Title: Human Serum/Plasma Glycoprotein Analysis by 1H-NMR, an Emerging Method of Inflammatory Assessment

Associations among NMR-measured inflammatory and metabolic biomarkers and accelerated aging in cardiac catheterization patients

Research into aging has grown substantially with the creation of molecular biomarkers of biological age that can be used to determine age acceleration. Concurrently, nuclear magnetic resonance (NMR) assessment of biomarkers of inflammation and metabolism provides researchers with new ways to examine intermediate risk factors for chronic disease. We used data from a cardiac catheterization cohort to examine associations between biomarkers of cardiometabolic health and accelerated aging assessed using both gene expression (Transcriptomic Age) and DNA methylation (Hannum Age, GrimAge, Horvath Age, and Phenotypic Age). Linear regression models were used to associate accelerated aging with each outcome (cardiometabolic health biomarkers) while adjusting for chronological age, sex, race, and neighborhood socioeconomic status. Our study shows a robust association between GlycA and GrimAge (5.71, 95% CI = 4.36, 7.05, P = 7.94 × 10-16), Hannum Age (1.81, 95% CI = 0.65, 2.98, P = 2.30 × 10-3), and Phenotypic Age (2.88, 95% CI = 1.91, 3.87, P = 1.21 × 10-8). We also saw inverse associations between apolipoprotein A-1 and aging biomarkers. These associations provide insight into the relationship between aging and cardiometabolic health that may be informative for vulnerable populations.

Quantification of lipoproteins by proton nuclear magnetic resonance spectroscopy (1H-NMRS) improves the prediction of cardiac autonomic dysfunction in patients with type 1 diabetes

AIMS

To assess if advanced characterization of serum glycoprotein and lipoprotein profile, measured by proton nuclear magnetic resonance spectroscopy (1H-NMRS) improves a predictive clinical model of cardioautonomic neuropathy (CAN) in subjects with type 1 diabetes (T1D).

METHODS

Cross-sectional study (ClinicalTrials.gov Identifier: NCT04950634). CAN was diagnosed using Ewing's score. Advanced characterization of macromolecular complexes including glycoprotein and lipoprotein profiles in serum samples were measured by 1H-NMRS. We addressed the relationships between these biomarkers and CAN using correlation and regression analyses. Diagnostic performance was assessed by analyzing their areas under the receiver operating characteristic curves (AUCROC).

RESULTS

Three hundred and twenty-three patients were included (46% female, mean age and duration of diabetes of 41 ± 13 years and 19 ± 11 years, respectively). The overall prevalence of CAN was 28% [95% confidence interval (95%CI): 23; 33]. Glycoproteins such as N-acetylglucosamine/galactosamine and sialic acid showed strong correlations with inflammatory markers such as high-sensitive C-reactive protein, fibrinogen, IL-10, IL-6, and TNF-α. On the contrary, we did not find any association between the former and CAN. A stepwise binary logistic regression model (R2 = 0.078; P = 0.003) retained intermediate-density lipoprotein-triglycerides (IDL-TG) [β:0.082 (95%CI: 0.005; 0.160); P = 0.039], high-density lipoprotein-triglycerides (HDL-TGL)/HDL-Cholesterol [β:3.633 (95%CI: 0.873; 6.394); P = 0.010], and large-HDL particle number [β: 3.710 (95%CI: 0.677; 6.744); P = 0.001] as statistically significant determinants of CAN. Adding these lipoprotein particles to a clinical prediction model of CAN that included age, duration of diabetes, and A1c enhanced its diagnostic performance, improving AUCROC from 0.546 (95%CI: 0.404; 0.688) to 0.728 (95%CI: 0.616; 0.840).

CONCLUSIONS

When added to clinical variables, 1H-NMRS-lipoprotein particle profiles may be helpful to identify those patients with T1D at risk of CAN.

Multiomics tools for improved atherosclerotic cardiovascular disease management

Multiomics studies offer accurate preventive and therapeutic strategies for atherosclerotic cardiovascular disease (ASCVD) beyond traditional risk factors. By using artificial intelligence (AI) and machine learning (ML) approaches, it is possible to integrate multiple 'omics and clinical data sets into tools that can be utilized for the development of personalized diagnostic and therapeutic approaches. However, currently multiple challenges in data quality, integration, and privacy still need to be addressed. In this opinion, we emphasize that joined efforts, exemplified by the AtheroNET COST Action, have a pivotal role in overcoming the challenges to advance multiomics approaches in ASCVD research, with the aim to foster more precise and effective patient care.

HDL-Related Parameters and COVID-19 Mortality: The Importance of HDL Function

COVID-19, caused by the SARS-CoV-2 coronavirus, emerged as a global pandemic in late 2019, resulting in significant global public health challenges. The emerging evidence suggests that diminished high-density lipoprotein (HDL) cholesterol levels are associated with the severity of COVID-19, beyond inflammation and oxidative stress. Here, we used nuclear magnetic resonance spectroscopy to compare the lipoprotein and metabolic profiles of COVID-19-infected patients with non-COVID-19 pneumonia. We compared the control group and the COVID-19 group using inflammatory markers to ensure that the differences in lipoprotein levels were due to COVID-19 infection. Our analyses revealed supramolecular phospholipid composite (SPC), phenylalanine, and HDL-related parameters as key discriminators between COVID-19-positive and non-COVID-19 pneumonia patients. More specifically, the levels of HDL parameters, including apolipoprotein A-I (ApoA-I), ApoA-II, HDL cholesterol, and HDL phospholipids, were significantly different. These findings underscore the potential impact of HDL-related factors in patients with COVID-19. Significantly, among the HDL-related metrics, the cholesterol efflux capacity (CEC) displayed the strongest negative association with COVID-19 mortality. CEC is a measure of how well HDL removes cholesterol from cells, which may affect the way SARS-CoV-2 enters cells. In summary, this study validates previously established markers of COVID-19 infection and further highlights the potential significance of HDL functionality in the context of COVID-19 mortality.

Towards clinical application of GlycA and GlycB for early detection of inflammation associated with (pre)diabetes and cardiovascular disease: recent evidence and updates

Cardiometabolic diseases are associated with low-grade inflammation early in life and persists into old age. The long latency period presents opportunities for early detection, lifestyle modification and intervention. However, the performance of conventional biomarker assays to detect low-grade inflammation has been variable, particularly for early-stage cardiometabolic disorder including prediabetes and subclinical atherosclerotic vascular inflammation. During the last decade, the application of nuclear magnetic resonance (NMR) spectroscopy for metabolic profiling of biofluids in translational and epidemiological research has advanced to a stage approaching clinical application. Proton (1H)-NMR profiling induces no destructible physical changes to specimens, and generates quantitative signals from deconvoluted spectra that are highly repeatable and reproducible. Apart from quantitative analysis of amino acids, lipids/lipoproteins, metabolic intermediates and small proteins, 1H-NMR technology is unique in being able to detect composite signals of acute-phase and low-grade inflammation indicated by glycosylated acetyls (GlycA) and N-acetylneuraminic acid (sialic acid) moieties (GlycB). Different from conventional immunoassays that target epitopes and are susceptible to conformational variation in protein structure and binding, GlycA and GlycB signals are stable over time, and maybe complementary as well as superior to high-sensitivity C-reactive protein and other inflammatory cytokines. Here we review the physicochemical principles behind 1H-NMR profiling of GlycA and GlycB, and the available evidence supporting their potential clinical application for the prediction of incident (pre)diabetes, cardiovascular disease, and adverse outcomes.

GlycA as a Novel Biomarker of Systemic Inflammation in Hypothyroidism

Background: GlycA is a novel glycoprotein biomarker of systemic inflammation and cardiovascular risk. Our objective was to assess the levels of GlycA in individuals with hypothyroidism. We also explored whether levothyroxine (LT4)-treated patients had different levels of GlycA, with attention to thyrotropin (TSH) levels. Methods: We performed a cross-sectional analysis, using baseline data from the ELSA-Brasil cohort study. We included only participants with serum TSH and GlycA levels measurements, using magnetic resonance spectroscopy (n = 4745). We excluded individuals with endogenous hyperthyroidism and those using drugs impacting thyroid function. Participants not taking LT4 and whose serum TSH was 0.4-4.0 mIU/L were classified as euthyroid (EU) and those with elevated TSH as undiagnosed hypothyroidism (UH). For those on LT4 (n = 345), adequacy of treatment was defined as TSH within the reference range. Those with TSH <0.4 mIU/L were considered over-treated (OT), and those >4.0 mIU/L, under-treated (UT). Both (UT+OT) were considered inadequately treated (IT). Group comparisons were performed by Kruskal-Wallis, adjusted Chi-square, and the post hoc Dunn test. Additional subgroup analysis were performed in patients with circulating thyroperoxidase antibodies (TPO-Ab+). Respective multivariable analyses were performed to evaluate the relationship between thyroid-related variables and GlycA levels (Generalized Linear Model), as well as an abnormal GlycA (>400 μmol/L; Logistic Binary Regression). Results: The prevalence rate of UH was 9.8% (467/4745) and, among those on LT4, only 61.7% (213/345) were adequately treated (AT). GlycA levels were higher in IT in comparison to EU (429 vs. 410 μmol/L, p < 0.01) but did not differ between UH (413 μmol/L) and euthyroidism. However, the subgroup analysis of those TPO-Ab+ showed that not only those with IT, but also those with UH, had higher levels of GlycA in comparison to euthyroidism (423 and 424 vs. 402 μmol/L, p = 0.04). This association between higher levels of GlycA and IT was maintained even in multivariable analysis (odds ratio 1.53, confidence interval 1.03 to 2.31) Lower levels of GlycA were detected in AT (405 μmol/L,) compared with OT (432 μmol/L, 0.04) and UT (423 μmol/L, p = 0.02). Conclusions: Patients with IT, both OT and UT, had higher GlycA levels, which may be associated with low-grade systemic inflammation and, possibly, increased cardiovascular risk.

