| 1. |
- Beger, Richard D., et al.
(författare)
-
Metabolomics enables precision medicine : "A White Paper, Community Perspective"
- 2016
-
Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 12:10
-
Tidskriftsartikel (refereegranskat)abstract
- INTRODUCTION BACKGROUND TO METABOLOMICS: Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates.OBJECTIVES OF WHITE PAPER—EXPECTED TREATMENT OUTCOMES AND METABOLOMICS ENABLING TOOL FOR PRECISION MEDICINE: We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine.CONCLUSIONS KEY SCIENTIFIC CONCEPTS AND RECOMMENDATIONS FOR PRECISION MEDICINE: Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.
|
|
| 2. |
- Fall, Tove, 1979-, et al.
(författare)
-
Non-targeted metabolomics combined with genetic analyses identifies bile acid synthesis and phospholipid metabolism as being associated with incident type 2 diabetes
- 2016
-
Ingår i: Diabetologia. - : Springer Science and Business Media LLC. - 0012-186X .- 1432-0428. ; 59:10, s. 2114-2124
-
Tidskriftsartikel (refereegranskat)abstract
- Aims/hypothesisIdentification of novel biomarkers for type 2 diabetes and their genetic determinants could lead to improved understanding of causal pathways and improve risk prediction.MethodsIn this study, we used data from non-targeted metabolomics performed using liquid chromatography coupled with tandem mass spectrometry in three Swedish cohorts (Uppsala Longitudinal Study of Adult Men [ULSAM], n = 1138; Prospective Investigation of the Vasculature in Uppsala Seniors [PIVUS], n = 970; TwinGene, n = 1630). Metabolites associated with impaired fasting glucose (IFG) and/or prevalent type 2 diabetes were assessed for associations with incident type 2 diabetes in the three cohorts followed by replication attempts in the Cooperative Health Research in the Region of Augsburg (KORA) S4 cohort (n = 855). Assessment of the association of metabolite-regulating genetic variants with type 2 diabetes was done using data from a meta-analysis of genome-wide association studies.ResultsOut of 5961 investigated metabolic features, 1120 were associated with prevalent type 2 diabetes and IFG and 70 were annotated to metabolites and replicated in the three cohorts. Fifteen metabolites were associated with incident type 2 diabetes in the four cohorts combined (358 events) following adjustment for age, sex, BMI, waist circumference and fasting glucose. Novel findings included associations of higher values of the bile acid deoxycholic acid and monoacylglyceride 18:2 and lower concentrations of cortisol with type 2 diabetes risk. However, adding metabolites to an existing risk score improved model fit only marginally. A genetic variant within the CYP7A1 locus, encoding the rate-limiting enzyme in bile acid synthesis, was found to be associated with lower concentrations of deoxycholic acid, higher concentrations of LDL-cholesterol and lower type 2 diabetes risk. Variants in or near SGPP1, GCKR and FADS1/2 were associated with diabetes-associated phospholipids and type 2 diabetes.Conclusions/interpretationWe found evidence that the metabolism of bile acids and phospholipids shares some common genetic origin with type 2 diabetes.Access to research materialsMetabolomics data have been deposited in the Metabolights database, with accession numbers MTBLS93 (TwinGene), MTBLS124 (ULSAM) and MTBLS90 (PIVUS).
|
|
| 3. |
- Herman, Stephanie
(författare)
-
Towards an Earlier Detection of Progressive Multiple Sclerosis using Metabolomics and Machine Learning
- 2020
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Decision-making guided by advanced analytics is becoming increasingly common in many fields. Implementing computationally driven healthcare solutions does, however, pose ethical dilemmas as it involves human health. Therefore, augmenting clinical expertise with advanced analytical insights to support decision-making in healthcare is probably a more feasible strategy.Multiple sclerosis is a debilitating neurological disease with two subtypes; relapsing-remitting multiple sclerosis (RRMS) and the typically late-stage progressive subtype (PMS). Progressive multiple sclerosis is a neurodegenerative phenotype, with a vague functional definition, that currently is diagnosed retrospectively. The challenge of diagnosing PMS earlier is a great example where data-driven insights might prove useful.This thesis addresses the need for an earlier detection of patients developing the progressive and neurodegenerative subtype of multiple sclerosis, using primarily metabolomics and machine learning approaches. In Paper I, the biochemical differences in cerebrospinal fluid (CSF) from RRMS and PMS patients were characterised, leading to the conclusion that it is possible to distinguish PMS patients based on biochemical alterations. In addition, pathway analysis revealed several metabolic pathways that were affected in the transition to PMS, including tryptophan metabolism and pyrimidine metabolism. In Paper II and III, the possibility of generating a concise PMS signature based on solely low-molecular measurements (III) or in combination with radiological and protein measures (II) was explored. In both cases, it was concluded that it is plausible to generate a condensed set of highly informative markers that can distinguish PMS patients from RRMS patients. In Paper III, the classifier was complemented with conformal prediction that enabled an estimate of confidence in single patient predictions and a personalised evaluation of current disease state. Finally, in Paper IV, the extracted low-molecular marker candidates were characterised in isolation, revealing that several metabolites were distinctively altered in the CSF of PMS patients, including increased levels of 4-acetamidobutanoate, 4-hydroxybenzoate and thymine.Overall, the results from this work indicate that it is possible to detect PMS at an earlier stage and that advanced analytical algorithms can support healthcare.
|
|
| 4. |
- Quell, Jan D., et al.
(författare)
-
Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies
- 2017
-
Ingår i: Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences. - : Elsevier BV. - 1570-0232 .- 1873-376X. ; 1071, s. 58-67
-
Tidskriftsartikel (refereegranskat)abstract
- Identification of metabolites in non-targeted metabolomics continues to be a bottleneck in metabolomics studies in large human cohorts. Unidentified metabolites frequently emerge in the results of association studies linking metabolite levels to, for example, clinical phenotypes. For further analyses these unknown metabolites must be identified. Current approaches utilize chemical information, such as spectral details and fragmentation characteristics to determine components of unknown metabolites. Here, we propose a systems biology model exploiting the internal correlation structure of metabolite levels in combination with existing biochemical and genetic information to characterize properties of unknown molecules.Levels of 758 metabolites (439 known, 319 unknown) in human blood samples of 2279 subjects were measured using a non-targeted metabolomics platform (LC-MS and GC-MS). We reconstructed the structure of biochemical pathways that are imprinted in these metabolomics data by building an empirical network model based on 1040 significant partial correlations between metabolites. We further added associations of these metabolites to 134 genes from genome-wide association studies as well as reactions and functional relations to genes from the public database Recon 2 to the network model. From the local neighborhood in the network, we were able to predict the pathway annotation of 180 unknown metabolites. Furthermore, we classified 100 pairs of known and unknown and 45 pairs of unknown metabolites to 21 types of reactions based on their mass differences. As a proof of concept, we then looked further into the special case of predicted dehydrogenation reactions leading us to the selection of 39 candidate molecules for 5 unknown metabolites. Finally, we could verify 2 of those candidates by applying LC-MS analyses of commercially available candidate substances. The formerly unknown metabolites X-13891 and X-13069 were shown to be 2-dodecendioic acid and 9-tetradecenoic acid, respectively.Our data-driven approach based on measured metabolite levels and genetic associations as well as information from public resources can be used alone or together with methods utilizing spectral patterns as a complementary, automated and powerful method to characterize unknown metabolites.
|
|
