differences were observed in the microbiome, with the variance explained by genotype at 4 and 18 months of age rising from 0.9 to 9% and 0.3 to 8% for the cecal and fecal samples, respectively. Bacteria at significantly higher abundances in AD mice include multiple Alistipes spp., two Ligilactobacillus spp., and Lactobacillus sp. P38, while multiple species of Turicibacter, Lactobacillus johnsonii, and Romboutsia ilealis were less abundant. Turicibacter is similarly depleted in people with AD, and members of this genus both consume and induce the production of gut-derived serotonin. Contradicting previous findings in humans, serotonin is significantly more concentrated in the blood of older 5xfAD animals compared to their WT littermates. 5xfAD animals exhibited significantly lower plasma concentrations of carnosine and the lysophospholipid lysoPC a C18:1. Correlations between the microbiome and metabolome were also explored. Taken together, these findings strengthen the link between Turicibacter abundance and AD, provide a basis for further microbiome studies of murine models for AD, and suggest that greater control over animal model microbiomes is needed in AD research. IMPORTANCE Microorganisms residing within the gastrointestinal tract are implicated in the onset and progression of Alzheimer's disease (AD) through the mediation of inflammation, exchange of small-molecules across the blood-brain barrier, and stimulation of the vagus nerve. Unfortunately, most animal models for AD are housed under conditions that do not reflect real-world human microbial exposure and do not sufficiently account for (or meaningfully consider) variations in the microbiome. An improved understanding of AD model animal microbiomes will increase model efficacy and the translatability of research findings into humans. Here, we present the characterization of the microbiome and metabolome of the 5xfAD mouse model, which is one of the most common animal models for familial AD. The manuscript highlights the importance of considering the microbiome in study design and aims to lay the groundwork for future studies involving mouse models for AD.

Alzheimer's disease: a scoping review of biomarker research and development for effective disease diagnosis

INTRODUCTION

Alzheimer's disease (AD) is regarded as the foremost reason for neurodegeneration that prominently affects the geriatric population. Characterized by extracellular accumulation of amyloid-beta (Aβ), intracellular aggregation of hyperphosphorylated tau (p-tau), and neuronal degeneration that causes impairment of memory and cognition. Amyloid/tau/neurodegeneration (ATN) classification is utilized for research purposes and involves amyloid, tau, and neuronal injury staging through MRI, PET scanning, and CSF protein concentration estimations. CSF sampling is invasive, and MRI and PET scanning requires sophisticated radiological facilities which limit its widespread diagnostic use. ATN classification lacks effectiveness in preclinical AD.

AREAS COVERED

This publication intends to collate and review the existing biomarker profile and the current research and development of a new arsenal of biomarkers for AD pathology from different biological samples, microRNA (miRNA), proteomics, metabolomics, artificial intelligence, and machine learning for AD screening, diagnosis, prognosis, and monitoring of AD treatments.

EXPERT OPINION

It is an accepted observation that AD-related pathological changes occur over a long period of time before the first symptoms are observed providing ample opportunity for detection of biological alterations in various biological samples that can aid in early diagnosis and modify treatment outcomes.

The functional connectivity of basal forebrain is associated with superior memory performance in older adults: a case-control study

Background

Aging is related with memory deterioration. However, some older adults demonstrate superior performance compared to age- and education-matched adults, who are referred to as superagers. To explore the neural mechanisms that mediate their unusually successful memory is important not only for the ameliorate the effects of aging in brain, but also for the prevention of neurodegenerative diseases, including Alzheimer’s disease. This case-control study is aimed to investigate the effects of volume and function of basal forebrain cholinergic neurons on the cognition of superagers.

Methods

The morphometric and resting-state functional MRI analysis, including 34 superagers and 48 typical older adults, were conducted. We compared the basal forebrain gray matter density and related resting-state functional connectivity (FC) in the two groups. To investigate the relationship of FC with cognition, we measure the correlation of significant altered FC and individual cognitive domain.

Results

No significant differences of gray matter density was observed between superagers and typical older adults. The superagers had stronger cortical FC of Ch1-3 with left putamen and insular cortex. The strength of FC positively correlated with global cognition, memory and executive function.

Conclusions

These findings demonstrated that the stronger FC of basal forebrain correlated with specific cognitive difference in global cognition and domains of memory and executive function in superagers.

Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data

Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.

Blood-based biomarkers of agitation in Alzheimer's disease: Advances and future prospects

Agitation is a common neuropsychiatric symptom that becomes more prevalent as Alzheimer's disease (AD) increases in severity. The treatment of agitation is an urgent and unmet need due to the poor outcomes associated with it, its disruptive impact on patients and caregivers, and the lack of efficacious and safe treatments. Recent research on agitation in AD with blood-based biomarkers has advanced the search for its biomarkers beyond the brain and provides new insights to understand its mechanisms and improve treatments. Here, we reviewed studies of blood-based biomarkers of agitation in AD, which show that inflammatory biomarkers are increased in patients with agitation, may predict the development of agitation, and are associated with symptom severity. In addition, they may also track symptom severity and response to treatment. Other biomarkers associated with agitation include markers of oxidative stress, brain cholesterol metabolism, motor activity, and clusterin, a chaperone protein. These results are promising and need to be replicated. Preliminary evidence suggests a role for these biomarkers in interventional studies for agitation to predict and monitor treatment response, which may eventually help enrich study samples and deliver therapy likely to benefit individual patients. Advances in blood-based biomarkers of AD including those identified in "-omic" studies and high sensitivity assays provide opportunities to identify new biomarkers of agitation. Future studies of agitation and its treatment should investigate blood-based biomarkers to yield novel insights into the neurobiological mechanisms of agitation, monitoring symptoms and response to treatment, and to identify patients likely to respond to treatments.

A metabolite panel that differentiates Alzheimer's disease from other dementia types

Introduction

Alzheimer's disease (AD) is associated with altered metabolites. This study aimed to determine the validity of using circulating metabolites to differentiate AD from other dementias.

