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Context Polycystic ovary syndrome (PCOS) is a common endocrine disorder associated with low-grade systemic inflammation and increased risk of pregnancy complications. Metformin treatment reduces the risk of late miscarriage and preterm birth in pregnant women with PCOS. Whether the protective effect of metformin involves immunological changes has not been determined. Objective To investigate the effect of metformin on the maternal immunological status in women with PCOS. Methods A post-hoc analysis was performed of two randomized controlled trials, PregMet and PregMet2, including longitudinal maternal serum samples from 615 women with PCOS. Women were randomized to metformin or placebo from first trimester to delivery. Twenty-two cytokines and C-reactive protein were measured in serum sampled at gestational weeks 5 to 12, 19, 32, and 36. Results Metformin treatment was associated with higher serum levels of several multifunctional cytokines throughout pregnancy, with the strongest effect on eotaxin (P < .001), interleukin-17 (P = .03), and basic fibroblast growth factor (P = .04). Assessment of the combined cytokine development confirmed the impact of metformin on half of the 22 cytokines. The immunomodulating effect of metformin was more potent in normal weight and overweight women than in obese women. Moreover, normoandrogenic women had the strongest effect of metformin in early pregnancy, whereas hyperandrogenic women presented increasing effect throughout pregnancy. Conclusion It appears that metformin has immunomodulating rather than anti-inflammatory properties in pregnancy. Its effect on the serum levels of many multifunctional cytokines demonstrates robust, persisting, and body mass–dependent immune mobilization in pregnant women with PCOS.

Metabolic profiling is widely used for large-scale association studies, based on biobank material. The main obstacle to the translation of metabolomic findings into clinical application is the lack of standardization, making validation in independent cohorts challenging. One reason for this is sensitivity of metabolites to preanalytical conditions. We present a systematic investigation of the effect of delayed centrifugation on the levels of NMR-measured metabolites and lipoproteins in serum and plasma samples. Blood was collected from 20 anonymous donors, of which 10 were recruited from an obesity clinic. Samples were stored at room temperature until centrifugation after 30 min, 1, 2, 4, or 8 h, which is within a realistic time scenario in clinical practice. The effect of delaying centrifugation on plasma and serum metabolic concentrations, and on concentrations of lipoprotein subfractions, was investigated. Our results show that lipoproteins are only minimally affected by a delay in centrifugation while metabolite levels are more sensitive to a delay. Metabolites significantly increased or decreased in concentration depending on delay duration. Further, we describe differences in the stability of serum and plasma, showing that plasma is more stable for metabolites, while lipoprotein subfractions are equally stable for both types of matrices.

Context Polycystic ovary syndrome (PCOS) is a common endocrine disorder associated with low-grade inflammation and increased incidence of pregnancy complications, but its influence on the maternal immune system in pregnancy is unknown. Longitudinal serum cytokine profiling is a sensitive measure of the complex immunological dynamics of pregnancy. Objective This work aimed to determine the immunological dynamics of serum cytokines throughout pregnancy in women with PCOS and compare it to pregnancy in women without PCOS. Methods A post hoc analysis was conducted of longitudinal serum samples from 2 randomized, placebo-controlled multicenter studies of pregnant women with PCOS and 2 studies of pregnant women without PCOS. Pregnant women with PCOS (n = 358) and without PCOS (n = 258, controls) provided 1752 serum samples from 4 time points in pregnancy (weeks 10, 19, 32, and 36). Main outcome measures included maternal serum levels of 22 cytokines and C-reactive protein (CRP) at 4 time points in pregnancy. Results Women with PCOS showed marked immunological changes in serum cytokines throughout pregnancy. Compared to controls, women with PCOS showed higher levels of 17 cytokines and CRP at week 10 of pregnancy and a distinct cytokine development throughout pregnancy. The immunological dynamics in women with PCOS was significantly affected by maternal body mass index, smoking, and fetal sex. Conclusion Pregnancy in women with PCOS was associated with a strong early mobilization of inflammatory and other serum cytokines persisting throughout pregnancy, indicating a more activated immune status. These findings provide a novel basis for further study of PCOS and pregnancy complications.

