Automated quantum mechanical total line shape fitting model for quantitative NMR-based profiling of human serum metabolites

Metabolite and Lipid Biomarkers Associated With Intraocular Pressure and Inner Retinal Morphology: 1H NMR Spectroscopy Results From the UK Biobank

Purpose

The purpose of this study was to assess metabolites associated with intraocular pressure (IOP) and inner retina structure.

Methods

We cross-sectionally assessed 168 non-fasting plasma metabolites measured by nuclear magnetic resonance (NMR) spectroscopy with IOP (n = 28,195), macular retinal nerve fiber layer thickness (mRNFL; n = 10,584), and macular ganglion cell inner plexiform layer thickness (mGCIPL; n = 10,554) in the UK Biobank. We used multiple linear regression models adjusting for various covariates with probit-transformed metabolite levels as predictors for each outcome. Each estimate represents the difference in outcome variable per standard deviation increase in the probit-transformed metabolite values. We used the number of effective (NEF) tests and false discovery rate (FDR) to adjust for multiple comparisons for metabolites and metabolite classes, respectively.

Results

In individual metabolite analysis, multiple amino acids, especially branched-chain amino acids, were associated with lower IOP (-0.12 mm Hg; 95% confidence interval = -0.16 to -0.07; NEF = 2.7E-05). Albumin, 3 hydroxybutyrate, lactate, and several lipids were associated with higher IOP (range = 0.07 to 0.18 mm Hg, NEF = ≤ 0.039). In IOP-adjusted analyses, five HDL-related metabolites were associated with thinner mRNFL (-0.15 microns for all metabolites, NEF = ≤ 0.027), whereas five LDL-related metabolites were associated with thicker mGCIPL (range = 0.17 to 0.20 microns; NEF = ≤ 0.044). In metabolite class analysis, the lipid components of lipoproteins (cholesterol, triglycerides, etc.) were not associated with our outcomes (FDR > 0.2 for all); yet multiple lipoproteins were significantly (FDR < 0.05) associated with all outcomes.

Conclusions

Branched-chain amino acids were associated with lower IOP, HDL metabolites were associated with thinner mRNFL, and LDL metabolites were associated with thicker mGCIPL.

Plasma metabolite profile for primary open-angle glaucoma in three US cohorts and the UK Biobank

Glaucoma is a progressive optic neuropathy and a leading cause of irreversible blindness worldwide. Primary open-angle glaucoma is the most common form, and yet the etiology of this multifactorial disease is poorly understood. We aimed to identify plasma metabolites associated with the risk of developing POAG in a case-control study (599 cases and 599 matched controls) nested within the Nurses' Health Studies, and Health Professionals' Follow-Up Study. Plasma metabolites were measured with LC-MS/MS at the Broad Institute (Cambridge, MA, USA); 369 metabolites from 18 metabolite classes passed quality control analyses. For comparison, in a cross-sectional study in the UK Biobank, 168 metabolites were measured in plasma samples from 2,238 prevalent glaucoma cases and 44,723 controls using NMR spectroscopy (Nightingale, Finland; version 2020). Here we show higher levels of diglycerides and triglycerides are adversely associated with glaucoma in all four cohorts, suggesting that they play an important role in glaucoma pathogenesis.

Studying Metabolism by NMR-Based Metabolomics

During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.

Biomarker Discovery in Atherosclerotic Diseases Using Quantitative Nuclear Magnetic Resonance Metabolomics

Despite great progress in the management of atherosclerosis (AS), its subsequent cardiovascular disease (CVD) remains the leading cause of morbidity and mortality. This is probably due to insufficient risk detection using routine lipid testing; thus, there is a need for more effective approaches relying on new biomarkers. Quantitative nuclear magnetic resonance (qNMR) metabolomics is able to phenotype holistic metabolic changes, with a unique advantage in regard to quantifying lipid-protein complexes. The rapidly increasing literature has indicated that qNMR-based lipoprotein particle number, particle size, lipid components, and some molecular metabolites can provide deeper insight into atherogenic diseases and could serve as novel promising determinants. Therefore, this article aims to offer an updated review of the qNMR biomarkers of AS and CVD found in epidemiological studies, with a special emphasis on lipoprotein-related parameters. As more researches are performed, we can envision more qNMR metabolite biomarkers being successfully translated into daily clinical practice to enhance the prevention, detection and intervention of atherosclerotic diseases.

