Assessment of reproducibility and biological variability of fasting and postprandial plasma metabolite concentrations using 1H NMR spectroscopy

Associations of childhood BMI, general and visceral fat mass with metabolite profiles at school-age

BACKGROUND

Childhood obesity increases metabolic disease risk. Underlying mechanisms remain unknown. We examined associations of body mass index (BMI), total body fat mass, and visceral fat mass with serum metabolites at school-age, and explored whether identified metabolites improved the identification of children at risk of a metabolically unhealthy phenotype.

METHODS

We performed a cross-sectional analysis among 497 children with a mean age of 9.8 (95% range 9.1, 10.6) years, participating in a population-based cohort study. We measured BMI, total body fat mass using DXA, and visceral fat mass using MRI. Serum concentrations of amino-acids, non-esterified-fatty-acids, phospholipids, and carnitines were determined using LC-MS/MS. Children were categorized as metabolically healthy or metabolically unhealthy, according to BMI, blood pressure, lipids, glucose, and insulin levels.

RESULTS

Higher BMI and total body fat mass were associated with altered concentrations of branched-chain amino-acids, essential amino-acids, and free carnitines. Higher BMI was also associated with higher concentrations of aromatic amino-acids and alkyl-lysophosphatidylcholines (FDR-corrected p-values < 0.05). The strongest associations were present for Lyso.PC.a.C14.0 and SM.a.C32.2 (FDR-corrected p-values < 0.01). Higher visceral fat mass was only associated with higher concentrations of 6 individual metabolites, particularly Lyso.PC.a.C14.0, PC.aa.C32.1, and SM.a.C32.2. We selected 15 metabolites that improved the prediction of a metabolically unhealthy phenotype, compared to BMI only (AUC: BMI: 0.59 [95% CI 0.47,0.71], BMI + Metabolites: 0.91 [95% CI 0.85,0.97]).

CONCLUSIONS

An adverse childhood body fat profile, characterized by higher BMI and total body fat mass, is associated with metabolic alterations, particularly in amino acids, phospholipids, and carnitines. Fewer associations were present for visceral fat mass. We identified a metabolite profile that improved the identification of impaired cardiometabolic health in children, compared to BMI only.

Circulating metabolic biomarkers and risk of new-onset hypertension: findings from the UK Biobank

OBJECTIVE

The evidence regarding the associations of circulating metabolic biomarkers with hypertension risk is scarce. We aimed to examine the associations between circulating metabolites and risk of hypertension.

METHODS

We included 49 422 individuals free of hypertension at baseline with a mean (SD) age of 53.5 (8.0) years from the UK Biobank. Nuclear magnetic resonance spectroscopy was used to quantify 143 individual metabolites. Multivariable-adjusted Cox regression models were used to estimate hazard ratios and 95% confidence intervals (CIs).

RESULTS

During a mean (SD) follow-up of 11.2 (1.8) years, 2686 incident hypertension cases occurred. Out of 143 metabolites, 76 were associated with incident hypertension, among which phenylalanine (hazard ratio: 1.40; 95% CI: 1.24-1.58) and apolipoprotein A1 (hazard ratio: 0.76; 95% CI: 0.66-0.87) had the strongest association when comparing the highest to the lowest quintile. In general, very-low-density lipoprotein (VLDL) particles were positively, whereas high-density lipoprotein (HDL) particles were inversely associated with risk of hypertension. Similar patterns of cholesterol, phospholipids, and total lipids within VLDL and HDL particles were observed. Triglycerides within all lipoproteins were positively associated with hypertension risk. Other metabolites showed significant associations with risk of hypertension included amino acids, fatty acids, ketone bodies, fluid balance and inflammation markers. Adding 10 selected metabolic biomarkers to the traditional hypertension risk model modestly improved discrimination (C-statistic from 0.745 to 0.752, P < 0.001) for prediction of 10-year hypertension incidence.

CONCLUSION

Among UK adults, disturbances in metabolic biomarkers are associated with incident hypertension. Comprehensive metabolomic profiling may provide potential novel biomarkers to identify high-risk individuals.

Circulating fatty acids and risk of hepatocellular carcinoma and chronic liver disease mortality in the UK Biobank

Nuclear magnetic resonance (NMR)-based plasma fatty acids are objective biomarkers of many diseases. Herein, we aim to explore the associations of NMR-based plasma fatty acids with the risk of hepatocellular carcinoma (HCC) and chronic liver disease (CLD) mortality in 252,398 UK Biobank participants. Here we show plasma levels of n-3 poly-unsaturated fatty acids (PUFA) and n-6 PUFA are negatively associated with the risk of incident HCC [HRQ4vsQ1: 0.48 (95% CI: 0.33-0.69) and 0.48 (95% CI: 0.28-0.81), respectively] and CLD mortality [HRQ4vsQ1: 0.21 (95% CI: 0.13-0.33) and 0.15 (95% CI: 0.08-0.30), respectively], whereas plasma levels of saturated fatty acids are positively associated with these outcomes [HRQ4vsQ1: 3.55 (95% CI: 2.25-5.61) for HCC and 6.34 (95% CI: 3.68-10.92) for CLD mortality]. Furthermore, fibrosis stage significantly modifies the associations between PUFA and CLD mortality. This study contributes to the limited prospective evidence on the associations between plasma-specific fatty acids and end-stage liver outcomes.

Untargeted serum metabolomic profiles and breast density in young women

PURPOSE OF THE STUDY

Breast density is an established risk factor for breast cancer. However, little is known about metabolic influences on breast density phenotypes. We conducted untargeted serum metabolomics analyses to identify metabolic signatures associated with breast density phenotypes among young women.

