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In this community effort, we compare measurements between 34 laboratories from 19 countries, utilizing mixtures of labelled authentic synthetic standards, to quantify by mass spectrometry four clinically used ceramide species in the NIST (National Institute of Standards and Technology) human blood plasma Standard Reference Material (SRM) 1950, as well as a set of candidate plasma reference materials (RM 8231). Participants either utilized a provided validated method and/or their method of choice. Mean concentration values, and intra- and inter-laboratory coefficients of variation (CV) were calculated using single-point and multi-point calibrations, respectively. These results are the most precise (intra-laboratory CVs ≤ 4.2%) and concordant (inter-laboratory CVs < 14%) community-derived absolute concentration values reported to date for four clinically used ceramides in the commonly analyzed SRM 1950. We demonstrate that calibration using authentic labelled standards dramatically reduces data variability. Furthermore, we show how the use of shared RM can correct systematic quantitative biases and help in harmonizing lipidomics. Collectively, the results from the present study provide a significant knowledge base for translation of lipidomic technologies to future clinical applications that might require the determination of reference intervals (RIs) in various human populations or might need to estimate reference change values (RCV), when analytical variability is a key factor for recall during multiple testing of individuals. Here, the authors compared measurements between 34 laboratories from 19 countries, to quantify by mass spectrometry four ceramides of clinical relevance in human blood plasma Standard Reference Materials. The main goals were to evaluate concordance obtained in a large inter-laboratory trial and to report absolute concentrations of four circulating lipids in a publicly available standard.

Polyphenols are an important class of secondary metabolites that possess antioxidant or anti-inflammatory properties and are associated with many health benefits. It has been reported that extracts of fruit juices or the fruit juices themselves are able to influence lipid metabolism. The aims of this study were to establish a reliable analytical method and thereafter investigate the influence of a polyphenol-rich fruit juice during an eight-week intervention on plasma lipid profiles in healthy male subjects. A placebo-controlled intervention study with 36 healthy male subjects was carried out. Volunteers consumed 750 mL of a polyphenol-rich or placebo beverage on a daily basis. With the established untargeted LC-IMS-qTof method, lipids could be identified, and changes in the lipidome could be detected. For the first time, a comparison of the lipidome of the control vs. treatment group allowed for the identification of differences in lipid profiles. The observed changes suggest that polyphenol intake leads to the targeted re-modeling of the lipidome, affecting bioactive lipid mediators and membrane components in particular. In the future, our identified lipid markers may be established as potential biomarker candidates related to health.

Background The pathophysiology of sarcopenia is complex and multifactorial and has not been fully elucidated. The impact of resistance training and nutritional support (RTNS) on metabolomics and lipodomics in older adults with sarcopenia remains uncertain. This study aimed to explore potential biomarkers of sarcopenia and clinical indicators of RTNS in older sarcopenic adults. Methods Older individuals diagnosed with sarcopenia through routine health checkups at a community hospital were recruited for a 12‐week randomized controlled trial focusing on RTNS. Plasma metabolomic and lipidomic profiles of 45 patients with sarcopenia and 47 matched controls were analysed using 1H‐nuclear magnetic resonance (1H‐NMR) and liquid chromatography‐mass spectrometer (LC–MS). Results At baseline, the patient and control groups had similar age, sex, and height distribution. The patient group had significantly lower weight, BMI, grip strength, gait speed, skeletal muscle index, lean mass of both the upper and lower limbs, and lower limb bone mass. There was a significant difference in 12 metabolites between the control and patient groups. They are isoleucine (patient/control fold change [FC] = 0.86 ± 0.04, P = 0.0005), carnitine (FC = 1.05 ± 0.01, P = 0.0110), 1‐methylhistamine/3‐methylhistamine (FC = 1.24 ± 0.14, P = 0.0039), creatinine (FC = 0.71 ± 0.04, P < 0.0001), carnosine (FC = 0.71 ± 0.04, P = 0.0007), ureidopropionic acid (FC = 0.61 ± 0.10, P = 0.0107), uric acid (FC = 0.88 ± 0.03, P = 0.0083), PC (18:2/20:0) (FC = 0.69 ± 0.03, P = 0.0010), PC (20:2/18:0) (FC = 0.70 ± 0.06, P = 0.0014), PC (18:1/20:1) (FC = 0.74 ± 0.05, P = 0.0015), PI 32:1 (FC = 4.72 ± 0.17, P = 0.0006), and PI 34:3 (FC = 1.88 ± 0.13, P = 0.0003). Among them, carnitine, 1‐methylhistamine/3‐methylhistamine, creatinine, ureidopropionic acid, uric acid, PI 32:1, and PI 34:3 were first identified. Notably, PI 32:1 had highest diagnostic accuracy (0.938) for sarcopenia. 1‐Methylhistamine/3‐methylhistamine, carnosine, PC (18:2/20:0), PI 32:1, and PI 34:3 levels were not different from the control group after RTNS. These metabolites are involved in amino acid metabolism, lipid metabolism, and the PI3K‐AKT/mTOR signalling pathway through the ingenuity pathway analysis. Conclusions These findings provide information on metabolic changes, lipid perturbations, and the role of RTNS in patients with sarcopenia. They reveal new insights into its pathological mechanisms and potential therapies.

