Integrated analysis of genomics, longitudinal metabolomics, and Alzheimer's risk factors among 1,111 cohort participants

Mid-to-Late Life Healthy Lifestyle Modifies Genetic Risk for Longitudinal Cognitive Aging among Asymptomatic Individuals

IMPORTANCE

Genetic and lifestyle factors contribute to an individual's risk of developing Alzheimer's disease. However, it is unknown whether and how adherence to healthy lifestyles can mitigate the genetic risk of Alzheimer's.

OBJECTIVE

The aim of this study is to investigate whether adherence to healthy lifestyles can modify the impact of genetic predisposition to Alzheimer's disease on later-life cognitive decline.

DESIGN SETTING AND PARTICIPANTS

This prospective cohort study included 891 adults of European ancestry, aged 40 to 65, who were without dementia and had complete healthy-lifestyle and cognition data during the follow-up. Participants joined the Wisconsin Registry for Alzheimer's Prevention (WRAP) beginning in 2001. We conducted replication analyses using a subsample with similar baseline age range from the Health and Retirement Study (HRS).

EXPOSURES

We assessed participants' exposures using a continuous non-APOE polygenic risk score for Alzheimer's, a binary indicator for APOE-ε4 carrier status, and a weighted healthy-lifestyle score, including factors such as no current smoking, regular physical activity, healthy diet, light to moderate alcohol consumption, and frequent cognitive activities.

MAIN OUTCOMES AND MEASURES

We z-standardized cognitive scores for global (Preclinical Alzheimer's Cognitive Composite score 3 - PACC3) and domain-specific assessments (delayed recall and immediate learning).

RESULTS

We followed 891 individuals for up to 10 years (mean [SD] baseline age, 58 [6] years, 31% male, 38% APOE-ε4 carriers). After false discovery rate (FDR) correction, we found statistically significant PRS × lifestyle × age interactions on preclinical cognitive decline but the evidence is stronger among APOE-ε4 carriers. Among APOE-ε4 carriers, PRS-related differences in overall and memory-related domains between people scoring 0-1 and 4-5 regarding healthy lifestyles became evident around age 67 after FDR correction. These findings were robust across several sensitivity analyses and were replicated in the population-based HRS.

CONCLUSION

A favorable lifestyle can mitigate the genetic risk associated with current known non-APOE genetic variants for longitudinal cognitive decline, and these protective effects are particularly pronounced among APOE-ε4 carriers.

New approach for early and specific Alzheimer disease diagnosis from different plasma biomarkers

BACKGROUND

Alzheimer Disease (AD) is a complex pathology, in which several biochemical pathways could be involved. Therefore, the development of clinical studies combining different nature biomarkers in an AD diagnosis approach is required. Specifically, the present study evaluated blood biomarkers from different molecular pathways (epigenomics, lipid metabolism, lipid peroxidation), to obtain an early and specific AD diagnosis approach.

METHODS

The participants were classified into early AD (n = 53), and non-AD (healthy controls, other dementias) (n = 83). Blood samples were collected and biochemical determinations (microRNAs, lipids, lipid peroxidation compounds) were carried out by quantitative PCR and liquid chromatography coupled to mass spectrometry, respectively. Then, a logistic regression model with a Bayesian variable selection procedure was developed.

RESULTS

The Bayesian variable selection procedure for microRNAs did not show any relevant variable. Therefore, microRNA biomarkers were excluded. So, the developed model considered only lipids and lipid peroxidation compounds. The corresponding selected variables were age, 18:0 LPC, PGE2, isoprostanes and, isofurans. The validated model (by leave-one-out cross-validation) provided satisfactory diagnosis indexes (AUC 0.83, Sensitivity 87 %, Specificity 79 %).

CONCLUSION

The developed model included biomarkers from different pathways (lipid metabolism, oxidative stress), achieving a promising approach to early, specific and, minimally invasive AD diagnosis. Nevertheless, further work to validate clinically these preliminary results with an external cohort is required. Also, the integration of different compounds coming from several biochemical pathways could constitute a relevant research field for the development of AD therapeutic targets.