Methods

Blood metabolites were measured in three data sets. Data set 1 (controls, 27; AD, 28) was used for analyzing differential metabolites. Data set 2 (controls, 93; AD, 92) was used to establish a diagnostic AD model with use of a metabolite panel. The model was applied to Data set 3 (controls, 76; AD, 76; other dementias, 205) to verify its capacity for differentiating AD from other dementias.

Results

Data set 1 revealed 7 upregulated and 77 downregulated metabolites. In Data set 2, a panel of 11 metabolites was included in a model that could distinguish AD from controls. In Data set 3, this panel was used to successfully differentiate AD from other dementias.

Discussion

This study revealed an AD‐specific panel of 11 metabolites that may be used for AD diagnosis.

Resistance exercise improves learning and memory and modulates hippocampal metabolomic profile in aged rats

Physical activity has been considered an important non-medication intervention to preserve mnemonic processes during aging. However, how resistance exercise promotes such benefits remains unclear. A possible hypothesis is that brain-metabolic changes of regions responsible for memory consolidation is affected by muscular training. Therefore, we analyzed the memory, axiety and the metabolomic of aged male Wistar rats (19-20 months old in the 1st day of experiment) submitted to a 12-week resistance exercise protocol (EX, n = 11) or which remained without physical exercise (CTL, n = 13). Barnes maze, elevated plus maze and inhibitory avoidance tests were used to assess the animals' behaviour. The metabolomic profile was identified by nuclear magnetic resonance spectrometry. EX group had better performance in the tests of learning and spatial memory in Barnes maze, and an increase of short and long-term aversive memories formation in inhibitory avoidance. In addition, the exercised animals showed a greater amount of metabolites, such as 4-aminobutyrate, acetate, butyrate, choline, fumarate, glycerol, glycine, histidine, hypoxanthine, isoleucine, leucine, lysine, niacinamide, phenylalanine, succinate, tyrosine, valine and a reduction of ascorbate and aspartate compared to the control animals. These data indicate that the improvement in learning and memory of aged rats submitted to resistance exercise program is associated by changes in the hippocampal metabolomic profile.

A Multimodal Risk Network Predicts Executive Function Trajectories in Non-demented Aging

Background: Multiple modalities of Alzheimer's disease (AD) risk factors may operate through interacting networks to predict differential cognitive trajectories in asymptomatic aging. We test such a network in a series of three analytic steps. First, we test independent associations between three risk scores (functional-health, lifestyle-reserve, and a combined multimodal risk score) and cognitive [executive function (EF)] trajectories. Second, we test whether all three associations are moderated by the most penetrant AD genetic risk [Apolipoprotein E (APOE) ε4+ allele]. Third, we test whether a non-APOE AD genetic risk score further moderates these APOE × multimodal risk score associations. Methods: We assembled a longitudinal data set (spanning a 40-year band of aging, 53-95 years) with non-demented older adults (baseline n = 602; Mage = 70.63(8.70) years; 66% female) from the Victoria Longitudinal Study (VLS). The measures included for each modifiable risk score were: (1) functional-health [pulse pressure (PP), grip strength, and body mass index], (2) lifestyle-reserve (physical, social, cognitive-integrative, cognitive-novel activities, and education), and (3) the combination of functional-health and lifestyle-reserve risk scores. Two AD genetic risk markers included (1) APOE and (2) a combined AD-genetic risk score (AD-GRS) comprised of three single nucleotide polymorphisms (SNPs; Clusterin[rs11136000], Complement receptor 1[rs6656401], Phosphatidylinositol binding clathrin assembly protein[rs3851179]). The analytics included confirmatory factor analysis (CFA), longitudinal invariance testing, and latent growth curve modeling. Structural path analyses were deployed to test and compare prediction models for EF performance and change. Results: First, separate analyses showed that higher functional-health risk scores, lifestyle-reserve risk scores, and the combined score, predicted poorer EF performance and steeper decline. Second, APOE and AD-GRS moderated the association between functional-health risk score and the combined risk score, on EF performance and change. Specifically, only older adults in the APOEε4- group showed steeper EF decline with high risk scores on both functional-health and combined risk score. Both associations were further magnified for adults with high AD-GRS. Conclusion: The present multimodal AD risk network approach incorporated both modifiable and genetic risk scores to predict EF trajectories. The results add an additional degree of precision to risk profile calculations for asymptomatic aging populations.

Metabolomic Alterations in the Blood and Brain in Association with Alzheimer's Disease: Evidence from in vivo to Clinical Studies

Alzheimer's disease (AD) has become a major health problem among the elderly population. Some evidence suggests that metabolic disturbance possibly plays a role in the pathophysiology of AD. Currently, the study of metabolomics has been used to explore changes in multiple metabolites in several diseases, including AD. Thus, the metabolomics research in AD might provide some information regarding metabolic dysregulations, and their possible associated pathophysiology. This review summarizes the information discovered regarding the metabolites in the brain and the blood from the metabolomics research of AD from both animal and clinical studies. Additionally, the correlation between the changes in metabolites and outcomes, such as pathological findings in the brain and cognitive impairment are discussed. We also deliberate on the findings of cohort studies, demonstrating the alterations in metabolites before changes of cognitive function. All of these findings can be used to inform the potential identity of specific metabolites as possible biomarkers for AD.

Targeting autonomic flexibility to enhance cognitive training outcomes in older adults with mild cognitive impairment: study protocol for a randomized controlled trial

Importance

Cognitive training with components that can further enhance the transferred and long-term effects and slow the progress of dementia is needed for preventing dementia.

Objective

The goal of the study is to test whether improving autonomic nervous system (ANS) flexibility via a resonance frequency breathing (RFB) training will strengthen the effects of a visual speed of processing (VSOP) cognitive training on cognitive and brain function, and slow the progress of dementia in older adults with mild cognitive impairment (MCI).

Design

Stage II double-blinded randomized controlled trial. The study was prospectively registered at ClinicalTrials.gov, with registration approved on 21 August 2020 (No. NCT04522791).

Setting

Study-related appointments will be conducted on-site at University of Rochester Medical Center locations. Data collection will be conducted from August 2020 to February 2025.

