Artery buckling affects the mechanical stress in atherosclerotic plaques

Background

Tortuous arteries are often seen in patients with hypertension and atherosclerosis. While the mechanical stress in atherosclerotic plaque under lumen pressure has been studied extensively, the mechanical stability of atherosclerotic arteries and subsequent effect on the plaque stress remain unknown. To this end, we investigated the buckling and post-buckling behavior of model stenotic coronary arteries with symmetric and asymmetric plaque.

Methods

Buckling analysis for a model coronary artery with symmetric and asymmetric plaque was conducted using finite element analysis based on the dimensions and nonlinear anisotropic materials properties reported in the literature.

Results

Artery with asymmetric plaque had lower critical buckling pressure compared to the artery with symmetric plaque and control artery. Buckling increased the peak stress in the plaque and led to the development of a high stress concentration in artery with asymmetric plaque. Stiffer calcified tissue and severe stenosis increased the critical buckling pressure of the artery with asymmetric plaque.

Conclusions

Arteries with atherosclerotic plaques are prone to mechanical buckling which leads to a high stress concentration in the plaques that can possibly make the plaques prone to rupture.

     

Matrix metalloproteinases 2 and 9 increase permeability of sheep pleura in vitro

Background

Matrix metalloproteinases (MMPs) 2 and 9 are two gelatinase members which have been found elevated in exudative pleural effusions. In endothelial cells these MMPs increase paracellular permeability via the disruption of tight junction (TJ) proteins occludin and claudin. In the present study it was investigated if MMP2 and MMP9 alter permeability properties of the pleura tissue by degradation of TJ proteins in pleural mesothelium.

Results

In the present study the transmesothelial resistance (R_TM) of sheep pleura tissue was recorded in Ussing chambers after the addition of MMP2 or MMP9. Both enzymes reduced RTM of the pleura, implying an increase in pleural permeability. The localization and expression of TJ proteins, occludin and claudin-1, were assessed after incubation with MMPs by indirect immunofluorescence and western blot analysis. Our results revealed that incubation with MMPs did not alter neither proteins localization at cell periphery nor their expression.

Conclusions

MMP2 and MMP9 increase the permeability of sheep pleura and this finding suggests a role for MMPs in pleural fluid formation. Tight junction proteins remain intact after incubation with MMPs, contrary to previous studies which have shown TJ degradation by MMPs. Probably MMP2 and MMP9 augment pleural permeability via other mechanisms.

     

Simulation of human atherosclerotic femoral plaque tissue: the influence of plaque material model on numerical results

Background

Due to the limited number of experimental studies that mechanically characterise human atherosclerotic plaque tissue from the femoral arteries, a recent trend has emerged in current literature whereby one set of material data based on aortic plaque tissue is employed to numerically represent diseased femoral artery tissue. This study aims to generate novel vessel-appropriate material models for femoral plaque tissue and assess the influence of using material models based on experimental data generated from aortic plaque testing to represent diseased femoral arterial tissue.

Methods

Novel material models based on experimental data generated from testing of atherosclerotic femoral artery tissue are developed and a computational analysis of the revascularisation of a quarter model idealised diseased femoral artery from a 90% diameter stenosis to a 10% diameter stenosis is performed using these novel material models. The simulation is also performed using material models based on experimental data obtained from aortic plaque testing in order to examine the effect of employing vessel appropriate material models versus those currently employed in literature to represent femoral plaque tissue.

Results

Simulations that employ material models based on atherosclerotic aortic tissue exhibit much higher maximum principal stresses within the plaque than simulations that employ material models based on atherosclerotic femoral tissue. Specifically, employing a material model based on calcified aortic tissue, instead of one based on heavily calcified femoral tissue, to represent diseased femoral arterial vessels results in a 487 fold increase in maximum principal stress within the plaque at a depth of 0.8 mm from the lumen.

Conclusions

Large differences are induced on numerical results as a consequence of employing material models based on aortic plaque, in place of material models based on femoral plaque, to represent a diseased femoral vessel. Due to these large discrepancies, future studies should seek to employ vessel-appropriate material models to simulate the response of diseased femoral tissue in order to obtain the most accurate numerical results.

     

Prospects for developing an accurate diagnostic biomarker panel for low prevalence cancers

Background

Early detection screening of asymptomatic populations for low prevalence cancers requires a highly specific test in order to limit the cost and anxiety produced by falsely positive identifications. Most solid cancers are a heterogeneous collection of diseases as they develop from various combinations of genetic lesions and epigenetic modifications. Therefore, it is unlikely that a single test will discriminate all cases of any particular cancer type. We propose a novel, intuitive biomarker panel design that accommodates disease heterogeneity by allowing for diverse biomarker selection that increases diagnostic accuracy.