METHODS

In a cross-sectional study of 173 young women aged 25-29 who participated in the Dietary Intervention Study in Children 2006 Follow-up Study, 449 metabolites were measured in fasting serum samples using ultra-high-performance liquid chromatography-tandem mass spectrometry. Multivariable-adjusted mixed-effects linear regression identified metabolites associated with magnetic resonance imaging measured breast density phenotypes: percent dense breast volume (%DBV), absolute dense breast volume (ADBV), and absolute non-dense breast volume (ANDBV). Metabolite results were corrected for multiple comparisons using a false discovery rate adjusted p-value (q).

RESULTS

The amino acids valine and leucine were significantly inversely associated with %DBV. For each 1 SD increase in valine and leucine, %DBV decreased by 20.9% (q = 0.02) and 18.4% (q = 0.04), respectively. ANDBV was significantly positively associated with 16 lipid and one amino acid metabolites, whereas no metabolites were associated with ADBV. Metabolite set enrichment analysis also revealed associations of distinct metabolic signatures with %DBV, ADBV, and ANDBV; branched chain amino acids had the strongest inverse association with %DBV (p = 0.002); whereas, diacylglycerols and phospholipids were positively associated with ANDBV (p ≤ 0.002), no significant associations were observed for ADBV.

CONCLUSION

Our results suggest an inverse association of branched chain amino acids with %DBV. Larger studies in diverse populations are needed.

Associations of Clinical Risk Factors and Novel Biomarkers With Age at Onset of Type 2 Diabetes

CONTEXT

Younger onset of type 2 diabetes (T2D) was associated with higher risks of vascular complications and mortality.

OBJECTIVE

To prospectively assess risk profiles for incident T2D stratified by age at onset.

METHODS

A total of 471 269 participants free of T2D at baseline were included from the UK Biobank. Approximately 70 clinical, lipid, lipoprotein, inflammatory, and metabolic markers, and genetic risk scores (GRSs) were analyzed. Stratified Cox proportional-hazards regression models were used to estimate hazard ratios (HRs) for T2D with age of diagnosis divided into 4 groups (≤50.0, 50.1-60.0, 60.1-70.0, and >70.0 years).

RESULTS

During 11 years of follow-up, 15 805 incident T2D were identified. Among clinical risk factors, obesity had the highest HR at any age, ranging from 13.16 (95% CI, 9.67-17.91) for 50.0 years and younger to 4.13 (3.78-4.51) for older than 70.0 years. Other risks associated with T2D onset at age 50.0 years and younger included dyslipidemia (3.50, 2.91-4.20), hypertension (3.21, 2.71-3.80), cardiovascular disease (2.87, 2.13-3.87), parental history of diabetes (2.42, 2.04-2.86), education lower than college (1.89, 1.57-2.27), physical inactivity (1.73, 1.43-2.10), smoking (1.38, 1.13-1.68), several lipoprotein particles, inflammatory markers, liver enzymes, fatty acids, amino acids, as well as GRS. Associations of most risk factors and biomarkers were markedly attenuated with increasing age at onset (P interaction <.05), and some were not significant for onset at age older than 70.0 years, such as smoking, systolic blood pressure, and apolipoprotein B.

CONCLUSION

Most risk factors or biomarkers had stronger relative risks for T2D at younger ages, which emphasizes the necessity of promoting primary prevention among younger individuals. Moreover, obesity should be prioritized.

Plasma metabolomics supports non-fasted sampling for metabolic profiling across a spectrum of glucose tolerance in the Nile rat model for type 2 diabetes

Type 2 diabetes is a challenge in modern healthcare, and animal models are necessary to identify underlying mechanisms. The Nile rat (Arvicanthis niloticus) develops diet-induced diabetes rapidly on a conventional rodent chow diet without genetic or chemical manipulation. Unlike common laboratory models, the outbred Nile rat model is diurnal and has a wide range of overt diabetes onset and diabetes progression patterns in both sexes, better mimicking the heterogeneous diabetic phenotype in humans. While fasted blood glucose has historically been used to monitor diabetic progression, postprandial blood glucose is more sensitive to the initial stages of diabetes. However, there is a long-held assumption that ad libitum feeding in rodent models leads to increased variance, thus masking diabetes-related metabolic changes in the plasma. Here we compared repeatability within triplicates of non-fasted or fasted plasma samples and assessed metabolic changes relevant to glucose tolerance in fasted and non-fasted plasma of 8-10-week-old male Nile rats. We used liquid chromatography-mass spectrometry lipidomics and polar metabolomics to measure relative metabolite abundances in the plasma samples. We found that, compared to fasted metabolites, non-fasted plasma metabolites are not only more strongly associated with glucose tolerance on the basis of unsupervised clustering and elastic net regression model, but also have a lower replicate variance. Between the two sampling groups, we detected 66 non-fasted metabolites and 32 fasted metabolites that were associated with glucose tolerance using a combined approach with multivariable elastic net and individual metabolite linear models. Further, to test if metabolite replicate variance is affected by age and sex, we measured non-fasted replicate variance in a cohort of mature 30-week-old male and female Nile rats. Our results support using non-fasted plasma metabolomics to study glucose tolerance in Nile rats across the progression of diabetes.