Aims/hypothesis We determined whether the time of day of exercise training (morning vs evening) would modulate the effects of consumption of a high-fat diet (HFD) on glycaemic control, whole-body health markers and serum metabolomics. Methods In this three-armed parallel-group randomised trial undertaken at a university in Melbourne, Australia, overweight/obese men consumed an HFD (65% of energy from fat) for 11 consecutive days. Participants were recruited via social media and community advertisements. Eligibility criteria for participation were male sex, age 30–45 years, BMI 27.0–35.0 kg/m2 and sedentary lifestyle. The main exclusion criteria were known CVD or type 2 diabetes, taking prescription medications, and shift-work. After 5 days, participants were allocated using a computer random generator to either exercise in the morning (06:30 hours), exercise in the evening (18:30 hours) or no exercise for the subsequent 5 days. Participants and researchers were not blinded to group assignment. Changes in serum metabolites, circulating lipids, cardiorespiratory fitness, BP, and glycaemic control (from continuous glucose monitoring) were compared between groups. Results Twenty-five participants were randomised (morning exercise n = 9; evening exercise n = 8; no exercise n = 8) and 24 participants completed the study and were included in analyses (n = 8 per group). Five days of HFD induced marked perturbations in serum metabolites related to lipid and amino acid metabolism. Exercise training had a smaller impact than the HFD on changes in circulating metabolites, and only exercise undertaken in the evening was able to partly reverse some of the HFD-induced changes in metabolomic profiles. Twenty-four-hour glucose concentrations were lower after 5 days of HFD compared with the participants’ habitual diet (5.3 ± 0.4 vs 5.6 ± 0.4 mmol/l, p = 0.001). There were no significant changes in 24 h glucose concentrations for either exercise group but lower nocturnal glucose levels were observed in participants who trained in the evening, compared with when they consumed the HFD alone (4.9 ± 0.4 vs 5.3 ± 0.3 mmol/l, p = 0.04). Compared with the no-exercise group, peak oxygen uptake improved after both morning (estimated effect 1.3 ml min−1 kg−1 [95% CI 0.5, 2.0], p = 0.003) and evening exercise (estimated effect 1.4 ml min−1 kg−1 [95% CI 0.6, 2.2], p = 0.001). Fasting blood glucose, insulin, cholesterol, triacylglycerol and LDL-cholesterol concentrations decreased only in participants allocated to evening exercise training. There were no unintended or adverse effects. Conclusions/interpretation A short-term HFD in overweight/obese men induced substantial alterations in lipid- and amino acid-related serum metabolites. Improvements in cardiorespiratory fitness were similar regardless of the time of day of exercise training. However, improvements in glycaemic control and partial reversal of HFD-induced changes in metabolic profiles were only observed when participants exercise trained in the evening. Trial registration anzctr.org.au registration no. ACTRN12617000304336. Funding This study was funded by the Novo Nordisk Foundation (NNF14OC0011493). Graphical abstract