Elemental and molecular characterization of degrading blood pools

Blood is a commonly encountered type of biological evidence and can provide critical information about the crime that occurred. The ability to accurately and precisely determine the time since deposition (TSD) of a bloodstain is highly sought after in the field of forensic science. Current spectral methods for determining TSD are typically developed using small volume bloodstains, we investigate the applicability to larger volume blood pools where drying and degradation mechanics are different. We explored the differences that exist between the surface and bulk of dried segments from fragments collected from 15 mL dried blood pools and identified heterogeneity using RGB colour analysis and hierarchical cluster analysis (HCA). The physical, molecular, and atomic differences between the layers were further investigated using scanning electron microscopy (SEM), X-Ray photoelectron spectroscopy (XPS), attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy, and Raman spectroscopy. SEM identified different morphology on the surface and the bulk indicative of density-dependant cellular settling. XPS revealed that iron was not present on the surface but rather was present in the bulk where the red blood cells had settled. The oxidation state of the iron was quantified over three weeks in which it transitioned from entirely Fe2+ to primarily Fe3+, as expected for ex vivo degradation of hemoglobin. Further, indications of amide saponification occurring at the blood-air interface were identified in the increased quantity of the C-O moiety relative to CO, and the formation of free amines and OC-ONa groups over time. ATR-FTIR and Raman spectroscopy provided insights into differences in the molecular composition of the layers, suggesting that the surface consists of more nucleic acids, lipids, and glycoproteins than the bulk, which was dominated by proteins (p < 0.001% using principal component analysis (PCA)). Additionally, spectral band trends previously reported to have applicability to the estimation of TSD were observed for the bulk portion of the blood pool as the Hb underwent predictable time dependant changes from oxyHb to metHb. PCA was performed based on all spectral data which demonstrated statistically significant differences between the surface and bulk, as well as proof-of-concept for linear TSD estimation models.

Towards a Precise NMR Quantification of Acute Phase Inflammation Proteins from Human Serum

Nuclear Magnetic Resonance (NMR) spectra of human serum and plasma show, besides metabolites and lipoproteins, two characteristic signals termed GlycA and B arising from the acetyl groups of glycoprotein glycans from acute phase proteins, which constitute good markers for inflammatory processes. Here, we report a comprehensive assignment of glycoprotein glycan NMR signals observed in human serum, showing that GlycA and GlycB signals originate from Neu5Ac and GlcNAc moieties from N-glycans, respectively. Diffusion-edited NMR experiments demonstrate that signal components can be associated with specific acute phase proteins. Conventionally determined concentrations of acute phase glycoproteins correlate well with distinct features in NMR spectra (R2 up to 0.9422, p-value <0.001), allowing the simultaneous quantification of several acute phase inflammation proteins. Overall, a proteo-metabolomics NMR signature of significant diagnostic potential is obtained within 10-20 min acquisition time. This is exemplified in serum samples from COVID-19 and cardiogenic shock patients showing significant changes in several acute phase proteins compared to healthy controls.

Special Issue: New Insight into the Molecular Role of Lipids and Lipoproteins in Vascular Diseases

Lipids and lipoproteins play a key role in cardiovascular diseases (CVD), mainly in the development of atherosclerosis [...].

Gradual Increase in Inflammation-Linked Glycoproteins and a Proatherogenic Lipoprotein Profile in the Early Stages of Psychosis as Characterized by 1H NMR Blood Analysis

Minimally invasive prognostic markers of inflammation and dyslipidemia in individuals with a risk of psychosis, also called "at-risk mental state" (ARMS), or in the first episode of psychosis (FEP) are of utmost clinical importance to prevent cardiovascular disorders. We analyzed the plasma concentration of inflammation-linked glycoproteins (Glycs) and lipoprotein subclasses by proton nuclear magnetic resonance (¹H NMR) in a single acquisition. Study participants were healthy controls (HCs, N = 67) and patients with ARMS (N = 58), FEP (N = 110), or early psychosis diagnosis with ≥2 episodes (critical period (CP), N = 53). Clinical biomarkers such as high-sensitivity C-reactive protein, interleukin 6, fibrinogen, insulin, and lipoproteins were also measured. Although all participants had normal lipoprotein profiles and no inflammation according to conventional biomarkers, a gradual increase in the Glyc ¹H NMR levels was observed from HCs to CP patients; this increase was statistically significant for GlycA (CP vs HC). In parallel, a progressive and significant proatherogenic ¹H NMR lipoprotein profile was also identified across stages of psychosis (ARMS and CP vs HC). These findings highlight the potential of using ¹H NMR Glyc and lipoprotein profiling to identify blood changes in individuals with ARMS or FEP and pave the way for applications using this technology to monitor metabolic and cardiovascular risks in clinical psychiatry.

Blood Metabolite Profiling of Antarctic Expedition Members: An 1H NMR Spectroscopy-Based Study

Serum samples from eight participants during the XV winter-over at Concordia base (Antarctic expedition) collected at defined time points, including predeparture, constituted the key substrates for a specific metabolomics study. To ascertain acute changes and chronic adaptation to hypoxia, the metabolic profiles of the serum samples were analyzed using NMR spectroscopy, with principal components analysis (PCA) followed by partial least squares and orthogonal partial least squares discriminant analyses (PLS-DA and OPLS-DA) used as supervised classification methods. Multivariate data analyses clearly highlighted an adaptation period characterized by an increase in the levels of circulating glutamine and lipids, mobilized to supply the body energy needs. At the same time, a reduction in the circulating levels of glutamate and N-acetyl glycoproteins, stress condition indicators, and proinflammatory markers were also found in the NMR data investigation. Subsequent pathway analysis showed possible perturbations in metabolic processes, potentially related to the physiological adaptation, predominantly found by comparing the baseline (at sea level, before mission onset), the base arrival, and the mission ending collected values.

Metabolic signature of healthy lifestyle and risk of rheumatoid arthritis: observational and Mendelian randomization study

BACKGROUND

While substantial evidence reveals that healthy lifestyle behaviors are associated with a lower risk of rheumatoid arthritis (RA), the underlying metabolic mechanisms remain unclear.

OBJECTIVES

This study aimed to identify the metabolic signature reflecting a healthy lifestyle and investigate its observational and genetic linkage with RA risk.

METHODS

This study included 87,258 UK Biobank participants (557 cases of incident RA) aged 37 to 73 years with complete lifestyle, genotyping and nuclear magnetic resonance (NMR) metabolomics data. A healthy lifestyle was assessed based on five factors: healthy diet, regular exercise, not smoking, moderate alcohol consumption, and normal body mass index. The metabolic signature was developed by summing selected metabolites' concentrations weighted by the coefficients using elastic net regression. We used multivariate Cox model to assess the associations between metabolic signatures and RA risk, and examined the mediating role of the metabolic signature in the impact of a healthy lifestyle on RA. We performed genome-wide association analysis (GWAS) to obtain genetic variants associated with the metabolic signature, then conducted Mendelian randomization (MR) analyses to detect causality.

RESULTS

The metabolic signature comprised of 81 metabolites, robustly correlated with healthy lifestyle ( r = 0.45, P = 4.2 × 10^-15). The metabolic signature was inversely associated with RA risk (HR per SD increment: 0.76, 95% CI: 0.70-0.83), and largely explained protective effects of healthy lifestyle on RA with 64% (95%CI: 50.4-83.3) mediation proportion. One and two-sample MR analyses also consistently showed the associations of genetically inferred per SD increment in metabolic signature with a reduction in RA risk (HR: 0.84, 95% CI: 0.75-0.94, P = 0.002 and OR: 0.84, 95% CI: 0.73-0.97, P = 0.02 respectively).

CONCLUSION

Our findings implicate the metabolic signature reflecting healthy lifestyle as a potential causal mediator in the development of RA, highlighting the importance of early lifestyle intervention and metabolic tracking for precise prevention of RA.

Ex vivo proton spectroscopy (1 H-NMR) analysis of inborn errors of metabolism: Automatic and computer-assisted analyses

There are about 1500 genetic metabolic diseases. A small number of treatable diseases are diagnosed by newborn screening programs, which are continually being developed. However, most diseases can only be diagnosed based on clinical symptoms or metabolic findings. The main biological fluids used are urine, plasma and, in special situations, cerebrospinal fluid. In contrast to commonly used methods such as gas chromatography and high performance liquid chromatography mass spectrometry, ex vivo proton spectroscopy (¹ H-NMR) is not yet used in routine clinical practice, although it has been recommended for more than 30 years. Automatic analysis and improved NMR technology have also expanded the applications used for the diagnosis of inborn errors of metabolism. We provide a mini-overview of typical applications, especially in urine but also in plasma, used to diagnose common but also rare genetic metabolic diseases with ¹ H-NMR. The use of computer-assisted diagnostic suggestions can facilitate interpretation of the profiles. In a proof of principle, to date, 182 reports of 59 different diseases and 500 reports of healthy children are stored. The percentage of correct automatic diagnoses was 74%. Using the same ¹ H-NMR profile-targeted analysis, it is possible to apply an untargeted approach that distinguishes profile differences from healthy individuals. Thus, additional conditions such as lysosomal storage diseases or drug interferences are detectable. Furthermore, because ¹ H-NMR is highly reproducible and can detect a variety of different substance categories, the metabolomic approach is suitable for monitoring patient treatment and revealing additional factors such as nutrition and microbiome metabolism. Besides the progress in analytical techniques, a multiomics approach is most effective to combine metabolomics with, for example, whole exome sequencing, to also diagnose patients with nondetectable metabolic abnormalities in biological fluids. In this mini review we also provide our own data to demonstrate the role of NMR in a multiomics platform in the field of inborn errors of metabolism.