Dietary biomarkers-an update on their validity and applicability in epidemiological studies

The aim of this literature review was to identify and provide a summary update on the validity and applicability of the most promising dietary biomarkers reflecting the intake of important foods in the Western diet for application in epidemiological studies. Many dietary biomarker candidates, reflecting intake of common foods and their specific constituents, have been discovered from intervention and observational studies in humans, but few have been validated. The literature search was targeted for biomarker candidates previously reported to reflect intakes of specific food groups or components that are of major importance in health and disease. Their validity was evaluated according to 8 predefined validation criteria and adapted to epidemiological studies; we summarized the findings and listed the most promising food intake biomarkers based on the evaluation. Biomarker candidates for alcohol, cereals, coffee, dairy, fats and oils, fruits, legumes, meat, seafood, sugar, tea, and vegetables were identified. Top candidates for all categories are specific to certain foods, have defined parent compounds, and their concentrations are unaffected by nonfood determinants. The correlations of candidate dietary biomarkers with habitual food intake were moderate to strong and their reproducibility over time ranged from low to high. For many biomarker candidates, critical information regarding dose response, correlation with habitual food intake, and reproducibility over time is yet unknown. The nutritional epidemiology field will benefit from the development of novel methods to combine single biomarkers to generate biomarker panels in combination with self-reported data. The most promising dietary biomarker candidates that reflect commonly consumed foods and food components for application in epidemiological studies were identified, and research required for their full validation was summarized.

Mediating role of host metabolites in strontium's effect on osteoporosis among older individuals: Findings from Wuhan, China

Strontium is receiving widespread attention due to its remarkable biological qualities in preventing bone resorption and fostering osteogenesis. However, the chemical processes behind strontium's dual activities on bone cells are not yet fully understood. This study used the metabolomic technique to identify and examine potential biomarkers between strontium exposure and osteoporosis (OP) risk. A total of 806 participants were recruited for the detection of plasma strontium content via inductively coupled plasma-mass spectrometry. Plasma metabolites were profiled in 254 participants through an untargeted metabolomics technique. Generalized linear models were primarily used to analyze associations among plasma strontium, metabolites, and OP. The mediating effects of metabolites on the strontium-OP association were further investigated. A total of 31 differential metabolites were observed, 10 of which were upregulated and 21 were downregulated in the OP group compared with the non-OP group. Five metabolites (3-phenoxybenzoic acid, Cer (t18:0/16:1), HexCer(t16:1/12:1(2OH)), HexCer(t14:2/18:1(2OH)), and TG(16:0-18:1-24:4)) were selected as potential mediators based on their significant association with OP risk and with femoral neck and lumbar spine bone mineral density (BMD). Moreover, all except TG(16:0-18:1-24:4) were involved in the OP discrimination model with excellent power combined with several traditional variables. 3-Phenoxybenzoic acid and Cer(t18:0/16:1) had significant indirect effects on the strontium-OP association. The five candidate metabolites mediated 83.79 % of the strontium-OP association. Plasma strontium level was associated with reduced OP risk in the Han population in Wuhan. Thus, plasma metabolite profiling revealed five BMD/OP-associated metabolites that acted as mediators in the strontium-OP association. Our findings provided evidence of the mediating role of host plasma metabolites in strontium's effect on OP pathology.

Metal mixture and osteoporosis risk: Insights from plasma metabolite profiling

The pathophysiology of osteoporosis (OP) is influenced by exposure to nonessential harmful metals and insufficient or excessive intake of necessary metals. Investigating multiple plasma metals, metabolites, and OP risk among older adults may reveal novel clues of underlying mechanisms for metal toxicity on bone mass. A total of 294 adults ≥ 55 years from Wuhan communities were included. Plasma concentrations of 23 metals and metabolites were measured via inductively coupled plasma-mass spectrometry and global metabolite detection. To investigate the relationships between plasma metals, OP risk, and OP-related metabolites, three different statistical techniques were used: generalized linear regression model, two-way orthogonal partial least-squares analysis (O2PLS), and weighted quantile sum (WQS). The mean ages were 66.82 and 66.21 years in OP (n = 115) and non-OP (n = 179) groups, respectively. Of all 2999 metabolites detected, 111 differential between-group members were observed. The OP risk decreased by 58.5% (OR=0.415, 95% CI: 0.237, 0.727) per quartile increment in the WQS index indicative of metal mixture exposure. Consistency remained for bone mineral density (BMD) measurements. The O2PLS model identified the top five OP-related metabolites, namely, DG(18:2_22:6), 3-phenoxybenzoic acid, TG(16:1_16:1_22:6), TG(16:0_16:0_20:4), and TG(14:1_18:2_18:3), contributing most to the joint covariation between the metal mixture and metabolites. Significant correlations between each of them and the metal mixture were found using WQS regression. Furthermore, the five metabolites mediated the associations of the metal mixtures, BMD, and OP risk. Our findings shed additional light on the mediation functions of plasma metabolites in the connection between multiple metal co-exposure and OP pathogenesis and offer new insights into the probable mechanisms underpinning the bone effects of the metal mixture.

Associations of circulating fatty acids with incident coronary heart disease: a prospective study of 89,242 individuals in UK Biobank

BACKGROUND

The role of fatty acids in coronary heart disease (CHD) remains uncertain. There is little evidence from large-scale epidemiological studies on the relevance of circulating fatty acids levels to CHD risk. This study aims to examine the independent associations of the major circulating types of fatty acids with CHD risk.

METHODS

UK Biobank is a prospective study of adults aged 40-69 in 2006-2010; in 2012-2013, a subset of the participants were resurveyed. Analyses were restricted to 89,242 participants with baseline plasma fatty acids (measured using nuclear magnetic resonance spectroscopy) and without prior CHD. Cox proportional hazards models were used to estimate hazard ratios (HRs) for the associations with incidence CHD, defined as the first-ever myocardial infarction, unstable angina pectoris, coronary-related death, or relevant procedure. And the major types of fatty acids were mutually adjusted to examine the independent associations. Hazard ratios were corrected for regression dilution using the correlation of baseline and resurvey fatty acids measures.