Participants

Older adults with MCI (n = 114) will be randomly assigned to an 8-week combined intervention (RFB+VSOP), VSOP with guided imagery relaxation (IR) control, and a IR-only control, with periodical booster training sessions at follow-ups. Mechanistic and distal outcomes include ANS flexibility, measured by heart rate variability, and multiple markers of dementia progress. Data will be collected across a 14-month period.

Discussion

This will be among the first RCTs to examine in older persons with MCI a novel, combined intervention targeting ANS flexibility, an important contributor to overall environmental adaptation, with an ultimate goal for slowing neurodegeneration.

Trial registration

ClinicalTrials.gov NCT04522791. Registered on 21 August 2020

Protocol version: STUDY00004727; IRB protocol version 2, approved on 30 July 2020.

Potential Neuroregenerative and Neuroprotective Effects of Uridine/Choline-Enriched Multinutrient Dietary Intervention for Mild Cognitive Impairment: A Narrative Review

In mild cognitive impairment (MCI) due to Alzheimer disease (AD), also known as prodromal AD, there is evidence for a pathologic shortage of uridine, choline, and docosahexaenoic acid [DHA]), which are key nutrients needed by the brain. Preclinical and clinical evidence shows the importance of nutrient bioavailability to support the development and maintenance of brain structure and function in MCI and AD. Availability of key nutrients is limited in MCI, creating a distinct nutritional need for uridine, choline, and DHA. Evidence suggests that metabolic derangements associated with ageing and disease-related pathology can affect the body's ability to generate and utilize nutrients. This is reflected in lower levels of nutrients measured in the plasma and brains of individuals with MCI and AD dementia, and progressive loss of cognitive performance. The uridine shortage cannot be corrected by normal diet, making uridine a conditionally essential nutrient in affected individuals. It is also challenging to correct the choline shortfall through diet alone, because brain uptake from the plasma significantly decreases with ageing. There is no strong evidence to support the use of single-agent supplements in the management of MCI due to AD. As uridine and choline work synergistically with DHA to increase phosphatidylcholine formation, there is a compelling rationale to combine these nutrients. A multinutrient enriched with uridine, choline, and DHA developed to support brain function has been evaluated in randomized controlled trials covering a spectrum of dementia from MCI to moderate AD. A randomized controlled trial in subjects with prodromal AD showed that multinutrient intervention slowed brain atrophy and improved some measures of cognition. Based on the available clinical evidence, nutritional intervention should be considered as a part of the approach to the management of individuals with MCI due to AD, including adherence to a healthy, balanced diet, and consideration of evidence-based multinutrient supplements.

Data-driven biomarker analysis using computational omics approaches to assess neurodegenerative disease progression

The complexity of biological systems suggests that current definitions of molecular dysfunctions are essential distinctions of a complex phenotype. This is well seen in neurodegenerative diseases (ND), such as Alzheimer's disease (AD) and Parkinson's disease (PD), multi-factorial pathologies characterized by high heterogeneity. These challenges make it necessary to understand the effectiveness of candidate biomarkers for early diagnosis, as well as to obtain a comprehensive mapping of how selective treatment alters the progression of the disorder. A large number of computational methods have been developed to explain network-based approaches by integrating individual components for modeling a complex system. In this review, high-throughput omics methodologies are presented for the identification of potent biomarkers associated with AD and PD pathogenesis as well as for monitoring the response of dysfunctional molecular pathways incorporating multilevel clinical information. In addition, principles for efficient data analysis pipelines are being discussed that can help address current limitations during the experimental process by increasing the reproducibility of benchmarking studies.

Plasma metabolomics of presymptomatic PSEN1-H163Y mutation carriers: a pilot study

BACKGROUND AND OBJECTIVE

PSEN1-H163Y carriers, at the presymptomatic stage, have reduced ¹⁸ FDG-PET binding in the cerebrum of the brain (Scholl et al., Neurobiol Aging 32:1388-1399, 2011). This could imply dysfunctional energy metabolism in the brain. In this study, plasma of presymptomatic PSEN1 mutation carriers was analyzed to understand associated metabolic changes.

METHODS

We analyzed plasma from noncarriers (NC, n = 8) and presymptomatic PSEN1-H163Y mutation carriers (MC, n = 6) via untargeted metabolomics using gas and liquid chromatography coupled with mass spectrometry, which identified 1199 metabolites. All the metabolites were compared between MC and NC using univariate analysis, as well as correlated with the ratio of Aβ_{1-42/A β 1-40} , using Spearman's correlation. Altered metabolites were subjected to Ingenuity Pathway Analysis (IPA).

RESULTS

Based on principal component analysis the plasma metabolite profiles were divided into dataset A and dataset B. In dataset A, when comparing between presymptomatic MC and NC, the levels of 79 different metabolites were altered. Out of 79, only 14 were annotated metabolites. In dataset B, 37 metabolites were significantly altered between presymptomatic MC and NC and nine metabolites were annotated. In both datasets, annotated metabolites represent amino acids, fatty acyls, bile acids, hexoses, purine nucleosides, carboxylic acids, and glycerophosphatidylcholine species. 1-docosapentaenoyl-GPC was positively correlated, uric acid and glucose were negatively correlated with the ratio of plasma Aβ_1-42 /Aβ_1-40 (P < 0.05).

INTERPRETATION

This study finds dysregulated metabolite classes, which are changed before the disease symptom onset. Also, it provides an opportunity to compare with sporadic Alzheimer's Disease. Observed findings in this study need to be validated in a larger and independent Familial Alzheimer's Disease (FAD) cohort.

A Community-Based Study Identifying Metabolic Biomarkers of Mild Cognitive Impairment and Alzheimer's Disease Using Artificial Intelligence and Machine Learning

BACKGROUND

Currently, there is no objective, clinically available tool for the accurate diagnosis of Alzheimer's disease (AD). There is a pressing need for a novel, minimally invasive, cost friendly, and easily accessible tool to diagnose AD, assess disease severity, and prognosticate course. Metabolomics is a promising tool for discovery of new, biologically, and clinically relevant biomarkers for AD detection and classification.