Multifaceted Analysis of Cerebrospinal Fluid and Serum from Progressive Multiple Sclerosis Patients: Potential Role of Vitamin C and Metal Ion Imbalance in the Divergence of Primary Progressive Multiple Sclerosis and Secondary Progressive Multiple Sclerosis

The progressive forms of multiple sclerosis (MS) primary progressive MS (PPMS) and secondary progressive MS (SPMS) are clinically distinguished by the rate at which symptoms worsen. Little is however known about the pathological mechanisms underlying the differential rate of accumulation of pathological changes. In this study, ¹H NMR spectroscopy was used to measure low-molecular-weight metabolites in paired cerebrospinal fluid (CSF) and serum of PPMS, SPMS, and control patients, as well as to determine lipoproteins and glycoproteins in serum samples. Additionally, neurodegenerative and inflammatory markers, neurofilament light (NFL) and chitinase-3-like protein 1 (CHI3L1), and the concentration of seven metal elements, Mg, Mn, Cu, Fe, Pb, Zn, and Ca, were also determined in both CSF and serum. The results indicate that the pathological changes associated with progressive MS are mainly localized in the central nervous system (CNS). More so, PPMS and SPMS patients with comparable disability status are pathologically similar in relation to neurodegeneration, neuroinflammation, and some metabolites that distinguish them from controls. However, the rapid progression of PPMS from the onset may be driven by a combination of neurotoxicity induced by heavy metals coupled with diminished CNS antioxidative capacity associated with differential intrathecal ascorbate retention and imbalance of Mg and Cu.

Glycoprotein Serum Concentrations Assessed By 1H-NMR are Increased in Patients With High Blood Pressure

BACKGROUND

Patients with hypertension present a permanent state of low-grade inflammation, as the disease activates several pro-inflammatory cells and inflammatory pathways. Glycoproteins A, B, and F, determined by proton nuclear magnetic resonance, provide a highly sensitive method for determining a group of liver-derived pro-inflammatory proteins, and their role has not yet been explored in patients with hypertension. In this study, we evaluated the impact of plasma concentrations of these glycoproteins in patients with hypertension.

METHODS

This cross-sectional study involved 340 patients attending our vascular and metabolism medicine unit. Of them, 129 were normotensive and 211 were hypertensive. Standard biochemistry and carotid ultrasound measures were performed. Serum concentrations of glycoproteins A, B, and F were determined by proton nuclear magnetic resonance.

RESULTS

Hypertensive patients presented a higher prevalence of obesity, metabolic syndrome, and diabetes and higher glycoprotein A, B, and F concentrations. Glycoproteins A, B, and F were positively correlated with systolic and diastolic blood pressure. Multivariate logistic models showed that glycoproteins A, B, and F were associated with higher odds of being hypertensive. Machine learning methods corroborated the relationship between glycoproteins and high blood pressure. The higher prevalence of carotid plaques in patients with high blood pressure was partially mediated by glycoproteins A and F.

CONCLUSIONS

Patients with hypertension present systemic, subclinical inflammation as assessed by liver-derived glycoprotein A, B, and F serum levels. These results support the effect of hypertension on the mechanisms of systemic inflammation. Hypertension-associated systemic inflammation plays a role in hypertension-associated vascular injury and probably in hypertension-induced damage to other organs.

PCSK9 Inhibitors Have Apolipoprotein C-III-Related Anti-Inflammatory Activity, Assessed by 1H-NMR Glycoprotein Profile in Subjects at High or very High Cardiovascular Risk

Atherosclerosis is a chronic inflammatory disease caused by the accumulation of cholesterol in the intima. Proprotein convertase subtilisin/kexin type 9 inhibitors (iPCSK9) can reduce low-density lipoprotein (LDL) cholesterol levels by 60%, but there is still no evidence that they can lower markers of systemic inflammation such as high-sensitivity C-reactive protein (hsCRP). Acute-phase serum glycoproteins are upregulated in the liver during systemic inflammation, and their role as inflammatory biomarkers is under clinical evaluation. In this observational study, we evaluate the effects of iPCSK9 on glycoproteins (Glyc) A, B and F. Thirty-nine patients eligible for iPCSK9 therapy were enrolled. One sample before and after one to six months of iPCSK9 therapy with alirocumab was obtained from each patient. Lipids, apolipoproteins, hsCRP and PCSK9 levels were measured by biochemical analyses, and the lipoprotein and glycoprotein profiles were measured by 1H nuclear magnetic resonance (1H-NMR). The PCSK9 inhibitor reduced total (36.27%, p < 0.001), LDL (55.05%, p < 0.001) and non-high-density lipoprotein (HDL) (45.11%, p < 0.001) cholesterol, apolipoprotein (apo) C-III (10%, p < 0.001), triglycerides (9.92%, p < 0.001) and glycoprotein signals GlycA (11.97%, p < 0.001), GlycB (3.83%, p = 0.017) and GlycF (7.26%, p < 0.001). It also increased apoA-I (2.05%, p = 0.043) and HDL cholesterol levels (11.58%, p < 0.001). Circulating PCSK9 levels increased six-fold (626.28%, p < 0.001). The decrease in Glyc signals positively correlated with the decrease in triglycerides and apoC-III. In conclusion, in addition to LDL cholesterol, iPCSK9 therapy also induces a reduction in systemic inflammation measured by 1H-NMR glycoprotein signals, which correlates with a decrease in triglycerides and apoC-III.

Glycoprotein and Lipoprotein Profiles Assessed by 1H-NMR and Its Relation to Ascending Aortic Dilatation in Bicuspid Aortic Valve Disease

INTRODUCTION

The bicuspid aortic valve (BAV) confers a high risk of ascending aorta dilatation (AAoD), although its progression seems highly variable. Furthermore, the implication of lipoprotein metabolism and inflammation in the mechanisms that underlie AAoD is not fully established. The aim of this study consisted of evaluating the impact of the lipoprotein and glycoprotein profiles in AAOD as well as its progression in BAV aortopathy.

METHODS

Using 1H-nuclear magnetic resonance (1H-NMR), we analyzed and compared the lipoprotein and glycoprotein profiles of plasma samples from 152 BAV patients with dilated and nondilated ascending aorta. Additionally, these profiles were also compared for 119 of these patients who were prospectively followed-up clinically and by echocardiography in the long-term (5 years). Ascending aorta dilation velocity (mm/year) was calculated for this analysis.

RESULTS

Several parameters related to the lipoprotein profile including remnant cholesterol, small LDL and IDL-cholesterol were found to be significantly increased in the dilated group compared to those in the nondilated group. The glycoprotein A-nuclear magnetic resonance (NMR) signal, a novel inflammation biomarker, was also observed to be increased in the dilated group. After performing multivariate analysis, remnant cholesterol remained an independent variable related to AAoD. In the long-term follow-up, proatherogenic lipoprotein parameters were related to ascending aorta dilatation velocity ascending. After a lineal regression analysis, non-HDL particles remained as an independent predictor of ascending aorta dilation velocity.

CONCLUSIONS

Patients with BAV and AAoD presented a more pro-atherogenic profile assessed by 1H-NMR, especially related to triglyceride-rich lipoproteins. This pro-atherogenic profile seems to contribute to the higher growth rate of ascending aorta diameter.

Quantitative Serum NMR Spectroscopy Stratifies COVID-19 Patients and Sheds Light on Interfaces of Host Metabolism and the Immune Response with Cytokines and Clinical Parameters

The complex manifestations of COVID-19 are still not fully decoded on the molecular level. We combined quantitative the nuclear magnetic resonance (NMR) spectroscopy serum analysis of metabolites, lipoproteins and inflammation markers with clinical parameters and a targeted cytokine panel to characterize COVID-19 in a large (534 patient samples, 305 controls) outpatient cohort of recently tested PCR-positive patients. The COVID-19 cohort consisted of patients who were predominantly in the initial phase of the disease and mostly exhibited a milder disease course. Concerning the metabolic profiles of SARS-CoV-2-infected patients, we identified markers of oxidative stress and a severe dysregulation of energy metabolism. NMR markers, such as phenylalanine, inflammatory glycoproteins (Glyc) and their ratio with the previously reported supramolecular phospholipid composite (Glyc/SPC), showed a predictive power comparable to laboratory parameters such as C-reactive protein (CRP) or ferritin. We demonstrated interfaces between the metabolism and the immune system, e.g., we could trace an interleukin (IL-6)-induced transformation of a high-density lipoprotein (HDL) to a pro-inflammatory actor. Finally, we showed that metadata such as age, sex and constitution (e.g., body mass index, BMI) need to be considered when exploring new biomarkers and that adding NMR parameters to existing diagnoses expands the diagnostic toolbox for patient stratification and personalized medicine.

The novel inflammatory biomarker GlycA and triglyceride-rich lipoproteins are associated with the presence of subclinical myocardial dysfunction in subjects with type 1 diabetes mellitus

BACKGROUND

Subjects with Type 1 diabetes mellitus (T1DM) have an increased incidence of heart failure (HF). Several pathophysiological mechanisms have been involved in its development. The aim of this study was to analyze the potential contribution of the advanced lipoprotein profile and plasma glycosylation (GlycA) to the presence of subclinical myocardial dysfunction in subjects with T1DM.

METHODS

We included subjects from a Danish cohort of T1DM subjects (Thousand & 1 study) with either diastolic and/or systolic subclinical myocardial dysfunction, and a control group without myocardial dysfunction, matched by age, sex and HbA1c. All underwent a transthoracic echocardiogram and an advanced lipoprotein profile obtained by using the NMR-based Liposcale® test. GlycA NMR signal was also analyzed. Systolic dysfunction was defined as left ventricular ejection fraction ≤ 45% and diastolic dysfunction was considered as E/e'≥12 or E/e' 8-12 + volume of the left atrium > 34 ml/m2. To identify a metabolic profile associated with the presence of subclinical myocardial dysfunction, a multivariate supervised model of classification based on least squares regression (PLS-DA regression) was performed.

