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Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research., Understanding the role of genetic predispositions and their interaction with environmental factors in complex chronic diseases is key to the development of safe and efficient therapies, to diagnosis and to [...]

Background: Bile acids (BAs) are byproducts of cholesterol metabolism involved in lipid absorption, signaling and energy homeostasis. BA synthesis and metabolism, regulated by human and gut bacteria co‐metabolism, are influenced by the exposome. Our prior studies at a population level illustrated a correlation between altered BA profiles and cognitive and brain imaging changes in MCI and AD. More information is needed to determine if these are causal in sub‐populations, and if their modulation can impact disease course. Method: Absolute serum levels of 33 primary, secondary, and conjugated BAs were measured on a targeted mass spectrometry‐based platform in 1117 fasted subjects (502 women) drawn from the AD Neuroimaging Initiative (ADNI‐1/GO‐2; AD (N = 180), MCI (N = 607) and CN (N = 330). Analysis centered on adjusted and/or stratified linear models with an interpretative focus emphasizing individual‐level granularity. Result: In stratified analysis, men but not women showed strong association between levels and ratios of BAs and diagnosis. Inclusion of two (informative) bile acids and their ratios increases AUCs for AD vs control from ∼0.72 to ∼0.82‐0.85 (p<0.001) in APOEε4 genotype‐stratified analyses. Some specific associations were APOEε4 genotype‐dependent; some specific microbiome‐mediated ratios in BAs were associated with AD diagnosis in APOEε4− only and secondary bile acid synthesis was associated with AD diagnosis in APOEε4+ only. Other associations were APOEε4 genotype‐independent ‐ higher ratios of primary BAs to total BAs were associated with reduced AD prevalence. Levels of cholic acid below sharp thresholds 25 or 50 nM (in APOEε4+ and APOEε4−, respectively) at 24 months were associated with 5‐ to 7‐fold increased relative risk of MCI to AD progression, establishing a potential biomarker for a resilience phenotype. Conclusion: Leveraging the combined power of ADNI and metabolomics with a focus on granularity and complementarity revealed previously unobserved relationships between BAs, sex, APOEε4 genotype, and AD etiology. These data identify a resilience class and provide a roadmap for stratifying subjects and targeting therapies in a precision medicine approach while gaining deeper insights linking peripheral‐central metabolism influenced by the exposome. BAs are a potential modifiable factor that mediates causal linkages between endogenous and exogenous factors in the susceptibility or resilience against AD. [ABSTRACT FROM AUTHOR]

Background: Brain glucose hypometabolism is among the earliest pathogenic changes in Alzheimer's disease (AD). This metabolic dysfunction points to the personal bioenergetic capacity, defined as the ability to maintain energy homeostasis under all circumstances including deregulated glucose uptake, as a potential source of resilience to the disease. Fasting blood acylcarnitine profiles are a central readout for this capacity in the absence of dietary glucose and capture the activity and efficiency of glucose‐independent routes of mitochondrial energy metabolism. Method: We used fasting serum acylcarnitine profiles of 1,531 participants (465 with normal cognition, 762 with mild cognitive impairment, and 304 with clinical AD) from the AD Neuroimaging Initiative to perform unsupervised subgroup identification using hierarchical clustering. Identified subgroups were investigated for differences in A/T/N biomarker profiles and cognitive status. The contributions of genetic and potentially modifiable factors defining the subgroups were quantified using analysis of explained variance. The influence of the strongest determining factors on longitudinal cognitive trajectories was estimated using linear mixed‐effects models and gene‐by‐environment interaction analysis. Result: We found several bioenergetically distinct subgroups with significant differences in AD biomarker profiles and cognitive function. The strongest genetic contribution to this bioenergetic endophenotype seems to be specifically linked to succinylcarnitine metabolism and significantly modulates the rate of future cognitive decline. In contrast, potentially modifiable sustainment of beta‐oxidation efficiency seems to decelerate bioenergetic aging, thus creating a bioenergetic reserve that delays progression of cognitive decline. Using gene‐by‐environment interaction analysis, we demonstrate that this molecular framework identifies a subgroup of individuals that is likely to benefit significantly from personalized therapeutic, dietary or lifestyle interventions tailored to increase resilience against bioenergetic disturbances in AD. Conclusion: Our study reports on a set of genetic and metabolic markers that define bioenergetically distinct subgroups with significant differences on the AD biomarker and cognitive level. Longitudinal data suggest that targeting the modifiable fraction of this endophenotype might be a promising strategy to slow down disease progression in individuals with specific allelic configurations. [ABSTRACT FROM AUTHOR]

