Multi-omic signature of body weight change: results from a population-based cohort study

Circulating metabolomic biomarkers of 5-year body weight and composition change in a biracial cohort of community-dwelling older adults

Unintentional weight loss in older populations is linked to greater mortality and morbidity risks. This study aims to understand the metabolic mechanisms of unintentional weight loss and their relationship with body composition changes in older adults. We investigated plasma metabolite associations with weight and body composition changes over 5 years in 1335 participants (mean age 73.4 years at Year 1, 51% women, and 33% Black) from the Health, Aging and Body Composition (Health ABC) study. Multinomial logistic regressions were used to examine associations of the 442 metabolites with weight loss > 5% over 5 years with/without an intention, weight gain > 5%, and fluctuating weight relative to weight stability. Metabolite associations with unintentional weight loss differed from other weight change patterns. Lower levels of essential amino acids, phospholipids, long-chain polyunsaturated triglycerides, cholesterol esters, and uridine were associated with higher odds of unintentional weight loss versus weight stability after adjusting for age, sex, race, and Year 1 BMI categories. Losses in fat mass and muscle mass each attenuated > 20% of the associations between many metabolites, such as phospholipids and essential amino acids, and unintentional weight loss. DXA whole-body fat mass loss (mean 3% annually) further attenuated 9 metabolite associations by > 50% after CT muscle loss (mean 2% annually) adjustment. Lipids and amino acids related to energy and protein balance were associated with unintentional weight loss in older adults. Fat and muscle mass losses partially attenuated these associations, suggesting connections of these metabolic pathways with muscle, and particularly adiposity dynamics.

Markov field network model of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques

Analysis of multi-modal datasets can identify multi-scale interactions underlying biological systems but can be beset by spurious connections due to indirect impacts propagating through an unmapped biological network. For example, studies in macaques have shown that Bacillus Calmette-Guerin (BCG) vaccination by an intravenous route protects against tuberculosis, correlating with changes across various immune data modes. To eliminate spurious correlations and identify critical immune interactions in a public multi-modal dataset (systems serology, cytokines, and cytometry) of vaccinated macaques, we applied Markov fields (MFs), a data-driven approach that explains vaccine efficacy and immune correlations via multivariate network paths, without requiring large numbers of samples (i.e., macaques) relative to multivariate features. We find that integrating multiple data modes with MFs helps remove spurious connections. Finally, we used the MF to predict outcomes of perturbations at various immune nodes, including an experimentally validated B cell depletion that induced network-wide shifts without reducing vaccine protection.

Metabolomic characterization of unintentional weight loss among community-dwelling older Black and White men and women

This study aims to understand the metabolic mechanisms of unintentional weight loss in older adults. We investigated plasma metabolite associations of subsequent weight change over 2 years in 1536 previously weight stable participants (mean age 74.6 years, 50% women, 35% Black) from the Health, Aging and Body Composition (Health ABC) Study. Multinomial logistic regressions were used to examine associations of the 442 metabolites with weight loss with/without an intention and weight gain >3% annually relative to weight stability. The metabolite associations of unintentional weight loss differed from those of intentional weight loss and weight gain. Lower levels of aromatic amino acids, phospholipids, long-chain poly-unsaturated triglycerides, and higher levels of amino acid derivatives, poly-unsaturated fatty acids, and carbohydrates were associated with higher odds of unintentional weight loss after adjusting for age, sex, race, and BMI categories. Prevalent diseases attenuated four and lower mid-thigh muscle mass and poorer appetite each attenuated 2 of 77 identified metabolite associations by >20%, respectively. Other factors (e.g., energy expenditure, diet, and medication) attenuated all associations by <20%. While 16 metabolite associations were attenuated by 20%-48% when adjusting for all these risk factors, 47 metabolite associations remained significant. Altered amino acid metabolism, impaired mitochondrial fatty acid oxidation, and inflammaging implicated by identified metabolites appear to precede unintentional weight loss in Health ABC older adults. Furthermore, these pathways seem to be associated with prevalent diseases especially diabetes, lower muscle mass, and poorer appetite.

Markov Field network model of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques

Analysis of multi-modal datasets can identify multi-scale interactions underlying biological systems, but can be beset by spurious connections due to indirect impacts propagating through an unmapped biological network. For example, studies in macaques have shown that BCG vaccination by an intravenous route protects against tuberculosis, correlating with changes across various immune data modes. To eliminate spurious correlations and identify critical immune interactions in a public multi-modal dataset (systems serology, cytokines, cytometry) of vaccinated macaques, we applied Markov Fields (MF), a data-driven approach that explains vaccine efficacy and immune correlations via multivariate network paths, without requiring large numbers of samples (i.e. macaques) relative to multivariate features. Furthermore, we find that integrating multiple data modes with MFs helps to remove spurious connections. Finally, we used the MF to predict outcomes of perturbations at various immune nodes, including a B-cell depletion that induced network-wide shifts without reducing vaccine protection, which we validated experimentally.

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Metabolic liability for weight gain in early adulthood

While weight gain is associated with a host of chronic illnesses, efforts in obesity have relied on single "snapshots" of body mass index (BMI) to guide genetic and molecular discovery. Here, we study >2,000 young adults with metabolomics and proteomics to identify a metabolic liability to weight gain in early adulthood. Using longitudinal regression and penalized regression, we identify a metabolic signature for weight liability, associated with a 2.6% (2.0%-3.2%, p = 7.5 × 10-19) gain in BMI over ≈20 years per SD higher score, after comprehensive adjustment. Identified molecules specified mechanisms of weight gain, including hunger and appetite regulation, energy expenditure, gut microbial metabolism, and host interaction with external exposure. Integration of longitudinal and concurrent measures in regression with Mendelian randomization highlights the complexity of metabolic regulation of weight gain, suggesting caution in interpretation of epidemiologic or genetic effect estimates traditionally used in metabolic research.

The metabolomic signature of weight loss and remission in the Diabetes Remission Clinical Trial (DiRECT)

AIMS/HYPOTHESIS

High-throughput metabolomics technologies in a variety of study designs have demonstrated a consistent metabolomic signature of overweight and type 2 diabetes. However, the extent to which these metabolomic patterns can be reversed with weight loss and diabetes remission has been weakly investigated. We aimed to characterise the metabolomic consequences of a weight-loss intervention in individuals with type 2 diabetes.

METHODS

We analysed 574 fasted serum samples collected within an existing RCT (the Diabetes Remission Clinical Trial [DiRECT]) (N=298). In the trial, participating primary care practices were randomly assigned (1:1) to provide either a weight management programme (intervention) or best-practice care by guidelines (control) treatment to individuals with type 2 diabetes. Here, metabolomics analysis was performed on samples collected at baseline and 12 months using both untargeted MS and targeted 1H-NMR spectroscopy. Multivariable regression models were fitted to evaluate the effect of the intervention on metabolite levels.

