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

A metabolomic approach to identifying biomarkers in blood of Alzheimer's disease

Objective

This study aims to identify metabolites with altered levels of expression in patients with early and progressive stages of Alzheimer's disease (AD).

Methods

All participants of the study underwent genetic screening and were diagnosed using both neuropsychological assessment and amyloid imaging before metabolome analysis. According to these assessments, the patients were classified as normal (n = 15), with mild cognitive impairment (n = 10), and with AD (n = 15).

Results

Using a targeted metabolomic approach, we found that plasma levels of C3, C5, and C5-DC acylcarnitines, arginine, phenylalanine, creatinine, symmetric dimethylarginine (SDMA) and phosphatidylcholine ae C38:2 were significantly altered in patients with early and progressive stages of AD. We created a predictive model based on the decision tree that included three main parameters: age, arginine and C5 plasma concentrations. The model distinguished AD patients from other participants with 60% sensitivity and 86.7% specificity. For healthy controls, the sensitivity was 85.7% and specificity was 61.5%. Multivariate ROC analysis to develop a decision tree showed that our model reached moderate diagnostic power in differentiating between older adults who are cognitively normal (AUC = 0.77) and those with AD (AUC = 0.72).

Interpretation

The plasma levels of arginine and valeryl carnitine, together with subject age, are promising as biomarkers for the diagnosis of AD in older adults.

Omics-based Biomarkers for the Early Alzheimer Disease Diagnosis and Reliable Therapeutic Targets Development

BACKGROUND

Alzheimer's disease (AD), the most common cause of dementia in adulthood, has great medical, social, and economic impact worldwide. Available treatments result in symptomatic relief, and most of them are indicated from the early stages of the disease. Therefore, there is an increasing body of research developing accurate and early diagnoses, as well as diseasemodifying therapies.

OBJECTIVE

Advancing the knowledge of AD physiopathological mechanisms, improving early diagnosis and developing effective treatments from omics-based biomarkers.

METHODS

Studies using omics technologies to detect early AD, were reviewed with a particular focus on the metabolites/lipids, micro-RNAs and proteins, which are identified as potential biomarkers in non-invasive samples.

RESULTS

This review summarizes recent research on metabolomics/lipidomics, epigenomics and proteomics, applied to early AD detection. Main research lines are the study of metabolites from pathways, such as lipid, amino acid and neurotransmitter metabolisms, cholesterol biosynthesis, and Krebs and urea cycles. In addition, some microRNAs and proteins (microglobulins, interleukins), related to a common network with amyloid precursor protein and tau, have been also identified as potential biomarkers. Nevertheless, the reproducibility of results among studies is not good enough and a standard methodological approach is needed in order to obtain accurate information.

CONCLUSION

The assessment of metabolomic/lipidomic, epigenomic and proteomic changes associated with AD to identify early biomarkers in non-invasive samples from well-defined participants groups will potentially allow the advancement in the early diagnosis and improvement of therapeutic interventions.

Mass spectrometry is a multifaceted weapon to be used in the battle against Alzheimer's disease: Amyloid beta peptides and beyond

Amyloid-β peptide (Aβ) accumulation and aggregation have been considered for many years the main cause of Alzheimer's disease (AD), and therefore have been the principal target of investigation as well as of the proposed therapeutic approaches (Grasso [2011] Mass Spectrom Rev. 30: 347-365). However, the amyloid cascade hypothesis, which considers Aβ accumulation the only causative agent of the disease, has proven to be incomplete if not wrong. In recent years, actors such as metal ions, oxidative stress, and other cofactors have been proposed as possible co-agents or, in some cases, main causative factors of AD. In this scenario, MS investigation has proven to be fundamental to design possible diagnostic strategies of this elusive disease, as well as to understand the biomolecular mechanisms involved, in the attempt to find a possible therapeutic solution. We review the current applications of MS in the search for possible Aβ biomarkers of AD to help the diagnosis of the disease. Recent examples of the important contributions that MS has given to prove or build theories on the molecular pathways involved with such terrible disease are also reviewed.