To study the possible effects of a dietary intervention with minimal and unprocessed foods, high in natural saturated fats on the lipid profile and body mass index of children. This study combines three intervention studies; one non-randomized retrospective cohort study and two randomized controlled trials, to a pooled analysis. The intervention group received a dietary intervention of minimal and unprocessed foods for three to six months, consisting of five times per week green vegetables, three times per week beef, daily 200-300 mL whole cow's milk (3.4% fat) and whole dairy butter (80% fat) on each slice of bread. The control group continued their usual dietary habits. Raw data of the three intervention studies where combined into one single dataset for data analysis, using mixed effects analysis of covariance to test the effects of the dietary advice on the main study outcomes, which are measurements of the lipid profile. In total, 267 children aged 1 to 16 years were followed. 135 children were included in the intervention group and 139 children in the control group. Characteristics (age, gender and follow-up period) were equally distributed between the groups at baseline. In the intervention group HDL-cholesterol increased significantly from 1.22 mmol/L, 95% confidence interval (CI) 1.14-1.32 to 1.42 mmol/L 95% CI 1.30-1.65 (p = 0.007). The increase over time in HDL cholesterol in the intervention group was significantly different compared to the increase in the control group (from 1.26 mmol/L, 95% CI 1.19-1.35, to 1.30 mmol/L, 95% CI 1.26-1.37) (p = 0.04). Due to the increased HDL concentration in the intervention group, the total cholesterol/HDL cholesterol ratio decreased significantly from 3.70 mmol/L, 95% CI 3.38-3.87, to 3.25 mmol/L, 95% CI 2.96-3.31 (p = 0.05). Consumption of minimal and unprocessed foods (high in natural saturated fats) has favourable effects on HDL cholesterol in children. Therefore, this dietary advice can safely be recommended to children.

Abstract Context Meal timing affects metabolic homeostasis and body weight, but how composition and timing of meals affect plasma lipidomics in humans is not well studied. Objective We used high throughput shotgun plasma lipidomics to investigate effects of timing of carbohydrate and fat intake on lipid metabolism and its relation to glycemic control. Design 29 nondiabetic men consumed (1) a high-carb test meal (MTT-HC) at 09.00 and a high-fat meal (MTT-HF) at 15.40; or (2) MTT-HF at 09.00 and MTT-HC at 15.40. Blood was sampled before and 180 minutes after completion of each MTT. Subcutaneous adipose tissue (SAT) was collected after overnight fast and both MTTs. Prior to each investigation day, participants consumed a 4-week isocaloric diet of the same composition: (1) high-carb meals until 13.30 and high-fat meals between 16.30 and 22:00 or (2) the inverse order. Results 12 hour daily lipid patterns showed a complex regulation by both the time of day (67.8%) and meal composition (55.4%). A third of lipids showed a diurnal variation in postprandial responses to the same meal with mostly higher responses in the morning than in the afternoon. Triacylglycerols containing shorter and more saturated fatty acids were enriched in the morning. SAT transcripts involved in fatty acid synthesis and desaturation showed no diurnal variation. Diurnal changes of 7 lipid classes were negatively associated with insulin sensitivity, but not with glucose and insulin response or insulin secretion. Conclusions This study identified postprandial plasma lipid profiles as being strongly affected by meal timing and associated with insulin sensitivity.