OBJECTIVE

Utilizing artificial intelligence and machine learning, we aim to assess whether a panel of metabolites as detected in plasma can be used as an objective and clinically feasible tool for the diagnosis of mild cognitive impairment (MCI) and AD.

METHODS

Using a community-based sample cohort acquired from different sites across the US, we adopted an approach combining Proton Nuclear Magnetic Resonance Spectroscopy (1H NMR), Liquid Chromatography coupled with Mass Spectrometry (LC-MS) and various machine learning statistical approaches to identify a biomarker panel capable of identifying those patients with AD and MCI from healthy controls.

RESULTS

Of the 212 measured metabolites, 5 were identified as optimal to discriminate between controls, and individuals with MCI or AD. Our models performed with AUC values in the range of 0.72-0.76, with the sensitivity and specificity values ranging from 0.75-0.85 and 0.69-0.81, respectively. Univariate and pathway analysis identified lipid metabolism as the most perturbed biochemical pathway in MCI and AD.

CONCLUSION

A comprehensive method of acquiring metabolomics data, coupled with machine learning techniques, has identified a strong panel of diagnostic biomarkers capable of identifying individuals with MCI and AD. Further, our data confirm what other groups have reported, that lipid metabolism is significantly perturbed in those individuals suffering with dementia. This work may provide additional insight into AD pathogenesis and encourage more in-depth analysis of the AD lipidome.

Novel biomarkers in Alzheimer's disease using high resolution proteomics and metabolomics: miRNAS, proteins and metabolites

Alzheimer's disease (AD) is the most common form of dementia. It affects approximately 6% of people over the age of 65 years. It is a clinicopathological, degenerative, chronical and progressive disease that exhibits a deterioration of memory, orientation, speech and other functions. Factors contributing to the pathogenesis of the disease are the presence of extracellular amyloid deposits, called neuritic senile plaques, and fibrillary protein deposits inside neurons, known as neurofibrillary bundles, that appear mainly in the frontal and temporal lobes. AD has a long preclinical latency and is difficult to diagnose and prevent at early stages. Despite the advent of novel high-throughput technologies, it is a great challenge to identify precise biomarkers to understand the progression of the disease and the development of new treatments. In this sense, important knowledge is emerging regarding novel molecular and biological candidates with diagnostic potential, including microRNAs that have a key role in gene repression. On the other hand, proteomic approaches offer a platform for the comprehensive analysis of the whole proteome in a certain physiological time. Proteomic technology investigates protein expression directly and reveals post-translational modifications known to be determinant for many human diseases. Clinically, there is growing evidence for the role of proteomic and metabolomic technologies in AD biomarker discovery. This review discusses the role of several miRNAs identified using genomic technologies, and the importance of novel proteomic and metabolomic approaches to identify new proteins and metabolites that may be useful as biomarkers for monitoring the progression and treatment of AD.

Metabolomics Research Conducted by Nurse Scientists: A Systematic Scoping Review

Metabolomics, one of the newest omics, allows for investigation of holistic responses of living systems to myriad biological, behavioral, and environmental factors. Researcher use metabolomics to examine the underlying mechanisms of clinically observed phenotypes. However, these methods are complex, potentially impeding their uptake by scientists. In this scoping review, we summarize literature illustrating nurse scientists' use of metabolomics. Using electronic search methods, we identified metabolomics investigations conducted by nurse scientists and published in English-language journals between 1990 and November 2019. Of the studies included in the review (N = 30), 9 (30%) listed first and/or senior authors that were nurses. Studies were conducted predominantly in the United States and focused on a wide array of clinical conditions across the life span. The upward trend we note in the use of these methods by nurse scientists over the past 2 decades mirrors a similar trend across scientists of all backgrounds. A broad range of study designs were represented in the literature we reviewed, with the majority involving untargeted metabolomics (n = 16, 53.3%) used to generate hypotheses (n = 13, 76.7%) of potential metabolites and/or metabolic pathways as mechanisms of clinical conditions. Metabolomics methods match well with the unique perspective of nurse researchers, who seek to integrate the experiences of individuals to develop a scientific basis for clinical practice that emphasizes personalized approaches. Although small in number, metabolomics investigations by nurse scientists can serve as the foundation for robust programs of research to answer essential questions for nursing.

Understanding brain resilience in superagers: a systematic review

PURPOSE

Superagers are older adults presenting excellent memory performance that may reflect resilience to the conventional pathways of aging. Our contribution aims to shape the evidence body of the known distinctive biomarkers of superagers and their connections with the Brain and Cognitive Reserve and Brain Maintenance concepts.

METHODS

We performed a systematic literature search in PubMed and ScienceDirect with no limit on publication date for studies that evaluated potential biomarkers in superagers classified by validated neuropsychological tests. Methodological quality was assessed using the QUADAS-2 tool.

RESULTS

Twenty-one studies were included, the majority in neuroimaging, followed by histological, genetic, cognition, and a single one on blood plasma analysis. Superagers exhibited specific regions of cortical preservation, rather than global cortical maintenance, standing out the anterior cingulate and hippocampus regions. Both superagers and controls showed similar levels of amyloid deposition. Moreover, the functional oscillation patterns in superagers resembled those described in young adults. Most of the quality assessment for the included studies showed medium risks of bias.

CONCLUSION

This systematic review supports selective cortical preservation in superagers, comprehending regions of the default mode, and salience networks, overlapped by stronger functional connectivity. In this context, the anterior cingulate cortex is highlighted as an imaging and histologic signature of these subjects. Besides, the biomarkers included pointed out that the Brain and Cognitive Reserve and Brain Maintenance concepts are independent and complementary in the superagers' setting.