Background: Adjuvant breast cancer treatment may cause metabolic perturbations, such as dyslipidaemia, potentially exacerbating risk of cardiometabolic disease as well as risk of breast cancer recurrence. Physical exercise may have beneficial metabolic effects, but it’s effect on serum lipoprotein- and metabolite profiles during adjuvant breast cancer treatment including chemotherapy is not yet well established. Methods: The women participating in this pilot study of Energy Balance and Breast Cancer Aspects (EBBA)-II, were aged 38-69 years and diagnosed with stage I-II breast cancer. 60 breast cancer patients were randomized after surgery to a control group (n = 29, usual care) or an intervention group (n = 31, intervention), stratified by menopausal status. The patients in the intervention group received a detailed exercise program and met for supervised training sessions in groups of 10-12 women for 60 minutes twice a week during a 12 month period, and were in addition asked to perform at least 60 minutes of exercise at home (a total of 180 minutes of exercise weekly). Fasting serum samples were collected pre-surgery and after six months, and analysed by nuclear magnetic resonance (NMR)-spectroscopy and mass spectrometry. 170 metabolites and 109 lipoprotein subclass variables were quantified and analysed using orthogonalized partial least squares discriminant analysis. Statistical significance was assessed by permutation testing. Single variables were tested with Mann Whitney U-tests or multiple linear regression (NCT02240836). Results: The breast cancer patients (n = 60) had at pre-surgery the following means: Age at diagnosis of 55.4 years (38-69 years), low density lipoprotein (LDL)-cholesterol 145.4 mg/dl (3.76 mmol/L), high density lipoprotein (HDL)-cholesterol 70.4 mg/dl (1.82 mmol/L), and triglycerides 101.9 mg/dl (1.15 mmol/L), and 58.3 % of the patients underwent chemotherapy (paclitaxel/docetaxel/5-FU/epirubicin/cyclophosphamide based adjuvant chemotherapy). Physical exercise ameliorated chemotherapy-induced increases in very low density lipoprotein (VLDL)- and intermediate density lipoprotein (IDL)-associated lipids, and reduced triglyceride enrichment in LDL and HDL compared with chemotherapy controls (p = 0.003). Physical exercise also significantly increased apoA1 (4.6 % increase vs 11.3 % decrease, q = 0.02) and apoA2 (5.2 % increase vs 13.0 % decrease, q = 0.01) compared with chemotherapy control patients. The NMR-measured lipid signal at 1.55-1.60 ppm increased after six months in chemotherapy recipients, but this was attenuated among chemotherapy recipients in the intervention group. No statistically significant effect of physical exercise on serum levels of small-molecular metabolites was detected. Conclusion: Our findings suggest that physical exercise may prevent atherogenic alterations in lipoprotein profile induced by chemotherapy. The results indicate increased HDL particle number- and function, as well as increased triglyceride clearance in the intervention group. Thus, atherogenic alterations in lipoprotein profile may play a role in evaluating breast cancer treatment, and could potentially be biomarkers of importance for breast cancer prognosis and co-morbidity. Citation Format: Torfinn Støve Madssen, Vidar Gordon Flote, Inger Thune, Gro Falkener Bertheussen, Anders Husøy, Steinar Lundgren, Hanne Frydenberg, Erik Wist, Ellen Schlichting, Jon Lømo, Anne McTiernan, Tone Frost Bathen, Guro Fanneløb Giskeødegård. Lipoprotein and metabolite responses to physical exercise during adjuvant breast cancer treatment [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-13-01.

BackgroundThe aim of this study was to investigate if serum lipoprotein and metabolic profiles of healthy women can predict the risk of developing breast cancer in the future, and to gain a better understanding of the etiology of the disease.MethodsFrom a cohort of 70 000 participants within the Trøndelag Health Study (HUNT study), we identified 1199 women who developed breast cancer within a 22 year follow-up period. Through a nested case-control study design, future breast cancer patients and matching controls (n = 2398) were analysed. Using nuclear magnetic resonance (NMR) spectroscopy, 28 metabolites and 112 lipoprotein subfractions were quantified from prediagnostic serum samples. Logistic regression was used to test metabolites and lipoprotein subfractions for associations with breast cancer risk and partial least-squares discriminant analysis (PLS-DA) models were built to predict future disease.ResultsAmong premenopausal women (554 cases) 14 lipoprotein subfractions were associated with long-term breast cancer risk. In specific, different subfractions of VLDL particles (in particular VLDL-2, VLDL-3 and VLDL-4) were inversely associated with breast cancer. For total VLDL: apolipoprotein B, cholesterol, free cholesterol and phospholipids were inversely associated with premenopausal breast cancer risk, and in addition total and HDL-4 triglycerides. No significant association was found in postmenopausal women.ConclusionsWe identified several associations between lipoprotein subfractions and long-term risk of breast cancer in premenopausal women. Inverse associations between several VLDL subfractions and breast cancer risk were found, revealing an altered metabolism in the endogenous lipid pathway many years prior to a breast cancer diagnosis.