Neurobiological findings associated with high cognitive performance in older adults: a systematic review

ABSTRACTObjectives:to perform a comprehensive literature review of studies on older adults with exceptional cognitive performance.

DESIGN

We performed a systematic review using two major databases (MEDLINE and Web of Science) from January 2002 to November 2017.

RESULTS

Quantitative analysis included nine of 4,457 studies and revealed that high-performing older adults have global preservation of the cortex, especially the anterior cingulate region, and hippocampal volumes larger than normal agers. Histological analysis of this group also exhibited decreased amyloid burden and neurofibrillary tangles compared to cognitively normal older controls. High performers that maintained memory ability after three years showed reduced amyloid positron emission tomography at baseline compared with high performers that declined. A single study on blood plasma found a set of 12 metabolites predicting memory maintenance of this group.

CONCLUSION

Structural and molecular brain preservation of older adults with high cognitive performance may be associated with brain maintenance. The operationalized definition of high-performing older adults must be carefully addressed using appropriate age cut-off and cognitive evaluation, including memory and non-memory tests. Further studies with a longitudinal approach that include a younger control group are essential.

Metabolomics in the study of retinal health and disease

Metabolomics is the qualitative and quantitative assessment of the metabolites (small molecules < 1.5 kDa) in body fluids. The metabolites are the downstream of the genetic transcription and translation processes and also downstream of the interactions with environmental exposures; thus, they are thought to closely relate to the phenotype, especially for multifactorial diseases. In the last decade, metabolomics has been increasingly used to identify biomarkers in disease, and it is currently recognized as a very powerful tool with great potential for clinical translation. The metabolome and the associated pathways also help improve our understanding of the pathophysiology and mechanisms of disease. While there has been increasing interest and research in metabolomics of the eye, the application of metabolomics to retinal diseases has been limited, even though these are leading causes of blindness. In this manuscript, we perform a comprehensive summary of the tools and knowledge required to perform a metabolomics study, and we highlight essential statistical methods for rigorous study design and data analysis. We review available protocols, summarize the best approaches, and address the current unmet need for information on collection and processing of tissues and biofluids that can be used for metabolomics of retinal diseases. Additionally, we critically analyze recent work in this field, both in animal models and in human clinical disease, including diabetic retinopathy and age-related macular degeneration. Finally, we identify opportunities for future research applying metabolomics to improve our current assessment and understanding of mechanisms of vitreoretinal diseases, and to hence improve patient assessment and care.

Potential Metabolomic Linkage in Blood between Parkinson's Disease and Traumatic Brain Injury

The etiologic basis for sporadic forms of neurodegenerative diseases has been elusive but likely represents the product of genetic predisposition and various environmental factors. Specific gene-environment interactions have become more salient owing, in part, to the elucidation of epigenetic mechanisms and their impact on health and disease. The linkage between traumatic brain injury (TBI) and Parkinson's disease (PD) is one such association that currently lacks a mechanistic basis. Herein, we present preliminary blood-based metabolomic evidence in support of potential association between TBI and PD. Using untargeted and targeted high-performance liquid chromatography-mass spectrometry we identified metabolomic biomarker profiles in a cohort of symptomatic mild TBI (mTBI) subjects (n = 75) 3⁻12 months following injury (subacute) and TBI controls (n = 20), and a PD cohort with known PD (n = 20) or PD dementia (PDD) (n = 20) and PD controls (n = 20). Surprisingly, blood glutamic acid levels in both the subacute mTBI (increased) and PD/PDD (decreased) groups were notably altered from control levels. The observed changes in blood glutamic acid levels in mTBI and PD/PDD are discussed in relation to other metabolite profiling studies. Should our preliminary results be replicated in comparable metabolomic investigations of TBI and PD cohorts, they may contribute to an "excitotoxic" linkage between TBI and PD/PDD.