RESULTS

During a median follow-up of 11.8 years, 3,815 incident cases of CHD occurred. Independently of other fatty acids, CHD risk was positively associated with saturated fatty acids (SFA) and monounsaturated fatty acids (MUFA), inversely associated with omega-3 polyunsaturated fatty acids (PUFA), but there was no strong evidence of an association with omega-6 PUFA: HR per standard deviation higher were 1.14 (95% CI, 1.09-1.20), 1.15 (1.10-1.21), 0.91 (0.87-0.94), and 1.04 (0.99-1.09) respectively. Independently of triglycerides and cholesterol, the inverse association with omega-3 PUFA was not materially changed, but the positive associations with SFA and MUFA attenuated to null after adjusting for triglycerides levels.

CONCLUSIONS

This large-scale study has quantitated the independent associations of circulating fatty acids with CHD risk. Omega-3 PUFA was inversely related to CHD risk, independently of other fatty acids and major lipid fractions. By contrast, independently of other fatty acids, the positive associations of circulating SFA and MUFA with CHD risk were mostly attributed to their relationship with triglycerides.

NMR-Metabolomics Reveals a Metabolic Shift after Surgical Resection of Non-Small Cell Lung Cancer

Background: Lung cancer can be detected by measuring the patient’s plasma metabolomic profile using nuclear magnetic resonance (NMR) spectroscopy. This NMR-based plasma metabolomic profile is patient-specific and represents a snapshot of the patient’s metabolite concentrations. The onset of non-small cell lung cancer (NSCLC) causes a change in the metabolite profile. However, the level of metabolic changes after complete NSCLC removal is currently unknown. Patients and methods: Fasted pre- and postoperative plasma samples of 74 patients diagnosed with resectable stage I-IIIA NSCLC were analyzed using 1H-NMR spectroscopy. NMR spectra (s = 222) representing two preoperative and one postoperative plasma metabolite profile at three months after surgical resection were obtained for all patients. In total, 228 predictors, i.e., 228 variables representing plasma metabolite concentrations, were extracted from each NMR spectrum. Two types of supervised multivariate discriminant analyses were used to train classifiers presenting a strong differentiation between the pre- and postoperative plasma metabolite profiles. The validation of these trained classification models was obtained by using an independent dataset. Results: A trained multivariate discriminant classification model shows a strong differentiation between the pre- and postoperative NSCLC profiles with a specificity of 96% (95% CI [86–100]) and a sensitivity of 92% (95% CI [81–98]). Validation of this model results in an excellent predictive accuracy of 90% (95% CI [77–97]) and an AUC value of 0.97 (95% CI [0.93–1]). The validation of a second trained model using an additional preoperative control sample dataset confirms the separation of the pre- and postoperative profiles with a predictive accuracy of 93% (95% CI [82–99]) and an AUC value of 0.97 (95% CI [0.93–1]). Metabolite analysis reveals significantly increased lactate, cysteine, asparagine and decreased acetate levels in the postoperative plasma metabolite profile. Conclusions: The results of this paper demonstrate that surgical removal of NSCLC generates a detectable metabolic shift in blood plasma. The observed metabolic shift indicates that the NSCLC metabolite profile is determined by the tumor’s presence rather than donor-specific features. Furthermore, the ability to detect the metabolic difference before and after surgical tumor resection strongly supports the prospect that NMR-generated metabolite profiles via blood samples advance towards early detection of NSCLC recurrence.

Metabolic profiling of MRI-measured liver fat in the UK Biobank

OBJECTIVE

Liver fat associates with obesity-related metabolic disturbances and may precede incident diseases. Metabolomic profiles of liver fat in the UK Biobank were investigated.

METHODS

Regression models assessed the associations between 180 metabolites and proton density liver fat fraction (PDFF) measured 5 years later through magnetic resonance imaging, as the difference (in SD units) of each log metabolite measure with 1-SD higher PDFF among those without chronic disease and not taking statins, and by diabetes and cardiovascular diseases.

RESULTS

After accounting for confounders, multiple metabolites were associated positively with liver fat (p < 0.0001 for 152 traits), particularly extremely large and very large lipoprotein particle concentrations, very low-density lipoprotein triglycerides, small high-density lipoprotein particles, glycoprotein acetyls, monounsaturated and saturated fatty acids, and amino acids. Extremely large and large high-density lipoprotein concentrations had strong inverse associations with liver fat. Associations were broadly comparable among those with versus without vascular metabolic conditions, although negative, rather than positive, associations were observed between intermediate-density and large low-density lipoprotein particles among those with BMI ≥25 kg/m² , diabetes, or cardiovascular diseases. Metabolite principal components showed a 15% significant improvement in risk prediction for PDFF relative to BMI, which was twice as great (but nonsignificant) compared with conventional high-density lipoprotein cholesterol and triglycerides.

CONCLUSIONS

Hazardous metabolomic profiles are associated with ectopic hepatic fat and are relevant to risk of vascular-metabolic disease.

Single and Joined Behaviour of Circulating Biomarkers and Metabolic Parameters in High-Fit and Low-Fit Healthy Females

Biomarkers are important in the assessment of health and disease, but are poorly studied in still healthy individuals with a (potential) different risk for metabolic disease. This study investigated, first, how single biomarkers and metabolic parameters, functional biomarker and metabolic parameter categories, and total biomarker and metabolic parameter profiles behave in young healthy female adults of different aerobic fitness and, second, how these biomarkers and metabolic parameters are affected by recent exercise in these healthy individuals. A total of 102 biomarkers and metabolic parameters were analysed in serum or plasma samples from 30 young, healthy, female adults divided into a high-fit (V̇O2peak ≥ 47 mL/kg/min, N = 15) and a low-fit (V̇O2peak ≤ 37 mL/kg/min, N = 15) group, at baseline and overnight after a single bout of exercise (60 min, 70% V̇O2peak). Our results show that total biomarker and metabolic parameter profiles were similar between high-fit and low-fit females. Recent exercise significantly affected several single biomarkers and metabolic parameters, mostly related to inflammation and lipid metabolism. Furthermore, functional biomarker and metabolic parameter categories corresponded to biomarker and metabolic parameter clusters generated via hierarchical clustering models. In conclusion, this study provides insight into the single and joined behavior of circulating biomarkers and metabolic parameters in healthy females, and identified functional biomarker and metabolic parameter categories that may be used for the characterisation of human health physiology.

