Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data

Adjusting for covariates and assessing modeling fitness in machine learning using MUVR2

Motivation

Machine learning (ML) methods are frequently used in Omics research to examine associations between molecular data and for example exposures and health conditions. ML is also used for feature selection to facilitate biological interpretation. Our previous MUVR algorithm was shown to generate predictions and variable selections at state-of-the-art performance. However, a general framework for assessing modeling fitness is still lacking. In addition, enabling to adjust for covariates is a highly desired, but largely lacking trait in ML. We aimed to address these issues in the new MUVR2 framework.

Results

The MUVR2 algorithm was developed to include the regularized regression framework elastic net in addition to partial least squares and random forest modeling. Compared with other cross-validation strategies, MUVR2 consistently showed state-of-the-art performance, including variable selection, while minimizing overfitting. Testing on simulated and real-world data, we also showed that MUVR2 allows for the adjustment for covariates using elastic net modeling, but not using partial least squares or random forest.

Availability and implementation

Algorithms, data, scripts, and a tutorial are open source under GPL-3 license and available in the MUVR2 R package at https://github.com/MetaboComp/MUVR2.

Altered serum metabolome as an indicator of paraneoplasia or concomitant cancer in patients with rheumatic disease

OBJECTIVES

A timely diagnosis is imperative for curing cancer. However, in patients with rheumatic musculoskeletal diseases (RMDs) or paraneoplastic syndromes, misleading symptoms frequently delay cancer diagnosis. As metabolic remodelling characterises both cancer and RMD, we analysed if a metabolic signature can indicate paraneoplasia (PN) or reveal concomitant cancer in patients with RMD.

METHODS

Metabolic alterations in the sera of rheumatoid arthritis (RA) patients with (n=56) or without (n=52) a history of invasive cancer were quantified by nuclear magnetic resonance analysis. Metabolites indicative of cancer were determined by multivariable regression analyses. Two independent RA and spondyloarthritis (SpA) cohorts with or without a history of invasive cancer were used for blinded validation. Samples from patients with active cancer or cancer treatment, pulmonary and lymphoid type cancers, paraneoplastic syndromes, non-invasive (NI) precancerous lesions and non-melanoma skin cancer and systemic lupus erythematosus and samples prior to the development of malignancy were used to test the model performance.

RESULTS

Based on the concentrations of acetate, creatine, glycine, formate and the lipid ratio L1/L6, a diagnostic model yielded a high sensitivity and specificity for cancer diagnosis with AUC=0.995 in the model cohort, AUC=0.940 in the blinded RA validation cohort and AUC=0.928 in the mixed RA/SpA cohort. It was equally capable of identifying cancer in patients with PN. The model was insensitive to common demographic or clinical confounders or the presence of NI malignancy like non-melanoma skin cancer.

CONCLUSIONS

This new set of metabolic markers reliably predicts the presence of cancer in arthritis or PN patients with high sensitivity and specificity and has the potential to facilitate a rapid and correct diagnosis of malignancy.

Metabolomic-Based Approaches for Endometrial Cancer Diagnosis and Prognosis: A Review

Endometrial cancer, the most prevalent gynecological malignancy in developed countries, is experiencing a sustained rise in both its incidence and mortality rates, primarily attributed to extended life expectancy and lifestyle factors. Currently, the absence of precise diagnostic tools hampers the effective management of the expanding population of women at risk of developing this disease. Furthermore, patients diagnosed with endometrial cancer require precise risk stratification to align with optimal treatment planning. Metabolomics technology offers a unique insight into the molecular landscape of endometrial cancer, providing a promising approach to address these unmet needs. This comprehensive literature review initiates with an overview of metabolomic technologies and their intrinsic workflow components, aiming to establish a fundamental understanding for the readers. Subsequently, a detailed exploration of the existing body of research is undertaken with the objective of identifying metabolite biomarkers capable of enhancing current strategies for endometrial cancer diagnosis, prognosis, and recurrence monitoring. Metabolomics holds vast potential to revolutionize the management of endometrial cancer by providing accuracy and valuable insights into crucial aspects.

