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Metabolomics studies hold promise for the discovery of pathways linked to disease processes. Cardiovascular disease (CVD) represents the leading cause of death and morbidity worldwide. Here we used a metabolomics approach to generate unbiased small-molecule metabolic profiles in plasma that predict risk for CVD. Three metabolites of the dietary lipid phosphatidylcholine—choline, trimethylamine N -oxide (TMAO) and betaine—were identified and then shown to predict risk for CVD in an independent large clinical cohort. Dietary supplementation of mice with choline, TMAO or betaine promoted upregulation of multiple macrophage scavenger receptors linked to atherosclerosis, and supplementation with choline or TMAO promoted atherosclerosis. Studies using germ-free mice confirmed a critical role for dietary choline and gut flora in TMAO production, augmented macrophage cholesterol accumulation and foam cell formation. Suppression of intestinal microflora in atherosclerosis-prone mice inhibited dietary-choline-enhanced atherosclerosis. Genetic variations controlling expression of flavin monooxygenases, an enzymatic source of TMAO, segregated with atherosclerosis in hyperlipidaemic mice. Discovery of a relationship between gut-flora-dependent metabolism of dietary phosphatidylcholine and CVD pathogenesis provides opportunities for the development of new diagnostic tests and therapeutic approaches for atherosclerotic heart disease. Heart disease is a gut issue Stanley Hazen and colleagues show that gut flora can influence cardiovascular disease by metabolizing a dietary phospholipid. A targeted metabolomics approach was used to identify plasma metabolites whose levels predict future risk for experiencing a non-fatal heart attack, stroke or death in subjects undergoing cardiac evaluation. Plasma levels of three metabolites of dietary phosphatidylcholine — choline, betaine and trimethylamine N -oxide (TMAO) — are associated with increased risk of cardiovascular disease. The gut flora is known to have a role in TMAO formation from choline. In addition, experiments in atherosclerosis-prone mice show that dietary choline enhances macrophage foam-cell formation and lesion formation — but not if the gut flora is depleted with antibiotics. This work suggests new diagnostic and therapeutic approaches for atherosclerotic heart disease. This paper shows that gut flora can influence cardiovascular disease, by metabolizing a dietary phospholipid. Using a metabolomics approach it is found that plasma levels of three metabolites of dietary phosphatidylcholine—choline, betaine and TMAO—are associated with increased risk of cardiovascular disease in humans. The gut flora is known to have a role in TMAO formation from choline, and this paper shows that dietary choline supplementation enhances macrophage foam cell formation and lesion formation in atherosclerosis-prone mice, but not if the gut flora are depleted with antibiotics.

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread to nearly every continent, registering over 1,250,000 deaths worldwide. The effects of SARS-CoV-2 on host targets remains largely limited, hampering our understanding of Coronavirus Disease 2019 (COVID-19) pathogenesis and the development of therapeutic strategies. The present study used a comprehensive untargeted metabolomic and lipidomic approach to capture the host response to SARS-CoV-2 infection. We found that several circulating lipids acted as potential biomarkers, such as phosphatidylcholine 14:0_22:6 (area under the curve (AUC) = 0.96), phosphatidylcholine 16:1_22:6 (AUC = 0.97), and phosphatidylethanolamine 18:1_20:4 (AUC = 0.94). Furthermore, triglycerides and free fatty acids, especially arachidonic acid (AUC = 0.99) and oleic acid (AUC = 0.98), were well correlated to the severity of the disease. An untargeted analysis of non-critical COVID-19 patients identified a strong alteration of lipids and a perturbation of phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, aminoacyl-tRNA degradation, arachidonic acid metabolism, and the tricarboxylic acid (TCA) cycle. The severity of the disease was characterized by the activation of gluconeogenesis and the metabolism of porphyrins, which play a crucial role in the progress of the infection. In addition, our study provided further evidence for considering phospholipase A2 (PLA2) activity as a potential key factor in the pathogenesis of COVID-19 and a possible therapeutic target. To date, the present study provides the largest untargeted metabolomics and lipidomics analysis of plasma from COVID-19 patients and control groups, identifying new mechanisms associated with the host response to COVID-19, potential plasma biomarkers, and therapeutic targets.