SummaryThe Trøndelag Health Study (HUNT) is a population-based cohort of ∼229,000 individuals recruited in four waves beginning in 1984 in Trøndelag County, Norway. ∼88,000 of these individuals have available genetic data from array genotyping. HUNT participants were recruited during 4 community-based recruitment waves and provided information on health-related behaviors, self-reported diagnoses, family history of disease, and underwent physical examinations. Linkage via the Norwegian personal identification number integrates digitized health care information from doctor visits and national health registries including death, cancer and prescription registries. Genome-wide association studies of HUNT participants have provided insights into the mechanism of cardiovascular, metabolic, osteoporotic and liver-related diseases, among others. Unique features of this cohort that facilitate research include nearly 40 years of longitudinal follow-up in a motivated and well-educated population, family data, comprehensive phenotyping, and broad availability of DNA, RNA, urine, fecal, plasma, and serum samples.

Nuclear magnetic resonance (NMR) spectroscopy allows for simultaneous detection of a wide range of metabolites and lipids. As metabolites act together in complex metabolic networks, they are often highly correlated, and optimal biological insight is achieved when using methods that take the correlation into account. For this reason, latent-variable-based methods, such as principal component analysis and partial least-squares discriminant analysis, are widely used in metabolomic studies. However, with increasing availability of larger population cohorts, and a shift from analysis of spectral data to using quantified metabolite levels, both more traditional statistical approaches and alternative machine learning methods have become more widely used. This review aims at providing an overview of the current state-of-the-art multivariate methods for the analysis of NMR-based metabolomic data as well as alternative methods, highlighting their strengths and limitations.

Background The prognosis for women with locally advanced breast cancer (LABC) is poor and there is a need for better treatment stratification. Gray-level co-occurrence matrix (GLCM) texture analysis of magnetic resonance (MR) images has been shown to predict pathological response and could become useful in stratifying patients to more targeted treatments. Purpose To evaluate the ability of GLCM textural features obtained before neoadjuvant chemotherapy to predict overall survival (OS) seven years after diagnosis of patients with LABC. Material and Methods This retrospective study includes data from 55 patients with LABC. GLCM textural features were extracted from segmented tumors in pre-treatment dynamic contrast-enhanced 3-T MR images. Prediction of OS by GLCM textural features was assessed and compared to predictions using traditional clinical variables. Results Linear mixed-effect models showed significant differences in five GLCM features (f1, f2, f5, f10, f11) between survivors and non-survivors. Using discriminant analysis for prediction of survival, GLCM features from 2 min post-contrast images achieved a classification accuracy of 73% ( P 1, f2, f10, and f11 provided significantly different survival curves in Kaplan–Meier analysis. Conclusion This study shows a clear association between textural features from post-contrast images obtained before neoadjuvant chemotherapy and OS seven years after diagnosis. Further studies in larger cohorts should be undertaken to investigate how this prognostic information can be used to benefit treatment stratification.

Metabolomics analysis of biofluids is a feasible tool for disease characterization and monitoring due to its minimally invasive nature. To reduce unwanted variation in biobanks and clinical studies, it is important to determine the effect of external factors on metabolic profiles of biofluids. In this study we examined the effect of sample collection and sample processing procedures on NMR measured serum lipoproteins and small-molecule metabolites in serum and urine, using a cohort of men diagnosed with either prostate cancer or benign prostatic hyperplasia. We determined day-to-day reliability of metabolites by systematic sample collection at two different days, in both fasting and non-fasting conditions. Study participants received prostate massage the first day to assess the differences between urine with and without prostate secretions. Further, metabolic differences between first-void and mid-stream urine samples, and the effect of centrifugation of urine samples before storage were assessed. Our results show that day-to-day reliability is highly variable between metabolites in both serum and urine, while lipoprotein subfractions possess high reliability. Further, fasting status clearly influenced the metabolite concentrations, demonstrating the importance of keeping this condition constant within a study cohort. Day-to-day reliabilities were however comparable in fasting and non-fasting samples. Urine sampling procedures such as sampling of first-void or mid-stream urine, and centrifugation or not before sample storage, were shown to only have minimal effect on the overall metabolic profile, and is thus unlikely to constitute a confounder in clinical studies utilizing NMR derived metabolomics.