The metabolic interplay between dietary carbohydrate and exercise and its role in acute appetite regulation in males: a randomized controlled study

An understanding of the metabolic determinants of postexercise appetite regulation would facilitate development of adjunctive therapeutics to suppress compensatory eating behaviours and improve the efficacy of exercise as a weight-loss treatment. Metabolic responses to acute exercise are, however, dependent on pre-exercise nutritional practices, including carbohydrate intake. We therefore aimed to determine the interactive effects of dietary carbohydrate and exercise on plasma hormonal and metabolite responses and explore mediators of exercise-induced changes in appetite regulation across nutritional states. In this randomized crossover study, participants completed four 120 min visits: (i) control (water) followed by rest; (ii) control followed by exercise (30 min at ∼75% of maximal oxygen uptake); (iii) carbohydrate (75 g maltodextrin) followed by rest; and (iv) carbohydrate followed by exercise. An ad libitum meal was provided at the end of each 120 min visit, with blood sample collection and appetite assessment performed at predefined intervals. We found that dietary carbohydrate and exercise exerted independent effects on the hormones glucagon-like peptide 1 (carbohydrate, 16.8 pmol/L; exercise, 7.4 pmol/L), ghrelin (carbohydrate, -48.8 pmol/L; exercise: -22.7 pmol/L) and glucagon (carbohydrate, 9.8 ng/L; exercise, 8.2 ng/L) that were linked to the generation of distinct plasma 1 H nuclear magnetic resonance metabolic phenotypes. These metabolic responses were associated with changes in appetite and energy intake, and plasma acetate and succinate were subsequently identified as potential novel mediators of exercise-induced appetite and energy intake responses. In summary, dietary carbohydrate and exercise independently influence gastrointestinal hormones associated with appetite regulation. Future work is warranted to probe the mechanistic importance of plasma acetate and succinate in postexercise appetite regulation. KEY POINTS: Carbohydrate and exercise independently influence key appetite-regulating hormones. Temporal changes in postexercise appetite are linked to acetate, lactate and peptide YY. Postexercise energy intake is associated with glucagon-like peptide 1 and succinate levels.

Pathobionts in the tumour microbiota predict survival following resection for colorectal cancer

BACKGROUND AND AIMS

The gut microbiota is implicated in the pathogenesis of colorectal cancer (CRC). We aimed to map the CRC mucosal microbiota and metabolome and define the influence of the tumoral microbiota on oncological outcomes.

METHODS

A multicentre, prospective observational study was conducted of CRC patients undergoing primary surgical resection in the UK (n = 74) and Czech Republic (n = 61). Analysis was performed using metataxonomics, ultra-performance liquid chromatography-mass spectrometry (UPLC-MS), targeted bacterial qPCR and tumour exome sequencing. Hierarchical clustering accounting for clinical and oncological covariates was performed to identify clusters of bacteria and metabolites linked to CRC. Cox proportional hazards regression was used to ascertain clusters associated with disease-free survival over median follow-up of 50 months.

RESULTS

Thirteen mucosal microbiota clusters were identified, of which five were significantly different between tumour and paired normal mucosa. Cluster 7, containing the pathobionts Fusobacterium nucleatum and Granulicatella adiacens, was strongly associated with CRC (P_FDR = 0.0002). Additionally, tumoral dominance of cluster 7 independently predicted favourable disease-free survival (adjusted p = 0.031). Cluster 1, containing Faecalibacterium prausnitzii and Ruminococcus gnavus, was negatively associated with cancer (P_FDR = 0.0009), and abundance was independently predictive of worse disease-free survival (adjusted p = 0.0009). UPLC-MS analysis revealed two major metabolic (Met) clusters. Met 1, composed of medium chain (MCFA), long-chain (LCFA) and very long-chain (VLCFA) fatty acid species, ceramides and lysophospholipids, was negatively associated with CRC (P_FDR = 2.61 × 10^-11); Met 2, composed of phosphatidylcholine species, nucleosides and amino acids, was strongly associated with CRC (P_FDR = 1.30 × 10^-12), but metabolite clusters were not associated with disease-free survival (p = 0.358). An association was identified between Met 1 and DNA mismatch-repair deficiency (p = 0.005). FBXW7 mutations were only found in cancers predominant in microbiota cluster 7.