Background Antipsychotic-induced weight gain (AIWG) is a common side effect of antipsychotic drugs and may lead to cardiometabolic comorbidities. There is an urgent public health need to identify patients at high risk of AIWG and determine potential biomarkers for AIWG. Methods In the Sequential Multiple-Assignment Randomized Trials to Compare Antipsychotic Treatments (SMART-CAT) trail, first-episode schizophrenia patients were randomly assigned to olanzapine, risperidone, perphenazine, amisulpride or aripiprazole for 8 weeks. We applied absolute quantitative lipidomics at baseline and after 8 weeks of antipsychotic treatment in 80 patients. To evaluate the effects of AIWG on lipid profile, 25 patients with ≥ 7% weight changes (weight gain, WG) and 28 patients with <|3|% weight changes (weight stable, WS) were investigated, separately. Results We found that baseline CerP(d40:3) and PC(20:1_22:6) were positively associated with weight changes at follow-up ( r  > 0.4, pFDR < 0.05). Additionally, baseline CerP(d40:3) and PC(20:1_22:6) independently predicted rapid weight gain, with receiver operating curve (ROC) of 0.76 (95% CI: 0.63–0.90), and 0.75 (95% CI: 0.62–0.88), respectively. Compared with baseline, levels of 45 differential lipid metabolites (fold change > 1.2, VIP > 1 and pFDR < 0.05) were significantly higher in the WG group. Interestingly, no differential lipid metabolites were identified in the WS group. The LASSO regression model identified 18 AIWG lipid signatures, including 2 cholesterol esters (ChEs), 1 diglyceride (DG), 12 phosphatidylcholines (PCs), 1 phosphatidylglycerol (PG), 1 phosphatidylinositol (PI), and 1 sphingomyelin (SM), with the ChE(16:1) contributing the most. Furthermore, the level changes of ChE(16:1) were positively associated with weight gain( r  = 0.67, pFDR < 0.05). Conclusion Our findings indicate that lipid profile may serve as predictors of rapid weight gain in schizophrenia and provide useful markers for AIWG intervention.

Background: Exercise or vitamin D intervention can reduce the risk of arterial stiffness; however, the underlying mechanisms of lipid metabolism remain unexplored. To examine the effects of a 12-week moderate and vigorous exercise program (65–80% maximal heart rate, 60 min/time, 2~3 times/week) with or without vitamin D supplementation (1000 IU/day) on the reduction in arterial stiffness and further explore whether the effects of interventions could be associated with the basal lipidome among patients with Type 2 diabetes mellitum (T2DM). Method: 61 patients with T2DM were randomly assigned to the following groups: control (CON, n = 15), exercise (EX, n = 14), vitamin D (VD, n = 16), and exercise + vitamin D (EX + VD, n = 16). Arterial stiffness risk factors (ankle–brachial index (ABI); brachial–ankle pulse wave velocity (baPWV), systolic blood pressure (SBP), and diastolic blood pressure (DBP)) were evaluated before and after the intervention. The plasma lipidome was determined using ultra-performance liquid chromatography coupled with tandem mass spectrometry. Machine learning was applied to establish prediction models for the responsiveness to arterial stiffness. Result: Vitamin D supplementation could inhibit the decrease in the ankle–brachial index (mean ± SD: EX + VD and VD, −0.001 ± 0.058; EX + CON, −0.047 ± −0.089; p = 0.03). We observed high inter-individual variability in the arterial stiffness risk factors in response to the interventions. We also found that optimally selecting the lipid predictors at baseline, such as SM d44:6, LPE 18:2, and Hex2Cer 29:0, could enhance the predictive power by 100% for arm SBP changes in the exercise group. Basal levels of Cer (33:1) and GM3 (44:4) could enhance the predictive power by 100% for changes in baPWV in the vitamin D group. Conclusions: A 12-week vitamin D supplementation was beneficial in preventing arterial stiffness. Compared with traditional clinical risk factors, specific lipids at baseline could significantly improve the ability to predict intervention-induced changes in the reduction of arterial stiffness.