Plasma microRNA markers of upper limb recovery following human stroke

Preclinical investigators have implicated several microRNAs as regulators of gene expression promoting neural plasticity following experimental stroke in rodent models. Our goal was to determine whether similar microRNAs might be identifiable in plasma of humans with variable recovery from stroke. Plasma was collected 19 days post-stroke from 27 participants with mild-moderate upper extremity impairment enrolled in the Critical Periods After Stroke Study (CPASS). MicroRNA expression was assessed using TaqMan microRNA assays. Good clinical recovery was defined as ≥6 point change in the Action Research Arm Test (ARAT) score from baseline to 6 months, with 22 subjects showing good and 5 showing poor recovery. When comparing the good versus poor recovery groups, six microRNAs showed significantly decreased expression – miR-371-3p, miR-524, miR-520g, miR-1255A, miR-453, and miR-583, while 3 showed significantly increased expression - miR-941, miR-449b, and miR-581. MiR-371-3p and miR-941 have previously been associated with neural repair mechanisms; none of the significant microRNAs have previously been associated with stroke. The 9 microRNAs converge on pathways associated with axonal guidance, developmental biology, and cancer. We conclude that plasma microRNAs may be informative regarding human neural repair mechanisms during stroke recovery and probably differ from those seen in experimental stroke models.

Operationalized definition of older adults with high cognitive performance

Recently, there has been an increasing number of studies on exceptional cognitive aging. Herein, we aim to objectively provide the operationalized characterization of older adults with unusually high memory ability. Some authors have defined them as "SuperAgers", individuals aged 80 years or older with memory ability similar or superior to middle-aged subjects. On the other hand, the terminology "high-performing older adults" (HPOA) seems to appropriately conceptualize these individuals without exaggeration. A threshold for age is not a reliable criterion, but may be defined as 75 and 80 years of age for developing and developed countries, respectively. We propose that HPOA may exhibit episodic memory test scores equal to or greater than those of individuals aged 50-60 years, according to the validated tables for the respective country. This group must also have global cognition scores within expected average values for age and education. Executive functioning may play a central role in the exceptional memory performance of this group. Further studies are essential to confirm existing findings and may provide important evidence for cognitive aging theory and the neurobiology of dementia.

Plasma metabolomic biomarkers accurately classify acute mild traumatic brain injury from controls

Past and recent attempts at devising objective biomarkers for traumatic brain injury (TBI) in both blood and cerebrospinal fluid have focused on abundance measures of time-dependent proteins. Similar independent determinants would be most welcome in diagnosing the most common form of TBI, mild TBI (mTBI), which remains difficult to define and confirm based solely on clinical criteria. There are currently no consensus diagnostic measures that objectively define individuals as having sustained an acute mTBI. Plasma metabolomic analyses have recently evolved to offer an alternative to proteomic analyses, offering an orthogonal diagnostic measure to what is currently available. The purpose of this study was to determine whether a developed set of metabolomic biomarkers is able to objectively classify college athletes sustaining mTBI from non-injured teammates, within 6 hours of trauma and whether such a biomarker panel could be effectively applied to an independent cohort of TBI and control subjects.

A 6-metabolite panel was developed from biomarkers that had their identities confirmed using tandem mass spectrometry (MS/MS) in our Athlete cohort. These biomarkers were defined at ≤6 hours following mTBI and objectively classified mTBI athletes from teammate controls, and provided similar classification of these groups at the 2, 3, and 7 days post-mTBI. The same 6-metabolite panel, when applied to a separate, independent cohort provided statistically similar results despite major differences between the two cohorts. Our confirmed plasma biomarker panel objectively classifies acute mTBI cases from controls within 6 hours of injury in our two independent cohorts. While encouraged by our initial results, we expect future studies to expand on these initial observations.