CONCLUSIONS

Networks of pathobionts in the tumour mucosal niche are associated with tumour mutation and metabolic subtypes and predict favourable outcome following CRC resection. Video Abstract.

Demographic, Health and Lifestyle Factors Associated with the Metabolome in Older Women

Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women’s Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2–0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings.

Controlling the confounding effect of metabolic gene expression to identify actual metabolite targets in microsatellite instability cancers

BACKGROUND

The metabolome is the best representation of cancer phenotypes. Gene expression can be considered a confounding covariate affecting metabolite levels. Data integration across metabolomics and genomics to establish the biological relevance of cancer metabolism is challenging. This study aimed to eliminate the confounding effect of metabolic gene expression to reflect actual metabolite levels in microsatellite instability (MSI) cancers.

METHODS

In this study, we propose a new strategy using covariate-adjusted tensor classification in high dimensions (CATCH) models to integrate metabolite and metabolic gene expression data to classify MSI and microsatellite stability (MSS) cancers. We used datasets from the Cancer Cell Line Encyclopedia (CCLE) phase II project and treated metabolomic data as tensor predictors and data on gene expression of metabolic enzymes as confounding covariates.

RESULTS

The CATCH model performed well, with high accuracy (0.82), sensitivity (0.66), specificity (0.88), precision (0.65), and F1 score (0.65). Seven metabolite features adjusted for metabolic gene expression, namely, 3-phosphoglycerate, 6-phosphogluconate, cholesterol ester, lysophosphatidylethanolamine (LPE), phosphatidylcholine, reduced glutathione, and sarcosine, were found in MSI cancers. Only one metabolite, Hippurate, was present in MSS cancers. The gene expression of phosphofructokinase 1 (PFKP), which is involved in the glycolytic pathway, was related to 3-phosphoglycerate. ALDH4A1 and GPT2 were associated with sarcosine. LPE was associated with the expression of CHPT1, which is involved in lipid metabolism. The glycolysis, nucleotide, glutamate, and lipid metabolic pathways were enriched in MSI cancers.

CONCLUSIONS

We propose an effective CATCH model for predicting MSI cancer status. By controlling the confounding effect of metabolic gene expression, we identified cancer metabolic biomarkers and therapeutic targets. In addition, we provided the possible biology and genetics of MSI cancer metabolism.

Characterizing the metabolic effects of the selective inhibition of gut microbial β-glucuronidases in mice

The hydrolysis of xenobiotic glucuronides by gut bacterial glucuronidases reactivates previously detoxified compounds resulting in severe gut toxicity for the host. Selective bacterial β-glucuronidase inhibitors can mitigate this toxicity but their impact on wider host metabolic processes has not been studied. To investigate this the inhibitor 4-(8-(piperazin-1-yl)-1,2,3,4-tetrahydro-[1,2,3]triazino[4',5':4,5]thieno[2,3-c]isoquinolin-5-yl)morpholine (UNC10201652, Inh 9) was administered to mice to selectively inhibit a narrow range of bacterial β-glucuronidases in the gut. The metabolomic profiles of the intestinal contents, biofluids, and several tissues involved in the enterohepatic circulation were measured and compared to control animals. No biochemical perturbations were observed in the plasma, liver or gall bladder. In contrast, the metabolite profiles of urine, colon contents, feces and gut wall were altered compared to the controls. Changes were largely restricted to compounds derived from gut microbial metabolism. This work establishes that inhibitors targeted towards bacterial β-glucuronidases modulate the functionality of the intestinal microbiota without adversely impacting the host metabolic system.

Multi-omic phenotyping reveals host-microbe responses to bariatric surgery, glycaemic control and obesity

Background

Resolution of type 2 diabetes (T2D) is common following bariatric surgery, particularly Roux-en-Y gastric bypass. However, the underlying mechanisms have not been fully elucidated.

