Serum Metabolomic Profiling of Patients with Lipedema.

Lipedema is a chronic condition characterized by disproportionate and symmetrical enlargement of adipose tissue, predominantly affecting the lower limbs of women. This study investigated the use of metabolomics in lipedema research, with the objective of identifying complex metabolic disturbances and potential biomarkers for early detection, prognosis, and treatment strategies. The study group (n = 25) comprised women diagnosed with lipedema. The controls were 25 lean women and 25 obese females, both matched for age. In the patients with lipedema, there were notable changes in the metabolite parameters. Specifically, lower levels of histidine and phenylalanine were observed, whereas pyruvic acid was elevated compared with the weight controls. The receiver operating characteristic (ROC) curves for the diagnostic accuracy of histidine, phenylalanine, and pyruvic acid concentrations in distinguishing between patients with lipedema and those with obesity but without lipedema revealed good diagnostic ability for all parameters, with pyruvic acid being the most promising (area under the curve (AUC): 0.9992). Subgroup analysis within matched body mass index (BMI) ranges (30.0 to 39.9 kg/m2) further revealed that differences in pyruvic acid, phenylalanine, and histidine levels are likely linked to lipedema pathology rather than BMI variations. Changes in low-density lipoprotein (LDL)-6 TG levels and significant reductions in various LDL-2-carried lipids of patients with lipedema, compared with the lean controls, were observed. However, these lipids were similar between the lipedema patients and the obese controls, suggesting that these alterations are related to adiposity. Metabolomics is a valuable tool for investigating lipedema, offering a comprehensive view of metabolic changes and insights into lipedema's underlying mechanisms.

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.

Distinct metabolomic and lipidomic profiles in serum samples of patients with primary sclerosing cholangitis.

Intoduction: Identification of specific metabolome and lipidome profile of patients with primary sclerosing cholangitis (PSC) is crucial for diagnosis, targeted personalized therapy, and more accurate risk stratification. Methods: Nuclear magnetic resonance (NMR) spectroscopy revealed an altered metabolome and lipidome of 33 patients with PSC [24 patients with inflammatory bowel disease (IBD) and 9 patients without IBD] compared with 40 age-, sex-, and body mass index (BMI)-matched healthy controls (HC) as well as 64 patients with IBD and other extraintestinal manifestations (EIM) but without PSC. Results: In particular, higher concentrations of pyruvic acid and several lipoprotein subfractions were measured in PSC in comparison to HC. Of clinical relevance, a specific amino acid and lipid profile was determined in PSC compared with IBD and other EIM. Discussion: These results have the potential to improve diagnosis by differentiating PSC patients from HC and those with IBD and EIM.

Optimizing sleep staging on multimodal time series: Leveraging borderline synthetic minority oversampling technique and supervised convolutional contrastive learning.

Sleep is an important research area in nutritional medicine that plays a crucial role in human physical and mental health restoration. It can influence diet, metabolism, and hormone regulation, which can affect overall health and well-being. As an essential tool in the sleep study, the sleep stage classification provides a parsing of sleep architecture and a comprehensive understanding of sleep patterns to identify sleep disorders and facilitate the formulation of targeted sleep interventions. However, the class imbalance issue is typically salient in sleep datasets, which severely affects classification performances. To address this issue and to extract optimal multimodal features of EEG, EOG, and EMG that can improve the accuracy of sleep stage classification, a Borderline Synthetic Minority Oversampling Technique (B-SMOTE)-Based Supervised Convolutional Contrastive Learning (BST-SCCL) is proposed, which can avoid the risk of data mismatch between various sleep knowledge domains (varying health conditions and annotation rules) and strengthening learning characteristics of the N1 stage from the pair-wise segments comparison strategy. The lightweight residual network architecture with a novel truncated cross-entropy loss function is designed to accommodate multimodal time series and boost the training speed and performance stability. The proposed model has been validated on four well-known public sleep datasets (Sleep-EDF-20, Sleep-EDF-78, ISRUC-1, and ISRUC-3) and its superior performance (overall accuracy of 91.31-92.34%, MF1 of 88.21-90.08%, and Cohen's Kappa coefficient k of 0.87-0.89) has further demonstrated its effectiveness. It shows the great potential of contrastive learning for cross-domain knowledge interaction in precision medicine.

High-Dose Spermidine Supplementation Does Not Increase Spermidine Levels in Blood Plasma and Saliva of Healthy Adults: A Randomized Placebo-Controlled Pharmacokinetic and Metabolomic Study.