Cognitively supernormal older adults maintain a unique structural connectome that is resistant to Alzheimer's pathology

Studying older adults with excellent cognitive capacities (Supernormals) provides a unique opportunity for identifying factors related to cognitive success - a critical topic across lifespan. There is a limited understanding of Supernormals' neural substrates, especially whether any of them attends shaping and supporting superior cognitive function or confer resistance to age-related neurodegeneration such as Alzheimer's disease (AD). Here, applying a state-of-the-art diffusion imaging processing pipeline and finite mixture modelling, we longitudinally examine the structural connectome of Supernormals. We find a unique structural connectome, containing the connections between frontal, cingulate, parietal, temporal, and subcortical regions in the same hemisphere that remains stable over time in Supernormals, relatively to typical agers. The connectome significantly classifies positive vs. negative AD pathology at 72% accuracy in a new sample mixing Supernormals, typical agers, and AD risk [amnestic mild cognitive impairment (aMCI)] subjects. Among this connectome, the mean diffusivity of the connection between right isthmus cingulate cortex and right precuneus most robustly contributes to predicting AD pathology across samples. The mean diffusivity of this connection links negatively to global cognition in those Supernormals with positive AD pathology. But this relationship does not exist in typical agers or aMCI. Our data suggest the presence of a structural connectome supporting cognitive success. Cingulate to precuneus white matter integrity may be useful as a structural marker for monitoring neurodegeneration and may provide critical information for understanding how some older adults maintain or excel cognitively in light of significant AD pathology.

A multiomics approach to heterogeneity in Alzheimer's disease: focused review and roadmap

Aetiological and clinical heterogeneity is increasingly recognized as a common characteristic of Alzheimer's disease and related dementias. This heterogeneity complicates diagnosis, treatment, and the design and testing of new drugs. An important line of research is discovery of multimodal biomarkers that will facilitate the targeting of subpopulations with homogeneous pathophysiological signatures. High-throughput 'omics' are unbiased data-driven techniques that probe the complex aetiology of Alzheimer's disease from multiple levels (e.g. network, cellular, and molecular) and thereby account for pathophysiological heterogeneity in clinical populations. This review focuses on data reduction analyses that identify complementary disease-relevant perturbations for three omics techniques: neuroimaging-based subtypes, metabolomics-derived metabolite panels, and genomics-related polygenic risk scores. Neuroimaging can track accrued neurodegeneration and other sources of network impairments, metabolomics provides a global small-molecule snapshot that is sensitive to ongoing pathological processes, and genomics characterizes relatively invariant genetic risk factors representing key pathways associated with Alzheimer's disease. Following this focused review, we present a roadmap for assembling these multiomics measurements into a diagnostic tool highly predictive of individual clinical trajectories, to further the goal of personalized medicine in Alzheimer's disease.

Metabolic correlates of prevalent mild cognitive impairment and Alzheimer's disease in adults with Down syndrome

INTRODUCTION

Disruption of metabolic function is a recognized feature of late onset Alzheimer's disease (LOAD). We sought to determine whether similar metabolic pathways are implicated in adults with Down syndrome (DS) who have increased risk for Alzheimer's disease (AD).

METHODS

We examined peripheral blood from 292 participants with DS who completed baseline assessments in the Alzheimer's Biomarkers Consortium-Down Syndrome (ABC-DS) using untargeted mass spectrometry (MS). Our sample included 38 individuals who met consensus criteria for AD (DS-AD), 43 who met criteria for mild cognitive impairment (DS-MCI), and 211 who were cognitively unaffected and stable (CS).

RESULTS

We measured relative abundance of 8,805 features using MS and 180 putative metabolites were differentially expressed (DE) among the groups at false discovery rate-corrected q< 0.05. From the DE features, a nine-feature classifier model classified the CS and DS-AD groups with receiver operating characteristic area under the curve (ROC AUC) of 0.86 and a two-feature model classified the DS-MCI and DS-AD groups with ROC AUC of 0.88. Metabolite set enrichment analysis across the three groups suggested alterations in fatty acid and carbohydrate metabolism.

DISCUSSION

Our results reveal metabolic alterations in DS-AD that are similar to those seen in LOAD. The pattern of results in this cross-sectional DS cohort suggests a dynamic time course of metabolic dysregulation which evolves with clinical progression from non-demented, to MCI, to AD. Metabolomic markers may be useful for staging progression of DS-AD.

Blood-based systems biology biomarkers for next-generation clinical trials in Alzheimer's disease

Alzheimer's disease (AD)-a complex disease showing multiple pathomechanistic alterations-is triggered by nonlinear dynamic interactions of genetic/epigenetic and environmental risk factors, which, ultimately, converge into a biologically heterogeneous disease. To tackle the burden of AD during early preclinical stages, accessible blood-based biomarkers are currently being developed. Specifically, next-generation clinical trials are expected to integrate positive and negative predictive blood-based biomarkers into study designs to evaluate, at the individual level, target druggability and potential drug resistance mechanisms. In this scenario, systems biology holds promise to accelerate validation and qualification for clinical trial contexts of use-including proof-of-mechanism, patient selection, assessment of treatment efficacy and safety rates, and prognostic evaluation. Albeit in their infancy, systems biology-based approaches are poised to identify relevant AD "signatures" through multifactorial and interindividual variability, allowing us to decipher disease pathophysiology and etiology. Hopefully, innovative biomarker-drug codevelopment strategies will be the road ahead towards effective disease-modifying drugs.
.

Longitudinal Functional Brain Mapping in Supernormals

Prevention of age-related cognitive decline is an increasingly important topic. Recently, increased attention is being directed at understanding biological models of successful cognitive aging. Here, we examined resting-state brain regional low-frequency oscillations using functional magnetic resonance imaging in 19 older adults with excellent cognitive abilities (Supernormals), 28 older adults with normative cognition, 57 older adults with amnestic mild cognitive impairment, and 26 with Alzheimer's disease. We identified a "Supernormal map", a set of regions whose oscillations were resistant to the aging-associated neurodegenerative process, including the right fusiform gyrus, right middle frontal gyrus, right anterior cingulate cortex, left middle temporal gyrus, left precentral gyrus, and left orbitofrontal cortex. The map was unique to the Supernormals, differentiated this group from cognitive average-ager comparisons, and predicted a 1-year change in global cognition (indexed by the Montreal Cognitive Assessment scores, adjusted R2 = 0.68). The map was also correlated to Alzheimer's pathophysiological features (beta-amyloid/pTau ratio, adjusted R2 = 0.66) in participants with and without cognitive impairment. These findings in phenotypically successful cognitive agers suggest a divergent pattern of brain regions that may either reflect inherent neural integrity that contributes to Supernormals' cognitive success, or alternatively indicate adaptive reorganization to the demands of aging.