Methods

To address this we compare the integrated serum, urine and faecal metabolic profiles of participants with obesity ± T2D (n = 80, T2D = 42) with participants who underwent Roux-en-Y gastric bypass or sleeve gastrectomy (pre and 3-months post-surgery; n = 27), taking diet into account. We co-model these data with shotgun metagenomic profiles of the gut microbiota to provide a comprehensive atlas of host-gut microbe responses to bariatric surgery, weight-loss and glycaemic control at the systems level.

Results

Here we show that bariatric surgery reverses several disrupted pathways characteristic of T2D. The differential metabolite set representative of bariatric surgery overlaps with both diabetes (19.3% commonality) and body mass index (18.6% commonality). However, the percentage overlap between diabetes and body mass index is minimal (4.0% commonality), consistent with weight-independent mechanisms of T2D resolution. The gut microbiota is more strongly correlated to body mass index than T2D, although we identify some pathways such as amino acid metabolism that correlate with changes to the gut microbiota and which influence glycaemic control.

Conclusion

We identify multi-omic signatures associated with responses to surgery, body mass index, and glycaemic control. Improved understanding of gut microbiota - host co-metabolism may lead to novel therapies for weight-loss or diabetes. However, further experiments are required to provide mechanistic insight into the role of the gut microbiota in host metabolism and establish proof of causality.

Weight-loss surgery is a highly effective treatment of type 2 diabetes in people with obesity. Interestingly, the improvement in diabetes after weight-loss surgery occurs before any significant weight-loss. Through better understanding of this metabolic improvement, weight-loss surgery provides a unique avenue to identify novel ways of treating diabetes and obesity. 

Here we combine measurements of metabolism, gut bacteria and diet in people with obesity, with or without type 2 diabetes and in patients before and after weight-loss surgery. We have used these data to identify changes associated with weight-loss surgery, obesity and diabetes. 

Improved understanding of the mechanisms behind these changes, including how changes to gut bacteria influence metabolism, may lead to new treatments for weight-loss or diabetes.

Penney et al. conduct microbial and metabolic profiling in people with obesity, with or without type 2 diabetes, undergoing two types of bariatric surgery. Integrative analysis identifies multi-omic signatures associated with response to surgery, body mass index, and glycaemic control.

Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension

Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing’s syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.

Postnatal prebiotic supplementation in rats affects adult anxious behaviour, hippocampus, electrophysiology, metabolomics, and gut microbiota

We have shown previously that prebiotic (Bimuno galacto-oligosacharides, B-GOS®) administration to neonatal rats increased hippocampal NMDAR proteins. The present study has investigated the effects of postnatal B-GOS® supplementation on hippocampus-dependent behavior in young, adolescent, and adult rats and applied electrophysiological, metabolomic and metagenomic analyses to explore potential underlying mechanisms. The administration of B-GOS® to suckling, but not post-weaned, rats reduced anxious behavior until adulthood. Neonatal prebiotic intake also reduced the fast decay component of hippocampal NMDAR currents, altered age-specific trajectories of the brain, intestinal, and liver metabolomes, and reduced abundance of fecal Enterococcus and Dorea bacteria. Our data are the first to show that prebiotic administration to rats during a specific postnatal period has long-term effects on behavior and hippocampal physiology. The study also suggests that early-life prebiotic intake may affect host brain function through the reduction of stress-related gut bacteria rather than increasing the proliferation of beneficial microbes.

Reflection on modern methods: building causal evidence within high-dimensional molecular epidemiological studies of moderate size

This commentary provides a practical perspective on epidemiological analysis within a single high-dimensional study of moderate size to consider a causal question. In this setting, non-causal confounding is important. This occurs when a factor is a determinant of outcome and the underlying association between exposure and the factor is non-causal. That is, the association arises due to chance, confounding or other bias rather than reflecting that exposure and the factor are causally related. In particular, the influence of technical processing factors must be accounted for by pre-processing measures to remove artefact or to control for these factors such as batch run. Work steps include the evaluation of alternative non-causal explanations for observed exposure-disease associations and strategies to obtain the highest level of causal inference possible within the study. A systematic approach is required to work through a question set and obtain insights on not only the exposure-disease association but also the multifactorial causal structure of the underlying data where possible. The appropriate inclusion of molecular findings will enhance the quest to better understand multifactorial disease causation in modern observational epidemiological studies.