RESULTS

Decreases in branched-chain amino acids, sugars and LDL triglycerides, and increases in sphingolipids, plasmalogens and metabolites related to fatty acid metabolism were associated with the intervention (Holm-corrected p<0.05). In individuals who lost more than 9 kg between baseline and 12 months, those who achieved diabetes remission saw greater reductions in glucose, fructose and mannose, compared with those who did not achieve remission.

CONCLUSIONS/INTERPRETATION

We have characterised the metabolomic effects of an integrated weight management programme previously shown to deliver weight loss and diabetes remission. A large proportion of the metabolome appears to be modifiable. Patterns of change were largely and strikingly opposite to perturbances previously documented with the development of type 2 diabetes.

DATA AVAILABILITY

The data used for analysis are available on a research data repository ( https://researchdata.gla.ac.uk/ ) with access given to researchers subject to appropriate data sharing agreements. Metabolite data preparation, data pre-processing, statistical analyses and figure generation were performed in R Studio v.1.0.143 using R v.4.0.2. The R code for this study has been made publicly available on GitHub at: https://github.com/lauracorbin/metabolomics_of_direct .

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

BACKGROUND

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

METHODS

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks.

RESULTS

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

CONCLUSIONS

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

An atlas of genome-wide gene expression and metabolite associations and possible mediation effects towards body mass index

Investigating the cross talk of different omics layers is crucial to understand molecular pathomechanisms of metabolic diseases like obesity. Here, we present a large-scale association meta-analysis of genome-wide whole blood and peripheral blood mononuclear cell (PBMC) gene expressions profiled with Illumina HT12v4 microarrays and metabolite measurements from dried blood spots (DBS) characterized by targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) in three large German cohort studies with up to 7706 samples. We found 37,295 associations comprising 72 amino acids (AA) and acylcarnitine (AC) metabolites (including ratios) and 8579 transcripts. We applied this catalogue of associations to investigate the impact of associating transcript-metabolite pairs on body mass index (BMI) as an example metabolic trait. This is achieved by conducting a comprehensive mediation analysis considering metabolites as mediators of gene expression effects and vice versa. We discovered large mediation networks comprising 27,023 potential mediation effects within 20,507 transcript-metabolite pairs. Resulting networks of highly connected (hub) transcripts and metabolites were leveraged to gain mechanistic insights into metabolic signaling pathways. In conclusion, here, we present the largest available multi-omics integration of genome-wide transcriptome data and metabolite data of amino acid and fatty acid metabolism and further leverage these findings to characterize potential mediation effects towards BMI proposing candidate mechanisms of obesity and related metabolic diseases. KEY MESSAGES: Thousands of associations of 72 amino acid and acylcarnitine metabolites and 8579 genes expand the knowledge of metabolome-transcriptome associations. A mediation analysis of effects on body mass index revealed large mediation networks of thousands of obesity-related gene-metabolite pairs. Highly connected, potentially mediating hub genes and metabolites enabled insight into obesity and related metabolic disease pathomechanisms.

Metabolomics and lipidomics strategies in modern drug discovery and development

Metabolomics and lipidomics have an increasingly pivotal role in drug discovery and development. In the context of drug discovery, monitoring changes in the levels or composition of metabolites and lipids relative to genetic variations yields functional insights, bolstering human genetics and (meta)genomic methodologies. This approach also sheds light on potential novel targets for therapeutic intervention. In the context of drug development, metabolite and lipid biomarkers contribute to enhanced success rates, promising a transformative impact on precision medicine. In this review, we deviate from analytical chemist-focused perspectives, offering an overview tailored to drug discovery. We provide introductory insight into state-of-the-art mass spectrometry (MS)-based metabolomics and lipidomics techniques utilized in drug discovery and development, drawing from the collective expertise of our research teams. We comprehensively outline the application of metabolomics and lipidomics in advancing drug discovery and development, spanning fundamental research, target identification, mechanisms of action, and the exploration of biomarkers.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

Background

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remain underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

Methods

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N=651) and the Netherlands Twin Register (NTR) (N=665). Follow-up comprised four BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated using latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. The sources of genetic and environmental variation underlying the protein abundances were quantified using twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) using mixed-effect models and correlation networks.

Results

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 6 and 4 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with many metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

Conclusions

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

Multi-view spectral clustering with latent representation learning for applications on multi-omics cancer subtyping

Driven by multi-omics data, some multi-view clustering algorithms have been successfully applied to cancer subtypes prediction, aiming to identify subtypes with biometric differences in the same cancer, thereby improving the clinical prognosis of patients and designing personalized treatment plan. Due to the fact that the number of patients in omics data is much smaller than the number of genes, multi-view spectral clustering based on similarity learning has been widely developed. However, these algorithms still suffer some problems, such as over-reliance on the quality of pre-defined similarity matrices for clustering results, inability to reasonably handle noise and redundant information in high-dimensional omics data, ignoring complementary information between omics data, etc. This paper proposes multi-view spectral clustering with latent representation learning (MSCLRL) method to alleviate the above problems. First, MSCLRL generates a corresponding low-dimensional latent representation for each omics data, which can effectively retain the unique information of each omics and improve the robustness and accuracy of the similarity matrix. Second, the obtained latent representations are assigned appropriate weights by MSCLRL, and global similarity learning is performed to generate an integrated similarity matrix. Third, the integrated similarity matrix is used to feed back and update the low-dimensional representation of each omics. Finally, the final integrated similarity matrix is used for clustering. In 10 benchmark multi-omics datasets and 2 separate cancer case studies, the experiments confirmed that the proposed method obtained statistically and biologically meaningful cancer subtypes.

A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways

The integrative method approaches are continuously evolving to provide accurate insights from the data that is received through experimentation on various biological systems. Multi-omics data can be integrated with predictive machine learning algorithms in order to provide results with high accuracy. This protocol chapter defines the steps required for the ML-multi-omics integration methods that are applied on biological datasets for its analysis and the visual interpretation of the results thus obtained.