Apolipoprotein E moderates the association between non-APOE polygenic risk score for Alzheimer's disease and aging on preclinical cognitive function

INTRODUCTION

Variation in preclinical cognitive decline suggests additional genetic factors related to Alzheimer's disease (eg, a non-APOE polygenic risk score [PRS]) may interact with the APOE ε4 allele to influence cognitive decline.

METHODS

We tested the PRS × APOE ε4 × age interaction on preclinical cognition using longitudinal data from the Wisconsin Registry for Alzheimer's Prevention. All analyses were fitted using a linear mixed-effects model and adjusted for within individual/family correlation among 1190 individuals.

RESULTS

We found statistically significant PRS × APOE ε4 × age interactions on immediate learning (P = 0.038), delayed recall (P < 0.001), and Preclinical Alzheimer's Cognitive Composite 3 score (P = 0.026). PRS-related differences in overall and memory-related cognitive domains between people with and without APOE ε4 emerge around age 70, with a much stronger adverse PRS effect among APOE ε4 carriers. The findings were replicated in a population-based cohort.

DISCUSSIONS

APOE ε4 can modify the association between PRS and cognition decline.

HIGHLIGHTS

APOE ε4 can modify the association between polygenic risk scores (PRSs) and longitudinal cognition decline, with the modifying effects more pronounced when the PRS is constructed using a conservative P threshold (eg, P < 5e-8 ). The adverse genetic effect caused by the combined effect of the currently known genetic variants is more detrimental among APOE ε4 carriers around age 70. Individuals who are APOE ε4 carriers with high PRSs are the most vulnerable to the harmful effects caused by genetic burden.

Inferring Alzheimer's Disease Pathologic Traits from Clinical Measures in Living Adults

Background

Alzheimer's disease neuropathologic changes (AD-NC) are important to identify people with high risk for AD dementia (ADD) and subtyping ADD.

Objective

Develop imputation models based on clinical measures to infer AD-NC.

Methods

We used penalized generalized linear regression to train imputation models for four AD-NC traits (amyloid-β, tangles, global AD pathology, and pathologic AD) in Rush Memory and Aging Project decedents, using clinical measures at the last visit prior to death as predictors. We validated these models by inferring AD-NC traits with clinical measures at the last visit prior to death for independent Religious Orders Study (ROS) decedents. We inferred baseline AD-NC traits for all ROS participants at study entry, and then tested if inferred AD-NC traits at study entry predicted incident ADD and postmortem pathologic AD.

Results

Inferred AD-NC traits at the last visit prior to death were related to postmortem measures with R2 = (0.188,0.316,0.262) respectively for amyloid-β, tangles, and global AD pathology, and prediction Area Under the receiver operating characteristic Curve (AUC) 0.765 for pathologic AD. Inferred baseline levels of all four AD-NC traits predicted ADD. The strongest prediction was obtained by the inferred baseline probabilities of pathologic AD with AUC = (0.919,0.896) for predicting the development of ADD in 3 and 5 years from baseline. The inferred baseline levels of all four AD-NC traits significantly discriminated pathologic AD profiled eight years later with p-values < 1.4×10-10.

Conclusions

Inferred AD-NC traits based on clinical measures may provide effective AD biomarkers that can estimate the burden of AD-NC traits in aging adults.

Large-scale proteome and metabolome analysis of CSF implicates altered glucose and carbon metabolism and succinylcarnitine in Alzheimer's disease

INTRODUCTION

A hallmark of Alzheimer's disease (AD) is the aggregation of proteins (amyloid beta [A] and hyperphosphorylated tau [T]) in the brain, making cerebrospinal fluid (CSF) proteins of particular interest.

METHODS

We conducted a CSF proteome-wide analysis among participants of varying AT pathology (n = 137 participants; 915 proteins) with nine CSF biomarkers of neurodegeneration and neuroinflammation.