Metabolomic Profile in Venous Thromboembolism (VTE)

Venous thromboembolism (VTE) is a condition comprising deep venous thrombosis (DVT) and pulmonary embolism (PE). The prevalence of this disease is constantly increasing and it is also a chief reason for morbidity. Therefore, the primary prevention of VTE remains a highly important public health issue. At present, its diagnosis generally relies on subjective clinical examination and ultrasound imaging. D-dimer is also used as a biomarker, but it is considered to be poorly specific and only moderately sensitive. There are also no reliable methods that could accurately guide the type of treatment and potentially identify patients who may benefit from more aggressive therapies without the risk of bleeding. The application of metabolomics profiling in the area of vascular diseases may become a turning point in early diagnosis and patient management. Among the most described metabolites possibly related to VTE are carnitine species, glucose, phenylalanine, 3-hydroxybutarate, lactic acid, tryptophan and some monounsaturated and polyunsaturated fatty acids. The cell response to acute PE was suggested to involve the uncoupling between glycolysis and oxidative phosphorylation. Despite technological advancement in the identification of metabolites and their alteration in thrombosis, we still do not understand the mechanisms and pathways responsible for the occurrence of observed alterations.

Statistical analysis in metabolic phenotyping

Metabolic phenotyping is an important tool in translational biomedical research. The advanced analytical technologies commonly used for phenotyping, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, generate complex data requiring tailored statistical analysis methods. Detailed protocols have been published for data acquisition by liquid NMR, solid-state NMR, ultra-performance liquid chromatography (LC-)MS and gas chromatography (GC-)MS on biofluids or tissues and their preprocessing. Here we propose an efficient protocol (guidelines and software) for statistical analysis of metabolic data generated by these methods. Code for all steps is provided, and no prior coding skill is necessary. We offer efficient solutions for the different steps required within the complete phenotyping data analytics workflow: scaling, normalization, outlier detection, multivariate analysis to explore and model study-related effects, selection of candidate biomarkers, validation, multiple testing correction and performance evaluation of statistical models. We also provide a statistical power calculation algorithm and safeguards to ensure robust and meaningful experimental designs that deliver reliable results. We exemplify the protocol with a two-group classification study and data from an epidemiological cohort; however, the protocol can be easily modified to cover a wider range of experimental designs or incorporate different modeling approaches. This protocol describes a minimal set of analyses needed to rigorously investigate typical datasets encountered in metabolic phenotyping.

Multiomic features associated with mucosal healing and inflammation in paediatric Crohn's disease

BACKGROUND

The gastrointestinal microbiota has an important role in mucosal immune homoeostasis and may contribute to maintaining mucosal healing in Crohn's disease (CD).

AIM

To identify changes in the microbiota, metabolome and protease activity associated with mucosal healing in established paediatric CD METHODS: Twenty-five participants aged 3-18 years with CD, disease duration of over 6 months, and maintenance treatment with biological therapy were recruited. They were divided into a low calprotectin group (faecal calprotectin <100 μg/g, "mucosal healing," n = 11), and a high calprotectin group (faecal calprotectin >100 μg/g, "mucosal inflammation," n = 11). 16S gene-based metataxonomics, ¹ H-NMR spectroscopy-based metabolic profiling and protease activity assays were performed on stool samples.

RESULTS

Relative abundance of Dialister species was six-times greater in the low calprotectin group (q = 0.00999). Alpha and beta diversity, total protease activity and inferred metagenomic profiles did not differ between groups. Pentanoate (valerate) and lysine were principal discriminators in a machine-learning model which differentiated high and low calprotectin samples using NMR spectra (R² 0.87, Q² 0.41). Mean relative concentration of pentanoate was 1.35-times greater in the low calprotectin group (95% CI 1.03-1.68, P = 0.036) and was positively correlated with Dialister. Mean relative concentration of lysine was 1.54-times greater in the high calprotectin group (95% CI 1.05-2.03, P = 0.028).

CONCLUSIONS

This multiomic study identified an increase in Dialister species and pentanoate, and a decrease in lysine, in patients with "mucosal healing." It supports further investigation of these as potential novel therapeutic targets in CD.