Angiogenesis is necessary for tumor growth and has been targeted in breast cancer; however, it is unclear which patients will respond and benefit from antiangiogenic therapy. We report noninvasive monitoring of patient response to neoadjuvant chemotherapy given alone or in combination with anti‐vascular endothelial growth factor (bevacizumab) in a randomized clinical trial. At four time points during neoadjuvant chemotherapy ± bevacizumab of receptor tyrosine‐protein kinase erbB‐2‐negative breast cancers, we measured metabolites and inflammation‐related markers in patient's serum. We report significant changes in the levels of several molecules induced by bevacizumab, the most prominent being an increase in pentraxin 3 (PTX3) and von Willebrand factor (VWF). Serum levels of AXL, VWF and pulmonary and activation‐regulated cytokine (PARC/CCL18) reflected response to chemotherapy alone or in combination with bevacizumab. We further analyzed serum cytokines in relation to tumor characteristics such as gene expression, tumor metabolites and tumor infiltrating leukocytes. We found that VWF and growth‐differentiation factor 15 tumor mRNA levels correlated with their respective serum protein levels suggesting that these cytokines may be produced by tumors and outflow to the bloodstream while influencing the tumor microenvironment locally. Finally, we used binomial logistic regression which allowed to predict patient's response using only 10 noninvasive biomarkers. Our study highlights the potential of monitoring circulating levels of cytokines and metabolites during breast cancer therapy © 2019 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Levels of zinc, along with its mechanistically related metabolites citrateand aspartate, are widely reported as reduced in prostate cancer compared to healthytissue and are therefore pointed out as potential cancer biomarkers. Previously, it hasonly been possible to analyze zinc and metabolites by separate detection methods.Through matrix-assisted laser desorption/ionization mass spectrometry imaging (MSI),we were for thefirst time able to demonstrate, in two different sample sets (n= 45 andn= 4), the simultaneous spatial detection of zinc, in the form of ZnCl3−, together withcitrate, aspartate, andN-acetylaspartate on human prostate cancer tissues. The reliabilityof the ZnCl3−detection was validated by total zinc determination using laser ablationinductively coupled plasma MSI on adjacent serial tissue sections. Zinc, citrate, andaspartate were correlated with each other (ranger= 0.46 to 0.74) and showed asignificant reduction in cancer compared to non-cancer epithelium (p< 0.05, log2foldchange range:−0.423 to−0.987), while no significant difference between cancer andstroma tissue was found. Simultaneous spatial detection of zinc and its metabolites is notonly a valuable tool for analyzing the role of zinc in prostate metabolism but might alsoprovide a fast and simple method to detect zinc, citrate, and aspartate levels as abiomarker signature for prostate cancer diagnostics and prognostics. This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.

Altered metabolism is one of the key molecular characteristics of prostate cancer (PCa) development, and a number of studies have searched for metabolic biomarkers in patient-derived samples. Reported metabolic changes in PCa tissue compared with normal tissue include reduced levels of citrate and spermine, elevated lipid synthesis and fatty acid oxidation, and higher levels of the amino acids alanine, glutamine, and glutamate. Recent studies have investigated the tumor microenvironment, such as reactive stroma, and so-called metabolic field effects. Furthermore, analysis of blood and urine samples reveals other, but significant, metabolic differences between patients with PCa and controls. Large-cohort studies show promising results for assessing future PCa risk from blood samples. In addition, metabolic profiling of extracellular vesicles from urine has gained interest as a noninvasive and more prostate-specific approach to diagnose PCa. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Acetaminophen (APAP) is one of the most commonly used analgesics worldwide, and overdoses are associated with lactic acidosis, hepatocyte toxicity, and acute liver failure due to oxidative stress and mitochondrial dysfunction. Hepatoma cell lines typically lack the CYP450 activity to generate the reactive metabolite of APAP observed in vivo, but are still subject to APAP cytotoxicity. In this study, we employed metabolic profiling and isotope labelling approaches to investigate the metabolic impact of acute exposure to cytotoxic doses of APAP on the widely used HepG2 cell model. We found that APAP exposure leads to limited cellular death and substantial growth inhibition. Metabolically, we observed an up-regulation of glycolysis and lactate production with a concomitant reduction in carbon from glucose entering the pentose-phosphate pathway and the TCA cycle. This was accompanied by a depletion of cellular NADPH and a reduction in the de novo synthesis of fatty acids and the amino acids serine and glycine. These events were not associated with lower reduced glutathione levels and no glutathione conjugates were seen in cell extracts. Co-treatment with a specific inhibitor of the lactate/H+ transporter MCT1, AZD3965, led to increased apoptosis in APAP-treated cells, suggesting that lactate accumulation could be a cause of cell death in this model. In conclusion, we show that APAP toxicity in HepG2 cells is largely independent of oxidative stress, and is linked instead to a decoupling of glycolysis from the TCA cycle, lactic acidosis, reduced NADPH production, and subsequent suppression of the anabolic pathways required for rapid growth.