RESULTS

We identified 61 proteins significantly associated with the AT category (P < 5.46 × 10-5 ) and 636 significant protein-biomarker associations (P < 6.07 × 10-6 ). Proteins from glucose and carbon metabolism pathways were enriched among amyloid- and tau-associated proteins, including malate dehydrogenase and aldolase A, whose associations with tau were replicated in an independent cohort (n = 717). CSF metabolomics identified and replicated an association of succinylcarnitine with phosphorylated tau and other biomarkers.

DISCUSSION

These results implicate glucose and carbon metabolic dysregulation and increased CSF succinylcarnitine levels with amyloid and tau pathology in AD.

HIGHLIGHTS

Cerebrospinal fluid (CSF) proteome enriched for extracellular, neuronal, immune, and protein processing. Glucose/carbon metabolic pathways enriched among amyloid/tau-associated proteins. Key glucose/carbon metabolism protein associations independently replicated. CSF proteome outperformed other omics data in predicting amyloid/tau positivity. CSF metabolomics identified and replicated a succinylcarnitine-phosphorylated tau association.

Assessing the Biological Mechanisms Linking Smoking Behavior and Cognitive Function: A Mediation Analysis of Untargeted Metabolomics

(1) Smoking is the most significant preventable health hazard in the modern world. It increases the risk of vascular problems, which are also risk factors for dementia. In addition, toxins in cigarettes increase oxidative stress and inflammation, which have both been linked to the development of Alzheimer's disease and related dementias (ADRD). This study identified potential mechanisms of the smoking-cognitive function relationship using metabolomics data from the longitudinal Wisconsin Registry for Alzheimer's Prevention (WRAP). (2) 1266 WRAP participants were included to assess the association between smoking status and four cognitive composite scores. Next, untargeted metabolomic data were used to assess the relationships between smoking and metabolites. Metabolites significantly associated with smoking were then tested for association with cognitive composite scores. Total effect models and mediation models were used to explore the role of metabolites in smoking-cognitive function pathways. (3) Plasma N-acetylneuraminate was associated with smoking status Preclinical Alzheimer Cognitive Composite 3 (PACC3) and Immediate Learning (IMM). N-acetylneuraminate mediated 12% of the smoking-PACC3 relationship and 13% of the smoking-IMM relationship. (4) These findings provide links between previous studies that can enhance our understanding of potential biological pathways between smoking and cognitive function.

Metabolomic epidemiology offers insights into disease aetiology

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

CSF metabolites associated with biomarkers of Alzheimer's disease pathology

Introduction

Metabolomics technology facilitates studying associations between small molecules and disease processes. Correlating metabolites in cerebrospinal fluid (CSF) with Alzheimer's disease (AD) CSF biomarkers may elucidate additional changes that are associated with early AD pathology and enhance our knowledge of the disease.

Methods

The relative abundance of untargeted metabolites was assessed in 161 individuals from the Wisconsin Registry for Alzheimer's Prevention. A metabolome-wide association study (MWAS) was conducted between 269 CSF metabolites and protein biomarkers reflecting brain amyloidosis, tau pathology, neuronal and synaptic degeneration, and astrocyte or microglial activation and neuroinflammation. Linear mixed-effects regression analyses were performed with random intercepts for sample relatedness and repeated measurements and fixed effects for age, sex, and years of education. The metabolome-wide significance was determined by a false discovery rate threshold of 0.05. The significant metabolites were replicated in 154 independent individuals from then Wisconsin Alzheimer's Disease Research Center. Mendelian randomization was performed using genome-wide significant single nucleotide polymorphisms from a CSF metabolites genome-wide association study.

Results

Metabolome-wide association study results showed several significantly associated metabolites for all the biomarkers except Aβ42/40 and IL-6. Genetic variants associated with metabolites and Mendelian randomization analysis provided evidence for a causal association of metabolites for soluble triggering receptor expressed on myeloid cells 2 (sTREM2), amyloid β (Aβ40), α-synuclein, total tau, phosphorylated tau, and neurogranin, for example, palmitoyl sphingomyelin (d18:1/16:0) for sTREM2, and erythritol for Aβ40 and α-synuclein.

Discussion

This study provides evidence that CSF metabolites are associated with AD-related pathology, and many of these associations may be causal.