Reproducibility of the Blood and Urine Exposome: A Systematic Literature Review and Meta-Analysis

Endogenous and exogenous metabolite concentrations may be susceptible to variation over time. This variability can lead to misclassification of exposure levels and in turn to biased results. To assess the reproducibility of metabolites, the intraclass correlation coefficient (ICC) is computed. A literature search in three databases from 2000 to May 2021 was conducted to identify studies reporting ICCs for blood and urine metabolites. This review includes 192 studies, of which 31 studies are included in the meta-analyses. The ICCs of 359 single metabolites are reported, and the ICCs of 10 metabolites were meta-analyzed. The reproducibility of the single metabolites ranges from poor to excellent and is highly compound-dependent. The reproducibility of bisphenol A (BPA), mono-ethyl phthalate (MEP), mono-n-butyl phthalate (MnBP), mono-2-ethylhexyl phthalate (MEHP), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono-benzyl phthalate (MBzP), mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP), methylparaben, and propylparaben is poor to moderate (ICC median: 0.32; range: 0.15-0.49), and for 25-hydroxyvitamin D [25(OH)D], it is excellent (ICC: 0.95; 95% CI, 0.90-0.99). Pharmacokinetics, mainly the half-life of elimination and exposure patterns, can explain reproducibility. This review describes the reproducibility of the blood and urine exposome, provides a vast dataset of ICC estimates, and hence constitutes a valuable resource for future reproducibility and clinical epidemiologic studies.

Metabolic linkages between zinc exposure and lung cancer risk: A nested case-control study

Epidemiologic studies have suggested that elevated concentrations of zinc are associated with a decreased risk of lung cancer, but the underlying mechanisms remain to be investigated. The metabolites are highly sensitive to environmental stress, which will help to reveal the linkages between zinc exposure and lung cancer risk. We designed a nested case-control study including 101 incident lung cancer cases and 1:2 age- and sex-frequency-matched 202 healthy controls from the Dongfeng-Tongji (DFTJ) cohort. Their plasma level of zinc was determined by using inductively coupled plasma-mass spectrometry (ICP-MS) and plasma profiles of metabolites were detected by using an untargeted metabolomics approach. The generalized linear models (GLM) were applied to assess the associations of plasma zinc with metabolites, and the mediation effects of zinc-related metabolites on zinc-lung cancer association were further testified. The concentrations of 55 metabolites had linear dose-response relationships with plasma zinc at a false discovery rate (FDR) < 0.05, among which L-proline, phosphatidylcholine (PC, 34:2), phosphatidylethanolamine (PE, O-36:5), L-altrose, and sphingomyelin (SM, 40:3) showed different levels between lung cancer cases and healthy controls (fold change = 0.92, 0.95, 1.07, 0.90, and 1.08, respectively, and all P < 0.05). The plasma concentration of SM(40:3) was negatively associated with incident risk of lung cancer [OR(95%CI) = 0.71(0.55, 0.91), P = 0.007] and could mediate 41.7% of the association between zinc and lung cancer risk (P = 0.004). Moreover, compared to the traditional factors, addition of SM(40:3) exerted improved prediction performance for incident risk of lung cancer [AUC(95%CIs) = 0.714(0.654, 0.775) vs. 0.663(0.600, 0.727), P = 0.030]. Our findings revealed metabolic profiles with zinc exposure and provide new insight into the alternations of metabolites underpinning the links between zinc exposure and lung cancer development.

Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study

BACKGROUND

Effective targeted prevention of type 2 diabetes (T2D) depends on accurate prediction of disease risk. We assessed the role of metabolomic profiling in improving T2D risk prediction beyond conventional risk factors.

METHODS

Nuclear magnetic resonance (NMR) metabolomic profiling was undertaken on baseline plasma samples in 65,684 UK Biobank participants without diabetes and not taking lipid-lowering medication. Among a subset of 50,519 participants with data available on all relevant co-variates (sociodemographic characteristics, parental history of diabetes, lifestyle-including dietary-factors, anthropometric measures and fasting time), Cox regression yielded adjusted hazard ratios for the associations of 143 individual metabolic biomarkers (including lipids, lipoproteins, fatty acids, amino acids, ketone bodies and other low molecular weight metabolic biomarkers) and 11 metabolic biomarker principal components (PCs) (accounting for 90% of the total variance in individual biomarkers) with incident T2D. These 11 PCs were added to established models for T2D risk prediction among the full study population, and measures of risk discrimination (c-statistic) and reclassification (continuous net reclassification improvement [NRI], integrated discrimination index [IDI]) were assessed.