Sudden cardiac death in diabetes and obesity: mechanisms and therapeutic strategies

Ventricular arrhythmias and sudden cardiac death (SCD) occur most frequently in the setting of coronary artery disease, cardiomyopathy and heart failure, but are also increasingly observed in individuals suffering from diabetes mellitus and obesity. The incidence of these metabolic disorders is rising in Western countries, but adequate prevention and treatment of arrhythmias and SCD in affected patients is limited due to our incomplete knowledge of the underlying disease mechanisms. Here, an overview is presented of the prevalence of electrophysiological disturbances, ventricular arrhythmias and SCD in the clinical setting of diabetes and obesity. Experimental studies are reviewed, which have identified disease pathways and associated modulatory factors, in addition to pro-arrhythmic mechanisms. Key processes are discussed, including mitochondrial dysfunction, oxidative stress, cardiac structural derangements, abnormal cardiac conduction, ion channel dysfunction, prolonged repolarization and dysregulation of intracellular sodium and calcium homeostasis. In addition, the recently identified pro-arrhythmic effects of dysregulated branched chain amino acid metabolism, a common feature in patients with metabolic disorders, are addressed. Finally, current management options are discussed, in addition to the potential development of novel preventive and therapeutic strategies based on recent insight gained from translational studies.

Decreasing severity of obesity from early to late adolescence and young adulthood associates with longitudinal metabolomic changes implicated in lower cardiometabolic disease risk

BACKGROUND

Obesity in childhood is associated with metabolic dysfunction, adverse subclinical cardiovascular phenotypes and adult cardiovascular disease. Longitudinal studies of youth with obesity investigating changes in severity of obesity with metabolomic profiles are sparse. We investigated associations between (i) baseline body mass index (BMI) and follow-up metabolomic profiles; (ii) change in BMI with follow-up metabolomic profiles; and (iii) change in BMI with change in metabolomic profiles (mean interval 5.5 years).

METHODS

Participants (n = 98, 52% males) were recruited from the Childhood Overweight Biorepository of Australia study. At baseline and follow-up, BMI and the % >95th BMI-centile (percentage above the age-, and sex-specific 95th BMI-centile) indicate severity of obesity, and nuclear magnetic resonance spectroscopy profiling of 72 metabolites/ratios, log-transformed and scaled to standard deviations (SD), was performed in fasting serum. Fully adjusted linear regression analyses were performed.

RESULTS

Mean (SD) age and % >95th BMI-centile were 10.3 (SD 3.5) years and 134.6% (19.0) at baseline, 15.8 (3.7) years and 130.7% (26.2) at follow-up. Change in BMI over time, but not baseline BMI, was associated with metabolites at follow-up. Each unit (kg/m²) decrease in sex- and age-adjusted BMI was associated with change (SD; 95% CI; p value) in metabolites of: alanine (-0.07; -0.11 to -0.04; p < 0.001), phenylalanine (-0.07; -0.10 to -0.04; p < 0.001), tyrosine (-0.07; -0.10 to -0.04; p < 0.001), glycoprotein acetyls (-0.06; -0.09 to -0.04; p < 0.001), degree of fatty acid unsaturation (0.06; 0.02 to 0.10; p = 0.003), monounsaturated fatty acids (-0.04; -0.07 to -0.01; p = 0.004), ratio of ApoB/ApoA1 (-0.05; -0.07 to -0.02; p = 0.001), VLDL-cholesterol (-0.04; -0.06 to -0.01; p = 0.01), HDL cholesterol (0.05; 0.08 to 0.1; p = 0.01), pyruvate (-0.08; -0.11 to -0.04; p < 0.001), acetoacetate (0.07; 0.02 to 0.11; p = 0.005) and 3-hydroxybuturate (0.07; 0.02 to 0.11; p = 0.01). Results using the % >95th BMI-centile were largely consistent with age- and sex-adjusted BMI measures.

CONCLUSIONS

In children and young adults with obesity, decreasing the severity of obesity was associated with changes in metabolomic profiles consistent with lower cardiovascular and metabolic disease risk in adults.

The Relation Between Adult Weight Gain, Adipocyte Volume, and the Metabolic Profile at Middle Age

CONTEXT

Weight gain during adulthood increases cardiometabolic disease risk, possibly through adipocyte hypertrophy.

OBJECTIVE

We aimed to study the specific metabolomic profile of adult weight gain, and to examine its association with adipocyte volume.

METHODS

Nuclear magnetic resonance-based metabolomics were measured in the Netherlands Epidemiology of Obesity (NEO) study (n = 6347, discovery) and Oxford Biobank (n = 6317, replication). Adult weight gain was calculated as the absolute difference between body mass index (BMI) at middle age and recalled BMI at age 20 years. We performed linear regression analyses with both exposures BMI at age 20 years and weight gain, and separately with BMI at middle age in relation to 149 serum metabolomic measures, adjusted for age, sex, and multiple testing. Additionally, subcutaneous abdominal adipocyte biopsies were collected in a subset of the Oxford Biobank (n = 114) to estimate adipocyte volume.

RESULTS

Mean (SD) weight gain was 4.5 (3.7) kg/m2 in the NEO study and 3.6 (3.7) kg/m2 in the Oxford Biobank. Weight gain, and not BMI at age 20 nor middle age, was associated with concentrations of 7 metabolomic measures after successful replication, which included polyunsaturated fatty acids, small to medium low-density lipoproteins, and total intermediate-density lipoprotein. One SD weight gain was associated with 386 μm3 (95% CI, 143-629) higher median adipocyte volume. Adipocyte volume was associated with lipoprotein particles specific for adult weight gain.

CONCLUSION

Adult weight gain is associated with specific metabolomic alterations of which the higher lipoprotein concentrations were likely contributed by larger adipocyte volumes, presumably linking weight gain to cardiometabolic disease.

Multi-omics integration in biomedical research - A metabolomics-centric review

Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.

A metabolome-wide association study in the general population reveals decreased levels of serum laurylcarnitine in people with depression

Depression constitutes a leading cause of disability worldwide. Despite extensive research on its interaction with psychobiological factors, associated pathways are far from being elucidated. Metabolomics, assessing the final products of complex biochemical reactions, has emerged as a valuable tool for exploring molecular pathways. We conducted a metabolome-wide association analysis to investigate the link between the serum metabolome and depressed mood (DM) in 1411 participants of the KORA (Cooperative Health Research in the Augsburg Region) F4 study (discovery cohort). Serum metabolomics data comprised 353 unique metabolites measured by Metabolon. We identified 72 (5.1%) KORA participants with DM. Linear regression tests were conducted modeling each metabolite value by DM status, adjusted for age, sex, body-mass index, antihypertensive, cardiovascular, antidiabetic, and thyroid gland hormone drugs, corticoids and antidepressants. Sensitivity analyses were performed in subcohorts stratified for sex, suicidal ideation, and use of antidepressants. We replicated our results in an independent sample of 968 participants of the SHIP-Trend (Study of Health in Pomerania) study including 52 (5.4%) individuals with DM (replication cohort). We found significantly lower laurylcarnitine levels in KORA F4 participants with DM after multiple testing correction according to Benjamini/Hochberg. This finding was replicated in the independent SHIP-Trend study. Laurylcarnitine remained significantly associated (p value < 0.05) with depression in samples stratified for sex, suicidal ideation, and antidepressant medication. Decreased blood laurylcarnitine levels in depressed individuals may point to impaired fatty acid oxidation and/or mitochondrial function in depressive disorders, possibly representing a novel therapeutic target.