Research has shown that diabetes mellitus is not just a disorder of blood sugar, but a complex metabolic imbalance that also profoundly involves lipid metabolism. Patients with diabetes often show alterations in their lipid profile, which contribute to the risk of cardiovascular complications. The EmDia clinical trial, a randomized, placebo-controlled study, has investigated the impact of empagliflozin, a sodium-glucose cotransporter-2 inhibitor (SGLT2i), on the plasma lipidome in patients with type 2 diabetes (T2DM). The goal was to identify molecular alterations that could explain the drug’s cardiovascular and renal benefits, which extend beyond simple glucose reduction. Using an untargeted lipidomics approach and advanced statistical techniques like sparse group LASSO regression, the study identified significant and reproducible alterations in specific lipid classes, most notably lysophosphatidylcholines (LPCs), after one and twelve weeks of treatment. These changes in the lipid profile correlated with clinical parameters such as estimated glomerular filtration rate (eGFR), uric acid, and blood pressure, suggesting that LPCs could serve as biomarkers of drug response. The discovery of LPCs as potential markers of empagliflozin’s effect opens new avenues for developing pharmacodynamic biomarkers and understanding the metabolic mechanisms of action, including the relationship between lipid metabolism and kidney health.

Prostate cancer patients using cholesterol-lowering statins have 30% lower risk of prostate cancer death compared to non-users. The effect is attributed to the inhibition of the mevalonate pathway in prostate cancer cells. Moreover, statin use causes lipoprotein metabolism changes in the serum. Statin effect on serum or intraprostatic lipidome profiles in prostate cancer patients has not been explored. We studied changes in the serum metabolomic and prostatic tissue lipidome after high-dose 80 mg atorvastatin intervention to expose biological mechanisms causing the observed survival benefit. Our randomized, double-blind, placebo-controlled clinical trial consisted of 103 Finnish men with prostate cancer. We observed clear difference in post-intervention serum lipoprotein lipid profiles between the study arms (median classification error 11.7%). The atorvastatin effect on intraprostatic lipid profile was not as clear (median classification error 44.7%), although slightly differing lipid profiles by treatment arm was observed, which became more pronounced in men who used atorvastatin above the median of 27 days (statin group median classification error 27.2%). Atorvastatin lowers lipids important for adaptation for hypoxic microenvironment in the prostate suggesting that prostate cancer cell survival benefit associated with statin use might be mediated by both, local and systemic, lipidomic/metabolomic profile changes.

Androgen deprivation therapy (ADT) is the main treatment strategy for men with metastatic prostate cancer (PC). However, ADT is associated with various metabolic disturbances, including impaired glucose tolerance, insulin resistance and weight gain, increasing risk of diabetes and cardiovascular death. Much remains unknown about the metabolic pathways and disturbances altered by ADT and the mechanisms. We assessed the metabolomic effects of ADT in the serum of 20 men receiving ADT. Sera collected before (baseline), 3 and 6 months after initiation of ADT was used for the metabolomics and lipidomics analyses. The ADT‐associated metabolic changes were identified by univariable and multivariable statistical analysis, ANOVA, and Pearson correlation. We found multiple key changes. First, ADT treatments reduced the steroid synthesis as reflected by the lower androgen sulfate and other steroid hormones. Greater androgen reduction was correlated with higher serum glucose levels, supporting the diabetogenic role of ADT. Second, ADT consistently decreased the 3‐hydroxybutyric acid and ketogenesis. Third, many acyl‐carnitines were reduced, indicating the effects on the fatty acid metabolism. Fourth, ADT was associated with a corresponding reduction in 3‐formyl indole (a.k.a. indole‐3‐carboxaldehyde), a microbiota‐derived metabolite from the dietary tryptophan. Indole‐3‐carboxaldehyde is an agonist for the aryl hydrocarbon receptor and regulates the mucosal reactivity and inflammation. Together, these ADT‐associated metabolomic analyses identified reduction in steroid synthesis and ketogenesis as prominent features, suggesting therapeutic potential of restricted ketogenic diets, though this requires formal testing. ADT may also impact the microbial production of indoles related to the immune pathways. Future research is needed to determine the functional impact and underlying mechanisms to prevent ADT‐linked comorbidities and diabetes risk. ADT is standard therapy for advanced and metastatic PC with negative metabolic consequences. We determined the metabolomic effects of ADT during the PC treatments. Such analysis identify the unexpected reduced ketogenesis and microbiome‐associated metabolites during ADT