Cognitive trajectories diverge by genetic risk in a preclinical longitudinal cohort

INTRODUCTION

We sought to characterize the timing of changes in cognitive trajectories related to genetic risk using the apolipoprotein E (APOE) score, a continuous measure of Alzheimer's disease (AD) risk. We also aimed to determine whether that timing was different when genetic risk was measured using an AD polygenic risk score (PRS) that contains APOE.

METHODS

We analyzed trajectories (N ≈1135) for four neuropsychological composite scores using mixed effects regression for longitudinal change across APOE scores and PRS of participants in the Wisconsin Registry for Alzheimer's Prevention, a longitudinal study of adults aged 40 to 70 at baseline, with a median participant follow-up time of 7.8 years.

RESULTS

We found a significant non-linear age-by-APOE score interaction in predicting cognitive decline. Cognitive trajectories diverged by APOE score at approximately 65 years of age. A 0.5 standard deviation difference in cognition between extreme percentiles of the PRS was predicted to occur 1 to 2 years before that of the APOE score.

DISCUSSION

Cognitive decline differs across time and APOE score. Estimates did not substantially shift with the AD PRS.

HIGHLIGHTS

The apolipoprotein E (APOE) score, a continuous measure, accounts for non-linear genetic risk of Alzheimer's disease. Non-linear age interacts with the APOE score to affect cognition. Cognitive decline starts to differ by APOE score levels at approximately age 65. Cognitive decline timing by polygenic risk (including APOE) is similar to APOE alone.

Molecular insights into sex-specific metabolic alterations in Alzheimer's mouse brain using multi-omics approach

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that is characterized by altered cellular metabolism in the brain. Several of these alterations have been found to be exacerbated in females, known to be disproportionately affected by AD. We aimed to unravel metabolic alterations in AD at the metabolic pathway level and evaluate whether they are sex-specific through integrative metabolomic, lipidomic, and proteomic analysis of mouse brain tissue.

METHODS

We analyzed male and female triple-transgenic mouse whole brain tissue by untargeted mass spectrometry-based methods to obtain a molecular signature consisting of polar metabolite, complex lipid, and protein data. These data were analyzed using multi-omics factor analysis. Pathway-level alterations were identified through joint pathway enrichment analysis or by separately evaluating lipid ontology and known proteins related to lipid metabolism.

RESULTS

Our analysis revealed significant AD-associated and in part sex-specific alterations across the molecular signature. Sex-dependent alterations were identified in GABA synthesis, arginine biosynthesis, and in alanine, aspartate, and glutamate metabolism. AD-associated alterations involving lipids were also found in the fatty acid elongation pathway and lysophospholipid metabolism, with a significant sex-specific effect for the latter.

CONCLUSIONS

Through multi-omics analysis, we report AD-associated and sex-specific metabolic alterations in the AD brain involving lysophospholipid and amino acid metabolism. These findings contribute to the characterization of the AD phenotype at the molecular level while considering the effect of sex, an overlooked yet determinant metabolic variable.

The metabolomics of human aging: Advances, challenges, and opportunities

As the global population becomes older, understanding the impact of aging on health and disease becomes paramount. Recent advancements in multiomic technology have allowed for the high-throughput molecular characterization of aging at the population level. Metabolomics studies that analyze the small molecules in the body can provide biological information across a diversity of aging processes. Here, we review the growing body of population-scale metabolomics research on aging in humans, identifying the major trends in the field, implicated biological pathways, and how these pathways relate to health and aging. We conclude by assessing the main challenges in the research to date, opportunities for advancing the field, and the outlook for precision health applications.