(1) Background: Spermidine is a biogenic polyamine that plays a crucial role in mammalian metabolism. As spermidine levels decline with age, spermidine supplementation is suggested to prevent or delay age-related diseases. However, valid pharmacokinetic data regarding spermidine remains lacking. Therefore, for the first time, the present study investigated the pharmacokinetics of oral spermidine supplementation. (2) Methods: This study was designed as a randomized, placebo-controlled, triple-blinded, two-armed crossover trial with two 5-day intervention phases separated by a washout phase of 9 days. In 12 healthy volunteers, 15 mg/d of spermidine was administered orally, and blood and saliva samples were taken. Spermidine, spermine, and putrescine were quantified by liquid chromatography-mass spectrometry (LC-MS/MS). The plasma metabolome was investigated using nuclear magnetic resonance (NMR) metabolomics. (3) Results: Compared with a placebo, spermidine supplementation significantly increased spermine levels in the plasma, but it did not affect spermidine or putrescine levels. No effect on salivary polyamine concentrations was observed. (4) Conclusions: This study's results suggest that dietary spermidine is presystemically converted into spermine, which then enters systemic circulation. Presumably, the in vitro and clinical effects of spermidine are at least in part attributable to its metabolite, spermine. It is rather unlikely that spermidine supplements with doses <15 mg/d exert any short-term effects.

Uncovering the relationship between gut microbial dysbiosis, metabolomics, and dietary intake in type 2 diabetes mellitus and in healthy volunteers: a multi-omics analysis.

Type 2 Diabetes Mellitus has reached epidemic levels globally, and several studies have confirmed a link between gut microbial dysbiosis and aberrant glucose homeostasis among people with diabetes. While the assumption is that abnormal metabolomic signatures would often accompany microbial dysbiosis, the connection remains largely unknown. In this study, we investigated how diet changed the gut bacteriome, mycobiome and metabolome in people with and without type 2 Diabetes.1 Differential abundance testing determined that the metabolites Propionate, U8, and 2-Hydroxybutyrate were significantly lower, and 3-Hydroxyphenyl acetate was higher in the high fiber diet compared to low fiber diet in the healthy control group. Next, using multi-omics factor analysis (MOFA2), we attempted to uncover sources of variability that drive each of the different groups (bacterial, fungal, and metabolite) on all samples combined (control and DM II). Performing variance decomposition, ten latent factors were identified, and then each latent factor was tested for significant correlations with age, BMI, diet, and gender. Latent Factor1 was the most significantly correlated. Remarkably, the model revealed that the mycobiome explained most of the variance in the DM II group (12.5%) whereas bacteria explained most of the variance in the control group (64.2% vs. 10.4% in the DM II group). The latent Factor1 was significantly correlated with dietary intake (q < 0.01). Further analyses of the impact of bacterial and fungal genera on Factor1 determined that the nine bacterial genera (Phocaeicola, Ligilactobacillus, Mesosutterella, Acidaminococcus, Dorea A, CAG-317, Caecibacter, Prevotella and Gemmiger) and one fungal genus (Malassezia furfur) were found to have high factor weights (absolute weight > 0.6). Alternatively, a linear regression model was fitted per disease group for each genus to visualize the relationship between the factor values and feature abundances, showing Xylose with positive weights and Propionate, U8, and 2-Hydroxybutyrate with negative weights. This data provides new information on the microbially derived changes that influence metabolic phenotypes in response to different diets and disease conditions in humans.

Unique Metabolomic and Lipidomic Profile in Serum From Patients With Crohn's Disease and Ulcerative Colitis Compared With Healthy Control Individuals.

BACKGROUND: Accurate biomarkers for disease activity and progression in patients with inflammatory bowel disease (IBD) are a prerequisite for individual disease characterization and personalized therapy. We show that metabolic profiling of serum from IBD patients is a promising approach to establish biomarkers. The aim of this work was to characterize metabolomic and lipidomic serum profiles of IBD patients in order to identify metabolic fingerprints unique to the disease. METHODS: Serum samples were obtained from 55 patients with Crohn's disease (CD), 34 patients with ulcerative colitis (UC), and 40 healthy control (HC) individuals and analyzed using proton nuclear magnetic resonance spectroscopy. Classification of patients and HC individuals was achieved by orthogonal partial least squares discriminant analysis and univariate analysis approaches. Disease activity was assessed using the Gastrointestinal Symptom Rating Scale. RESULTS: Serum metabolome significantly differed between CD patients, UC patients, and HC individuals. The metabolomic differences of UC and CD patients compared with HC individuals were more pronounced than the differences between UC and CD patients. Differences in serum levels of pyruvic acid, histidine, and the branched-chain amino acids leucine and valine were detected. The size of low-density lipoprotein particles shifted from large to small dense particles in patients with CD. Of note, apolipoprotein A1 and A2 serum levels were decreased in CD and UC patients with higher fecal calprotectin levels. The Gastrointestinal Symptom Rating Scale is negatively associated with the concentration of apolipoprotein A2. CONCLUSIONS: Metabolomic assessment of serum samples facilitated the differentiation of IBD patients and HC individuals. These differences were constituted by changes in amino acid and lipoprotein levels. Furthermore, disease activity in IBD patients was associated with decreased levels of the atheroprotective apolipoproteins A1 and A2.