Discriminating Dietary Responses by Combining Transcriptomics and Metabolomics Data in Nutrition Intervention Studies

Scope

Combining different “omics” data types in a single, integrated analysis may better characterize the effects of diet on human health.

Methods and results

The performance of two data integration tools, similarity network fusion tool (SNFtool) and Data Integration Analysis for Biomarker discovery using Latent variable approaches for “Omics” (DIABLO; MixOmics), in discriminating responses to diet and metabolic phenotypes is investigated by combining transcriptomics and metabolomics datasets from three human intervention studies: a postprandial crossover study testing dairy foods (n = 7; study 1), a postprandial challenge study comparing obese and non‐obese subjects (n = 13; study 2); and an 8‐week parallel intervention study that assessed three diets with variable lipid content on fasting parameters (n = 39; study 3). In study 1, combining datasets using SNF or DIABLO significantly improve sample classification. For studies 2 and 3, the value of SNF integration depends on the dietary groups being compared, while DIABLO discriminates samples well but does not perform better than transcriptomic data alone.

Conclusion

The integration of associated “omics” datasets can help clarify the subtle signals observed in nutritional interventions. The performance of each integration tool is differently influenced by study design, size of the datasets, and sample size.

Plasma Biomarkers and Identification of Resilient Metabolic Disruptions in Patients With Venous Thromboembolism Using a Metabolic Systems Approach

OBJECTIVE

Deep vein thrombosis and pulmonary embolism referred as venous thromboembolism (VTE) are a common cause of morbidity and mortality. Plasma from healthy controls or individuals who have experienced a VTE were analyzed using metabolomics to characterize biomarkers and metabolic systems of patients with VTE. Approach and Results: Polar metabolite and lipidomic profiles from plasma collected 3 months after an incident VTE were obtained using liquid chromatography mass spectrometry. Fasting-state plasma samples from 42 patients with VTE and 42 healthy controls were measured. Plasma metabolomic profiling identified 512 metabolites forming 62 biological clusters. Multivariate analysis revealed a panel of 21 metabolites altogether capable of predicting VTE status with an area under the curve of 0.92 (P=0.00174, selectivity=0.857, sensitivity=0.971). Multiblock systems analysis revealed 25 of the 62 functional biological groups as significantly affected in the VTE group (P<0.05 to control). Complementary correlation network analysis of the dysregulated functions highlighted a subset of the lipidome composed mainly of n-3 long-chain polyunsaturated fatty acids within the predominant triglycerides as a potential regulator of the post-VTE event biological response, possibly controlling oxidative and inflammatory defence systems, and metabolic disorder associated dysregulations. Of interest was microbiota metabolites including trimethylamine N-oxide that remained associated to post incident VTE patients, highlighting a possible involvement of gut microbiota on VTE risk and relapse.

CONCLUSIONS

These findings show promise for the elucidation of underlying mechanisms and the design of a diagnostic test to assess the likely efficacy of clinical care in patients with VTE.

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

Multi-omics integration-a comparison of unsupervised clustering methodologies

With the recent developments in the field of multi-omics integration, the interest in factors such as data preprocessing, choice of the integration method and the number of different omics considered had increased. In this work, the impact of these factors is explored when solving the problem of sample classification, by comparing the performances of five unsupervised algorithms: Multiple Canonical Correlation Analysis, Multiple Co-Inertia Analysis, Multiple Factor Analysis, Joint and Individual Variation Explained and Similarity Network Fusion. These methods were applied to three real data sets taken from literature and several ad hoc simulated scenarios to discuss classification performance in different conditions of noise and signal strength across the data types. The impact of experimental design, feature selection and parameter training has been also evaluated to unravel important conditions that can affect the accuracy of the result.

Relationships Between Gut Microbiota, Metabolome, Body Weight, and Glucose Homeostasis of Obese Dogs Fed with Diets Differing in Prebiotic and Protein Content

Obesity is a major issue in pets and nutritional strategies need to be developed, like promoting greater protein and fiber intake. This study aimed to evaluate the effects of dietary protein levels and prebiotic supplementation on the glucose metabolism and relationships between the gut, microbiota, metabolome, and phenotype of obese dogs. Six obese Beagle dogs received a diet containing 25.6% or 36.9% crude protein, with or without 1% short-chain fructo-oligosaccharide (scFOS) or oligofructose (OF), in a Latin-square study design. Fecal and blood samples were collected for metabolite analysis, untargeted metabolomics, and 16S rRNA amplicon sequencing. A multi-block analysis was performed to build a correlation network to identify relationships between fecal microbiota, metabolome, and phenotypic variables. Diets did not affect energy homeostasis, but scFOS supplementation modulated fecal microbiota composition and induced significant changes of the fecal metabolome. Bile acids and several amino acids were related to glucose homeostasis while specific bacteria gathered in metavariables had a high number of links with phenotypic and metabolomic parameters. It also suggested that fecal aminoadipate and hippurate act as potential markers of glucose homeostasis. This preliminary study provides new insights into the relationships between the gut microbiota, the metabolome, and several phenotypic markers involved in obesity and associated metabolic dysfunctions.

Using metabolite profiling to construct and validate a metabolite risk score for predicting future weight gain

Background

Excess weight gain throughout adulthood can lead to adverse clinical outcomes and are influenced by complex factors that are difficult to measure in free-living individuals. Metabolite profiling offers an opportunity to systematically discover new predictors for weight gain that are relatively easy to measure compared to traditional approaches.

Methods and results

Using baseline metabolite profiling data of middle-aged individuals from the Framingham Heart Study (FHS; n = 1,508), we identified 42 metabolites associated (p < 0.05) with longitudinal change in body mass index (BMI). We performed stepwise linear regression to select 8 of these metabolites to build a metabolite risk score (MRS) for predicting future weight gain. We replicated the MRS using data from the Mexico City Diabetes Study (MCDS; n = 768), in which one standard deviation increase in the MRS corresponded to ~0.03 increase in BMI (kg/m²) per year (i.e. ~0.09 kg/year for a 1.7 m adult). We observed that none of the available anthropometric, lifestyle, and glycemic variables fully account for the MRS prediction of weight gain. Surprisingly, we found the MRS to be strongly correlated with baseline insulin sensitivity in both cohorts and to be negatively predictive of T2D in MCDS. Genome-wide association study of the MRS identified 2 genome-wide (p < 5 × 10⁻⁸) and 5 suggestively (p < 1 × 10⁻⁶) significant loci, several of which have been previously linked to obesity-related phenotypes.