Methods

Using characteristics of nine pancreatic ductal adenocarcinoma (PDAC) biomarkers measured in human sera, we modeled the behavior of biomarker panels consisting of a sum of indicator variables representing a subset of biomarkers within a larger biomarker data set. We then chose a cutoff for the sum to force specificity to be high and delineated the number of biomarkers required for adequate sensitivity of PDAC in our panel design.

Results

The model shows that a panel consisting of 40 non-correlated biomarkers characterized individually by 32% sensitivity at 95% specificity would require any 7 biomarkers to be above their respective thresholds and would result in a panel specificity and sensitivity of 99% each.

Conclusions

A highly accurate blood-based diagnostic panel can be developed from a reasonable number of individual serum biomarkers that are relatively weak classifiers when used singly. A panel constructed as described is advantageous in that a high level of specificity can be forced, accomplishing a prerequisite for screening asymptomatic populations for low-prevalence cancers.

     

Elevated IL-33 promotes expression of MMP2 and MMP9 via activating STAT3 in alveolar macrophages during LPS-induced acute lung injury

Background

Pulmonary inflammation and endothelial barrier permeability increase in acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) induced by pro-inflammatory cytokines and matrix metalloproteinases (MMPs). However, the relationship between pro-inflammatory cytokines and MMPs in ALI/ARDS remains poorly understood.

Methods

A lipopolysaccharide (LPS)-induced ALI rat model was established through intratracheal instillation. The wet/dry ratios of lung tissues were measured, and bronchoalveolar lavage fluid (BALF) was collected to test protein concentrations, total cell/macrophage numbers, and pro-inflammatory cytokine levels. LPS-treated alveolar macrophages were utilized in in vitro experiments. The expression and secretion of MMPs were respectively detected using quantitative PCR, Western blotting and ELISA assays.

Results

The levels of IL-33 and MMP2/9 in BALF increased in all the ALI rats with severe lung injury. LPS-induced IL-33 autocrine upregulated the expression of MMP2 and MMP9 through activating STAT3. Neutralizing IL-33 in culture medium with specific antibodies suppressed the expression and secretion of MMP2 and MMP9 in LPS-treated alveolar macrophages. Consistently, eliminating IL-33 decreased the levels of MMP2 and MMP9 in BALF and alleviated lung injury in ALI rats.

Conclusion

The IL-33/STAT3/MMP2/9 regulatory pathway is activated in alveolar macrophages during acute lung injury, which may exacerbate the pulmonary inflammation.

     

Metabolic characterization of hepatitis B virus-related liver cirrhosis using NMR-based serum metabolomics

Introduction

Liver cirrhosis (LC) is an advanced liver disease that can develop into hepatocellular carcinoma. Hepatitis B virus (HBV) infection is one of the main causes of LC. Therefore, there is an urgent need for developing a new method to monitor the progression of HBV-related LC (HBV-LC).

Objectives

In this study, we attempted to examine serum metabolic changes in healthy individuals as well as patients with HBV and HBV-LC. Furthermore, potential metabolite biomarkers were identified to evaluate patients progressed from health to HBV-LC.

Methods

Metabolic profiles in the serum of healthy individuals as well as patients with HBV and HBV-LC were detected using an NMR-based metabolomic approach. Univariate and multivariate analyses were conducted to analyze serum metabolic changes during HBV-LC progression. Moreover, potential metabolite biomarkers were explored by receiver operating characteristic curve analysis.

Results

Serum metabolic changes were closely associated with the progression of HBV-LC, mainly involving energy metabolism, protein metabolism, lipid metabolism and microbial metabolism. Serum histidine was identified as a potential biomarker for HBV patients. Acetate, formate, pyruvate and glutamine in the serum were identified as a potential biomarker panel for patients progressed from HBV to HBV-LC. In addition, phenylalanine, unsaturated lipid, n-acetylglycoprotein and acetone in the serum could be considered as a potential common biomarkers panel for these patients.

Conclusion

NMR-based serum metabolomic approach could be a promising tool to monitor the progression of liver disease. Different metabolites may reflect different stages of liver disease.

     

Best serum biomarker combination for ovarian cancer classification

Background

Screening test using CA-125 is the most common test for detecting ovarian cancer. However, the level of CA-125 is diverse by variable condition other than ovarian cancer. It has led to misdiagnosis of ovarian cancer.