RESULTS

During median 11.9 (IQR 11.1-12.6) years' follow-up, after accounting for multiple testing, 90 metabolic biomarkers showed independent associations with T2D risk among 50,519 participants (1211 incident T2D cases) and 76 showed associations after additional adjustment for HbA1c (false discovery rate controlled p < 0.01). Overall, 8 metabolic biomarker PCs were independently associated with T2D. Among the full study population of 65,684 participants, of whom 1719 developed T2D, addition of PCs to an established risk prediction model, including age, sex, parental history of diabetes, body mass index and HbA1c, improved T2D risk prediction as assessed by the c-statistic (increased from 0.802 [95% CI 0.791-0.812] to 0.830 [0.822-0.841]), continuous NRI (0.44 [0.38-0.49]) and relative (15.0% [10.5-20.4%]) and absolute (1.5 [1.0-1.9]) IDI. More modest improvements were observed when metabolic biomarker PCs were added to a more comprehensive established T2D risk prediction model additionally including waist circumference, blood pressure and plasma lipid concentrations (c-statistic, 0.829 [0.819-0.838] to 0.837 [0.831-0.848]; continuous NRI, 0.22 [0.17-0.28]; relative IDI, 6.3% [4.1-9.8%]; absolute IDI, 0.7 [0.4-1.1]).

CONCLUSIONS

When added to conventional risk factors, circulating NMR-based metabolic biomarkers modestly enhanced T2D risk prediction.

Maternal Body Mass Index, Early-Pregnancy Metabolite Profile, and Birthweight

CONTEXT

Maternal prepregnancy body mass index (BMI) has a strong influence on gestational metabolism, but detailed metabolic alterations are unknown.

OBJECTIVE

First, to examine the associations of maternal prepregnancy BMI with maternal early-pregnancy metabolite alterations. Second, to identify an early-pregnancy metabolite profile associated with birthweight in women with a higher prepregnancy BMI that improved prediction of birthweight compared to glucose and lipid concentrations.

DESIGN, SETTING, AND PARTICIPANTS

Prepregnancy BMI was obtained in a subgroup of 682 Dutch pregnant women from the Generation R prospective cohort study.

MAIN OUTCOME MEASURES

Maternal nonfasting targeted amino acids, nonesterified fatty acid, phospholipid, and carnitine concentrations measured in blood serum at mean gestational age of 12.8 weeks. Birthweight was obtained from medical records.

RESULTS

A higher prepregnancy BMI was associated with 72 altered amino acids, nonesterified fatty acid, phospholipid and carnitine concentrations, and 6 metabolite ratios reflecting Krebs cycle, inflammatory, oxidative stress, and lipid metabolic processes (P-values < 0.05). Using penalized regression models, a metabolite profile was selected including 15 metabolites and 4 metabolite ratios based on its association with birthweight in addition to prepregnancy BMI. The adjusted R2 of birthweight was 6.1% for prepregnancy BMI alone, 6.2% after addition of glucose and lipid concentrations, and 12.9% after addition of the metabolite profile.

CONCLUSIONS

A higher maternal prepregnancy BMI was associated with altered maternal early-pregnancy amino acids, nonesterified fatty acids, phospholipids, and carnitines. Using these metabolites, we identified a maternal metabolite profile that improved prediction of birthweight in women with a higher prepregnancy BMI compared to glucose and lipid concentrations.

Genetic Studies of Metabolomics Change After a Liquid Meal Illuminate Novel Pathways for Glucose and Lipid Metabolism

Humans spend the greater part of the day in a postprandial state. However, the genetic basis of postprandial blood measures is relatively uncharted territory. We examined the genetics of variation in concentrations of postprandial metabolites (t = 150 min) in response to a liquid mixed meal through genome-wide association studies (GWAS) performed in the Netherlands Epidemiology of Obesity (NEO) study (n = 5,705). The metabolite response GWAS identified an association between glucose change and rs10830963:G in the melatonin receptor 1B (β [SE] -0.23 [0.03], P = 2.15 × 10^-19). In addition, the ANKRD55 locus led by rs458741:C showed strong associations with extremely large VLDL (XXLVLDL) particle response (XXLVLDL total cholesterol: β [SE] 0.17 [0.03], P = 5.76 × 10^-10; XXLVLDL cholesterol ester: β [SE] 0.17 [0.03], P = 9.74 × 10^-10), which also revealed strong associations with body composition and diabetes in the UK Biobank (P < 5 × 10^-8). Furthermore, the associations between XXLVLDL response and insulinogenic index, HOMA-β, Matsuda insulin sensitivity index, and HbA_1c in the NEO study implied the role of chylomicron synthesis in diabetes (with false discovery rate-corrected q <0.05). To conclude, genetic studies of metabolomics change after a liquid meal illuminate novel pathways for glucose and lipid metabolism. Further studies are warranted to corroborate biological pathways of the ANKRD55 locus underlying diabetes.

Spousal associations of serum metabolomic profiles by nuclear magnetic resonance spectroscopy

Phenotype-based assortative mating is well established in humans, with the potential for further convergence through a shared environment. To assess the correlation within infertile couples of physical, social, and behavioural characteristics and 155 circulating metabolic measures. Cross sectional study at a tertiary medical center of 326 couples undertaking IVF. Serum lipids, lipoprotein subclasses, and low-molecular weight metabolites as quantified by NMR spectroscopy (155 metabolic measures). Multivariable and quantile regression correlations within couples of metabolite profiles. Couples exhibited statistical correlations of varying strength for most physical, social, and behavioural characteristics including age, height, alcohol consumption, education, smoking status, physical activity, family history and ethnicity, with correlation coefficients ranging from 0.22 to 0.73. There was no evidence of within couple associations for BMI and weight, where the correlation coefficients were - 0.03 (95% CI - 0.14, 0.08) and 0.01 (95% CI - 0.10, 0.12), respectively. Within spousal associations of the metabolite measurements were all positive but with weak to modest magnitudes, with the median correlation coefficient across all 155 measures being 0.12 (range 0.01-0.37 and interquartile range 0.10-0.18). With just four having associations stronger than 0.3: docosahexaenoic acid (0.37, 95% CI 0.22, 0.52), omega-3 fatty acids (0.32, 95% CI 0.20, 0.43) histidine (0.32, 95% CI 0.23, 0.41) and pyruvate (0.32, 95% CI 0.22, 0.43). Infertile couples exhibit spousal similarities for a range of demographic and serum metabolite measures, supporting initial assortative mating, with diet-derived metabolites suggesting possible subsequent convergence of their individual metabolic profile.