1. Introduction Metabolomics is an emerging 'omics' technology that focuses on the identification and quantification of all or, in practice, the largest possible set of low-molecular-weight metabolites in a biological [...], Metabolomics is an emerging field that is based on the quantitative measurement of as many small organic molecules occurring in a biological sample as possible. Due to recent technical advances, metabolomics can now be used widely as an analytical high-throughput technology in drug testing and epidemiological metabolome and genome wide association studies. Analogous to chip-based gene expression analyses, the enormous amount of data produced by modern kit-based metabolomics experiments poses new challenges regarding their biological interpretation in the context of various sample phenotypes. We developed metaP-server to facilitate data interpretation. metaP-server provides automated and standardized data analysis for quantitative metabolomics data, covering the following steps from data acquisition to biological interpretation: (i) data quality checks, (ii) estimation of reproducibility and batch effects, (iii) hypothesis tests for multiple categorical phenotypes, (iv) correlation tests for metric phenotypes, (v) optionally including all possible pairs of metabolite concentration ratios, (vi) principal component analysis (PCA), and (vii) mapping of metabolites onto colored KEGG pathway maps. Graphical output is clickable and cross-linked to sample and metabolite identifiers. Interactive coloring of PCA and bar plots by phenotype facilitates on-line data exploration. For users of commercial metabolomics kits, cross-references to the HMDB, LipidMaps, KEGG, PubChem, and CAS databases are provided. metaP-server is freely accessible at http://metabolomics.helmholtz-muenchen.de/metap2/.

Background: The cascade of molecular changes that leads to Alzheimer's disease (AD) onset and progression remains incompletely understood. Recently, researchers have utilized manifold learning techniques to construct pseudo‐temporal models of the disease using brain transcriptomics data and quantify the progression of individuals along this trajectory using pseudotime (Mukherjee et al. Nat Commun 2020;11:5781). Downstream analyses provided insights into potentially disease‐driving pathways, including links to mitochondrial dysfunction. Here, we embedded these models into a multi‐omics context to enable a more comprehensive molecular characterization of disease progression. Method: The AD Atlas (https://adatlas.org/) integrates multi‐scale molecular data from different studies and cohorts, including omics QTLs, correlation networks, differential expression data, as well as omics associations with AD and endophenotypes. Here, we used the AD Atlas to annotate metabolites that were significantly associated with brain‐based pseudotime estimates. Metabolite (n = 667) levels were measured in brain tissue samples (dorsolateral prefrontal cortex) from 154 female ROS/MAP participants using untargeted metabolomics and tested for association using linear regression. We used the resulting set of significant metabolites as input for the AD Atlas to extract a multi‐omics context network augmented with associations to AD. Subsequently, we applied pathway enrichment analysis to derive overrepresented biological processes potentially involved in disease progression. Result: In total, 89 of the 667 metabolites showed a significant association with pseudotime after Bonferroni adjustment, 34 of which could be mapped to the AD Atlas database. The resulting multi‐omics network contained a total of 619 genes, 197 metabolites and links to 12 AD‐related phenotypes, including CSF amyloid pathology, brain glucose uptake measured by FDG‐PET and cognitive measures. Five of the 34 metabolites and nearly one‐third of the genes contained in the network showed significant associations with AD, with transcriptional changes most pronounced in the temporal cortex (n = 193). Enrichment analysis revealed functional links to neurotransmission and bioenergetics, pathways previously implicated in the pathogenesis of AD. Conclusion: By using metabolites as proxies for transcriptome‐derived pseudotime, we were able to investigate the molecular underpinnings of AD progression in a multi‐omics context. Our analysis provides further molecular evidence for pathways implicated in AD and emphasizes the potential of such an approach for future studies. [ABSTRACT FROM AUTHOR]