RESULTS

One-hundred forty-six subjects had diastolic dysfunction and 18 systolic dysfunction. Compared to the control group, patients with myocardial dysfunction had longer duration of diabetes (p = 0.005), and higher BMI (p = 0.013), serum NTproBNP concentration (p = 0.001), systolic blood pressure (p < 0.001), albuminuria (p < 0.001), and incidence of advanced retinopathy (p < 0.001). The supervised classification model identified a specific pattern associated with myocardial dysfunction, with a capacity to discriminate patients with myocardial dysfunction from controls. PLS-DA showed that triglyceride-rich lipoproteins (TGRLs), such as VLDL (total VLDL particles, large VLDL subclass and VLDL-TG content) and IDL (IDL cholesterol content), as well as the plasma concentration of GlycA, were associated with the presence of subclinical myocardial dysfunction.

CONCLUSION

Proatherogenic TGRLs and the proinflammatory biomarker Glyc A are strongly associated to myocardial dysfunction in T1DM. These findings suggest a pivotal role of TGRLs and systemic inflammation in the development of subclinical myocardial dysfunction in T1DM.

Measurement of Serum N-Glycans in the Assessment of Early Vascular Aging (Arterial Stiffness) in Adults With Type 1 Diabetes

OBJECTIVE

Vascular aging (arterial stiffness [AS]) is an inflammation-linked process that predicts macro- and microvascular complications in adults with type 1 diabetes (T1D). We evaluated the utility of measuring the inflammation-linked N-glycans GlycA and GlycB to assess vascular aging in adults with T1D.

RESEARCH DESIGN AND METHODS

Eighty-four adults with T1D (>10-year duration without cardiovascular events) and 68 healthy control subjects were evaluated for clinical characteristics (including microvascular complications in patients with T1D), aortic pulse wave velocity (aPWV) (surrogate measure of AS), and serum GlycA and GlycB (peak area [concentration] and height/width [H/W] ratio) using 1H-nuclear magnetic resonance spectroscopy.

RESULTS

Patients with T1D had higher median (interquartile range) values than healthy control subjects for (P < 0.001 for all comparisons) aPWV 7.9 (6.9-9.1) vs. 6.1 (5.5-6.7) m/s, GlycA 850.4 (781.3-916.1) vs. 652.4 (581.5-727.1) μmoL; GlycB 386.1 (353.2-426.3) vs. 310.0 (280.5-331.9) μmol/L), H/W ratio of GlycA 16.5 (14.9-18.1) vs. 15.0 (13.7-16.7), and H/W ratio of GlycB 5.0 (4.6-5.5) vs. 4.0 (3.4-4.3). Moreover, aPWV correlated (P < 0.001 for all correlations) with GlycA (r = 0.550) and GlycB (r = 0.423) concentrations and with H/W ratios of GlycA (r = 0.453) and GlycB (r = 0.510). Adjusting for potential confounders, GlycA concentration (β = 0.212, P < 0.001) and the H/W ratios of GlycA (β = 0.150, P = 0.009) and GlycB (β = 0.155, P = 0.011) remained independently associated with aPWV. C-statistics for detecting individuals with aPWV >10 m/s were 0.866 (95% CI 0.794-0.937) for GlycA levels and 0.862 (0.780-0.943) for H/W ratio of GlycB.

CONCLUSIONS

Measurement of serum GlycA and GlycB may have utility in assessing vascular aging in adults with T1D of >10-year duration and no previous cardiovascular events.

Updates and Original Case Studies Focused on the NMR-Linked Metabolomics Analysis of Human Oral Fluids Part II: Applications to the Diagnosis and Prognostic Monitoring of Oral and Systemic Cancers

Human saliva offers many advantages over other biofluids regarding its use and value as a bioanalytical medium for the identification and prognostic monitoring of human diseases, mainly because its collection is largely non-invasive, is relatively cheap, and does not require any major clinical supervision, nor supervisory input. Indeed, participants donating this biofluid for such purposes, including the identification, validation and quantification of surrogate biomarkers, may easily self-collect such samples in their homes following the provision of full collection details to them by researchers. In this report, the authors have focused on the applications of metabolomics technologies to the diagnosis and progressive severity monitoring of human cancer conditions, firstly oral cancers (e.g., oral cavity squamous cell carcinoma), and secondly extra-oral (systemic) cancers such as lung, breast and prostate cancers. For each publication reviewed, the authors provide a detailed evaluation and critical appraisal of the experimental design, sample size, ease of sample collection (usually but not exclusively as whole mouth saliva (WMS)), their transport, length of storage and preparation for analysis. Moreover, recommended protocols for the optimisation of NMR pulse sequences for analysis, along with the application of methods and techniques for verifying and resonance assignments and validating the quantification of biomolecules responsible, are critically considered. In view of the authors’ specialisms and research interests, the majority of these investigations were conducted using NMR-based metabolomics techniques. The extension of these studies to determinations of metabolic pathways which have been pathologically disturbed in these diseases is also assessed here and reviewed. Where available, data for the monitoring of patients’ responses to chemotherapeutic treatments, and in one case, radiotherapy, are also evaluated herein. Additionally, a novel case study featured evaluates the molecular nature, levels and diagnostic potential of ¹H NMR-detectable salivary ‘acute-phase’ glycoprotein carbohydrate side chains, and/or their monomeric saccharide derivatives, as biomarkers for cancer and inflammatory conditions.

GlycA and GlycB as Inflammatory Markers in Chronic Heart Failure

The role of inflammation in heart failure (HF) has been extensively described, but it is uncertain whether inflammation exerts a different prognostic influence according to etiology. We aimed to examine the inflammatory state in chronic HF by measuring N-acetylglucosamine/galactosamine (GlycA) and sialic acid (GlycB), evolving proton nuclear magnetic resonance biomarkers of systemic inflammation, and explore their prognostic value in patients with chronic HF. The primary end point was a composite of all-cause death and HF readmission. A total of 429 patients were included. GlycB correlated with interleukin-1 receptor-like 1 in the whole cohort (r² = 0.14, p = 0.011) and the subgroup of nonischemic etiology (r² = 0.31, p <0.001). No association was found with New York Heart Association functional class or left ventricular ejection fraction. In patients with nonischemic HF (52.2%, n = 224), GlycA and GlycB exhibited significant association with the composite end point (hazard ratio [HR] 1.19, 95% confidence interval [CI] 1.06 to 1.33, p = 0.004 and HR 2.13, 95% CI 1.43 to 3.13, p <0.001; respectively) and GlycB with HF readmission after multivariable adjustment (HR 2.25, 95% CI 1.54 to 3.30, p <0.001). GlycB levels were also associated with a greater risk of HF-related recurrent admissions (adjusted incidence rate ratio 1.33, 95% CI = 1.07 to 1.65, p = 0.009). None of the markers were associated with the clinical end points in patients with ischemic HF. In conclusion, GlycA and GlycB represent an evolving approach to inflammation status with prognostic value in long-term outcomes in patients with nonischemic HF.

Infant inflammation predicts childhood emotional and behavioral problems and partially mediates socioeconomic disadvantage

BACKGROUND

Emotional and behavioral problems (EBP) are common in children. Environmental factors like socioeconomic disadvantage influence EBP pathogenesis and can trigger inflammation. However, the link between early inflammation-EBP in children is unclear. We investigated the associations between i) infant inflammatory biomarkers and subsequent EBP and ii) early life environmental factors and EBP and assessed whether infant inflammation mediated these associations.

METHODS

Inflammatory biomarkers glycoprotein acetyls (GlycA) and high-sensitivity C-reactive protein (hsCRP) were quantified at birth and 12 months in a population-derived birth cohort, the Barwon Infant Study. Early life factors including demographic, prenatal, and perinatal factors were collected from antenatal to the two-year period. Internalizing and externalizing problems at age two were measured by the Child Behavior Checklist. Prospective associations were examined by multivariable regression analyses adjusted for potential confounders. Indirect effects of early life factors on EBP through inflammation were identified using mediation analyses.

RESULTS

Elevated GlycA levels at birth (GlycAbirth) were associated with greater internalizing problems at age two (β = 1.32 per SD increase in GlycA; P = 0.001). Inflammation at birth had a stronger magnitude of effect with later EBP than at 12 months. GlycAbirth partially mediated the associations between lower household income (6%), multiparity (12%) and greater number of older siblings (13%) and EBP. Patterns were less evident for hsCRP or externalizing problems.

CONCLUSIONS

GlycAbirth was positively associated with EBP at age two and partially mediated the association between several indicators of socioeconomic disadvantage and EBP. Prenatal and perinatal inflammation may be relevant to early neurodevelopment and emotional health.

Innate Immune Activation and Circulating Inflammatory Markers in Preschool Children

Early childhood is characterised by repeated infectious exposures that result in inflammatory responses by the innate immune system. In addition, this inflammatory response to infection is thought to contribute to the epidemiological evidence linking childhood infection and adult non-communicable diseases. Consequently, the relationship between innate immune responses and inflammation during early life may inform prevention of NCDs later in life. In adults, non-genetic host factors such as age, sex, and obesity, strongly impact cytokine production and circulating mediators, but data in children are lacking. Here, we assessed cytokine responses and inflammatory markers in a population of healthy preschool children (mean age 4.2 years). We studied associations between cytokines, plasma inflammatory markers and non-genetic host factors, such as sex, age, adiposity, season, and immune cell composition. Similar to adults, boys had a higher inflammatory response than girls, with IL-12p70 and IL-10 upregulated following TLR stimulation. Adiposity and winter season were associated with increased circulating inflammatory markers but not cytokine production. The inflammatory markers GlycA and hsCRP were positively associated with production of a number of cytokines and may therefore reflect innate immune function and inflammatory potential. This dataset will be informative for future prospective studies relating immune parameters to preclinical childhood NCD phenotypes.