Longitudinal Multi-omics Reveals Subset-Specific Mechanisms Underlying Irritable Bowel Syndrome

The gut microbiome has been implicated in multiple human chronic gastrointestinal (GI) disorders. Determining its mechanistic role in disease has been difficult due to apparent disconnects between animal and human studies and lack of an integrated multi-omics view of disease-specific physiological changes. We integrated longitudinal multi-omics data from the gut microbiome, metabolome, host epigenome, and transcriptome in the context of irritable bowel syndrome (IBS) host physiology. We identified IBS subtype-specific and symptom-related variation in microbial composition and function. A subset of identified changes in microbial metabolites correspond to host physiological mechanisms that are relevant to IBS. By integrating multiple data layers, we identified purine metabolism as a novel host-microbial metabolic pathway in IBS with translational potential. Our study highlights the importance of longitudinal sampling and integrating complementary multi-omics data to identify functional mechanisms that can serve as therapeutic targets in a comprehensive treatment strategy for chronic GI diseases. VIDEO ABSTRACT.

Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study

BACKGROUND

Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D.

METHODS

We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n = 403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n = 458 individuals with new onset of T2D. A dietary metabolite profile model (T_pred) was constructed using multivariable regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous T_pred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models.

FINDINGS

A higher T_pred score was associated with healthier diets high in wholegrain (β=3.36 g, 95% CI 0.31, 6.40 and β=2.82 g, 95% CI 0.06, 5.57) and lower energy intake (β=-75.53 kcal, 95% CI -144.71, -2.35 and β=-122.51 kcal, 95% CI -186.56, -38.46), and saturated fat (β=-0.92 g, 95% CI -1.56, -0.28 and β=-0.98 g, 95% CI -1.53, -0.42 g), respectively for cohort 1 and 2. In both cohorts a higher T_pred score was also associated with lower total body adiposity and favourable lipid profiles HDL-cholesterol (β=0.07 mmol/L, 95% CI 0.03, 0.1), (β=0.08 mmol/L, 95% CI 0.04, 0.1), and triglycerides (β=-0.1 mmol/L, 95% CI -0.2, -0.03), (β=-0.2 mmol/L, 95% CI -0.3, -0.09), respectively for cohort 1 and 2. In cohort 2, the T_pred score was negatively associated with liver fat (β=-0.74%, 95% CI -0.67, -0.81), and lower fasting concentrations of HbA1c (β=-0.9 mmol/mol, 95% CI -1.5, -0.1), glucose (β=-0.2 mmol/L, 95% CI -0.4, -0.05) and insulin (β=-11.0 pmol/mol, 95% CI -19.5, -2.6). Longitudinal analysis showed at 18-month follow up a higher T_pred score was also associated lower total body adiposity in both cohorts and lower fasting glucose (β=-0.2 mmol/L, 95% CI -0.3, -0.01) and insulin (β=-9.2 pmol/mol, 95% CI -17.9, -0.4) concentrations in cohort 2.

INTERPRETATION

Plasma dietary metabolite profiling provides objective measures of diet intake, showing a relationship to glycaemic deterioration and cardiometabolic health.

FUNDING

This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115,317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies.

Identifying unknown metabolites using NMR-based metabolic profiling techniques

Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes but is hindered by a lack of automated annotation and standardized methods for structure elucidation of candidate disease biomarkers. Here we describe a system for identifying molecular species derived from nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping studies, with detailed information on sample preparation, data acquisition and data modeling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as Statistical Total Correlation Spectroscopy (STOCSY), Subset Optimization by Reference Matching (STORM) and Resolution-Enhanced (RED)-STORM to identify other signals in the NMR spectra relating to the same molecule. It also uses two-dimensional NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multi-dimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take 2 or 3 days. This approach to biomarker discovery is efficient and cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. It requires a basic understanding of MATLAB to use the statistical spectroscopic tools and analytical skills to perform solid phase extraction (SPE), liquid chromatography (LC) fraction collection, LC-NMR-mass spectroscopy and one-dimensional and two-dimensional NMR experiments.