Identification of Successful Cognitive Aging in the Alzheimer's Disease Neuroimaging Initiative Study

The present prospective observational study aimed to identify the existence of successful cognitive agers among a group of well-defined cognitively healthy older adults (n = 354, mean age = 75 years), and to examine baseline individual-level predictors and associated health outcomes over time. Episodic memory (EM) and executive function (EF) composite scores and multiple health outcomes were obtained annually over 5 years. Potential individual-level predictors that were related to Alzheimer's disease pathology or genetic risk, neurodegeneration, and vascular risks were collected at baseline. Three latent classes with matched age and education were identified using growth mixture modeling: a group of participants who exhibited high, stable EM and EF (40.7% of the sample, "successful agers"); a group who had initial high cognitive performance that declined over time (21.2%, "declining agers"); and a group who had normal (EM) or poor (EF) but stable cognitive performance over time (38.1%, "low stable agers"). The group classification predicted significant differences in the incidence of global cognitive impairment, the development of at least one depressive symptom, and everyday functional impairment. Sex, apolipoprotein E allele 4, amyloid-β1-42, and t-tau significantly contributed to the difference in cognitive trajectories between the successful agers and the other two groups. Characterizing successful cognitive agers who are relatively resistant to both tau and amyloid pathology provides potential pathways for promoting successful cognitive aging and preventing cognitive decline.

Human Plasma Metabolomics Study across All Stages of Age-Related Macular Degeneration Identifies Potential Lipid Biomarkers

PURPOSE

To characterize the plasma metabolomic profile of patients with age-related macular degeneration (AMD) using mass spectrometry (MS).

DESIGN

Cross-sectional observational study.

PARTICIPANTS

We prospectively recruited participants with a diagnosis of AMD and a control group (>50 years of age) without any vitreoretinal disease.

METHODS

All participants underwent color fundus photography, used for AMD diagnosis and staging, according to the Age-Related Eye Disease Study classification scheme. Fasting blood samples were collected and plasma was analyzed by Metabolon, Inc. (Durham, NC), using ultrahigh-performance liquid chromatography (UPLC) and high-resolution MS. Metabolon's hardware and software were used to identify peaks and control quality. Principal component analysis and multivariate regression were performed to assess differences in the metabolomic profiles of AMD patients versus controls, while controlling for potential confounders. For biological interpretation, pathway enrichment analysis of significant metabolites was performed using MetaboAnalyst.

MAIN OUTCOME MEASURES

The primary outcome measures were levels of plasma metabolites in participants with AMD compared with controls and among different AMD severity stages.

RESULTS

We included 90 participants with AMD (30 with early AMD, 30 with intermediate AMD, and 30 with late AMD) and 30 controls. Using UPLC and MS, 878 biochemicals were identified. Multivariate logistic regression identified 87 metabolites with levels that differed significantly between AMD patients and controls. Most of these metabolites (82.8%; n = 72), including the most significant metabolites, belonged to the lipid pathways. Analysis of variance revealed that of the 87 metabolites, 48 (55.2%) also were significantly different across the different stages of AMD. A significant enrichment of the glycerophospholipids pathway was identified (P = 4.7 × 10-9) among these metabolites.

CONCLUSIONS

Participants with AMD have altered plasma metabolomic profiles compared with controls. Our data suggest that the most significant metabolites map to the glycerophospholipid pathway. These findings have the potential to improve our understanding of AMD pathogenesis, to support the development of plasma-based metabolomics biomarkers of AMD, and to identify novel targets for treatment of this blinding disease.