Targeted Metabolomic Profiling Reveals Association Between Altered Amino Acids and Poor Functional Recovery After Stroke

Amino acids have been shown to be among the most important metabolites to be altered following stroke; however, they are a double-edged sword with regard to regulating hemostasis. In this study, we conducted a targeted metabolomic study to examine the association between serum levels of amino acids and functional recovery after stroke. Three hundred and fifty-one patients with stroke admitted to an acute rehabilitation hospital were screened, and 106 patients were selected based on inclusion and exclusion criteria. Recruited patients were stratified using Montebello Rehabilitation Factor Score (MRFS) efficiency. We selected the top (n = 20, 19%) and bottom (n = 20, 19%) of MRFS efficiency for metabolomic analysis. A total of 21 serum amino acids levels were measured using ultra high performance liquid chromatography and mass spectrometry. The normalized data were analyzed by multivariate approaches, and the selected potential biomarkers were combined in different combinations for prediction of stroke functional recovery. The results demonstrated that there were significant differences in leucine-isoleucine, proline, threonine, glutamic acid, and arginine levels between good and poor recovery groups. In the training (0.952) and test (0.835) sets, metabolite biomarker panels composed of proline, glutamic acid, and arginine had the highest sensitivity and specificity in distinguishing good recovery from poor. In particular, arginine was present in the top 10 combinations of the average area under the receiver operating characteristic curve (AUC) test set. Our findings suggest that amino acids related to energy metabolism and excitotoxicity may play an important role in functional recovery after stroke. Therefore, the level of serum arginine has predictive value for the recovery rate after stroke.

Oligosaccharides from Morinda officinalis Slow the Progress of Aging Mice by Regulating the Key Microbiota-Metabolite Pairs

The gut microbiota is considered an important factor in the progression of Alzheimer's disease (AD). Active research on the association between the metabolome and the gut microbiome is ongoing and can provide a large amount of beneficial information about the interactions between the microbiome and the metabolome. Previous studies have shown that the oligosaccharides from Morinda officinalis (OMO) can delay the progress of AD in model animals by regulating the diversity of the gut microbiome and metabolic components, and the correlation between the gut microbiome and metabolic components still needs to be further verified. This study applied a new two-level strategy to investigate and ensure the accuracy and consistency of the results. This strategy can be used to determine the association between the gut microbiome and serum metabolome in APP/PS1 transgenic mice and C57BL/6J male mice. The “4C0d-2 spp.-Cholesterol,” “CW040 spp.-L-valine,” “CW040 spp.-L-acetylcarnitine,” “RF39 spp.-L-valine,” “TM7-3 spp.-L-valine,” and “TM7-3 spp.-L-acetylcarnitine” associations among specific “microbiota-metabolite” pairs were further identified based on univariate and multivariate correlation analyses and functional analyses. The key relevant pairs were verified by an independent oligosaccharide intervention study, and the gut microbiome and serum metabolome of the OMO intervention group were similar to those of the normal group. The results indicate that OMO can significantly suppress Alzheimer's disease by regulating the key microbiota-metabolite pairs. Therefore, this two-level strategy is effective in identifying the principal correlations in large datasets obtained from combinations of multiomic studies and further enhancing our understanding of the correlation between the brain and gut in patients with AD.

Recent advances from metabolomics and lipidomics application in alzheimer's disease inspiring drug discovery

Introduction: Although age is a major risk factor for Alzheimer's disease (AD), it is not an inevitable consequence of aging nor is it exclusively an old-age disease. Several other major risk factors for AD are strongly associated with metabolism and include lack of exercise, obesity, diabetes, high blood pressure and cholesterol, over-consumption of alcohol and depression in addition to low educational level, social isolation, and cognitive inactivity. Approaches for Alzheimer prevention and treatment through manipulation of metabolism and utilization of active metabolites have great potential either as a primary or secondary treatment avenue or as a preventative strategy in high-risk individuals.Areas covered: This review outlines the current knowledge concerning the relationship between AD and metabolism and the novel treatments attempting to correct changes in AD patients determined through metabolomics or lipidomic analyses.Expert opinion: Metabolites are one of the main driving factors and indicators of AD and can offer many possible avenues for prevention and treatment. However, with the highly interconnected effects of metabolites and metabolism, as well as the many different routes for metabolism dysfunction, successful treatment would have to include the correction of metabolic errors as well as errors in transport and metabolite processing in order to affect and revert AD progression.

Amyloid and FDG PET of Successful Cognitive Aging: Global and Cingulate-Specific Differences

BACKGROUND

Some individuals, called Supernormals (SN), maintain excellent memory in old age. While brain structural and functional integrity in SN seem to be aging-resistant, their amyloidosis and neural injury status has not been well studied.

OBJECTIVE

The goal of this study was to compare cortical amyloid deposition and glucose metabolism between SN and older adults with normal cognition (NC), amnestic mild cognitive impairment (MCI), and Alzheimer's disease (AD).

METHODS

Subjects from the ADNI database were included if they received T1-weighted MRI, amyloid PET, FDG-PET, and cognitive testing within a 6-month period, yielding 27 AD, 69 MCI, 172 NC, and 122 SN. PET standardized uptake value ratios (SUVrs) were calculated for the whole cortex and 68 regions of interest, with whole cerebellum serving as reference.

RESULTS

SN had lower whole cortex amyloid than MCI, and higher glucose metabolism than all others. Regional analysis revealed that amyloid burden and glucose metabolism in the right isthmus cingulate cortex differed in SN compared to others, while SN glucose metabolism also differed from others in several frontal and temporal regions.

CONCLUSION

Preserved cortical glucose metabolism, and lower levels of amyloidosis and glucose hypometabolism in the right isthmus cingulate cortex, contributes to the Supernormal phenomenon. These findings may be informative for development of early screening biomarkers and therapeutic targets for modification of cognitive trajectories.