A Two-Way Interaction between Methotrexate and the Gut Microbiota of Male Sprague-Dawley Rats

Methotrexate (MTX) is a chemotherapeutic agent that can cause a range of toxic side effects including gastrointestinal damage, hepatotoxicity, myelosuppression, and nephrotoxicity and has potentially complex interactions with the gut microbiome. Following untargeted UPLC-qtof-MS analysis of urine and fecal samples from male Sprague-Dawley rats administered at either 0, 10, 40, or 100 mg/kg of MTX, dose-dependent changes in the endogenous metabolite profiles were detected. Semiquantitative targeted UPLC-MS detected MTX excreted in urine as well as MTX and two metabolites, 2,4-diamino-N-10-methylpteroic acid (DAMPA) and 7-hydroxy-MTX, in the feces. DAMPA is produced by the bacterial enzyme carboxypeptidase glutamate 2 (CPDG2) in the gut. Microbiota profiling (16S rRNA gene amplicon sequencing) of fecal samples showed an increase in the relative abundance of Firmicutes over the Bacteroidetes at low doses of MTX but the reverse at high doses. Firmicutes relative abundance was positively correlated with DAMPA excretion in feces at 48 h, which were both lower at 100 mg/kg compared to that seen at 40 mg/kg. Overall, chronic exposure to MTX appears to induce community and functionality changes in the intestinal microbiota, inducing downstream perturbations in CPDG2 activity, and thus may delay MTX detoxication to DAMPA. This reduction in metabolic clearance might be associated with increased gastrointestinal toxicity.

Nutriome-metabolome relationships provide insights into dietary intake and metabolism

Dietary assessment traditionally relies on self-reported data which are often inaccurate and may result in erroneous diet-disease risk associations. We illustrate how urinary metabolic phenotyping can be used as alternative approach for obtaining information on dietary patterns. We used two multi-pass 24-hr dietary recalls, obtained on two occasions on average three weeks apart, paired with two 24-hr urine collections from 1,848 U.S. individuals; 67 nutrients influenced the urinary metabotype measured with ¹H-NMR spectroscopy characterized by 46 structurally identified metabolites. We investigated the stability of each metabolite over time and showed that the urinary metabolic profile is more stable within individuals than reported dietary patterns. The 46 metabolites accurately predicted healthy and unhealthy dietary patterns in a free-living U.S. cohort and replicated in an independent U.K. cohort. We mapped these metabolites into a host-microbial metabolic network to identify key pathways and functions. These data can be used in future studies to evaluate how this set of diet-derived, stable, measurable bioanalytical markers are associated with disease risk. This knowledge may give new insights into biological pathways that characterize the shift from a healthy to unhealthy metabolic phenotype and hence give entry points for prevention and intervention strategies.

RETRACTED ARTICLE: Dietary metabotype modelling predicts individual responses to dietary interventions

Habitual consumption of poor quality diets is linked directly to risk factors for many non-communicable diseases. This has resulted in the vast majority of countries and the World Health Organization developing policies for healthy eating to reduce the prevalence of non-communicable diseases in the population. However, there is mounting evidence of variability in individual metabolic responses to any dietary intervention. We have developed a method for applying a pipeline for understanding interindividual differences in response to diet, based on coupling data from highly controlled dietary studies with deep metabolic phenotyping. In this feasibility study, we create an individual Dietary Metabotype Score (DMS) that embodies interindividual variability in dietary response and captures consequent dynamic changes in concentrations of urinary metabolites. We find an inverse relationship between the DMS and blood glucose concentration. There is also a relationship between the DMS and urinary metabolic energy loss. Furthermore, we use a metabolic entropy approach to visualize individual and collective responses to dietary interventions. Potentially, the DMS offers a method to target and to enhance dietary response at the individual level, thereby reducing the burden of non-communicable diseases at the population level.