Application of Metabolomics in Alzheimer's Disease

Progress toward the development of efficacious therapies for Alzheimer’s disease (AD) is halted by a lack of understanding early underlying pathological mechanisms. Systems biology encompasses several techniques including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Metabolomics is the newest omics platform that offers great potential for the diagnosis and prognosis of neurodegenerative diseases as an individual’s metabolome reflects alterations in genetic, transcript, and protein profiles and influences from the environment. Advancements in the field of metabolomics have demonstrated the complexity of dynamic changes associated with AD progression underscoring challenges with the development of efficacious therapeutic interventions. Defining systems-level alterations in AD could provide insights into disease mechanisms, reveal sex-specific changes, advance the development of biomarker panels, and aid in monitoring therapeutic efficacy, which should advance individualized medicine. Since metabolic pathways are largely conserved between species, metabolomics could improve the translation of preclinical research conducted in animal models of AD into humans. A summary of recent developments in the application of metabolomics to advance the AD field is provided below.

Systems healthcare: a holistic paradigm for tomorrow

Systems healthcare is a holistic approach to health premised on systems biology and medicine. The approach integrates data from molecules, cells, organs, the individual, families, communities, and the natural and man-made environment. Both extrinsic and intrinsic influences constantly challenge the biological networks associated with wellness. Such influences may dysregulate networks and allow pathobiology to evolve, resulting in early clinical presentation that requires astute assessment and timely intervention for successful mitigation. Herein, we describe the components of relevant biological systems and the nature of progression from at-risk to manifest disease. We illustrate the systems approach by examining two relevant clinical examples: Alzheimer's and cardiovascular diseases. The implications of systems healthcare management are examined through the lens of economics, ethics, policy and the law. Finally, we propose the need to develop new educational paradigms to enhance the training of the health professional in an era of systems medicine.

Amyloid β42 peptide is toxic to non-neural cells in Drosophila yielding a characteristic metabolite profile and the effect can be suppressed by PI3K

The human Aβ42 peptide is associated with Alzheimer's disease through its deleterious effects in neurons. Expressing the human peptide in adult Drosophila in a tissue- and time-controlled manner, we show that Aβ42 is also toxic in non-neural cells, neurosecretory and epithelial cell types in particular. This form of toxicity includes the aberrant signaling by Wingless morphogen leading to the eventual activation of Caspase 3. Preventing Caspase 3 activation by means of p53 keeps epithelial cells from elimination but maintains the Aβ42 toxicity yielding more severe deleterious effects to the organism. Metabolic profiling by nuclear magnetic resonance (NMR) of adult flies at selected ages post Aβ42 expression onset reveals characteristic changes in metabolites as early markers of the pathological process. All morphological and most metabolic features of Aβ42 toxicity can be suppressed by the joint overexpression of PI3K.

Summary: The Alzheimer's disease-related Aβ42 peptide is toxic for non-neural cells. This toxicity can be detected by specific metabolite changes and suppressed by the overexpression of the enzyme PI3K.

Personality and Performance in Specific Neurocognitive Domains Among Older Persons

OBJECTIVE

Certain Big 5 personality dimensions have been repeatedly linked to global measures of cognitive function and outcome categories. We examined whether the Big 5 or their specific components showed differential evidence of associations with specific neurocognitive domains.

METHODS

Participants were 179 older adults (70+) from a broader study on cognitive aging. The NEO-Five Factor Inventory and a comprehensive battery of neuropsychological tests were used.

RESULTS

Adjusted for age, gender, and years of education, probability values, Bayes Factors, and measures effect size from linear models suggested strong evidence for associations between better delayed recall memory and higher Conscientiousness (principally the facets of Goal-Striving and Dependability) and Openness (specifically the Intellectual Interest component). Better executive function and attention showed moderate to strong evidence of associations with lower Neuroticism (especially the Self-conscious Vulnerability facet) and higher Conscientiousness (mostly the Dependability facet). Better language functioning was linked to higher Openness (specifically, the Intellectual Interests facet). Worse visual-spatial function was strongly associated with higher Neuroticism.

CONCLUSION

Different tests of neurocognitive functioning show varying degrees of evidence for associations with different personality traits. Better understanding of the patterning of neurocognitive-personality linkages may facilitate grasp of underlying mechanisms and/or refine understanding of co-occurring clinical presentation of personality traits and specific cognitive deficits.