Major Depressive Disorder (MDD) is an often-chronic condition with substantial molecular alterations and pathway dysregulations involved. Single metabolite, pathway and targeted metabolomics platforms have indeed revealed several metabolic alterations in depression including energy metabolism, neurotransmission and lipid metabolism. More comprehensive coverage of the metabolome is needed to further specify metabolic dysregulation in depression and reveal previously untargeted mechanisms. Here we measured 820 metabolites using the metabolome-wide Metabolon platform in 2770 subjects from a large Dutch clinical cohort with extensive depression clinical phenotyping (1101 current MDD, 868 remitted MDD, 801 healthy controls) at baseline and 1805 subjects at 6-year follow up (327 current MDD, 1045 remitted MDD, 433 healthy controls). MDD diagnosis was based on DSM-IV psychiatric interviews. Depression severity was measured with the Inventory of Depressive Symptomatology self-report. Associations between metabolites and MDD status and depression severity were assessed at baseline and at the 6-year follow-up. Metabolites consistently associated with MDD status or depression severity on both occasions were examined in Mendelian randomization (MR) analysis using metabolite (N=14,000) and MDD (N=800,000) GWAS results. At baseline, 139 and 126 metabolites were associated with current MDD status and depression severity, respectively, with 79 overlapping metabolites. Six years later, 34 out of the 79 metabolite associations were subsequently replicated. Downregulated metabolites were enriched with long-chain monounsaturated (P=6.7e-07) and saturated (P=3.2e-05) fatty acids and upregulated metabolites with lysophospholipids (P=3.4e-4). Adding BMI to the models changed results only marginally. MR analyses showed that genetically-predicted higher levels of the lysophospholipid 1-linoleoyl-GPE (18:2) were associated with greater risk of depression. The identified metabolome-wide profile of depression (severity) indicated altered lipid metabolism with downregulation of long-chain fatty acids and upregulation of lysophospholipids, for which causal involvement was suggested using genetic tools. This metabolomics signature offers a window on depression pathophysiology and a potential access point for the development of novel therapeutic approaches., Competing Interests: Declaration of competing interest A. John Rush has received consulting fees from Compass Inc., Curbstone Consultant LLC, Emmes Corp., Evecxia Therapeutics, Inc., Holmusk, Johnson and Johnson (Janssen), Liva-Nova, Neurocrine Biosciences Inc., Otsuka-US; speaking fees from Liva-Nova, Johnson and Johnson (Janssen); and royalties from Guilford Press and the University of Texas Southwestern Medical Center, Dallas, TX (for the Inventory of Depressive Symptoms and its derivatives). He is also named co-inventor on two patents: U.S. Patent No. 7,795,033: Methods to Predict the Outcome of Treatment with Antidepressant Medication, Inventors: McMahon FJ, Laje G, Manji H, Rush AJ, Paddock S, Wilson AS; and U.S. Patent No. 7,906,283: Methods to Identify Patients at Risk of Developing Adverse Events During Treatment with Antidepressant Medication, Inventors: McMahon FJ, Laje G, Manji H, Rush AJ, Paddock S. M.A. and G.K. are co-inventors (through Duke University/Helmholtz Zentrum München) on patents on applications of metabolomics in diseases of the central nervous system and hold equity in Chymia LLC and IP in PsyProtix and Atai that are exploring the potential for therapeutic applications targeting mitochondrial metabolism in depression. R.R.K. is CEO of Rush University System for Health, Chairman of National Medical Research Council Ministry of Health Singapore, Chairman of Amyriad, BV Board member Community Health Systems, Advisory board SageRx, Verily Patents on Brain Computer Interface licensed to Psyber and ATAI, an inventor of Metabolomics patents in the CNS field including patents licensed to Chymia LLC and PsyProtix with royalties and ownership. R.K.D. is funded by National Institute on Aging [U01AG061359, R01AG057452, RF1AG051550, and R01AG046171] and National Institute of Mental Health [R01MH108348]. This funding enabled consortia that she leads including the Mood Disorder Precision Medicine Consortium, the Alzheimer’s disease Metabolomics Consortium, and the Alzheimer’s Gut Microbiome Project that contributed to acylcarnitine discoveries. She is an inventor on key patents in the field of Metabolomics and hold equity in Metabolon, a biotech company in North Carolina. In addition, she holds patents licensed to Chymia LLC and PsyProtix with royalties and ownership. The funders listed above had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the paper; and decision to submit the paper for publication. B.W.D. has received research support from Acadia, Compass, Aptinyx, NIMH, Sage, Otsuka, and Takeda, and has served as a consultant to Greenwich Biosciences, Myriad Neuroscience, Otsuka, Sage, and Sophren Therapeutics. All the other authors declare no conflict of interest.