A novel UHPLC-HRMS-based metabolomics strategy enables the discovery of potential neuroactive metabolites in mice plasma, following i.p. administration of the main Crocus sativus L. bioactive component

Trans-crocin 4 (TC4) is an important carotenoid constituent of saffron showing potential activity against Alzheimer's Disease (AD) due to its antioxidant and antiamyloidogenic properties. Metabolomics is an emerging scientific field that enhances biomarker discovery and reveals underlying biochemical mechanisms aiming towards the early subclinical diagnosis of diseases. To date, there are no reports on the changes induced to mice plasma metabolome after TC4 administration. We report a novel untargeted UHPLC-ESI HRMS metabolomics strategy to determine the alteration of the metabolic fingerprint following i.p. administration of TC4 in male and female mice. Blood samples from fiftysix mice treated with TC4 as well as from control animals were analyzed with UHPLC-ESI HRMS. Statistical evaluation of the results was achieved by multivariate analysis (MVA), i.e., principal component analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) in order to discover the variables that contributed to the discrimination between treated and untreated groups which were identified by online database searching (e.g., Metlin, HMDB, KEGG) aided by chemometric processing, e.g., covariance searching etc. Due to the high variability imposed by various factors, e.g., sex of the animals participating in the study, administration dose and time-points of sacrifice, multilevel sparse PLS-DA analysis, e.g., splitting variation to each individual component, has been employed as a more efficient approach for such designs. This methodology allowed the identification of the time sequence of metabolome changes due to the administration of TC4, whereas a sex-related effect on the metabolome is indicated, denoting that the administration in both sexes is indispensable in order to acquire safe conclusions as reliable metabolome pictures. The results demonstrated a number of annotated metabolites playing a potential role in neuroprotection while they are closely related to AD. Moreover, five additional annotated metabolites were involved in the steroid biosynthesis pathway while two of them may be considered as putative neuroprotective agents.

Diet-Related Metabolites Associated with Cognitive Decline Revealed by Untargeted Metabolomics in a Prospective Cohort

SCOPE

Untargeted metabolomics may reveal preventive targets in cognitive aging, including within the food metabolome.

METHODS AND RESULTS

A case-control study nested in the prospective Three-City study includes participants aged ≥65 years and initially free of dementia. A total of 209 cases of cognitive decline and 209 controls (matched for age, gender, education) with slower cognitive decline over up to 12 years are contrasted. Using untargeted metabolomics and bootstrap-enhanced penalized regression, a baseline serum signature of 22 metabolites associated with subsequent cognitive decline is identified. The signature includes three coffee metabolites, a biomarker of citrus intake, a cocoa metabolite, two metabolites putatively derived from fish and wine, three medium-chain acylcarnitines, glycodeoxycholic acid, lysoPC(18:3), trimethyllysine, glucose, cortisol, creatinine, and arginine. Adding the 22 metabolites to a reference predictive model for cognitive decline (conditioned on age, gender, education and including ApoE-ε4, diabetes, BMI, and number of medications) substantially increases the predictive performance: cross-validated Area Under the Receiver Operating Curve = 75% [95% CI 70-80%] compared to 62% [95% CI 56-67%].

CONCLUSIONS

The untargeted metabolomics study supports a protective role of specific foods (e.g., coffee, cocoa, fish) and various alterations in the endogenous metabolism responsive to diet in cognitive aging.

Plasma metabolites related to cellular energy metabolism are altered in adults with Down syndrome and Alzheimer's disease

Down syndrome (DS) is a well-known neurodevelopmental disorder most commonly caused by trisomy of chromosome 21. Because individuals with DS almost universally develop heavy amyloid burden and Alzheimer's disease (AD), biomarker discovery in this population may be extremely fruitful. Moreover, any AD biomarker in DS that does not directly involve amyloid pathology may be of high value for understanding broader mechanisms of AD generalizable to the neurotypical population. In this retrospective biomarker discovery study, we examined banked peripheral plasma samples from 78 individuals with DS who met clinical criteria for AD at the time of the blood draw (DS-AD) and 68 individuals with DS who did not (DS-NAD). We measured the relative abundance of approximately 5,000 putative features in the plasma using untargeted mass spectrometry (MS). We found significantly higher levels of a peak putatively annotated as lactic acid in the DS-AD group (q = .014), a finding confirmed using targeted MS (q = .011). Because lactate is the terminal product of glycolysis and subsequent lactic acid fermentation, we performed additional targeted MS focusing on central carbon metabolism which revealed significantly increased levels of pyruvic (q = .03) and methyladipic (q = .03) acids in addition to significantly lower levels of uridine (q = .007) in the DS-AD group. These data suggest that AD in DS is accompanied by a shift from aerobic respiration toward the less efficient fermentative metabolism and that bioenergetically derived metabolites observable in peripheral blood may be useful for detecting this shift.

Metabolomics Analyses of Saliva Detect Novel Biomarkers of Alzheimer's Disease

Using a non-invasive biofluid (saliva), we apply a powerful metabolomics workflow for unbiased biomarker discovery in Alzheimer's disease (AD). We profile and differentiate Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD groups. The workflow involves differential chemical isotope labeling liquid chromatography mass spectrometry using dansylation derivatization for in-depth profiling of the amine/phenol submetabolome. The total sample (N = 109) was divided in to the Discovery Phase (DP) (n = 82; 35 CN, 25 MCI, 22 AD) and a provisional Validation Phase (VP) (n = 27; 10 CN, 10 MCI, 7 AD). In DP we detected 6,230 metabolites. Pairwise analyses confirmed biomarkers for AD versus CN (63), AD versus MCI (47), and MCI versus CN (2). We then determined the top discriminating biomarkers and diagnostic panels. A 3-metabolite panel distinguished AD from CN and MCI (DP and VP: Area Under the Curve [AUC] = 1.000). The MCI and CN groups were best discriminated with a 2-metabolite panel (DP: AUC = 0.779; VP: AUC = 0.889). In addition, using positively confirmed metabolites, we were able to distinguish AD from CN and MCI with good diagnostic performance (AUC > 0.8). Saliva is a promising biofluid for both unbiased and targeted AD biomarker discovery and mechanism detection. Given its wide availability and convenient accessibility, saliva is a biofluid that can promote diversification of global AD biomarker research.