Conclusions

We have constructed and validated a generalizable MRS for future weight gain that is an independent predictor distinct from several other known risk factors. The MRS captures a composite biological picture of weight gain, perhaps hinting at the anabolic effects of preserved insulin sensitivity. Future investigation is required to assess the relationships between MRS-predicted weight gain and other obesity-related diseases.

Clinical and lifestyle related factors influencing whole blood metabolite levels - A comparative analysis of three large cohorts

Objective

Human blood metabolites are influenced by a number of lifestyle and environmental factors. Identification of these factors and the proper quantification of their relevance provides insights into human biological and metabolic disease processes, is key for standardized translation of metabolite biomarkers into clinical applications, and is a prerequisite for comparability of data between studies. However, so far only limited data exist from large and well-phenotyped human cohorts and current methods for analysis do not fully account for the characteristics of these data. The primary aim of this study was to identify, quantify and compare the impact of a comprehensive set of clinical and lifestyle related factors on metabolite levels in three large human cohorts. To achieve this goal, we improve current methodology by developing a principled analysis approach, which could be translated to other cohorts and metabolite panels.

Methods

63 Metabolites (amino acids, acylcarnitines) were quantified by liquid chromatography tandem mass spectrometry in three cohorts (total N = 16,222). Supported by a simulation study evaluating various analytical approaches, we developed an analysis pipeline including preprocessing, identification, and quantification of factors affecting metabolite levels. We comprehensively identified uni- and multivariable metabolite associations considering 29 environmental and clinical factors and performed metabolic pathway enrichment and network analyses.

Results

Inverse normal transformation of batch corrected and outlier removed metabolite levels accompanied by linear regression analysis proved to be the best suited method to deal with the metabolite data. Association analyses revealed numerous uni- and multivariable significant associations. 15 of the analyzed 29 factors explained >1% of variance for at least one of the metabolites. Strongest factors are application of steroid hormones, reticulocytes, waist-to-hip ratio, sex, haematocrit, and age. Effect sizes of factors are comparable across studies.

Conclusions

We introduced a principled approach for the analysis of MS data allowing identification, and quantification of effects of clinical and lifestyle factors with metabolite levels. We detected a number of known and novel associations broadening our understanding of the regulation of the human metabolome. The large heterogeneity observed between cohorts could almost completely be explained by differences in the distribution of influencing factors emphasizing the necessity of a proper confounder analysis when interpreting metabolite associations.

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Amino-acids and acylcarnitines analyzed in three studies with >16,000 individuals.

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Develop a generic and adaptable bioinformatics workflow.

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Analysis of the impact of 29 clinical and life-style factors on blood metabolites.

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Analysis of network between factors and metabolites.

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Comparison of results between studies.

Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective

It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.

The Metabolomic Signatures of Weight Change

Obesity represents a major health concern, not just in the West but increasingly in low and middle income countries. In order to develop successful strategies for losing weight, it is essential to understand the molecular pathogenesis of weight change. A number of pathways, implicating oxidative stress but also the fundamental regulatory of insulin, have been implicated in weight gain and in the regulation of energy expenditure. In addition, a considerable body of work has highlighted the role of metabolites generated by the gut microbiome, in particular short chain fatty acids, in both processes. The current review provides a brief understanding of the mechanisms underlying the associations of weight change with changes in lipid and amino acid metabolism, energy metabolism, dietary composition and insulin dynamics, as well as the influence of the gut microbiome. The changes in metabolomic profiles and the models outlined can be used as an accurate predictor for obesity and obesity related disorders.

Large-scale plasma metabolome analysis reveals alterations in HDL metabolism in migraine

OBJECTIVE

To identify a plasma metabolomic biomarker signature for migraine.

METHODS

Plasma samples from 8 Dutch cohorts (n = 10,153: 2,800 migraine patients and 7,353 controls) were profiled on a ¹H-NMR-based metabolomics platform, to quantify 146 individual metabolites (e.g., lipids, fatty acids, and lipoproteins) and 79 metabolite ratios. Metabolite measures associated with migraine were obtained after single-metabolite logistic regression combined with a random-effects meta-analysis performed in a nonstratified and sex-stratified manner. Next, a global test analysis was performed to identify sets of related metabolites associated with migraine. The Holm procedure was applied to control the family-wise error rate at 5% in single-metabolite and global test analyses.

RESULTS

Decreases in the level of apolipoprotein A1 (β -0.10; 95% confidence interval [CI] -0.16, -0.05; adjusted p = 0.029) and free cholesterol to total lipid ratio present in small high-density lipoprotein subspecies (HDL) (β -0.10; 95% CI -0.15, -0.05; adjusted p = 0.029) were associated with migraine status. In addition, only in male participants, a decreased level of omega-3 fatty acids (β -0.24; 95% CI -0.36, -0.12; adjusted p = 0.033) was associated with migraine. Global test analysis further supported that HDL traits (but not other lipoproteins) were associated with migraine status.

CONCLUSIONS

Metabolic profiling of plasma yielded alterations in HDL metabolism in migraine patients and decreased omega-3 fatty acids only in male migraineurs.

Metabolomic correlates of central adiposity and earlier-life body mass index

BMI is correlated with circulating metabolites, but few studies discuss other adiposity measures, and little is known about metabolomic correlates of BMI from early life. We investigated associations between different adiposity measures, BMI from childhood through adulthood, and metabolites quantified from serum using ¹H NMR spectroscopy in 900 British men and women aged 60-64. We assessed BMI, waist-to-hip ratio (WHR), android-to-gynoid fat ratio (AGR), and BMI from childhood through adulthood. Linear regression with Bonferroni adjustment was performed to assess adiposity and metabolites. Of 233 metabolites, 168; 126; and 133 were associated with BMI, WHR, and AGR at age 60-64, respectively. Associations were strongest for HDL, particularly HDL particle size-e.g., there was 0.08 SD decrease in HDL diameter (95% CI: 0.07-0.10) with each unit increase in BMI. BMI-adjusted AGR or WHR were associated with 31 metabolites where there was no metabolome-wide association with BMI. We identified inverse associations between BMI at age 7 and glucose or glycoprotein at age 60-64 and relatively large LDL cholesteryl ester with postadolescent BMI gains. In summary, we identified metabolomic correlates of central adiposity and earlier-life BMI. These findings support opportunities to leverage metabolomics in early prevention of cardiovascular risk attributable to body fatness.