Metabolomic serum abnormalities in dogs with hepatopathies

Hepatopathies can cause major metabolic abnormalities in humans and animals. This study examined differences in serum metabolomic parameters and patterns in left-over serum samples from dogs with either congenital portosystemic shunts (cPSS, n = 24) or high serum liver enzyme activities (HLEA, n = 25) compared to control dogs (n = 64). A validated targeted proton nuclear magnetic resonance spectroscopy platform was used to assess 123 parameters. Principal component analysis of the serum metabolome demonstrated distinct clustering among individuals in each group, with the cluster of HLEA being broader compared to the other groups, presumably due to the wider spectrum of hepatic diseases represented in these samples. While younger and older adult control dogs had very similar metabolomic patterns and clusters, there were changes in many metabolites in the hepatopathy groups. Higher phenylalanine and tyrosine concentrations, lower branched-chained amino acids (BCAAs) concentrations, and altered fatty acid parameters were seen in cPSS dogs compared to controls. In contrast, dogs with HLEA had increased concentrations of BCAAs, phenylalanine, and various lipoproteins. Machine learning based solely on the metabolomics data showed excellent group classification, potentially identifying a novel tool to differentiate hepatopathies. The observed changes in metabolic parameters could provide invaluable insight into the pathophysiology, diagnosis, and prognosis of hepatopathies.

Serum or Plasma (and Which Plasma), That Is the Question

Blood derivatives are the biofluids of choice for metabolomic clinical studies since blood can be collected with low invasiveness and is rich in biological information. However, the choice of the blood collection tubes has an undeniable impact on the plasma and serum metabolic content. Here, we compared the metabolomic and lipoprotein profiles of blood samples collected at the same time and place from six healthy volunteers but using different collection tubes (each enrolled volunteer provided multiple blood samples at a distance of a few weeks/months): citrate plasma, EDTA plasma, and serum tubes. All samples were analyzed via nuclear magnetic resonance spectroscopy. Several metabolites showed statistically significant alterations among the three blood matrices, and also metabolites’ correlations were shown to be affected. The effects of blood collection tubes on the lipoproteins’ profiles are relevant too, but less marked. Overcoming the issue associated with different blood collection tubes is pivotal to scale metabolomics and lipoprotein analysis at the level of epidemiological studies based on samples from multicenter cohorts. We propose a statistical solution, based on regression, that is shown to be efficient in reducing the alterations induced by the different collection tubes for both the metabolomic and lipoprotein profiles.

Triglyceride-Rich Lipoproteins and Glycoprotein A and B Assessed by 1H-NMR in Metabolic-Associated Fatty Liver Disease

High plasma triglyceride (TG) levels and chronic inflammation are important factors related to metabolic-associated fatty liver disease in patients at cardiovascular risk. Using nuclear magnetic resonance (1H-NMR), we aimed to study the triglyceride-rich lipoprotein (TRL) and acute-phase glycoprotein profiles of a cohort of patients with metabolic disease and their relationship with fatty liver. Plasma samples of 280 patients (type 2 diabetes, 81.1%; obesity, 63.3%; and metabolic syndrome, 91.8%) from the University Hospital Lipid Unit were collected for the measurement of small, medium and large TRL particle numbers and sizes and glycoprotein profiles (Glyc-A and Glyc-B) by 1H-NMR. Liver function parameters, including the fatty liver index (FLI) and fibrosis-4 (FIB-4) score, were assessed. Hepatic echography assessment was performed in 100 patients, and they were followed up for 10 years. TRL particle concentrations showed a strong positive association with Glyc-A and Glyc-B (ρ=0.895 and ρ=0.654, p<0.001, respectively) and with the liver function-related proteins ALT ρ=0.293, p<0.001), AST (ρ=0.318, p<0.001) and GGT (ρ=0.284, p<0.001). Likewise, TRL concentrations showed a positive association with FLI (ρ=0.425, p<0.001) but not with FIB-4. During the follow-up period of 10 years, 18 new cases of steatosis were observed among 64 patients who were disease-free at baseline. Baseline TRL particle numbers and glycoprotein levels were associated with the new development of metabolic-associated fatty liver disease (MAFLD) (AUC=0.692, p=0.018 and AUC=0.669, p=0.037, respectively). Overall, our results indicated that TRL number and acute-phase glycoproteins measured by 1H-NMR could be potential biomarkers of the development of hepatic steatosis in patients at metabolic risk.

NMR spectroscopy spotlighting immunogenicity induced by COVID-19 vaccination to mitigate future health concerns

In this review, the disease and immunogenicity affected by COVID-19 vaccination at the metabolic level are described considering the use of nuclear magnetic resonance (NMR) spectroscopy for the analysis of different biological samples. Consistently, we explain how different biomarkers can be examined in the saliva, blood plasma/serum, bronchoalveolar-lavage fluid (BALF), semen, feces, urine, cerebrospinal fluid (CSF) and breast milk. For example, the proposed approach for the given samples can allow one to detect molecular biomarkers that can be relevant to disease and/or vaccine interference in a system metabolome. The analysis of the given biomaterials by NMR often produces complex chemical data which can be elucidated by multivariate statistical tools, such as PCA and PLS-DA/OPLS-DA methods. Moreover, this approach may aid to improve strategies that can be helpful in disease control and treatment management in the future.

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NMR analysis of various bio-samples can explore disease course and vaccine interaction.

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Immunogenicity and reactogenicity caused by COVID-19 vaccination can be studied by NMR.

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Vaccine interaction alters metabolic pathway(s) at a certain stage, and this mechanism can be probed at the metabolic level.

Glycoprotein Profile Measured by a 1H-Nuclear Magnetic Resonance Based on Approach in Patients with Diabetes: A New Robust Method to Assess Inflammation

Patients with type 2 diabetes mellitus (T2DM) and atherogenic dyslipidemia (AD) are at higher risk of developing cardiovascular diseases (CVDs), so an interest in discovering inflammation biomarkers as indicators of processes related to CVD progression is increasing. This study aims (a) to characterize the plasma glycoprotein profile of a cohort of 504 participants, including patients with and without T2DM and/or AD and controls, and (b) to study the associations between the glycoprotein profile and other lipid and clinical variables in these populations. We characterized the plasma glycoprotein profiles by using ¹H-NMR. We quantified the two peaks associated with the concentration of plasma glycoproteins (GlycA and GlycB) and their height/width ratios (H/W GlycA and H/W GlycB), as higher and narrower signals have been related to inflammation. We also quantified GlycF, the signal of which is proportional to the concentration of the acetyl groups of free N-acetylglucosamine, N-acetylgalactosamine, and N-acetylneuraminic in the samples. The lipoprotein profile was also determined (Liposcale^®). Standard clinical and anthropometric measurements were taken. Multivariate classification models were developed to study the differences between the study groups. Reduced HDL-C levels, increased small dense LDL and HDL particles, and elevated TG levels were significantly associated with glycoprotein variables. Glycoprotein values in the diagnostic groups were significantly different from those in the CT groups. AD and DM conditions together contribute to a positive and significant synergetic effect on the GlycA area (<0.05) and the H/W ratios of GlycA (<0.01) and GlycB (<0.05). By adding the new glycoprotein variables to the traditionally used marker of inflammation C-reactive protein (CRP), the AUC increased sharply for classification models between the CT group and the rest (0.68 to 0.84), patients with and without dyslipidemia (0.54 to 0.86), and between patients with and without diabetes (0.55 to 0.75). ¹H-NMR-derived glycoproteins can be used as possible markers of the degree of inflammation associated with T2DM and AD.

Novel glycoproteins identify preclinical atherosclerosis among women with previous preeclampsia regardless of type 1 diabetes status

BACKGROUND AND AIMS

Information regarding inflammation and cardiovascular disease (CVD) risk in type 1 diabetes (T1D) or preeclampsia (PE) is scarce. We assessed differences in inflammation markers according to the presence of both conditions and their association with atherosclerosis.

METHODS AND RESULTS

We recruited 112 women without CVD and last pregnancy ≥5 years previously (n = 28 per group): a)T1D and PE; b)T1D without PE; c)PE without T1D; and d)Controls (without T1D or PE). Groups were matched by several CVD risk factors, and diabetes duration and retinopathy in T1D. Carotid intima-media thickness (IMT) and plaque presence (IMT ≥1.5 mm) were assessed by ultrasonography. Inflammatory markers included classical variables (leucocytes and high-sensitivity C-reactive protein [hsCRP]) and glycoproteins by nuclear magnetic resonance (¹H-NMR) spectroscopy (GlycA, GlycB, GlycF and the height/width [H/W] ratios of GlycA and GlycB). The age of the participants was 44.9 ± 7.8 years, and 20.5% harbored plaque. There were no differences in inflammatory markers among the four study groups. Overall, in multivariate-adjusted models, all ¹H-NMR-glycoproteins (except GlycB) were positively associated with IMT measures (IMT of bulb and maximum-IMT of any carotid segment; p < 0.05). After dividing the sample according to PE status, previous findings remained largely unchanged. Furthermore, GlycF was independently associated with carotid plaque only in PE group (OR 5.08 [1.03-25.01] per 0.1 log-increments, p = 0.046). Neither leucocytes nor hsCRP were related to atherosclerosis. Regarding T1D status, non-uniform results were observed.

CONCLUSIONS

High ¹H-NMR-glycoprotein concentrations have a negative impact on carotid atherosclerosis among women with preeclampsia, regardless of T1D status.