The protein corona formed on nanoparticles (NPs) has potential as a valuable diagnostic tool for improving plasma proteome coverage. Here, we show that spiking small molecules, including metabolites, lipids, vitamins, and nutrients into plasma can induce diverse protein corona patterns on otherwise identical NPs, significantly enhancing the depth of plasma proteome profiling. The protein coronas on polystyrene NPs when exposed to plasma treated with an array of small molecules allows for the detection of 1793 proteins marking an 8.25-fold increase in the number of quantified proteins compared to plasma alone (218 proteins) and a 2.63-fold increase relative to the untreated protein corona (681 proteins). Furthermore, we discovered that adding 1000 µg/ml phosphatidylcholine could singularly enable the detection of 897 proteins. At this specific concentration, phosphatidylcholine selectively depletes the four most abundant plasma proteins, including albumin, thus reducing the dynamic range of plasma proteome and enabling the detection of proteins with lower abundance. Employing an optimized data-independent acquisition approach, the inclusion of phosphatidylcholine leads to the detection of 1436 proteins in a single plasma sample. Our molecular dynamics results reveal that phosphatidylcholine interacts with albumin via hydrophobic interactions, H-bonds, and water bridges. The addition of phosphatidylcholine also enables the detection of 337 additional proteoforms compared to untreated protein corona using a top-down proteomics approach. Given the critical role of plasma proteomics in biomarker discovery and disease monitoring, we anticipate the widespread adoption of this methodology for the identification and clinical translation of biomarkers. The protein corona on nanoparticles has potential for application in protein diagnostics. Here, the authors report on the use of small molecules to change the protein and proteoform patterns of protein corona on otherwise identical nanoparticles, which can be leveraged to significantly enhance the depth of plasma proteome profiling.

Metabolomic discovery of biomarkers of type 2 diabetes (T2D) risk may reveal etiological pathways and help to identify individuals at risk for disease. We prospectively investigated the association between serum metabolites measured by targeted metabolomics and risk of T2D in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam (27,548 adults) among all incident cases of T2D (n = 800, mean follow-up 7 years) and a randomly drawn subcohort (n = 2,282). Flow injection analysis tandem mass spectrometry was used to quantify 163 metabolites, including acylcarnitines, amino acids, hexose, and phospholipids, in baseline serum samples. Serum hexose; phenylalanine; and diacyl-phosphatidylcholines C32:1, C36:1, C38:3, and C40:5 were independently associated with increased risk of T2D and serum glycine; sphingomyelin C16:1; acyl-alkyl-phosphatidylcholines C34:3, C40:6, C42:5, C44:4, and C44:5; and lysophosphatidylcholine C18:2 with decreased risk. Variance of the metabolites was largely explained by two metabolite factors with opposing risk associations (factor 1 relative risk in extreme quintiles 0.31 [95% CI 0.21–0.44], factor 2 3.82 [2.64–5.52]). The metabolites significantly improved T2D prediction compared with established risk factors. They were further linked to insulin sensitivity and secretion in the Tübingen Family study and were partly replicated in the independent KORA (Cooperative Health Research in the Region of Augsburg) cohort. The data indicate that metabolic alterations, including sugar metabolites, amino acids, and choline-containing phospholipids, are associated early on with a higher risk of T2D.

There is an urgent need for a better molecular understanding of the pathophysiology underlying development and progression of diabetic nephropathy. The aim of the current study was to identify novel associations between serum lipidomics and diabetic nephropathy. Non-targeted serum lipidomic analyses were performed with mass spectrometry in 669 individuals with type 1 diabetes. Cross-sectional associations of lipid species with estimated glomerular filtration rate (eGFR) and urinary albumin excretion were assessed. Moreover, associations with register-based longitudinal follow-up for progression to a combined renal endpoint including ≥30% decline in eGFR, ESRD and all-cause mortality were evaluated. Median follow-up time was 5.0–6.4 years. Adjustments included traditional risk factors and multiple testing correction. In total, 106 lipid species were identified. Primarily, alkyl-acyl phosphatidylcholines, triglycerides and sphingomyelins demonstrated cross-sectional associations with eGFR and macroalbuminuria. In longitudinal analyses, thirteen lipid species were associated with the slope of eGFR or albuminuria. Of these lipids, phosphatidylcholine and sphingomyelin species, PC(O-34:2), PC(O-34:3), SM(d18:1/24:0), SM(d40:1) and SM(d41:1), were associated with lower risk of the combined renal endpoint. PC(O-34:3), SM(d40:1) and SM(d41:1) were associated with lower risk of all-cause mortality while an SM(d18:1/24:0) was associated with lower risk of albuminuria group progression. We report distinct associations between lipid species and risk of renal outcomes in type 1 diabetes, independent of traditional markers of kidney function.