Use of Large and Diverse Datasets for 1H NMR Serum Metabolic Profiling of Early Lactation Dairy Cows

Most livestock metabolomic studies involve relatively small, homogenous populations of animals. However, livestock farming systems are non-homogenous, and large and more diverse datasets are required to ensure that biomarkers are robust. The aims of this study were therefore to (1) investigate the feasibility of using a large and diverse dataset for untargeted proton nuclear magnetic resonance (¹H NMR) serum metabolomic profiling, and (2) investigate the impact of fixed effects (farm of origin, parity and stage of lactation) on the serum metabolome of early-lactation dairy cows. First, we used multiple linear regression to correct a large spectral dataset (707 cows from 13 farms) for fixed effects prior to multivariate statistical analysis with principal component analysis (PCA). Results showed that farm of origin accounted for up to 57% of overall spectral variation, and nearly 80% of variation for some individual metabolite concentrations. Parity and week of lactation had much smaller effects on both the spectra as a whole and individual metabolites (< 3% and < 20%, respectively). In order to assess the effect of fixed effects on prediction accuracy and biomarker discovery, we used orthogonal partial least squares (OPLS) regression to quantify the relationship between NMR spectra and concentrations of the current gold standard serum biomarker of energy balance, β-hydroxybutyrate (BHBA). Models constructed using data from multiple farms provided reasonably robust predictions of serum BHBA concentration (0.05 ≤ RMSE ≤ 0.18). Fixed effects influenced the results biomarker discovery; however, these impacts could be controlled using the proposed method of linear regression spectral correction.

Metabolic maturation in the first 2 years of life in resource-constrained settings and its association with postnatal growths

Malnutrition continues to affect the growth and development of millions of children worldwide, and chronic undernutrition has proven to be largely refractory to interventions. Improved understanding of metabolic development in infancy and how it differs in growth-constrained children may provide insights to inform more timely, targeted, and effective interventions. Here, the metabolome of healthy infants was compared to that of growth-constrained infants from three continents over the first 2 years of life to identify metabolic signatures of aging. Predictive models demonstrated that growth-constrained children lag in their metabolic maturity relative to their healthier peers and that metabolic maturity can predict growth 6 months into the future. Our results provide a metabolic framework from which future nutritional programs may be more precisely constructed and evaluated.

A prospective cohort analysis of gut microbial co-metabolism in Alaska Native and rural African people at high and low risk of colorectal cancer

BACKGROUND

Alaska Native (AN) people have the world's highest recorded incidence of sporadic colorectal cancer (CRC) (∼91:100,000), whereas rural African (RA) people have the lowest risk (<5:100,000). Previous data supported the hypothesis that diet affected CRC risk through its effects on the colonic microbiota that produce tumor-suppressive or -promoting metabolites.

OBJECTIVES

We investigated whether differences in these metabolites may contribute to the high risk of CRC in AN people.

METHODS

A cross-sectional observational study assessed dietary intake from 32 AN and 21 RA healthy middle-aged volunteers before screening colonoscopy. Analysis of fecal microbiota composition by 16S ribosomal RNA gene sequencing and fecal/urinary metabolites by 1H-NMR spectroscopy was complemented with targeted quantification of fecal SCFAs, bile acids, and functional microbial genes.

RESULTS

Adenomatous polyps were detected in 16 of 32 AN participants, but not found in RA participants. The AN diet contained higher proportions of fat and animal protein and less fiber. AN fecal microbiota showed a compositional predominance of Blautia and Lachnoclostridium, higher microbial capacity for bile acid conversion, and low abundance of some species involved in saccharolytic fermentation (e.g., Prevotellaceae, Ruminococcaceae), but no significant lack of butyrogenic bacteria. Significantly lower concentrations of tumor-suppressive butyrate (22.5 ± 3.1 compared with 47.2 ± 7.3 SEM µmol/g) coincided with significantly higher concentrations of tumor-promoting deoxycholic acid (26.7 ± 4.2 compared with 11 ± 1.9 µmol/g) in AN fecal samples. AN participants had lower quantities of fecal/urinary metabolites than RA participants and metabolite profiles correlated with the abundance of distinct microbial genera in feces. The main microbial and metabolic CRC-associated markers were not significantly altered in AN participants with adenomatous polyps.

CONCLUSIONS

The low-fiber, high-fat diet of AN people and exposure to carcinogens derived from diet or environment are associated with a tumor-promoting colonic milieu as reflected by the high rates of adenomatous polyps in AN participants.

Analysis of NMR Metabolomics Data

In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.