Evolution of Serum Acute-Phase Glycoproteins Assessed by 1H-NMR in HIV Elite Controllers

Elite controllers (ECs) are an exceptional group of people living with HIV (PLWH) who maintain undetectable viral loads (VLs) despite not being on antiretroviral therapy (ART). However, this phenotype is heterogeneous, with some of these subjects losing virological control over time. In this longitudinal retrospective study, serum acute-phase glycoprotein profile assessed by proton nuclear magnetic resonance (¹H-NMR) was determined in 11 transient controllers (TCs) who spontaneously lost virological control and 11 persistent controllers (PCs) who persistently maintained virological control over time. Both PCs and TCs showed similar acute-phase glycoprotein profiles, even when TCs lost the virological control (GlycB, p = 0.824 and GlycA, p = 0.710), and the serum acute-phase glycoprotein signature in PCs did not differ from that in HIV-negative subjects (GlycB, p = 0.151 and GlycA, p = 0.243). Differences in serum glycoproteins A and B were significant only in ECs compared to HIV-typical progressors (TPs) with < 100 CD4+ T-cells (p < 0.001). ¹H-NMR acute-phase glycoprotein profile does not distinguish TCs form PCs before the loss of viral control. ECs maintain a low-grade inflammatory state compared to TPs. PCs revealed a closer serum signature to HIV-negative subjects, reaffirming this phenotype as a closer model of functional control of HIV.

Glycosylation and Cardiovascular Diseases

Cardiovascular disease (CVD) is the leading cause of death worldwide, accounting for approximately 18 million deaths in 2017. Coronary artery disease is the predominant cause of death from CVD, followed by stroke. Owing to recent technological advancements, glycans and glycosylation patterns of proteins have been investigated in association with CVD risk factors and clinical events. These studies have found significant associations of glycans as biomarkers of systemic inflammation and major CVD risk factors and events. While more limited, studies have also shown that glycans may be useful for monitoring response to anti-inflammatory therapies and may be responsive to changes in lifestyle, particularly in patients with chronic inflammatory diseases. Glycans capture summative risk information related to inflammatory, immune, and signaling pathways and are promising biomarkers for CVD risk prediction and therapeutic monitoring.

Inflammatory metabolic profile of South African patients with prostate cancer

Background

Men with African ancestry are more likely to develop aggressive prostate cancer (PCa) and to die from this disease. The study of PCa in the South African population represents an opportunity for biomedical research due to the high prevalence of aggressive PCa. While inflammation is known to play a significant role in PCa progression, its association with tumor stage in populations of African descent has not been explored in detail. Identification of new metabolic biomarkers of inflammation may improve diagnosis of patients with aggressive PCa.

Methods

Plasma samples were profiled from 41 South African men with PCa using nuclear magnetic resonance (NMR) spectroscopy. A total of 41 features, including metabolites, lipid classes, total protein, and the inflammatory NMR markers, GlycA, and GlycB, were quantified from each NMR spectrum. The Bruker’s B.I.-LISA protocols were used to characterize 114 parameters related to the lipoproteins. The unsupervised KODAMA method was used to stratify the patients of our cohort based on their metabolic profile.

Results

We found that the plasma of patients with very high risk, aggressive PCa and high level of C-reactive protein have a peculiar metabolic phenotype (metabotype) characterized by extremely high levels of GlycA and GlycB. The inflammatory processes linked to the higher level of GlycA and GlycB are characterized by a deep change of the plasma metabolome that may be used to improve the stratification of patients with PCa. We also identified a not previously known relationship between high values of VLDL and low level of GlycB in a different metabotype of patients characterized by lower-risk PCa.

Conclusions

For the first time, a portrait of the metabolic changes in African men with PCa has been delineated indicating a strong association between inflammation and metabolic profiles. Our findings indicate how the metabolic profile could be used to identify those patients with high level of inflammation, characterized by aggressive PCa and short life expectancy. Integrating a metabolomic analysis as a tool for patient stratification could be important for opening the door to the development of new therapies. Further investigations are needed to understand the prevalence of an inflammatory metabotype in patients with aggressive PCa.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40170-021-00265-6.

Metabolomic profiles discriminating anxiety from depression

OBJECTIVE

Depression has been associated with metabolomic alterations. Depressive and anxiety disorders are often comorbid diagnoses and are suggested to share etiology. We investigated whether differential metabolomic alterations are present between anxiety and depressive disorders and which clinical characteristics of these disorders are related to metabolomic alterations.

METHODS

Data were from the Netherlands Study of Depression and Anxiety (NESDA), including individuals with current comorbid anxiety and depressive disorders (N = 531), only a current depression (N = 304), only a current anxiety disorder (N = 548), remitted depressive and/or anxiety disorders (N = 897), and healthy controls (N = 634). Forty metabolites from a proton nuclear magnetic resonance lipid-based metabolomics panel were analyzed. First, we examined differences in metabolites between disorder groups and healthy controls. Next, we assessed whether depression or anxiety clinical characteristics (severity and symptom duration) were associated with metabolites.

RESULTS

As compared to healthy controls, seven metabolomic alterations were found in the group with only depression, reflecting an inflammatory (glycoprotein acetyls; Cohen's d = 0.12, p = 0.002) and atherogenic-lipoprotein-related (e.g., apolipoprotein B: Cohen's d = 0.08, p = 0.03, and VLDL cholesterol: Cohen's d = 0.08, p = 0.04) profile. The comorbid group showed an attenuated but similar pattern of deviations. No metabolomic alterations were found in the group with only anxiety disorders. The majority of metabolites associated with depression diagnosis were also associated with depression severity; no associations were found with anxiety severity or disease duration.

CONCLUSION

While substantial clinical overlap exists between depressive and anxiety disorders, this study suggests that altered inflammatory and atherogenic-lipoprotein-related metabolomic profiles are uniquely associated with depression rather than anxiety disorders.

Quantification of glycoproteins by nuclear magnetic resonance associated with preclinical carotid atherosclerosis in patients with type 1 diabetes

BACKGROUND AND AIMS

Glycoproteins play a key role in inflammatory and cardiometabolic processes. Their implication in atherosclerosis in type 1 diabetes (T1D) is unknown. We assessed the relationships between classic inflammatory markers, glycoproteins measured by nuclear magnetic resonance (¹H-NMR), and preclinical atherosclerosis in these patients.

METHODS AND RESULTS

We selected patients with T1D, without cardiovascular disease (CVD), with: age ≥40 years, nephropathy (micro/macroalbuminuria), or ≥10 years of evolution with another risk factor. The presence of plaque (intima-media thickness >1.5 mm) was determined by ultrasonography. Concentrations of high-sensitive C-reactive protein (hsCRP), circulating leukocytes (classical inflammation markers) and ¹H-NMR-glycoproteins (GlycA, GlycB, GlycF, and the height/width [H/W] ratios of GlycA and GlycB) were determined. We included 189 patients (58% male, age 47.0 [40.7-55.2] years). Thirty-five percent presented plaques (22%, ≥2 plaques). There was no association between hsCRP or leukocytes and atherosclerosis. However, in age- and sex-adjusted models, GlycA, GlycF, and the H/W ratios of GlycA and GlycB gradually increased with the number of plaques (0, 1, ≥2 plaques) only in patients without statins (p < 0.05), with no association in patients receiving this drug (p for interaction <0.05; in ≥2 plaques). Finally, in models adjusted for other classical and T1D-specific risk factors, GlycA and GlycB H/W ratios remained associated with carotid plaque (OR 1.39 [1.12-1.90] and OR 6.89 [1.85-25.62], respectively).

CONCLUSION

In T1D individuals without lipid-lowering treatment, ¹H-NMR-glycoproteins were independently associated with the presence and amount of carotid atherosclerosis, unlike other classical inflammatory markers. Further studies are needed to ascertain their utility as CVD biomarkers.

Serum glycoproteins A and B assessed by 1H-NMR in familial hypercholesterolemia

BACKGROUND AND AIMS

Inflammation is a pathophysiological mechanism of atherosclerosis, and several mediators have been proposed as biomarkers. Acute-phase serum glycoproteins are upregulated in the liver during systemic inflammation. Because of their unique biochemical characteristics, they can be measured by ¹H-NMR, and their role as subclinical inflammation markers is under clinical evaluation. We aimed to assess the clinical value of plasma glycoproteins in familial hypercholesterolemia (FH) patients.

METHODS

We recruited 295 FH patients (75.6% with FH-associated genetic variants). At baseline, a full glycoprotein profile, glycoprotein A and B (GlycA and B) concentrations and their height and width ratios (H/W) were analysed by ¹H-NMR. A carotid artery ultrasound study was performed at baseline and prospectively at the 5-year follow-up in 144 FH patients.

RESULTS

At baseline, the GlycA and GlycB concentrations and their H/W ratios were correlated with lipid profile and adiposity parameters, with the correlation between the GlycA and triglyceride concentrations (r = 0.780; p < 0001) being the strongest. Glycoprotein concentrations were also correlated with inflammation markers, mainly hsCRP. Higher glycoprotein concentrations were observed in patients with higher intima media thickness, arterial rigidity and presence of arteriosclerotic plaques. In the multivariate and random forest analyses, the baseline GlycB concentration showed a significant contribution to the detection of FH individuals prone to develop carotid plaques.

CONCLUSIONS

The concentrations of serum glycoproteins as assessed by ¹H-NMR are robust markers of subclinical inflammation. In FH patients, they are increased in the presence of subclinical vascular damage and could be considered atherosclerosis risk markers in the long term.