Diet-induced weight loss reduces postprandial dicarbonyl stress in abdominally obese men: Secondary analysis of a randomized controlled trial

AIMS

Dicarbonyl compounds contribute to the formation of advanced glycation endproducts (AGEs) and the development of insulin resistance and vascular complications. Dicarbonyl stress may already be detrimental in obesity. We evaluated whether diet-induced weight loss can effectively reverse dicarbonyl stress in abdominally obese men.

MATERIALS AND METHODS

Plasma samples were collected from lean (n = 25) and abdominally obese men (n = 52) in the fasting state, and during a mixed meal test (MMT). Abdominally obese men were randomized to 8 weeks of dietary weight loss or habitual diet, followed by a second MMT. The α-dicarbonyls methylglyoxal (MGO), glyoxal (GO) and 3-deoxyglucosone (3-DG) and AGEs were measured by UPLC-MS/MS. Skin autofluorescence (SAF) was measured using the AGE reader. T-tests were used for the cross-sectional analysis and ANCOVA to assess the treatment effect.

RESULTS

Postprandial glucose, MGO and 3-DG concentrations were higher in obese men as compared to lean men (p < 0.05 for all). Fasting dicarbonyls, AGEs, and SAF were not different between lean and obese men. After the weight loss intervention, fasting MGO levels tended to decrease by 25 nmol/L (95%-CI: -51-0.5; p = 0.054). Postprandial dicarbonyls were decreased after weight loss as compared to the control group: iAUC of MGO decreased by 57% (5280 nmol/L∙min; 95%-CI: 33-10526; p = 0.049), of GO by 66% (11,329 nmol/L∙min; 95%-CI: 495-22162; p = 0.041), and of 3-DG by 45% (20,175 nmol/L∙min; 95%-CI: 5351-35000; p = 0.009). AGEs and SAF did not change significantly after weight loss.

CONCLUSION

Abdominal obesity is characterized by increased postprandial dicarbonyl stress, which can be reduced by a weight loss intervention. Registered under ClinicalTrials.gov Identifier no. NCT01675401.

Effects of a whole diet approach on metabolic flexibility, insulin sensitivity and postprandial glucose responses in overweight and obese adults - A randomized controlled trial

BACKGROUND & AIMS

Metabolic flexibility is the ability to adapt fuel oxidation to fuel availability. Metabolic inflexibility has been associated with obesity, the metabolic syndrome and insulin resistance, and can be improved by exercise or weight loss. Dietary changes can modulate metabolic flexibility; however, the effect of a whole diet approach on metabolic flexibility has never been studied. Therefore, our objective was to assess the effect of a healthy diet (HD), as compared to a typical Western diet (WD), on several fasting and postprandial markers of metabolic flexibility and insulin sensitivity.

METHODS

In this parallel randomized trial, overweight or obese men and women (50-70 years; BMI 25-35 kg/m²) consumed a healthy diet (HD; high in fruits and vegetables, pulses, fibers, nuts, fatty fish, and low in high-glycemic carbohydrates; n = 19) or a typical Western diet (WD; n = 21) for six weeks, following a two-week run-in period. The change in respiratory quotient upon insulin stimulation (ΔRQ), and insulin sensitivity, expressed as the M-value, were both determined with a hyperinsulinemic euglycemic clamp. Additionally, other fasting and postprandial markers of metabolic flexibility were assessed during a 5-h high-fat high-glycemic mixed meal challenge.

RESULTS

ΔRQ (p = 0.730) and insulin sensitivity (p = 0.802) were not significantly affected by diet. Postprandial RQ did also not show significant differences (p = 0.610), whereas postprandial glucose excursions were significantly higher in the HD group at T30 (p = 0.014) and T45 (p = 0.026) after mixed meal ingestion (p = 0.037). Fasting glucose (p = 0.530) and HbA1c (p = 0.124) remained unchanged, whereas decreases in fasting insulin (p = 0.038) and the HOMA-IR (p = 0.050) were significantly more pronounced with the HD.

CONCLUSION

A healthy diet for six weeks, without further life-style changes, did not improve metabolic flexibility and whole-body insulin sensitivity, when compared to a Western-style diet. It remains to be determined whether the short time increase in postprandial glucose is physiologically relevant or detrimental to metabolic health. This trial was registered at clinicaltrials.gov as NCT02519127.