Neurocognitive SuperAging in Older Adults Living With HIV: Demographic, Neuromedical and Everyday Functioning Correlates

OBJECTIVES

Studies of neurocognitively elite older adults, termed SuperAgers, have identified clinical predictors and neurobiological indicators of resilience against age-related neurocognitive decline. Despite rising rates of older persons living with HIV (PLWH), SuperAging (SA) in PLWH remains undefined. We aimed to establish neuropsychological criteria for SA in PLWH and examined clinically relevant correlates of SA.

METHODS

734 PLWH and 123 HIV-uninfected participants between 50 and 64 years of age underwent neuropsychological and neuromedical evaluations. SA was defined as demographically corrected (i.e., sex, race/ethnicity, education) global neurocognitive performance within normal range for 25-year-olds. Remaining participants were labeled cognitively normal (CN) or impaired (CI) based on actual age. Chi-square and analysis of variance tests examined HIV group differences on neurocognitive status and demographics. Within PLWH, neurocognitive status differences were tested on HIV disease characteristics, medical comorbidities, and everyday functioning. Multinomial logistic regression explored independent predictors of neurocognitive status.

RESULTS

Neurocognitive status rates and demographic characteristics differed between PLWH (SA=17%; CN=38%; CI=45%) and HIV-uninfected participants (SA=35%; CN=55%; CI=11%). In PLWH, neurocognitive groups were comparable on demographic and HIV disease characteristics. Younger age, higher verbal IQ, absence of diabetes, fewer depressive symptoms, and lifetime cannabis use disorder increased likelihood of SA. SA reported increased independence in everyday functioning, employment, and health-related quality of life than non-SA.

CONCLUSIONS

Despite combined neurological risk of aging and HIV, youthful neurocognitive performance is possible for older PLWH. SA relates to improved real-world functioning and may be better explained by cognitive reserve and maintenance of cardiometabolic and mental health than HIV disease severity. Future research investigating biomarker and lifestyle (e.g., physical activity) correlates of SA may help identify modifiable neuroprotective factors against HIV-related neurobiological aging. (JINS, 2019, 25, 507-519).

Dysregulated Urinary Arginine Metabolism in Older Adults With Amnestic Mild Cognitive Impairment

Background: Urine samples, which capture an individual's metabolic profile, are ideal for the exploration of non-invasive biomarkers to confirm the amnestic mild cognitive impairment (aMCI) status of patients vs. unimpaired ones. Objective: We aimed to detect differentially metabolized amino acids, which are important objectives in metabolomics, garnering particular attention in biomedical pathogenesis from the urine of aMCI patients, which may give clinicians the possibility to intervene with early treatments that curb Alzheimer's disease (AD). Methods: The study included 208 subjects, 98 of whom were aMCI patients, and 110 who were control subjects without dementia. Urine samples were taken from each participant and supernatant was obtained for analysis. The concentrations of amino acids were measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Results: Urinary arginine levels in aMCI patients are obviously lower than in normal controls (q < 0.2 and p < 0.05). Meanwhile, aMCI patients had significant reduced urinary global arginine bioavailability ratio (GABR), and GABR in urine displayed a positive correlation with the score of CMMSE. Conclusion: Urinary dysregulated arginine metabolism that may serve as a helpful clinical diagnostic biomarker for aMCI in older adults.

New molecular approaches to Alzheimer's disease

Alzheimer's disease is a neurodegenerative disorder and the most common and devastating form of dementia. It affects mainly older people, accounting for 50-80% of dementia cases. The age is the main associated risk factor and based on the onset age, early-onset (EOAD) or late-onset (LOAD) forms are distinguished. AD has a strong impact both on the life-style of patients and their families and on the society, due to the high costs related to social and medical care. So far, despite the great advances in understanding of the AD pathogenesis, there is no a cure for this form of dementia and current available treatments are limited to temporarily relieve symptoms. In this review, firstly we give an overview of the current knowledge of the genetic basis of both forms of AD with a particular emphasis on the insights in the understanding of the pathogenic mechanisms of this disorder. Then we discuss the promising relevance of "omics sciences" and the open challenges of the application of Big Data in promoting precision medicine for AD.

Plasma metabolomics in early Alzheimer's disease patients diagnosed with amyloid biomarker

An untargeted metabolomics study has been carried out using plasma samples from patients with Mild Cognitive Impairment due to Alzheimer's disease patients (MCI-AD, n = 29) and healthy people (n = 29)). They have been classified following the National Institute on Aging and Alzheimer's Association (NIA-AA) recommendations and cerebrospinal fluid biomarkers. The analytical method was based on liquid chromatography coupled to high resolution mass spectrometry. The data process from the corresponding metabolic profiles retained 1158 molecular features in positive and 424 in negative ionization mode. Differences between metabolomic profiles from MCI-AD patients and healthy participants were investigated using a penalized logistic regression analysis (ElasticNet), and being able to select automatically the most informative variables (53 molecular features). From the molecular features selected for the elastic net models, 16 variables were preliminarily identified by The Human Metabolome Database (amino acids, lipids…). However, only 4 of these variables were tentatively identified by MS/MS and all ions fragmentation modes, being choline the only confirmed metabolite. Regarding their metabolic pathways, they could be involved in cholinergic system, energy metabolism, amino acids and lipids pathways. To conclude, this is a reliable approach to early AD mechanisms, and choline has been identified as a promising AD diagnosis metabolite. SIGNIFICANCE: The untargeted analysis carried out in human plasma samples from early Alzheimer's disease patients and healthy individuals, and the use of sophisticated statistical tools, identified some metabolic pathways and plasma biomarkers. Preliminarily, cholinergic system, energy metabolism, and aminoacids and lipids pathways may be involved in early Alzheimer's disease development.