Longitudinal intervention studies with repeated measurements over time are an important type of experimental design in biomedical research. Due to the advent of “omics”-sciences (genomics, transcriptomics, proteomics, metabolomics), longitudinal studies generate increasingly multivariate outcome data. Analysis of such data must take both the longitudinal intervention structure and multivariate nature of the data into account. The ASCA+-framework combines general linear models with principal component analysis and can be used to separate and visualize the multivariate effect of different experimental factors. However, this methodology has not yet been developed for the more complex designs often found in longitudinal intervention studies, which may be unbalanced, involve randomized interventions, and have substantial missing data. Here we describe a new methodology, repeated measures ASCA+ (RM-ASCA+), and show how it can be used to model metabolic changes over time, and compare metabolic changes between groups, in both randomized and non-randomized intervention studies. Tools for both visualization and model validation are discussed. This approach can facilitate easier interpretation of data from longitudinal clinical trials with multivariate outcomes., Author summary Clinical trials are increasingly generating large amounts of complex biological data. Examples can include measuring metabolism or gene expression in tissue or blood sampled repeatedly over the course of a treatment. In such cases, one might wish to compare changes in not one, but hundreds, or thousands of variables simultaneously. In order to effectively analyze such data, both the study design and the multivariate nature of the data should be considered during data analysis. ANOVA simultaneous component analysis+ (ASCA+) is a statistical method which combines general linear models with principal component analysis, and provides a way to separate and visualize the effects of different factors on complex biological data. In this work, we describe how repeated measures linear mixed models, a class of models commonly used when analyzing changes over time and treatment effects in longitudinal studies, can be used together with ASCA+ for analyzing clinical trials in a novel method called repeated measures-ASCA+ (RM-ASCA+).

Patients with locally advanced breast cancer have a worse prognosis compared to patients with localized tumors and require neoadjuvant treatment before surgery. The aim of this study was to characterize the systemic metabolic effect of neoadjuvant chemotherapy in patients with large primary breast cancers and to relate these changes to treatment response and long-term survival. This study included 132 patients with large primary breast tumors randomized to receive neoadjuvant chemotherapy with or without the addition of the antiangiogenic drug Bevacizumab. Tumor biopsies and serum were collected before and during treatment and, serum additionally 6 weeks after surgery. Samples were analyzed by nuclear magnetic resonance spectroscopy (NMR). Correlation analysis showed low correlations between metabolites measured in cancer tissue and serum. Multilevel partial least squares discriminant analysis (PLS-DA) showed clear changes in serum metabolite levels during treatment (p-values ≤ 0.001), including unfavorable changes in lipid levels. PLS-DA revealed metabolic differences between tissue samples from survivors and nonsurvivors collected 12 weeks into treatment with an accuracy of 72% (p-value = 0.005); however, this was not evident in serum samples. Our results demonstrate a potential clinical application for serum-metabolomics for patient monitoring during and after treatment, and indicate potential for tissue NMR spectroscopy for predicting patient survival.

Metabolic profiling of biofluids by nuclear magnetic resonance (NMR) spectroscopy serves as an important tool in disease characterization, and its accuracy largely depends on the quality of samples. We aimed to explore possible effects of repeated freeze–thaw cycles (FTCs) on concentrations of lipoprotein parameters in serum and metabolite concentrations in serum and urine samples. After one to five FTCs, serum and urine samples (n= 20) were analyzed by NMR spectroscopy, and 112 lipoprotein parameters, 20 serum metabolites, and 35 urine metabolites were quantified by a commercial analytical platform. Principal component analysis showed no systematic changes related to FTCs, and samples from the same donor were closely clustered, showing a higher between-subject variation than within-subject variation. The coefficients of variation were small (medians of 4.3%, 11.0%, and 4.9% for lipoprotein parameters and serum and urine metabolites, respectively). Minor, but significant accumulated freeze–thaw effects were observed for 32 lipoprotein parameters and one serum metabolite (acetic acid) when comparing FTC1 to further FTCs. Remaining lipoprotein and metabolite concentrations showed no significant change. In conclusion, five FTCs did not significantly alter the concentrations of urine metabolites and introduced only minor changes to serum lipoprotein parameters and metabolites evaluated by the NMR-based platform.