Robust 1D NMR lineshape fitting using real and imaginary data in the frequency domain

Quantitative NMR is intrinsically dependent on precise, accurate, and robust peak area calculation. In this work, we demonstrate how the use of complex-valued peak descriptions can improve peak fitting in the frequency domain - incorporating phase and baseline correction as well as apodization while working with commonly used Fourier-transformed data. The method has been implemented in an open source R package called rnmrfit that is available for download on GitHub (https://github.com/ssokolen/rnmrfit). Application to real data suggests that this approach can also result in dramatically higher precision than can be achieved with existing software. Simulation data indicates that coefficients of variation below 0.1% can be readily achieved at signal to noise (SNR) ratios of approximately 100. The use of complex-valued data in the frequency domain is demonstrated as a relatively simple and effective means of improving peak fitting for quantitative NMR analysis.

The value of universally available raw NMR data for transparency, reproducibility, and integrity in natural product research

Covering: up to 2018With contributions from the global natural product (NP) research community, and continuing the Raw Data Initiative, this review collects a comprehensive demonstration of the immense scientific value of disseminating raw nuclear magnetic resonance (NMR) data, independently of, and in parallel with, classical publishing outlets. A comprehensive compilation of historic to present-day cases as well as contemporary and future applications show that addressing the urgent need for a repository of publicly accessible raw NMR data has the potential to transform natural products (NPs) and associated fields of chemical and biomedical research. The call for advancing open sharing mechanisms for raw data is intended to enhance the transparency of experimental protocols, augment the reproducibility of reported outcomes, including biological studies, become a regular component of responsible research, and thereby enrich the integrity of NP research and related fields.

Global testing of shifts in metabolic phenotype

INTRODUCTION

Current metabolomics approaches to unravel impact of diet- or lifestyle induced phenotype variation and shifts predominantly deploy univariate or multivariate approaches, with a posteriori interpretation at pathway level. This however often provides only a fragmented view on the involved metabolic pathways.

OBJECTIVES

To demonstrate the feasibility of using Goeman's global test (GGT) for assessment of variation and shifts in metabolic phenotype at the level of a priori defined pathways.

METHODS

Two intervention studies with identified phenotype variations and shifts were examined. In a weight loss (WL) intervention study obese subjects received a mixed meal challenge before and after WL. In a polyphenol (PP) intervention study obese subjects received a high fat mixed meal challenge (61E% fat) before and after a PP intervention. Plasma samples were obtained at fasting and during the postprandial response. Besides WL- and PP-induced phenotype shifts, also correlation of plasma metabolome with phenotype descriptors was assessed at pathway level. The plasma metabolome covered organic acids, amino acids, biogenic amines, acylcarnitines and oxylipins.

RESULTS

For the population of the WL study, GGT revealed that HOMA correlated with the fasting levels of the TCA cycle, BCAA catabolism, the lactate, arginine-proline and phenylalanine-tyrosine pathways. For the population of the PP study, HOMA correlated with fasting metabolite levels of TCA cycle, fatty acid oxidation and phenylalanine-tyrosine pathways. These correlations were more pronounced for metabolic pathways in the fasting state, than during the postprandial response. The effect of the WL and PP intervention on a priori defined metabolic pathways, and correlation of pathways with insulin sensitivity as described by HOMA was in line with previous studies.

CONCLUSION

GGT confirmed earlier biological findings in a hypothesis led approach. A main advantage of GGT is that it provides a direct view on involvement of a priori defined pathways in phenotype shifts.

Validation and Automation of a High-Throughput Multitargeted Method for Semiquantification of Endogenous Metabolites from Different Biological Matrices Using Tandem Mass Spectrometry

The use of metabolomics profiling to understand the metabolism under different physiological states has increased in recent years, which created the need for robust analytical platforms. Here, we present a validated method for targeted and semiquantitative analysis of 102 polar metabolites that cover major metabolic pathways from 24 classes in a single 17.5-min assay. The method has been optimized for a wide range of biological matrices from various organisms, and involves automated sample preparation and data processing using an inhouse developed R-package. To ensure reliability, the method was validated for accuracy, precision, selectivity, specificity, linearity, recovery, and stability according to European Medicines Agency guidelines. We demonstrated an excellent repeatability of retention times (CV < 4%), calibration curves (R² ≥ 0.980) in their respective wide dynamic concentration ranges (CV < 3%), and concentrations (CV < 25%) of quality control samples interspersed within 25 batches analyzed over a period of one year. The robustness was demonstrated through a high correlation between metabolite concentrations measured using our method and the NIST reference values (R² = 0.967), including cross-platform comparability against the BIOCRATES AbsoluteIDQp180 kit (R² = 0.975) and NMR analyses (R² = 0.884). We have shown that our method can be successfully applied in many biomedical research fields and clinical trials, including epidemiological studies for biomarker discovery. In summary, a thorough validation demonstrated that our method is reproducible, robust, reliable, and suitable for metabolomics studies.

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.