Principal components from untargeted cerebrospinal fluid metabolomics associated with Alzheimer's disease biomarkers

Studying the correlation between cerebrospinal fluid (CSF) metabolites and the Alzheimer's Disease (AD) biomarkers may offer a window to the alterations of the brain metabolome and unveil potential biological mechanisms underlying AD. In this analysis, 308 CSF metabolites from 338 individuals of Wisconsin Registry for Alzheimer's Prevention and Wisconsin Alzheimer's Disease Research Center were included in a principal component analysis (PCA). The resulted principal components (PCs) were tested for association with CSF total tau (t-tau), phosphorylated tau (p-tau), amyloid β 42 (Aβ42), and Aβ42/40 ratio using linear regression models. Significant PCs were further tested with other CSF NeuroToolKit (NTK) and imaging biomarkers. Using a Bonferroni corrected p < 0.05, 5 PCs were significantly associated with CSF p-tau and t-tau and 3 PCs were significantly associated with CSF Aβ42. Pathway analysis suggested that these PCS were enriched in 6 pathways, including metabolism of caffeine and nicotinate and nicotinamide. This study provides evidence that CSF metabolites are associated with AD pathology through core AD biomarkers and other NTK markers and suggests potential pathways to follow up in future studies.

A Nuclear Magnetic Resonance Spectroscopy Method in Characterization of Blood Metabolomics for Alzheimer's Disease

There is currently a crucial need for improved diagnostic techniques and targeted treatment methods for Alzheimer's disease (AD), a disease which impacts millions of elderly individuals each year. Metabolomic analysis has been proposed as a potential methodology to better investigate and understand the progression of this disease. In this report, we present our AD metabolomics results measured with high resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) on human blood plasma samples obtained from AD and non-AD subjects. Our study centers on developments of AD and non-AD metabolomics differentiating models with procedures of quality assurance (QA) and quality control (QC) through pooled samples. Our findings suggest that analysis of blood plasma samples using HRMAS NMR has the potential to differentiate between diseased and healthy subjects, which has important clinical implications for future improvements in AD diagnosis methodologies.

Small phenolic and indolic gut-dependent molecules in the primate central nervous system: levels vs. bioactivity

INTRODUCTION

A rapidly growing body of data documents associations between disease of the brain and small molecules generated by gut-microbiota (GMB). While such metabolites can affect brain function through a variety of mechanisms, the most direct action would be on the central nervous system (CNS) itself.

OBJECTIVE

Identify indolic and phenolic GMB-dependent small molecules that reach bioactive concentrations in primate CNS.

METHODS

We conducted a PubMed search for metabolomic studies of the primate CNS [brain tissue or cerebrospinal fluid (CSF)] and then selected for phenolic or indolic metabolites that (i) had been quantified, (ii) were GMB-dependent. For each chemical we then conducted a search for studies of bioactivity conducted in vitro in human cells of any kind or in CNS cells from the mouse or rat.

RESULTS

36 metabolites of interests were identified in primate CNS through targeted metabolomics. Quantification was available for 31/36 and in vitro bioactivity for 23/36. The reported CNS range for 8 metabolites 2-(3-hydroxyphenyl)acetic acid, 2-(4-hydroxyphenyl)acetic acid, 3-(3-hydroxyphenyl)propanoic acid, (E)-3-(3,4-dihydroxyphenyl)prop-2-enoic acid [caffeic acid], 3-hydroxybenzoic acid, 4-hydroxybenzoic acid, 2-acetamido-3-(1H-indol-3-yl)propanoic acid [N-acetyltryptophan], 1H-indol-3-yl hydrogen sulfate [indoxyl-3-sulfate] overlapped with a bioactive concentration. However, the number and quality of relevant studies of CNS neurochemistry as well as of bioactivity were highly limited. Structural isomers, multiple metabolites and potential confounders were inadequately considered.

CONCLUSION

The potential direct bioactivity of GMB-derived indolic and phenolic molecules on primate CNS remains largely unknown. The field requires additional strategies to identify and prioritize screening of the most promising small molecules that enter the CNS.

Metabolomics Work Flow and Analytics in Systems Biology

Metabolomics is an omics approach of systems biology that involves the development and assessment of large-scale, comprehensive biochemical analysis tools for metabolites in biological systems. This review describes the metabolomics workflow and provides an overview of current analytic tools used for the quantification of metabolic profiles. We explain analytic tools such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, ionization techniques, and approaches for data extraction and analysis.