Defining Acute Coronary Syndrome through Metabolomics

As an emerging platform technology, metabolomics offers new insights into the pathomechanisms associated with complex disease conditions, including cardiovascular diseases. It also facilitates assessing the risk of developing the disease before its clinical manifestation. For this reason, metabolomics is of growing interest for understanding the pathogenesis of acute coronary syndromes (ACS), finding new biomarkers of ACS, and its associated risk management. Metabolomics-based studies in ACS have already demonstrated immense potential for biomarker discovery and mechanistic insights by identifying metabolomic signatures (e.g., branched-chain amino acids, acylcarnitines, lysophosphatidylcholines) associated with disease progression. Herein, we discuss the various metabolomics approaches and the challenges involved in metabolic profiling, focusing on ACS. Special attention has been paid to the clinical studies of metabolomics and lipidomics in ACS, with an emphasis on ischemia/reperfusion injury.

NMR Metabolite Profiles in Male Meat-Eaters, Fish-Eaters, Vegetarians and Vegans, and Comparison with MS Metabolite Profiles

Metabolomics may help to elucidate mechanisms underlying diet-disease relationships and identify novel risk factors for disease. To inform the design and interpretation of such research, evidence on diet-metabolite associations and cross-assay comparisons is needed. We aimed to compare nuclear magnetic resonance (NMR) metabolite profiles between meat-eaters, fish-eaters, vegetarians and vegans, and to compare NMR measurements to those from mass spectrometry (MS), clinical chemistry and capillary gas-liquid chromatography (GC). We quantified 207 serum NMR metabolite measures in 286 male participants of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Oxford cohort. Using univariate and multivariate analyses, we found that metabolite profiles varied by diet group, especially for vegans; the main differences compared to meat-eaters were lower levels of docosahexaenoic acid, total n-3 and saturated fatty acids, cholesterol and triglycerides in very-low-density lipoproteins, various lipid factions in high-density lipoprotein, sphingomyelins, tyrosine and creatinine, and higher levels of linoleic acid, total n-6, polyunsaturated fatty acids and alanine. Levels in fish-eaters and vegetarians differed by metabolite measure. Concentrations of 13 metabolites measured using both NMR and MS, clinical chemistry or GC were mostly similar. In summary, vegans' metabolite profiles were markedly different to those of men consuming animal products. The studied metabolomics platforms are complementary, with limited overlap between metabolite classes.

Reproducibility of Targeted Lipidome Analyses (Lipidyzer) in Plasma and Erythrocytes over a 6-Week Period

It is essential to measure lipid biomarkers with a high reproducibility to prevent biased results. We compared the lipid composition and inter-day reproducibility of lipid measurements in plasma and erythrocytes. Samples from 42 individuals (77% women, mean age 65 years, mean body mass index (BMI) 27 kg/m²), obtained non-fasted at baseline and after 6 weeks, were used for quantification of up to 1000 lipid species across 13 lipid classes with the Lipidyzer platform. Intraclass correlation coefficients (ICCs) were calculated to investigate the variability of lipid concentrations between timepoints. The ICC distribution of lipids in plasma and erythrocytes were compared using Wilcoxon tests. After data processing, the analyses included 630 lipids in plasma and 286 in erythrocytes. From these, 230 lipids overlapped between sample types. In plasma, 78% of lipid measurements were reproduced well to excellently, compared to 37% in erythrocytes. The ICC score distribution in plasma (median ICC 0.69) was significantly better than in erythrocytes (median ICC 0.51) (p-value < 0.001). At the class level, reproducibility in plasma was superior for triacylglycerols and cholesteryl esters while ceramides, diacylglycerols, (lyso)phosphatidylethanolamines, and sphingomyelins showed better reproducibility in erythrocytes. Although in plasma overall reproducibility was superior, differences at individual and class levels may favor the use of erythrocytes.

Association of the serum metabolomic profile by nuclear magnetic resonance spectroscopy with sperm parameters: a cross-sectional study of 325 men

OBJECTIVE

To determine whether 155 circulating metabolic measures relevant to lifestyle and metabolic health are associated with sperm parameters, as measured by concentration, motility, and total motile sperm count (TMSC).

STUDY DESIGN

Cross sectional.

SETTING

University hospital.

PATIENT(S)

Three hundred twenty-five men prospectively recruited between April 1, 2017 and March 31, 2019.

INTERVENTION(S)

Detailed demographic, lifestyle, fertility, medical history, and semen analysis with quantification of nonfasting serum lipids, lipoprotein subclasses, and low-molecular weight metabolites (including amino acids, glycolysis, and inflammatory markers) by nuclear magnetic resonance (NMR) spectroscopy.

MAIN OUTCOME MEASURE(S)

Association of serum metabolic profiles with sperm parameters.

RESULT(S)

The age of the participants was mean 37.2 years, with a median sperm concentration of 35 million/mL and median motility of 53%. Of these men, 76% had a TMSC >15 million, 10% had 5-15 million, and 14% had <5 million. In both univariate and confounder adjusted analyses, an extensive range of lipids and lipoproteins, glycolysis-related metabolites, amino acids, ketone bodies, creatinine, or albumin showed no strong statistically significant association with sperm concentration, motility, or the odds of having a reduced or low TMSC. Higher levels of glycolysis metabolites and ketone bodies were associated with an increased odds of TMSC <15 million compared with ≥15 million (odds ratios of ∼1.2-1.3), and several lipids/lipoprotein concentrations appeared to protect against very low TMSC (<5 million compared with ≥5 million) with odds ratios of ∼0.8 or greater.