NMR-based metabolomics has shown promise in the diagnosis of diseases as it enables identification and quantification of metabolic biomarkers. Using high-resolution magic-angle-spinning (HR-MAS) NMR spectroscopy, metabolic profiles from intact tissue specimens can be obtained with high spectral resolution. In addition, HR-MAS NMR requires minimal sample preparation and the sample is kept intact for subsequent analyses. In this chapter, we describe a typical protocol for NMR-based metabolomics of tissue samples. We cover all major steps ranging from tissue sample collection to determination of biomarkers, including experimental precautions taken to ensure reproducible and reliable reporting of data in the area of clinical application.

Background: Metabolomic characterization of tumours can potentially improve prediction of cancer prognosis and treatment response. Here, we describe efforts to validate previous metabolomic findings using a historical cohort of breast cancer patients and discuss challenges with using older biobanks collected with non-standardized sampling procedures. Methods: In total, 100 primary breast cancer samples were analysed by high-resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) and subsequently examined by histology. Metabolomic profiles were related to the presence of cancer tissue, hormone receptor status, T-stage, N-stage, and survival. RNA integrity number (RIN) and metabolomic profiles were compared with an ongoing breast cancer biobank. Results: The 100 samples had a median RIN of 4.3, while the ongoing biobank had a significantly higher median RIN of 6.3 (p = 5.86 × 10−7). A low RIN was associated with changes in choline-containing metabolites and creatine, and the samples in the older biobank showed metabolic differences previously associated with tissue degradation. The association between metabolomic profile and oestrogen receptor status was in accordance with previous findings, however, with a lower classification accuracy. Conclusions: Our findings highlight the importance of standardized biobanking procedures in breast cancer metabolomics studies. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-) license (http://creativecommons.org/licenses/by/4.0/).

NMR-based metabolomics has shown promise in the diagnosis of diseases as it enables identification and quantification of metabolic biomarkers. Using high-resolution magic-angle-spinning (HR-MAS) NMR spectroscopy, metabolic profiles from intact tissue specimens can be obtained with high spectral resolution. In addition, HR-MAS NMR requires minimal sample preparation and the sample is kept intact for subsequent analyses. In this chapter, we describe a typical protocol for NMR-based metabolomics of tissue samples. We cover all major steps ranging from tissue sample collection to determination of biomarkers, including experimental precautions taken to ensure reproducible and reliable reporting of data in the area of clinical application.

Introduction Human pregnancy is a state of elevated maternal systemic inflammation, and pregnancy complications are often associated with a dysfunctional immune response. The network of cytokines reflects this complex immune activity, and broad serum cytokine profiling provides a new tool to understand the changes in immune status during pregnancy. Objective This study aimed to determine how maternal serum cytokine patterns change during the first half of pregnancy. Methods Maternal peripheral serum samples collected at a mean gestation of 10, 13, 18 and 24 weeks were included from a prospective clinical study of healthy women (n = 110) in first half of normal pregnancy. The serum samples were analysed for 27 different cytokines using multiplex magnetic bead-based immunoassays, and high sensitivity C-reactive protein (CRP) was analysed by ELISA. Serum cytokine and CRP patterns were explored with linear mixed effects models (LMM) and multilevel partial least squares discriminant analysis (PLS-DA). Results Serum cytokine profiling provided partial overview of the maternal immune status and corresponding reference values for serum cytokine levels during the first half of pregnancy. Several cytokines decreased in concentration from first to second trimester. Cytokine pattern analysis revealed that chemokines provided the most sensitive measurement of variation with gestational age in normal pregnancies. The nine inflammatory cytokines showed the highest intra-group correlation during pregnancy, while CRP levels did not correlate with changes in the inflammatory cytokines. Conclusion Chemokines showed the greatest gestational variation and inflammatory cytokines showed a strong intra-group correlation during the first half of pregnancy.