Epigenomics and Lipidomics Integration in Alzheimer Disease: Pathways Involved in Early Stages

BACKGROUND

Alzheimer Disease (AD) is the most prevalent dementia. However, the physiopathological mechanisms involved in its development are unclear. In this sense, a multi-omics approach could provide some progress.

METHODS

Epigenomic and lipidomic analysis were carried out in plasma samples from patients with mild cognitive impairment (MCI) due to AD (n = 22), and healthy controls (n = 5). Then, omics integration between microRNAs (miRNAs) and lipids was performed by Sparse Partial Least Squares (s-PLS) regression and target genes for the selected miRNAs were identified.

RESULTS

25 miRNAs and 25 lipids with higher loadings in the sPLS regression were selected. Lipids from phosphatidylethanolamines (PE), lysophosphatidylcholines (LPC), ceramides, phosphatidylcholines (PC), triglycerides (TG) and several long chain fatty acids families were identified as differentially expressed in AD. Among them, several fatty acids showed strong positive correlations with miRNAs studied. In fact, these miRNAs regulated genes implied in fatty acids metabolism, as elongation of very long-chain fatty acids (ELOVL), and fatty acid desaturases (FADs).

CONCLUSIONS

The lipidomic-epigenomic integration showed that several lipids and miRNAs were differentially expressed in AD, being the fatty acids mechanisms potentially involved in the disease development. However, further work about targeted analysis should be carried out in a larger cohort, in order to validate these preliminary results and study the proposed pathways in detail.

Metabolism-Based Gene Differences in Neurons Expressing Hyperphosphorylated AT8- Positive (AT8+) Tau in Alzheimer's Disease

Metabolic adaptations in the brain are critical to the establishment and maintenance of normal cellular functions and to the pathological responses to disease processes. Here, we have focused on specific metabolic pathways that are involved in immune-mediated neuronal processes in brain using isolated neurons derived from human autopsy brain sections of normal individuals and individuals diagnosed as Alzheimer's disease (AD). Laser capture microscopy was used to select specific cell types in immune-stained thin brain sections followed by NanoString technology to identify and quantify differences in mRNA levels between age-matched control and AD neuronal samples. Comparisons were also made between neurons isolated from AD brain sections expressing pathogenic hyperphosphorylated AT8- positive (AT8+) tau and non-AT8+ AD neurons using double labeling techniques. The mRNA expression data showed unique patterns of metabolic pathway expression between the subtypes of captured neurons that involved membrane based solute transporters, redox factors, and arginine and methionine metabolic pathways. We also identified the expression levels of a novel metabolic gene, Radical-S-Adenosyl Domain1 (RSAD1) and its corresponding protein, Rsad1, that impact methionine usage and radical based reactions. Immunohistochemistry was used to identify specific protein expression levels and their cellular location in NeuN+ and AT8+ neurons. APOE4 vs APOE3 genotype-specific and sex-specific gene expression differences in these metabolic pathways were also observed when comparing neurons from individuals with AD to age-matched individuals.

CSF metabolites associate with CSF tau and improve prediction of Alzheimer's disease status

INTRODUCTION

Cerebrospinal fluid (CSF) total tau (t-tau) and phosphorylated tau (p-tau) are biomarkers of Alzheimer's disease (AD), yet much is unknown about AD-associated changes in tau metabolism and tau tangle etiology.

METHODS

We assessed the variation of t-tau and p-tau explained by 38 previously identified CSF metabolites using linear regression models in middle-age controls from the Wisconsin Alzheimer's Disease Research Center, and predicted AD/mild cognitive impairment (MCI) versus an independent set of older controls using metabolites selected by the least absolute shrinkage and selection operator (LASSO).

RESULTS

The 38 CSF metabolites explained 70.3% and 75.7% of the variance in t-tau and p-tau, respectively. Of these, seven LASSO-selected metabolites improved the prediction ability of AD/MCI versus older controls (area under the curve score increased from 0.92 to 0.97 and 0.78 to 0.93) compared to the base model.

DISCUSSION

These tau-correlated CSF metabolites increase AD/MCI prediction accuracy and may provide insight into tau tangle etiology.

Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

The study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.

Metabolites Associated with Early Cognitive Changes Implicated in Alzheimer's Disease

BACKGROUND

Understanding metabolic mechanisms associated with cognitive changes preceding an Alzheimer's disease (AD) diagnosis could advance our understanding of AD progression and inform preventive methods.

OBJECTIVE

We investigated the metabolomics of the early changes in executive function and delayed recall, the earliest aspects of cognitive function to change in the course of AD development, in order to better understand mechanisms that could contribute to early stages and progression of this disease.

METHODS

This investigation used longitudinal plasma samples from the Wisconsin Registry for Alzheimer's Prevention (WRAP), a cohort of participants who were dementia free at enrollment and enriched with a parental history of AD. Metabolomic profiles were quantified for 2,324 fasting plasma samples among 1,200 participants, each with up to three study visits, which occurred every two years. Metabolites were individually tested for association with executive function and delayed recall trajectories across age.

RESULTS

Of 1,097 metabolites tested, levels of seven were associated with executive function trajectories, including an amino acid cysteine S-sulfate and three fatty acids, including erucate (22 : 1n9), while none were associated with delayed recall trajectories. Replication was attempted for four of these metabolites that were present in the Vietnam Era Twin Study of Aging (VETSA). Although none reached statistical significance, three of these associations showed consistent effectdirections.

CONCLUSION

Our results suggest potential metabolomic mechanisms that could contribute to the earliest signs of cognitive decline. In particular, fatty acids may be associated with cognition in a manner that is more complex than previously suspected.

Metabolic Profiling of Cognitive Aging in Midlife

Alzheimer's dementia (AD) begins many years before its clinical symptoms. Metabolic dysfunction represents a core feature of AD and cognitive impairment, but few metabolomic studies have focused on cognitive aging in midlife. Using an untargeted metabolomics approach, we identified metabolic predictors of cognitive aging in midlife using fasting plasma sample from 30 middle-aged (mean age 57.2), male-male twin pairs enrolled in the Vietnam Era Twin Study of Aging (VETSA). For all twin pairs, one twin developed incident MCI, whereas his co-twin brother remained to be cognitively normal during an average 5.5-year follow-up. Linear mixed model was used to identify metabolites predictive of MCI conversion or cognitive change over time, adjusting for traditional risk factors. Results from twins were replicated in an independent cohort of middle-aged adults (mean age 59.1) in the Wisconsin Registry for Alzheimer's Prevention (WRAP). Results in twins showed that higher baseline levels of four plasma metabolites, including sphingomyelin (d18:1/20:1 and d18:2/20:0), sphingomyelin (d18:1/22:1, d18:2/22:0, and d16:1/24:1), DAG (18:2/20:4), and hydroxy-CMPF, were significantly associated with a slower decrease in one or more domains of cognitive function. The association of sphingomyelin (d18:1/20:1 and d18:2/20:0) was replicated in WRAP. Our results support that metabolic perturbation occurs many years before cognitive impairment and plasma metabolites may serve as early biomarkers for prediction or monitoring of cognitive aging and AD in midlife.

Current Status of Metabolomic Biomarker Discovery: Impact of Study Design and Demographic Characteristics

Widespread application of omic technologies is evolving our understanding of population health and holds promise in providing precise guidance for selection of therapeutic interventions based on patient biology. The opportunity to use hundreds of analytes for diagnostic assessment of human health compared to the current use of 10–20 analytes will provide greater accuracy in deconstructing the complexity of human biology in disease states. Conventional biochemical measurements like cholesterol, creatinine, and urea nitrogen are currently used to assess health status; however, metabolomics captures a comprehensive set of analytes characterizing the human phenotype and its complex metabolic processes in real-time. Unlike conventional clinical analytes, metabolomic profiles are dramatically influenced by demographic and environmental factors that affect the range of normal values and increase the risk of false biomarker discovery. This review addresses the challenges and opportunities created by the evolving field of clinical metabolomics and highlights features of study design and bioinformatics necessary to maximize the utility of metabolomics data across demographic groups.

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.