CONCLUSION(S)

Several metabolites exhibited potentially clinically relevant strength of association with the odds of a low TMSC and warrant replication.

Short-term variability of the human serum metabolome depending on nutritional and metabolic health status

The intra-individual variability of the human serum metabolome over a period of 4 weeks and its dependence on metabolic health and nutritional status was investigated in a single-center study under tightly controlled conditions in healthy controls, pre-diabetic individuals and patients with type-2 diabetes mellitus (T2DM, n = 10 each). Untargeted metabolomics in serum samples taken at three different days after overnight fasts and following intake of a standardized mixed meal showed that the human serum metabolome is remarkably stable: The median intra-class correlation coefficient (ICC) across all metabolites and all study participants was determined as 0.65. ICCs were similar for the three different health groups, before and after meal intake, and for different metabolic pathways. Only 147 out of 1438 metabolites (10%) had an ICC below 0.4 indicating poor stability over time. In addition, we confirmed previously identified metabolic signatures differentiating healthy, pre-diabetic and diabetic individuals. To our knowledge, this is the most comprehensive study investigating the temporal variability of the human serum metabolome under such tightly controlled conditions.

Biosensing based on surface-enhanced Raman spectroscopy as an emerging/next-generation point-of-care approach for acute myocardial infarction diagnosis

Cardiovascular disease is a major global health issue. In particular, acute myocardial infarction (AMI) requires urgent attention and early diagnosis. The use of point-of-care diagnostics has resulted in the improved management of cardiovascular disease, but a major drawback is that the performance of POC devices does not rival that of central laboratory tests. Recently, many studies and advances have been made in the field of surface-enhanced Raman scattering (SERS), including the development of POC biosensors that utilize this detection method. Here, we present a review of the strengths and limitations of these emerging SERS-based biosensors for AMI diagnosis. The ability of SERS to multiplex sensing against existing POC detection methods are compared and discussed. Furthermore, SERS calibration-free methods that have recently been explored to minimize the inconvenience and eliminate the limitations caused by the limited linear range and interassay differences found in the calibration curves are outlined. In addition, the incorporation of artificial intelligence (AI) in SERS techniques to promote multivariate analysis and enhance diagnostic accuracy are discussed. The future prospects for SERS-based POC devices that include wearable POC SERS devices toward predictive, personalized medicine following the Fourth Industrial Revolution are proposed.

A Multilevel Bayesian Approach to Improve Effect Size Estimation in Regression Modeling of Metabolomics Data Utilizing Imputation with Uncertainty

To ensure scientific reproducibility of metabolomics data, alternative statistical methods are needed. A paradigm shift away from the p-value toward an embracement of uncertainty and interval estimation of a metabolite's true effect size may lead to improved study design and greater reproducibility. Multilevel Bayesian models are one approach that offer the added opportunity of incorporating imputed value uncertainty when missing data are present. We designed simulations of metabolomics data to compare multilevel Bayesian models to standard logistic regression with corrections for multiple hypothesis testing. Our simulations altered the sample size and the fraction of significant metabolites truly different between two outcome groups. We then introduced missingness to further assess model performance. Across simulations, the multilevel Bayesian approach more accurately estimated the effect size of metabolites that were significantly different between groups. Bayesian models also had greater power and mitigated the false discovery rate. In the presence of increased missing data, Bayesian models were able to accurately impute the true concentration and incorporating the uncertainty of these estimates improved overall prediction. In summary, our simulations demonstrate that a multilevel Bayesian approach accurately quantifies the estimated effect size of metabolite predictors in regression modeling, particularly in the presence of missing data.

Serum GlycA Level is Elevated in Active Systemic Lupus Erythematosus and Correlates to Disease Activity and Lupus Nephritis Severity

Objective: Reliable non-invasive biomarkers are needed to assess disease activity and prognosis in patients with systemic lupus erythematosus (SLE). Glycoprotein acetylation (GlycA), a novel biomarker for chronic inflammation, has been reported to be increased in several inflammatory diseases. We investigated the relevance of serum GlycA in SLE patients exhibiting various levels of activity and severity, especially with regards to renal involvement. Methods: Serum GlycA was measured by nuclear magnetic resonance spectroscopy in samples from well characterized SLE patients and from both healthy controls and patients with other kidney diseases (KD). Disease activity was evaluated using the Systemic Lupus Erythematosus Activity Index 2000 (SLEDAI-2K). Renal severity was assessed by kidney biopsy. Results: Serum GlycA was elevated in active (n = 105) compared to quiescent SLE patients (n = 39, p < 10⁻⁶), healthy controls (n = 20, p = 0.009) and KD controls (n = 21, p = 0.04), despite a more severely altered renal function in the latter. GlycA level was correlated to disease activity (SLEDAI-2K, ρ = 0.37, p < 10⁻⁴), C-reactive protein, neutrophil count, triglyceride levels, proteinuria and inversely to serum albumin. In patients with biopsy-proven lupus nephritis (LN), GlycA levels were higher in proliferative (n = 26) than non-proliferative LN (n = 10) in univariate analysis (p = 0.04), and was shown to predict proliferative LN independently of renal parameters, immunological activity, neutrophil count and daily corticosteroid dosage by multivariate analysis (p < 5 × 10⁻³ for all models). In LN patients with repeated longitudinal GlycA measurement (n = 11), GlycA varied over time and seemed to peak at the time of the flare. Conclusions: GlycA, as a summary measure for different inflammatory processes, could be a valuable biomarker of disease activity in patients with SLE, and a non-invasive biomarker of pathological severity in the context of LN.