Clearing the undergrowth: detection and quantification of low level impurities using 19F NMR

A new method for the analysis of low level impurities in sparsely fluorinated species allows measurement of clean high dynamic range ¹⁹F spectra, fully decoupled and free of interfering signals from ¹³C isotopomers.

Assessment of dietary exposure and effect in humans: The role of NMR

In human nutritional science progress has always depended strongly on analytical measurements for establishing relationships between diet and health. This field has undergone significant changes as a result of the development of NMR and mass spectrometry methods for large scale detection, identification and quantification of metabolites in body fluids. This has allowed systematic studies of the metabolic fingerprints that biological processes leave behind, and has become the research field of metabolomics. As a metabolic profiling technique, NMR is at its best when its unbiased nature, linearity and reproducibility are exploited in well-controlled nutritional intervention and cross-sectional population screening studies. Although its sensitivity is less good than that of mass spectrometry, NMR has maintained a strong position in metabolomics through implementation of standardisation protocols, hyphenation with mass spectrometry and chromatographic techniques, accurate quantification and spectral deconvolution approaches, and high-throughput automation. Thus, NMR-based metabolomics has contributed uniquely to new insights into dietary exposure, in particular by unravelling the metabolic fates of phytochemicals and the discovery of dietary intake markers. NMR profiling has also contributed to the understanding of the subtle effects of diet on central metabolism and lipoprotein metabolism. In order to hold its ground in nutritional metabolomics, NMR will need to step up its performance in sensitivity and resolution; the most promising routes forward are the analytical use of dynamic nuclear polarisation and developments in microcoil construction and automated fractionation.

The Muscle Metabolome Differs between Healthy and Frail Older Adults

Populations around the world are aging rapidly. Age-related loss of physiological functions negatively affects quality of life. A major contributor to the frailty syndrome of aging is loss of skeletal muscle. In this study we assessed the skeletal muscle biopsy metabolome of healthy young, healthy older and frail older subjects to determine the effect of age and frailty on the metabolic signature of skeletal muscle tissue. In addition, the effects of prolonged whole-body resistance-type exercise training on the muscle metabolome of older subjects were examined. The baseline metabolome was measured in muscle biopsies collected from 30 young, 66 healthy older subjects, and 43 frail older subjects. Follow-up samples from frail older (24 samples) and healthy older subjects (38 samples) were collected after 6 months of prolonged resistance-type exercise training. Young subjects were included as a reference group. Primary differences in skeletal muscle metabolite levels between young and healthy older subjects were related to mitochondrial function, muscle fiber type, and tissue turnover. Similar differences were observed when comparing frail older subjects with healthy older subjects at baseline. Prolonged resistance-type exercise training resulted in an adaptive response of amino acid metabolism, especially reflected in branched chain amino acids and genes related to tissue remodeling. The effect of exercise training on branched-chain amino acid-derived acylcarnitines in older subjects points to a downward shift in branched-chain amino acid catabolism upon training. We observed only modest correlations between muscle and plasma metabolite levels, which pleads against the use of plasma metabolites as a direct read-out of muscle metabolism and stresses the need for direct assessment of metabolites in muscle tissue biopsies.

Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics

Metabolomics is becoming common in epidemiology due to recent developments in quantitative profiling technologies and appealing results from their applications for understanding health and disease. Our team has developed an automated high-throughput serum NMR metabolomics platform that provides quantitative molecular data on 14 lipoprotein subclasses, their lipid concentrations and composition, apolipoprotein A-I and B, multiple cholesterol and triglyceride measures, albumin, various fatty acids as well as on numerous low-molecular-weight metabolites, including amino acids, glycolysis related measures and ketone bodies. The molar concentrations of these measures are obtained from a single serum sample with costs comparable to standard lipid measurements. We have analyzed almost 250 000 samples from around 100 epidemiological cohorts and biobanks and the new international set-up of multiple platforms will allow an annual throughput of more than 250 000 samples. The molecular data have been used to study type 1 and type 2 diabetes etiology as well as to characterize the molecular reflections of the metabolic syndrome, long-term physical activity, diet and lipoprotein metabolism. The results have revealed new biomarkers for early atherosclerosis, type 2 diabetes, diabetic nephropathy, cardiovascular disease and all-cause mortality. We have also combined genomics and metabolomics in diverse studies. We envision that quantitative high-throughput NMR metabolomics will be incorporated as a routine in large biobanks; this would make perfect sense both from the biological research and cost point of view - the standard output of over 200 molecular measures would vastly extend the relevance of the sample collections and make many separate clinical chemistry assays redundant.