Profound Perturbation of the Metabolome in Obesity Is Associated with Health Risk

Obesity is a heterogeneous phenotype that is crudely measured by body mass index (BMI). There is a need for a more precise yet portable method of phenotyping and categorizing risk in large numbers of people with obesity to advance clinical care and drug development. Here, we used non-targeted metabolomics and whole-genome sequencing to identify metabolic and genetic signatures of obesity. We find that obesity results in profound perturbation of the metabolome; nearly a third of the assayed metabolites associated with changes in BMI. A metabolome signature identifies the healthy obese and lean individuals with abnormal metabolomes-these groups differ in health outcomes and underlying genetic risk. Specifically, an abnormal metabolome associated with a 2- to 5-fold increase in cardiovascular events when comparing individuals who were matched for BMI but had opposing metabolome signatures. Because metabolome profiling identifies clinically meaningful heterogeneity in obesity, this approach could help select patients for clinical trials.

Impact of dietary fiber supplementation on modulating microbiota-host-metabolic axes in obesity

Low dietary fiber intake is associated with higher rates of microbiota-associated chronic diseases such as obesity. Low-fiber diets alter not only microbial composition but also the availability of metabolic end products derived from fermentation of fiber. Our objective was to examine the effects of dietary fiber supplementation on gut microbiota and associated fecal and serum metabolites in relation to metabolic markers of obesity. We conducted a 12-week, single-center, double-blind, placebo-controlled trial with 53 adults with overweight or obesity. They were randomly assigned to a pea fiber (PF, 15 g/d in wafer form; n=29) or control (CO, isocaloric amount of wafers; n=24) group. Blood and fecal samples were collected at baseline and 12 weeks. Serum metabolomics, gut microbiota and fecal short-chain fatty acids (SCFAs) and bile acids (BAs) were examined. Within-group but not between-group analysis showed a significant effect of treatment on serum metabolites at 12 weeks compared to baseline. Fiber significantly altered fecal SCFAs and BAs with higher acetate and reduced isovalerate, cholate, deoxycholate and total BAs content in the PF group compared to baseline. Microbiota was differentially modulated in the two groups, including an increase in the SCFA producer Lachnospira in the PF group and decrease in the CO group. The change in body weight of participants showed a negative correlation with their change in Lachnospira (r=-0.463, P=.006) abundance. The current study provides insight into the actions of pea fiber and its impact on modulating microbiota-host-metabolic axes in obesity.

Machine Learning and Integrative Analysis of Biomedical Big Data

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.

Dietary Probiotic Effect of Lactococcus lactis WFLU12 on Low-Molecular-Weight Metabolites and Growth of Olive Flounder (Paralichythys olivaceus)

The use of probiotics is considered an attractive biocontrol method. It is effective in growth promotion in aquaculture. However, the mode of action of probiotics in fish in terms of growth promotion remains unclear. The objective of the present study was to investigate growth promotion effect of dietary administration of host-derived probiotics, Lactococcus lactis WFLU12, on olive flounder compared to control group fed with basal diet by analyzing their intestinal and serum metabolome using capillary electrophoresis mass spectrometry with time-of flight (CE-TOFMS). Results of CE-TOFMS revealed that 53 out of 200 metabolites from intestinal luminal metabolome and 5 out of 171 metabolites from serum metabolome, respectively, were present in significantly higher concentrations in the probiotic-fed group than those in the control group. Concentrations of metabolites such as citrulline, tricarboxylic acid cycle (TCA) intermediates, short chain fatty acids, vitamins, and taurine were significantly higher in the probiotic-fed group than those in the control group. The probiotic strain WFLU12 also possesses genes encoding enzymes to help produce these metabolites. Therefore, it is highly likely that these increased metabolites linked to growth promotion in olive flounder are due to supplementation of the probiotic strain. To the best of our knowledge, this is the first study to show that dietary probiotics can greatly influence metabolome in fish. Findings of the present study may reveal important implications for maximizing the efficiency of using dietary additives to optimize fish health and growth.

Weight loss moderately affects the mixed meal challenge response of the plasma metabolome and transcriptome of peripheral blood mononuclear cells in abdominally obese subjects

INTRODUCTION

The response to dietary challenges has been proposed as a more accurate measure of metabolic health than static measurements performed in the fasted state. This has prompted many groups to explore the potential of dietary challenge tests for assessment of diet and lifestyle induced shifts in metabolic phenotype.

OBJECTIVES

We examined whether the response to a mixed-meal challenge could provide a readout for a weight loss (WL)-induced phenotype shift in abdominally obese male subjects. The underlying assumption of a mixed meal challenge is that it triggers all aspects of phenotypic flexibility and provokes a more prolonged insulin response, possibly allowing for better differentiation between individuals.

METHODS

Abdominally obese men (n = 29, BMI = 30.3 ± 2.4 kg/m²) received a mixed-meal challenge prior to and after an 8-week WL or no-WL control intervention. Lean subjects (n = 15, BMI = 23.0 ± 2.0 kg/m²) only received the mixed meal challenge at baseline to have a benchmark for WL-induced phenotype shifts.

RESULTS

Levels of several plasma metabolites were significantly different between lean and abdominally obese at baseline as well as during postprandial metabolic responses. Genes related to oxidative phosphorylation in peripheral blood mononuclear cells (PBMCs) were expressed at higher levels in abdominally obese subjects as compared to lean subjects at fasting, which was partially reverted after WL. The impact of WL on the postprandial response was modest, both at the metabolic and gene expression level in PBMCs.

CONCLUSION

We conclude that mixed-meal challenges are not necessarily superior to measurements in the fasted state to assess metabolic health. Furthermore, the mechanisms accounting for the observed differences between lean and abdominally obese in the fasted state are different from those underlying the dissimilarity observed during the postprandial response.

Time-course changes in circulating branched-chain amino acid levels and metabolism in obese Yucatan minipig

OBJECTIVES

High-fat high-sucrose diet (HFHS) overfeeding is one of the main factors responsible for the increased prevalence of metabolic disorders. Elevated levels of branched-chain amino acids (BCAAs) have been associated with metabolic dysfunctions, including insulin resistance (IR). The aim of this study was to elucidate whether elevated BCAA levels are the cause or the consequence of IR and to determine the mechanisms and tissues involved in such a phenotype.

METHODS

We performed a 2-mo follow-up on minipigs overfed an HFHS diet and focused on kinetics fasting and postprandial (PP) BCAA levels and BCAA catabolism in key tissues.

RESULTS

The study of the fasting BCAA elevation reveals that BCAA accumulation in the plasma compartment is well correlated with IR markers and body weight. Furthermore, the PP excursion of BCAA levels after the last HFHS meal was exacerbated when compared with that of the first meal, suggesting a reduced amino acid oxidation potential. Although only minor changes in BCAA metabolism were observed in liver, muscle, and the visceral adipose tissue, the oxidative deamination potential of the subcutaneous adipose tissue was blunted after 60 d of HFHS feeding.