Identification of Metabolic Phenotypes in Young Adults with Obesity by 1H NMR Metabolomics of Blood Serum

(1) Since the obesity prevalence rate has been consistently increasing, it is necessary to find an effective way to prevent and treat it. Although progress is being made to reduce obesity in the young adult population, a better understanding of obesity-related metabolomics and related biochemical mechanisms is urgently needed for developing appropriate screening strategies. Therefore, the aim of this study is to identify the serum metabolic profile associated with young adult obesity and its metabolic phenotypes. (2) Methods: The serum metabolic profile of 30 obese and 30 normal-weight young adults was obtained using proton nuclear magnetic resonance spectroscopy (¹H NMR). ¹H NMR spectra were integrated into 24 integration regions, which reflect relative metabolites, and were used as statistical variables. (3) Results: The obese group showed increased levels of lipids, glucose, glutamate, N-acetyl glycoprotein, alanine, lactate, 3 hydroxybutyrate and branch chain amino acid (BCAA), and decreased levels of choline as compared with the normal-weight group. Non-hyperlipidemia obese adults showed lower levels of lipids and lactate, glutamate, acetoacetate, N-acetyl glycoprotein, isoleucine, and higher levels of choline and glutamine, as compared with hyperlipidemic obese adults. (4) Conclusions: This study reveals valuable findings in the field of metabolomics and young adult obesity. We propose several serum biomarkers that distinguish between normal weight and obese adults, i.e., glutamine (higher in the normal group, p < 0.05), and lactate, BCAAs, acetoacetate and 3-hydroxybutyrate (higher in the obese group, p < 0.05). In addition, visceral fat and serum TG, glutamate, acetoacetate, N-acetyl glycoprotein, unsaturated lipid, isoleucine, and VLDL/LDL are higher (p < 0.05) in the obese with hyperlipidemia. Therefore, they can be used as biomarkers to identify these two types of obesity.

Investigation of circulating metabolites associated with breast cancer risk by untargeted metabolomics: a case-control study nested within the French E3N cohort

BACKGROUND

Perturbations in circulating metabolites prior to a breast cancer diagnosis are not well characterised. We aimed to gain more detailed knowledge to help understand and prevent the disease.

METHODS

Baseline plasma samples from 791 breast cancer cases and 791 matched controls from the E3N (EPIC-France) cohort were profiled by nuclear magnetic resonance (NMR)-based untargeted metabolomics. Partial least-squares discriminant analysis (PLS-DA) models were built from NMR profiles to predict disease outcome, and odds ratios and false discovery rate (FDR)-adjusted CIs were calculated for 43 identified metabolites by conditional logistic regression.

RESULTS

Breast cancer onset was predicted in the premenopausal subgroup with modest accuracy (AUC 0.61, 95% CI: 0.49-0.73), and 10 metabolites associated with risk, particularly histidine (OR = 1.70 per SD increase, FDR-adjusted CI 1.19-2.41), N-acetyl glycoproteins (OR = 1.53, FDR-adjusted CI 1.18-1.97), glycerol (OR = 1.55, FDR-adjusted CI 1.11-2.18) and ethanol (OR = 1.44, FDR-adjusted CI 1.05-1.97). No predictive capacity or significant metabolites were found overall or for postmenopausal women.

CONCLUSIONS

Perturbed metabolism compared to controls was observed in premenopausal but not postmenopausal cases. Histidine and NAC have known involvement in inflammatory pathways, and the robust association of ethanol with risk suggests the involvement of alcohol intake.

Mitochondrial Dysfunction and Chronic Inflammation in Polycystic Ovary Syndrome

Polycystic ovarian syndrome (PCOS) is the most common endocrine-metabolic disorder affecting a vast population worldwide; it is linked with anovulation, mitochondrial dysfunctions and hormonal disbalance. Mutations in mtDNA have been identified in PCOS patients and likely play an important role in PCOS aetiology and pathogenesis; however, their causative role in PCOS development requires further investigation. As a low-grade chronic inflammation disease, PCOS patients have permanently elevated levels of inflammatory markers (TNF-α, CRP, IL-6, IL-8, IL-18). In this review, we summarise recent data regarding the role of mtDNA mutations and mitochondrial malfunctions in PCOS pathogenesis. Furthermore, we discuss recent papers dedicated to the identification of novel biomarkers for early PCOS diagnosis. Finally, traditional and new mitochondria-targeted treatments are discussed. This review intends to emphasise the key role of oxidative stress and chronic inflammation in PCOS pathogenesis; however, the exact molecular mechanism is mostly unknown and requires further investigation.

Investigating the relationships between unfavourable habitual sleep and metabolomic traits: evidence from multi-cohort multivariable regression and Mendelian randomization analyses

BACKGROUND

Sleep traits are associated with cardiometabolic disease risk, with evidence from Mendelian randomization (MR) suggesting that insomnia symptoms and shorter sleep duration increase coronary artery disease risk. We combined adjusted multivariable regression (AMV) and MR analyses of phenotypes of unfavourable sleep on 113 metabolomic traits to investigate possible biochemical mechanisms linking sleep to cardiovascular disease.

METHODS

We used AMV (N = 17,368) combined with two-sample MR (N = 38,618) to examine effects of self-reported insomnia symptoms, total habitual sleep duration, and chronotype on 113 metabolomic traits. The AMV analyses were conducted on data from 10 cohorts of mostly Europeans, adjusted for age, sex, and body mass index. For the MR analyses, we used summary results from published European-ancestry genome-wide association studies of self-reported sleep traits and of nuclear magnetic resonance (NMR) serum metabolites. We used the inverse-variance weighted (IVW) method and complemented this with sensitivity analyses to assess MR assumptions.

RESULTS

We found consistent evidence from AMV and MR analyses for associations of usual vs. sometimes/rare/never insomnia symptoms with lower citrate (- 0.08 standard deviation (SD)[95% confidence interval (CI) - 0.12, - 0.03] in AMV and - 0.03SD [- 0.07, - 0.003] in MR), higher glycoprotein acetyls (0.08SD [95% CI 0.03, 0.12] in AMV and 0.06SD [0.03, 0.10) in MR]), lower total very large HDL particles (- 0.04SD [- 0.08, 0.00] in AMV and - 0.05SD [- 0.09, - 0.02] in MR), and lower phospholipids in very large HDL particles (- 0.04SD [- 0.08, 0.002] in AMV and - 0.05SD [- 0.08, - 0.02] in MR). Longer total sleep duration associated with higher creatinine concentrations using both methods (0.02SD per 1 h [0.01, 0.03] in AMV and 0.15SD [0.02, 0.29] in MR) and with isoleucine in MR analyses (0.22SD [0.08, 0.35]). No consistent evidence was observed for effects of chronotype on metabolomic measures.

CONCLUSIONS

Whilst our results suggested that unfavourable sleep traits may not cause widespread metabolic disruption, some notable effects were observed. The evidence for possible effects of insomnia symptoms on glycoprotein acetyls and citrate and longer total sleep duration on creatinine and isoleucine might explain some of the effects, found in MR analyses of these sleep traits on coronary heart disease, which warrant further investigation.

Metabolomic profiling reveals plasma GlycA and GlycB as a potential biomarkers for treatment efficiency in rheumatoid arthritis

In this pilot study, we carried out metabolic profiling of patients with rheumatoid arthritis (RA) starting therapy with biological disease-modifying antirheumatic drugs (bDMARDs). The main aim of the study was to assess the occurring metabolic changes associated with therapy success and metabolic pathways involved. In particular, the potential of the metabolomics profiles was evaluated as therapeutically valuable prognostic indicators of the effectiveness of bDMARD treatment to identify responders versus non-responders prior to implementing treatment. Plasma metabolomic profiles of twenty-five patients with RA prior bDMARD treatment and after three months of therapy were obtained by ¹H NMR, liquid chromatography - mass spectrometry, and gas chromatography - mass spectrometry and evaluated by statistical and multivariate analyses. In the group of responders, significant differences in their metabolic patterns were seen after three months of the bDMARD therapy compared with profiles prior to treatment. We identified 24 metabolites that differed significantly between these two-time points mainly belonging to amino acid metabolism, peptides, lipids, cofactors, and vitamins and xenobiotics. Eleven metabolites differentiated responders versus non-responders before treatment. Additionally, N-acetylglucosamine and N-acetylgalactosamine (GlycA) and N-acetylneuraminic acid (GlycB) persisted significant in comparison responders to non-responders after three months of therapy. Moreover, those two metabolites indicated prediction of response potential by results of receiver-operating characteristic (ROC) curve analysis. The applied analysis provides novel insights into the metabolic pathways involved in RA patient's response to bDMARD and therapy effectiveness. GlycA and GlycB are promising biomarkers to identify responding patients prior onset of bDMARD therapy.

NMR Spectroscopic Windows on the Systemic Effects of SARS-CoV-2 Infection on Plasma Lipoproteins and Metabolites in Relation to Circulating Cytokines

To investigate the systemic metabolic effects of SARS-CoV-2 infection, we analyzed 1H NMR spectroscopic data on human blood plasma and co-modeled with multiple plasma cytokines and chemokines (measured in parallel). Thus, 600 MHz 1H solvent-suppressed single-pulse, spin-echo, and 2D J-resolved spectra were collected on plasma recorded from SARS-CoV-2 rRT-PCR-positive patients (n = 15, with multiple sampling timepoints) and age-matched healthy controls (n = 34, confirmed rRT-PCR negative), together with patients with COVID-19/influenza-like clinical symptoms who tested SARS-CoV-2 negative (n = 35). We compared the single-pulse NMR spectral data with in vitro diagnostic research (IVDr) information on quantitative lipoprotein profiles (112 parameters) extracted from the raw 1D NMR data. All NMR methods gave highly significant discrimination of SARS-CoV-2 positive patients from controls and SARS-CoV-2 negative patients with individual NMR methods, giving different diagnostic information windows on disease-induced phenoconversion. Longitudinal trajectory analysis in selected patients indicated that metabolic recovery was incomplete in individuals without detectable virus in the recovery phase. We observed four plasma cytokine clusters that expressed complex differential statistical relationships with multiple lipoproteins and metabolites. These included the following: cluster 1, comprising MIP-1β, SDF-1α, IL-22, and IL-1α, which correlated with multiple increased LDL and VLDL subfractions; cluster 2, including IL-10 and IL-17A, which was only weakly linked to the lipoprotein profile; cluster 3, which included IL-8 and MCP-1 and were inversely correlated with multiple lipoproteins. IL-18, IL-6, and IFN-γ together with IP-10 and RANTES exhibited strong positive correlations with LDL1-4 subfractions and negative correlations with multiple HDL subfractions. Collectively, these data show a distinct pattern indicative of a multilevel cellular immune response to SARS CoV-2 infection interacting with the plasma lipoproteome giving a strong and characteristic immunometabolic phenotype of the disease. We observed that some patients in the respiratory recovery phase and testing virus-free were still metabolically highly abnormal, which indicates a new role for these technologies in assessing full systemic recovery.