Metabolomic screening of fasting plasma from nondiabetic subjects identified α-hydroxybutyrate (α-HB) and linoleoyl-glycerophosphocholine (L-GPC) as joint markers of insulin resistance (IR) and glucose intolerance. To test the predictivity of α-HB and L-GPC for incident dysglycemia, α-HB and L-GPC measurements were obtained in two observational cohorts, comprising 1,261 nondiabetic participants from the Relationship between Insulin Sensitivity and Cardiovascular Disease (RISC) study and 2,580 from the Botnia Prospective Study, with 3-year and 9.5-year follow-up data, respectively. In both cohorts, α-HB was a positive correlate and L-GPC a negative correlate of insulin sensitivity, with α-HB reciprocally related to indices of β-cell function derived from the oral glucose tolerance test (OGTT). In follow-up, α-HB was a positive predictor (adjusted odds ratios 1.25 [95% CI 1.00–1.60] and 1.26 [1.07–1.48], respectively, for each standard deviation of predictor), and L-GPC was a negative predictor (0.64 [0.48–0.85] and 0.67 [0.54–0.84]) of dysglycemia (RISC) or type 2 diabetes (Botnia), independent of familial diabetes, sex, age, BMI, and fasting glucose. Corresponding areas under the receiver operating characteristic curve were 0.791 (RISC) and 0.783 (Botnia), similar in accuracy when substituting α-HB and L-GPC with 2-h OGTT glucose concentrations. When their activity was examined, α-HB inhibited and L-GPC stimulated glucose-induced insulin release in INS-1e cells. α-HB and L-GPC are independent predictors of worsening glucose tolerance, physiologically consistent with a joint signature of IR and β-cell dysfunction.

Non-small cell lung cancer (NSCLC) is one of the most common malignancies worldwide. In this study, we investigated Ultrahigh Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry and Gas Chromatography Time-of-Flight/Mass Spectrometry-based non-targeted metabolomic profiles of serum samples obtained from early-stage NSCLC patients and healthy controls (HC). Metabolic pathways and the biological relevance of potential biomarkers were extensively studied to gain insights into dysregulated metabolism in NSCLC. The identified biomarker candidates were further externally validated via a targeted metabolomics analysis. The global metabolomics profiles could clearly distinguish NSCLC patients from HC. Phosphatidylcholine (PC) levels were found to be dysregulated in glycerophospholipid (GPL) metabolism, which was the top altered pathway in early-stage NSCLC. Compared with those in HC, significant increases in the levels of saturated and monounsaturated PCs such as PC (15:0/18:1), PC (18:0/16:0) and PC (18:0/20:1) were observed in NSCLC. Additionally, relative to those in HC, the levels of 9 polyunsaturated PCs, namely, PC (17:2/2:0), PC (18:4/3:0), and PC (15:0/18:2), and so on were significantly decreased in NSCLC patients. A panel of 12 altered PCs had good diagnostic performance in differentiating early-stage NSCLC patients from HC, and these PCs may thus be used as serum biomarkers for the early diagnosis of NSCLC.

Previous observational studies have indicated that omega-3 may reduce the risk of various cancers. However, the relationship between omega-3 and the incidence of multiple myeloma (MM) remains unclear. Therefore, we conducted a systematic Mendelian randomization (MR) analysis to investigate the causal relationship between omega-3 and the risk of developing MM, while also exploring the potential mediating role of plasma lipids in this association. First, we conducted a two-sample MR study with MM using the omega-3 GWAS data from Richardson TG. We then repeated the validation with the other three omega-3 GWAS data and performed a meta-analysis of the MR results for a total of four omega-3 data. In the second step, we used multivariate Mendelian randomization (MVMR) analyses to adjust for the effects of confounders and explore the direct causal effects of omega-3 with MM. In the third step, we employed a two-step MR to investigate the potential mediating roles of 179 plasma lipids in the association between omega-3 and the risk of MM. Multiple sensitivity analyses were used to assess the robustness of the results. A two-sample MR analysis found that omega-3 can reduce the risk of MM (OR = 0.80, 95% CI 0.69–0.94; P  = 0.005). In subsequent validation, omega-3 data from both Kettunen J and Davyson E yielded similar results. However, data from Zhang S indicated that omega-3 was not associated with MM risk. Ultimately, the meta-analysis results demonstrated that omega-3 can lower the risk of MM (OR = 0.80, 95% CI 0.72–0.88; P  < 0.001). Furthermore, MVMR analysis, after adjusting for relevant risk factors such as obesity and type 2 diabetes, confirmed that omega-3 still reduces the risk of MM. Finally, two-step MR identified phosphatidylcholine (18:2_20:4) as a potential mediator of the causal relationship between omega-3 and MM. Various sensitivity analyses validated the robustness of these findings. Our study suggests that omega-3 may reduce the incidence risk of MM by increasing the levels of phosphatidylcholine (18:2_20:4). We hope that these findings will provide new insights for the prevention and treatment of MM.