CONCLUSIONS

To our knowledge, the present results demonstrated for the first time in a swine model of obesity and IR, the existence of a phenotype related to high-circulating BCAA levels and metabolic dysregulation. The oxidative BCAA capacity reduction specifically in the subcutaneous adipose tissue emerges, at least in the present swine model, as the more plausible metabolic explanation for the elevated blood BCAA phenotype.

Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies

Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.

Phenotype-driven identification of modules in a hierarchical map of multifluid metabolic correlations

The identification of phenotype-driven network modules in complex, multifluid metabolomics data poses a considerable challenge for statistical analysis and result interpretation. This is the case for phenotypes with only few associations ('sparse' effects), but, in particular, for phenotypes with a large number of metabolite associations ('dense' effects). Herein, we postulate that examining the data at different layers of resolution, from metabolites to pathways, will facilitate the interpretation of modules for both the sparse and the dense cases. We propose an approach for the phenotype-driven identification of modules on multifluid networks based on untargeted metabolomics data of plasma, urine, and saliva samples from the German Study of Health in Pomerania (SHIP-TREND) study. We generated a hierarchical, multifluid map of metabolism covering both metabolite and pathway associations using Gaussian graphical models. First, this map facilitates a fundamental understanding of metabolism within and across fluids for our study, and can serve as a valuable and downloadable resource. Second, based on this map, we then present an algorithm to identify regulated modules that associate with factors such as gender and insulin-like growth factor I (IGF-I) as examples of traits with dense and sparse associations, respectively. We found IGF-I to associate at the rather fine-grained metabolite level, while gender shows well-interpretable associations at pathway level. Our results confirm that a holistic and interpretable view of metabolic changes associated with a phenotype can only be obtained if different layers of metabolic resolution from multiple body fluids are considered.

Metabolism consists of complex interactions across various organs and body fluids, which poses a substantial challenge for the analysis of metabolic data. To address this problem, Jan Krumsiek from Helmholtz Zentrum München and colleagues used metabolomics measurements of plasma, urine, and saliva from 1000 people to statistically reconstruct a map of interactions in human metabolism. Based on this map, a novel approach that identifies highly correlated biochemical modules that are associated with a given phenotype, was tested for gender and insulin-like growth factor I (IGF-I). The identified modules provided insights into the interaction between metabolome and phenotype that reach beyond what can be found by commonly used statistical approaches for metabolomics. The approach is generic and can be readily applied to new datasets by other colleagues from the field.

Low level activity thresholds for changes in NMR biomarkers and genes in high risk subjects for Type 2 Diabetes

Our objectives were to determine if there are quantitative associations between amounts and intensities of physical activities (PA) on NMR biomarkers and changes in skeletal muscle gene expressions in subjects with high risk for type 2 diabetes (T2D) performing a 3-month PA intervention. We found that PA was associated with beneficial biomarker changes in a factor containing several VLDL and HDL subclasses and lipids in principal component analysis (P = <0.01). Division of PA into quartiles demonstrated significant changes in NMR biomarkers in the 2nd - 4th quartiles compared to the 1st quartile representing PA of less than 2850 daily steps (P = 0.0036). Mediation analysis of PA-related reductions in lipoproteins showed that the effects of PA was 4-15 times greater than those of body weight or fat mass reductions. In a subset study in highly active subjects' gene expressions of oxidative fiber markers, Apo D, and G0/G1 Switch Gene 2, controlling insulin signaling and glucose metabolism were significantly increased. Slow walking at speeds of 2-3 km/h exceeding 2895 steps/day attenuated several circulating lipoprotein lipids. The effects were mediated rather by PA than body weight or fat loss. Thus, lower thresholds for PA may exist for long term prevention of cardio-metabolic diseases in sedentary overweight subjects.

Metabolomic Profiling of Long-Term Weight Change: Role of Oxidative Stress and Urate Levels in Weight Gain

OBJECTIVE

To investigate the association between long-term weight change and blood metabolites.

METHODS

Change in BMI over 8.6 ± 3.79 years was assessed in 3,176 females from the TwinsUK cohort (age range: 18.3-79.6, baseline BMI: 25.11 ± 4.35) measured for 280 metabolites at follow-up. Statistically significant metabolites (adjusting for covariates) were included in a multivariable least absolute shrinkage and selection operator (LASSO) model. Findings were replicated in the Cooperative Health Research in the Region of Augsburg (KORA) study (n = 1,760; age range: 25-70, baseline BMI: 27.72 ± 4.53). The study examined whether the metabolites identified could prospectively predict weight change in KORA and in the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) study (n = 471; age range: 55-74, baseline BMI: 27.24 ± 5.37).

RESULTS

Thirty metabolites were significantly associated with change in BMI per year in TwinsUK using Bonferroni correction. Four were independently associated with weight change in the multivariable LASSO model and replicated in KORA: namely, urate (meta-analysis β [95% CI] = 0.05 [0.040 to 0.063]; P = 1.37 × 10^-19 ), gamma-glutamyl valine (β [95% CI] = 0.06 [0.046 to 0.070]; P = 1.23 × 10^-20 ), butyrylcarnitine (β [95% CI] = 0.04 [0.028 to 0.051]; P = 6.72 × 10^-12 ), and 3-phenylpropionate (β [95% CI] = -0.03 [-0.041 to -0.019]; P = 9.8 × 10^-8 ), all involved in oxidative stress. Higher levels of urate at baseline were associated with weight gain in KORA and PLCO.

CONCLUSIONS

Metabolites linked to higher oxidative stress are associated with increased long-term weight gain.

Epidemiology in Germany-general development and personal experience

Did you ever hear about epidemiology in Germany? Starting from an epidemiological desert the discipline has grown remarkably, especially during the last 10-15 years: research institutes have been established, research funding has improved, multiple curriculae in Epidemiology and Public Health are offered. This increase has been quite steep, and now the epidemiological infrastructure is much better. Several medium-sized and even big population cohorts are ongoing, and the number and quality of publications from German epidemiologists has reached a respectable level. My own career in epidemiology started in the field of environmental health. After German reunification I concentrated for many years on environmental problems in East Germany and observed the health benefits after improvement of the situation. Later, I concentrated on population-based cohorts in newborns (GINI/LISA) and adults (KORA, German National Cohort), and on biobanking. This Essay describes the development in Germany after worldwar 2, illustrated by examples of research results and build-up of epidemiological infractructures worth mentioning.