GlycA measured by NMR spectroscopy is associated with disease activity and cardiovascular disease risk in chronic inflammatory diseases

GlycA is a biomarker of systemic inflammation, quantifying both the protein concentrations and glycosylation states of several acute phase proteins. GlycA has been shown to be associated with both subclinical atherosclerosis and with cardiovascular disease (CVD). GlycA levels are higher in acute and chronic inflammation. During ongoing systemic inflammatory processes, GlycA specific acute phase reactants and proteins undergo circulating concentration and glycosylation pattern changes, and these alterations are reflected in the GlycA NMR signal. Additionally, levels associate with ongoing disease severity in individuals with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and psoriasis thus capturing active inflammation. Furthermore, in these disease states, GlycA is associated with cardiovascular disease (CVD) independent of traditional risk factors including C-reactive protein (CRP). Finally, GlycA levels decrease with exercise, weight loss, and systemic anti-inflammatory agents. Therefore, GlycA appears to be a promising new composite biomarker of active systemic inflammation including assessing CVD risk in patients with inflammatory diseases.

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Patients with chronic inflammatory disorders are at increased risk for cardiovascular diseasenot captured by traditional risk factors.

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GlycA is a biomarker of acute phase reactants by NMR spectroscopy which captures disease activity in human inflammatory diseases.

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GlycA associates with cardiovascular disease and offers a tool to monitor primary disease activity and assess CVD risk in inflammatory diseases.

Integrative Modeling of Quantitative Plasma Lipoprotein, Metabolic, and Amino Acid Data Reveals a Multiorgan Pathological Signature of SARS-CoV-2 Infection

The metabolic effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on human blood plasma were characterized using multiplatform metabolic phenotyping with nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS). Quantitative measurements of lipoprotein subfractions, α-1-acid glycoprotein, glucose, and biogenic amines were made on samples from symptomatic coronavirus disease 19 (COVID-19) patients who had tested positive for the SARS-CoV-2 virus (n = 17) and from age- and gender-matched controls (n = 25). Data were analyzed using an orthogonal-projections to latent structures (OPLS) method and used to construct an exceptionally strong (AUROC = 1) hybrid NMR-MS model that enabled detailed metabolic discrimination between the groups and their biochemical relationships. Key discriminant metabolites included markers of inflammation including elevated α-1-acid glycoprotein and an increased kynurenine/tryptophan ratio. There was also an abnormal lipoprotein, glucose, and amino acid signature consistent with diabetes and coronary artery disease (low total and HDL Apolipoprotein A1, low HDL triglycerides, high LDL and VLDL triglycerides), plus multiple highly significant amino acid markers of liver dysfunction (including the elevated glutamine/glutamate and Fischer's ratios) that present themselves as part of a distinct SARS-CoV-2 infection pattern. A multivariate training-test set model was validated using independent samples from additional SARS-CoV-2 positive patients and controls. The predictive model showed a sensitivity of 100% for SARS-CoV-2 positivity. The breadth of the disturbed pathways indicates a systemic signature of SARS-CoV-2 positivity that includes elements of liver dysfunction, dyslipidemia, diabetes, and coronary heart disease risk that are consistent with recent reports that COVID-19 is a systemic disease affecting multiple organs and systems. Metabolights study reference: MTBLS2014.

NMR of glycoproteins: profiling, structure, conformation and interactions

In glycoproteins, carbohydrates are responsible for the selective interaction and tight regulation of cellular processes, constituting the main information transducer interface in protein-glycoprotein interactions. Increasing experimental and computational evidence suggest that such interactions often induce allosteric changes in the host protein, underlining the importance of studying intact glycoproteins. Technical issues have precluded such studies for years but, nowadays, a promising era is emerging where NMR spectroscopy, among other techniques, allows the characterization of the composition, structure and segmental dynamics of glycoproteins. In this review, we discuss such advances and highlight some selected examples. This novel technology unravels multiple new functional mechanisms, subtly hidden within the sugar code.

Associations of GlycA and high-sensitivity C-reactive protein with measures of lipolysis in adults with obesity

BACKGROUND

Obesity-associated inflammation promotes metabolic dysfunction. However, it is unclear how different inflammatory biomarkers predict dysregulation in specific tissues/organs, particularly adipose tissue.

OBJECTIVE

The aim of our study was to examine whether GlycA, a nuclear magnetic resonance-measured biomarker of inflammation, is a better predictor of insulin-suppressible lipolysis and other measures of metabolic dysfunction compared with high-sensitivity C-reactive protein (hsCRP) in human obesity.

METHODS

This was a cross-sectional study of 58 nondiabetic adults with obesity (body mass index: 39.8 ± 7.0 kg/m², age 46.5 ± 12.2 years, 67.2% female) who underwent a frequently sampled intravenous glucose tolerance test in the fasted state. Noninsulin-suppressible (l₀), insulin-suppressible (l₂), and maximal (l₀+l₂) lipolysis rates, as well as insulin sensitivity and acute insulin response to glucose, were calculated by minimal model analysis. Nuclear magnetic resonance was used to measure GlycA. Body composition was determined by dual-energy X-ray absorptiometry.

RESULTS

GlycA was strongly correlated with hsCRP (r = +0.46; P < .001). GlycA and hsCRP were positively associated with l₂, l₀+l₂, and fat mass (Ps < .01). In linear regression models accounting for age, race, sex, and fat mass, GlycA remained significantly associated with l₂ and l₀+l₂ (Ps < .05), whereas hsCRP did not (Ps ≥ .20). Neither GlycA nor hsCRP was associated with l_0, insulin sensitivity, or acute insulin response to glucose.

CONCLUSIONS

GlycA was associated with elevated lipolysis, independent of adiposity, in adults with obesity. Our findings suggest that GlycA and hsCRP have distinct inflammation-mediated metabolic effects, with GlycA having a greater association with adipose tissue dysfunction. Further studies are warranted to investigate the mechanisms underlying these associations.

GlycA Levels during the Earliest Stages of Rheumatoid Arthritis: Potential Use as a Biomarker of Subclinical Cardiovascular Disease

This study aimed at evaluating the clinical relevance of glycoprotein profiles during the earliest phases of rheumatoid arthritis (RA) as biomarkers of cardiovascular (CV) risk and treatment response. Then, GlycA and GlycB serum levels were measured using 1H-nuclear magnetic resonance in 82 early RA patients, 14 clinically-suspect arthralgia (CSA), and 28 controls. Serum glycosyltransferase activity was assessed by a colorimetric assay. Subclinical CV disease was assessed by Doppler-ultrasound. We found that GlycA and GlycB serum levels were increased in RA (both p < 0.001), but not in CSA, independently of cardiometabolic risk factors. Increased serum glycosyltransferase activity paralleled GlycA (r = 0.405, p < 0.001) and GlycB levels (r = 0.327, p = 0.005) in RA. GlycA, but not GlycB, was associated with atherosclerosis occurrence (p = 0.012) and severity (p = 0.001). Adding GlycA to the mSCORE improved the identification of patients with atherosclerosis over mSCORE alone, increasing sensitivity (29.7 vs. 68.0%) and accuracy (55.8 vs. 76.6%) and allowing reclassification into more appropriate risk categories. GlycA-reclassification identified patients with impaired lipoprotein metabolism. Finally, baseline GlycA levels predicted poor clinical response upon anti-rheumatic treatment at 6 and 12 months in univariate and multivariate analysis. In sum, increased GlycA levels during the earliest stage of RA can be considered a powerful biomarker for CV risk stratification and treatment response.

A Tale of Two Biomarkers: Untargeted 1H NMR Metabolomic Fingerprinting of BHBA and NEFA in Early Lactation Dairy Cows

Disorders of energy metabolism, which can result from a failure to adapt to the period of negative energy balance immediately after calving, have significant negative effects on the health, welfare and profitability of dairy cows. The most common biomarkers of energy balance in dairy cows are β-hydroxybutyrate (BHBA) and non-esterified fatty acids (NEFA). While elevated concentrations of these biomarkers are associated with similar negative health and production outcomes, the phenotypic and genetic correlations between them are weak. In this study, we used an untargeted ¹H NMR metabolomics approach to investigate the serum metabolomic fingerprints of BHBA and NEFA. Serum samples were collected from 298 cows in early lactation (calibration dataset N = 248, validation N = 50). Metabolomic fingerprinting was done by regressing ¹H NMR spectra against BHBA and NEFA concentrations (determined using colorimetric assays) using orthogonal partial least squares regression. Prediction accuracies were high for BHBA models, and moderately high for NEFA models (R² of external validation of 0.88 and 0.75, respectively). We identified 16 metabolites that were significantly (variable importance of projection score > 1) correlated with the concentration of one or both biomarkers. These metabolites were primarily intermediates of energy, phospholipid, and/or methyl donor metabolism. Of the significant metabolites identified; (1) two (acetate and creatine) were positively correlated with BHBA but negatively correlated with NEFA, (2) nine had similar associations with both BHBA and NEFA, (3) two were correlated with only BHBA concentration, and (4) three were only correlated with NEFA concentration. Overall, our results suggest that BHBA and NEFA are indicative of similar metabolic states in clinically healthy animals, but that several significant metabolic differences exist that help to explain the weak correlations between them. We also identified several metabolites that may be useful intermediate phenotypes in genomic selection for improved metabolic health.