Reactive stroma is a tissue feature commonly observed in the tumor microenvironment of prostate cancer and has previously been associated with more aggressive tumors. The aim of this study was to detect differentially expressed genes and metabolites according to reactive stroma content measured on the exact same prostate cancer tissue sample. Reactive stroma was evaluated using histopathology from 108 fresh frozen prostate cancer samples gathered from 43 patients after prostatectomy (Biobank1). A subset of the samples was analyzed both for metabolic (n = 85) and transcriptomic alterations (n = 78) using high resolution magic angle spinning magnetic resonance spectroscopy (HR-MAS MRS) and RNA microarray, respectively. Recurrence-free survival was assessed in patients with clinical follow-up of minimum five years (n = 38) using biochemical recurrence (BCR) as endpoint. Multivariate metabolomics and gene expression analysis compared low (≤15%) against high reactive stroma content (≥16%). High reactive stroma content was associated with BCR in prostate cancer patients even when accounting for the influence of Grade Group (Cox hazard proportional analysis, p = 0.013). In samples with high reactive stroma content, metabolites and genes linked to immune functions and extracellular matrix (ECM) remodeling were significantly upregulated. Future validation of these findings is important to reveal novel biomarkers and drug targets connected to immune mechanisms and ECM in prostate cancer. The fact that high reactive stroma grading is connected to BCR adds further support for the clinical integration of this histopathological evaluation. © The Author(s) 2018. This article is licensed under a Creative Commons Attribution 4.0 International License.

Metabolic profiles reflect biological conditions as a result of biochemical changes within a living system. It is therefore possible to associate metabolic signatures with clinical endpoints of diseases, such as breast cancer. Nuclear magnetic resonance (NMR) spectroscopy is one of the most common techniques used for metabolic profiling, and produces high dimensional datasets from which meaningful biological information can be extracted. Here, we present an overview of data analysis techniques used to achieve this, describing key steps in the procedure. Moreover, examples of clinical endpoints of interest are provided. Although these are specific for breast cancer, the procedures for the analysis of NMR spectra as described here are applicable to any type of cancer and to other diseases.

Metabolomics is the branch of “omics” technologies that involves high‐throughput identification and quantification of small‐molecule metabolites in the metabolome. NMR‐based spectroscopy of biofluids represents a potential method for non‐invasive characterization of cancer. While the metabolism of cancer cells is altered compared with normal non‐proliferating cells, the metabolome of several biofluids (e.g. blood and urine) reflects the metabolism of the entire organism. This review provides an update on the current status of NMR metabolomics analysis of biofluids with respect to: (i) cancer risk assessment; (ii) cancer detection; (iii) disease characterization and prognosis; and (iv) treatment monitoring. We conclude that many studies show impressive associations between biofluid metabolomics and cancer progression, and suggest that NMR metabolomics can be used to provide information with prognostic or predictive value. However, translation of these findings to clinical practice is currently hindered by a lack of validation, difficulties in biological interpretation, and non‐standardized analytical procedures. Locked until 19.4.2019 due to copyright restrictions. This is the peer reviewed version of the following article: [NMR‐based metabolomics of biofluids in cancer], which has been published in final form at [https://doi.org/10.1002/nbm.3927]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

Background Breast cancer treatment has metabolic side effects, potentially affecting risk of cardiovascular disease (CVD) and recurrence. We aimed to compare alterations in serum metabolites and lipoproteins during treatment between recipients and non-recipients of chemotherapy, and describe metabolite profiles associated with treatment-related weight gain. Methods This pilot study includes 60 stage I/II breast cancer patients who underwent surgery and were treated according to national guidelines. Serum sampled pre-surgery and after 6 and 12 months was analysed by MR spectroscopy and mass spectrometry. In all, 170 metabolites and 105 lipoprotein subfractions were quantified. Results The metabolite and lipoprotein profiles of chemotherapy recipients and non-recipients changed significantly 6 months after surgery (p