An interaction map of circulating metabolites, immune gene networks, and their genetic regulation

BACKGROUND

Immunometabolism plays a central role in many cardiometabolic diseases. However, a robust map of immune-related gene networks in circulating human cells, their interactions with metabolites, and their genetic control is still lacking. Here, we integrate blood transcriptomic, metabolomic, and genomic profiles from two population-based cohorts (total N = 2168), including a subset of individuals with matched multi-omic data at 7-year follow-up.

RESULTS

We identify topologically replicable gene networks enriched for diverse immune functions including cytotoxicity, viral response, B cell, platelet, neutrophil, and mast cell/basophil activity. These immune gene modules show complex patterns of association with 158 circulating metabolites, including lipoprotein subclasses, lipids, fatty acids, amino acids, small molecules, and CRP. Genome-wide scans for module expression quantitative trait loci (mQTLs) reveal five modules with mQTLs that have both cis and trans effects. The strongest mQTL is in ARHGEF3 (rs1354034) and affects a module enriched for platelet function, independent of platelet counts. Modules of mast cell/basophil and neutrophil function show temporally stable metabolite associations over 7-year follow-up, providing evidence that these modules and their constituent gene products may play central roles in metabolic inflammation. Furthermore, the strongest mQTL in ARHGEF3 also displays clear temporal stability, supporting widespread trans effects at this locus.

CONCLUSIONS

This study provides a detailed map of natural variation at the blood immunometabolic interface and its genetic basis, and may facilitate subsequent studies to explain inter-individual variation in cardiometabolic disease.

Erythritol is a pentose-phosphate pathway metabolite and associated with adiposity gain in young adults

Metabolomic markers associated with incident central adiposity gain were investigated in young adults. In a 9-mo prospective study of university freshmen (n = 264). Blood samples and anthropometry measurements were collected in the first 3 d on campus and at the end of the year. Plasma from individuals was pooled by phenotype [incident central adiposity, stable adiposity, baseline hemoglobin A1c (HbA1c) > 5.05%, HbA1c < 4.92%] and assayed using GC-MS, chromatograms were analyzed using MetaboliteDetector software, and normalized metabolite levels were compared using Welch's t test. Assays were repeated using freshly prepared pools, and statistically significant metabolites were quantified in a targeted GC-MS approach. Isotope tracer studies were performed to determine if the potential marker was an endogenous human metabolite in men and in whole blood. Participants with incident central adiposity gain had statistically significantly higher blood erythritol [P < 0.001, false discovery rate (FDR) = 0.0435], and the targeted assay revealed 15-fold [95% confidence interval (CI): 13.27, 16.25] higher blood erythritol compared with participants with stable adiposity. Participants with baseline HbA1c > 5.05% had 21-fold (95% CI: 19.84, 21.41) higher blood erythritol compared with participants with lower HbA1c (P < 0.001, FDR = 0.00016). Erythritol was shown to be synthesized endogenously from glucose via the pentose-phosphate pathway (PPP) in stable isotope-assisted ex vivo blood incubation experiments and through in vivo conversion of erythritol to erythronate in stable isotope-assisted dried blood spot experiments. Therefore, endogenous production of erythritol from glucose may contribute to the association between erythritol and obesity observed in young adults.

Machine learning and systems genomics approaches for multi-omics data

In light of recent advances in biomedical computing, big data science, and precision medicine, there is a mammoth demand for establishing algorithms in machine learning and systems genomics (MLSG), together with multi-omics data, to weigh probable phenotype-genotype relationships. Software frameworks in MLSG are extensively employed to analyze hundreds of thousands of multi-omics data by high-throughput technologies. In this study, we reviewed the MLSG software frameworks and future directions with respect to multi-omics data analysis and integration. Our review was targeted at researching recent approaches and technical solutions for the MLSG software frameworks using multi-omics platforms.

Proteomics reveals the effects of sustained weight loss on the human plasma proteome

Sustained weight loss is a preferred intervention in a wide range of metabolic conditions, but the effects on an individual's health state remain ill‐defined. Here, we investigate the plasma proteomes of a cohort of 43 obese individuals that had undergone 8 weeks of 12% body weight loss followed by a year of weight maintenance. Using mass spectrometry‐based plasma proteome profiling, we measured 1,294 plasma proteomes. Longitudinal monitoring of the cohort revealed individual‐specific protein levels with wide‐ranging effects of losing weight on the plasma proteome reflected in 93 significantly affected proteins. The adipocyte‐secreted SERPINF1 and apolipoprotein APOF1 were most significantly regulated with fold changes of −16% and +37%, respectively (P < 10⁻¹³), and the entire apolipoprotein family showed characteristic differential regulation. Clinical laboratory parameters are reflected in the plasma proteome, and eight plasma proteins correlated better with insulin resistance than the known marker adiponectin. Nearly all study participants benefited from weight loss regarding a ten‐protein inflammation panel defined from the proteomics data. We conclude that plasma proteome profiling broadly evaluates and monitors intervention in metabolic diseases.

Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations

The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era.

Characterization of the metabolic profile associated with serum 25-hydroxyvitamin D: a cross-sectional analysis in population-based data

BACKGROUND

Numerous observational studies have observed associations between vitamin D deficiency and cardiometabolic diseases, but these findings might be confounded by obesity. A characterization of the metabolic profile associated with serum 25-hydroxyvitamin D [25(OH)D] levels, in general and stratified by abdominal obesity, may help to untangle the relationship between vitamin D, obesity and cardiometabolic health.

METHODS

Serum metabolomics measurements were obtained from a nuclear magnetic resonance spectroscopy (NMR)- and a mass spectrometry (MS)-based platform. The discovery was conducted in 1726 participants of the population-based KORA-F4 study, in which the associations of the concentrations of 415 metabolites with 25(OH)D levels were assessed in linear models. The results were replicated in 6759 participants (NMR) and 609 (MS) participants, respectively, of the population-based FINRISK 1997 study.

RESULTS

Mean [standard deviation (SD)] 25(OH)D levels were 15.2 (7.5) ng/ml in KORA F4 and 13.8 (5.9) ng/ml in FINRISK 1997; 37 metabolites were associated with 25(OH)D in KORA F4 at P < 0.05/415. Of these, 30 associations were replicated in FINRISK 1997 at P < 0.05/37. Among these were constituents of (very) large very-low-density lipoprotein and small low-density lipoprotein subclasses and related measures like serum triglycerides as well as fatty acids and measures reflecting the degree of fatty acid saturation. The observed associations were independent of waist circumference and generally similar in abdominally obese and non-obese participants.

CONCLUSIONS

Independently of abdominal obesity, higher 25(OH)D levels were associated with a metabolite profile characterized by lower concentrations of atherogenic lipids and a higher degree of fatty acid polyunsaturation. These results indicate that the relationship between vitamin D deficiency and cardiometabolic diseases is unlikely to merely reflect obesity-related pathomechanisms.