The metabolic and lipidomic serum profile of patients with inflammatory bowel disease was analyzed using proton nuclear magnetic resonance spectroscopy. A significantly altered profile in comparison with healthy control individuals was identified, characterized by more atherogenic properties.

mRNA vaccines against SARS-CoV-2 induce comparably low long-term IgG Fc galactosylation and sialylation levels but increasing long-term IgG4 responses compared to an adenovirus-based vaccine.

Background: The new types of mRNA-containing lipid nanoparticle vaccines BNT162b2 and mRNA-1273 and the adenovirus-based vaccine AZD1222 were developed against SARS-CoV-2 and code for its spike (S) protein. Several studies have investigated short-term antibody (Ab) responses after vaccination. Objective: However, the impact of these new vaccine formats with unclear effects on the long-term Ab response - including isotype, subclass, and their type of Fc glycosylation - is less explored. Methods: Here, we analyzed anti-S Ab responses in blood serum and the saliva of SARS-CoV-2 naïve and non-hospitalized pre-infected subjects upon two vaccinations with different mRNA- and adenovirus-based vaccine combinations up to day 270. Results: We show that the initially high mRNA vaccine-induced blood and salivary anti-S IgG levels, particularly IgG1, markedly decrease over time and approach the lower levels induced with the adenovirus-based vaccine. All three vaccines induced, contrary to the short-term anti-S IgG1 response with high sialylation and galactosylation levels, a long-term anti-S IgG1 response that was characterized by low sialylation and galactosylation with the latter being even below the corresponding total IgG1 galactosylation level. Instead, the mRNA, but not the adenovirus-based vaccines induced long-term IgG4 responses - the IgG subclass with inhibitory effector functions. Furthermore, salivary anti-S IgA levels were lower and decreased faster in naïve as compared to pre-infected vaccinees. Predictively, age correlated with lower long-term anti-S IgG titers for the mRNA vaccines. Furthermore, higher total IgG1 galactosylation, sialylation, and bisection levels correlated with higher long-term anti-S IgG1 sialylation, galactosylation, and bisection levels, respectively, for all vaccine combinations. Conclusion: In summary, the study suggests a comparable "adjuvant" potential of the newly developed vaccines on the anti-S IgG Fc glycosylation, as reflected in relatively low long-term anti-S IgG1 galactosylation levels generated by the long-lived plasma cell pool, whose induction might be driven by a recently described TH1-driven B cell response for all three vaccines. Instead, repeated immunization of naïve individuals with the mRNA vaccines increased the proportion of the IgG4 subclass over time which might influence the long-term Ab effector functions. Taken together, these data shed light on these novel vaccine formats and might have potential implications for their long-term efficacy.

Metabolic and Lipidomic Markers Differentiate COVID-19 From Non-Hospitalized and Other Intensive Care Patients.

Coronavirus disease 2019 (COVID-19) is a viral infection affecting multiple organ systems of great significance for metabolic processes. Thus, there is increasing interest in metabolic and lipoprotein signatures of the disease, and early analyses have demonstrated a metabolic pattern typical for atherosclerotic and hepatic damage in COVID-19 patients. However, it remains unclear whether this is specific for COVID-19 and whether the observed signature is caused by the disease or rather represents an underlying risk factor. To answer this question, we have analyzed 482 serum samples using nuclear magnetic resonance metabolomics, including longitudinally collected samples from 12 COVID-19 and 20 cardiogenic shock intensive care patients, samples from 18 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody-positive individuals, and single time point samples from 58 healthy controls. COVID-19 patients showed a distinct metabolic serum profile, including changes typical for severe dyslipidemia and a deeply altered metabolic status compared with healthy controls. Specifically, very-low-density lipoprotein and intermediate-density lipoprotein particles and associated apolipoprotein B and intermediate-density lipoprotein cholesterol were significantly increased, whereas cholesterol and apolipoprotein A2 were decreased. Moreover, a similarly perturbed profile was apparent when compared with other patients with cardiogenic shock who are in the intensive care unit when looking at a 1-week time course, highlighting close links between COVID-19 and lipid metabolism. The metabolic profile of COVID-19 patients distinguishes those from healthy controls and also from patients with cardiogenic shock. In contrast, anti-SARS-CoV-2 antibody-positive individuals without acute COVID-19 did not show a significantly perturbed metabolic profile compared with age- and sex-matched healthy controls, but SARS-CoV-2 antibody-titers correlated significantly with metabolic parameters, including levels of glycine, ApoA2, and small-sized low- and high-density lipoprotein subfractions. Our data suggest that COVID-19 is associated with dyslipidemia, which is not observed in anti-SARS-CoV-2 antibody-positive individuals who have not developed severe courses of the disease. This suggests that lipoprotein profiles may represent a confounding risk factor for COVID-19 with potential for patient stratification.