Glucose metabolism becomes progressively impaired with older age. Fasting glucose and insulin resistance are risk factors for premature death and other adverse outcomes. We aimed to identifying plasma metabolites associated with altered glucose metabolism and insulin resistance in older community-dwelling adults. A targeted metabolomics approach was used to identify plasma metabolites associated with impaired fasting plasma glucose, 2-hour plasma glucose on oral glucose tolerance testing, and homeostatic model assessment insulin resistance (HOMA-IR) in 472 participants who participated in the Baltimore Longitudinal Study of Aging, with a mean (SD) age of 70.7 (9.9) years. We measured 143 plasma metabolites. In ordinal logistic regression analyses, using a false discovery rate of 5% and adjusting for potential confounders, we found that alanine, glutamic acid, and proline were significantly associated with increased odds of abnormal fasting plasma glucose. Phosphatidylcholine (diacyl C34:4, alkyl-acyl C32:1, C32:2, C34:2, C34:3, and C36:3) was associated with decreased odds of abnormal fasting plasma glucose. Glutamic and acid phosphatidylcholine alkyl-acyl C34:2 were associated with increased and decreased odds of 2-hour plasma glucose, respectively. Glutamic acid was associated with increased odds of higher tertiles of HOMA-IR. Glycine; phosphatidylcholine (diacyl C32:0, alkyl-acyl C32:1, C32:2, C34:1, C34:2, C34:3, C36:2, C36:3, C40:5, C40:6, C42:3, C42:4, and C42:5); sphingomyelin C16:0, C24:1, and C26:1; and lysophosphatidylcholine C18:1 were associated with decreased odds of abnormal HOMA-IR. Targeted metabolomics identified four plasma amino acids and 16 plasma lipid species, primarily containing polyunsaturated fatty acids, that were associated with abnormal glucose metabolism and insulin resistance in older adults.

To test whether type 2 diabetic patients have an elevated level of advanced glycation end-products (AGEs) and responsible for altered phosphatidylcholine metabolism, which we recently found to be associated with osteoarthritis (OA) and diabetes mellitus (DM), synovial fluid (SF) and plasma samples were collected from OA patients with and without DM. Hyperglycemia-related AGEs including methylglyoxal (MG), free methylglyoxal-derived hydroimidazolone (MG-H1), and protein bound N-(Carboxymethyl)lysine (CML) and N-(Carboxyethyl)lysine (CEL) levels were measured in both SF and plasma samples using liquid chromatography coupled tandem mass spectrometry methodology. The correlation between these AGEs and phosphatidylcholine acyl-alkyl C34:3 (PC ae C34:3) and C36:3 (PC ae C36:3) were examined. Eighty four patients with knee OA, including 46 with DM and 38 without DM, were included in the study. There was no significant difference in plasma levels of MG, MG-H1, CML, and CEL between OA patients with and without DM. However, the levels of MG and MG-H1, but not CML and CEL in SF were significantly higher in OA patients with DM than in those without (all p ≤0.04). This association strengthened after adjustment for age, body mass index (BMI), sex and hexose level (p<0.02). Moreover, the levels of MG-H1 in SF was negatively and significantly correlated with PC ae C34:3 (ρ = -0.34; p = 0.02) and PC ae C36:3 (ρ = -0.39; P = 0.03) after the adjustment of age, BMI, sex and hexose level. Our data indicated that the production of non-protein bound AGEs was increased within the OA-affected joint of DM patients. This is associated with changes in phosphatidylcholine metabolism and might be responsible for the observed epidemiological association between OA and DM.