Methods

In this paper, we explore the 16 serum biomarker for finding alternative biomarker combination to reduce misdiagnosis. For experiment, we use the serum samples that contain 101 cancer and 92 healthy samples. We perform two major tasks: Marker selection and Classification. For optimal marker selection, we use genetic algorithm, random forest, T-test and logistic regression. For classification, we compare linear discriminative analysis, K-nearest neighbor and logistic regression.

Results

The final results show that the logistic regression gives high performance for both tasks, and HE4-ELISA, PDGF-AA, Prolactin, TTR is the best biomarker combination for detecting ovarian cancer.

Conclusions

We find the combination which contains TTR and Prolactin gives high performance for cancer detection. Early detection of ovarian cancer can reduce high mortality rates. Finding a combination of multiple biomarkers for diagnostic tests with high sensitivity and specificity is very important.

     

TIPE2 acts as a biomarker for tumor aggressiveness and suppresses cell invasiveness in papillary thyroid cancer (PTC)

Background

Tumor necrosis factor (TNF)-alpha-induced protein 8-like 2 (TIPE2 or TNFAIP8L2) is a newly described negative immune regulator and is closely associated with various tumors. However, the expression and roles of TIPE2 in PTC is unknown.

Results

In the present study, TIPE2 upregulation in PTC tissues was found to be negatively associated with tumor size, capsule infiltration, peripheral infiltration and tumor T stage, which could be used to predict tumor invasiveness. TIPE2 overexpression significantly suppressed the viability, proliferation, migration and invasion of PTC cells. Moreover, TIPE2 suppressed tumor invasiveness by inhibiting Rac1, leading to decreased expression of uPA and MMP9.

Conclusions

These results indicate that TIPE2 is a potential biomarker for predicting tumor aggressiveness and suppresses tumor invasiveness in a Rac1-dependent manner.

     

Serum metabolomic study for detecting biomarkers of non-traumatic osteonecrosis of the femoral head

Introduction

Non-traumatic osteonecrosis of the femoral head (NTONFH) is a progressive disease, always leading to hip dysfunction if no early intervention was applied. The difficulty for early diagnosis of NTONFH is due to the slight symptoms at early stages as well as the high cost for screening patients by using magnetic resonance imaging.

Objective

The aim was to detect biomarkers of early-stage NTONFH, which was beneficial to the exploration of a cost-effective approach for the early diagnose of the disease.

Methods

Metabolomic approaches were employed in this study to detect biomarkers of early-stage NTONFH (22 patients, 23 controls), based on the platform of ultra-performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) and the uses of multivariate statistic analysis, putative metabolite identification, metabolic pathway analysis and biomarker analysis.

Results

In total, 33 serum metabolites were found altered between NTONFH group and control group. In addition, glycerophospholipid metabolism and pyruvate metabolism were highly associated with the disease.

Conclusion

The combination of LysoPC (18:3), l-tyrosine and l-leucine proved to have a high diagnostic value for early-stage NTONFH. Our findings may contribute to the protocol for early diagnosis of NTONFH and further elucidate the underlying mechanisms of the disease.

     

Metabolomics approach for predicting response to neoadjuvant chemotherapy for colorectal cancer

Introduction

Colorectal cancer (CRC) is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. Only some CRC patients will benefit from neoadjuvant chemotherapy (NACT).

Objectives

An accurate prediction of response to NACT in CRC patients would greatly facilitate optimal personalized management, which could improve their long-term survival and clinical outcomes.

Methods

In this study, plasma metabolite profiling was performed to identify potential biomarker candidates that can predict response to NACT for CRC. Metabolic profiles of plasma from non-response (n?=?30) and response (n?=?27) patients to NACT were studied using UHPLC–quadruple time-of-flight)/mass spectrometry analyses and statistical analysis methods.

Results

The concentrations of nine metabolites were significantly different when comparing response to NACT. The area under the receiver operating characteristic curve value of the potential biomarkers was up to 0.83 discriminating the non-response and response group to NACT, superior to the clinical parameters (carcinoembryonic antigen and carbohydrate antigen 199).

Conclusion

These results show promise for larger studies that could result in more personalized treatment protocols for CRC patients.

     

Identification of novel biomarker candidates by proteomic analysis of cerebrospinal fluid from patients with moyamoya disease using SELDI-TOF-MS

Background

Moyamoya disease (MMD) is an uncommon cerebrovascular condition with unknown etiology characterized by slowly progressive stenosis or occlusion of the bilateral internal carotid arteries associated with an abnormal vascular network. MMD is a major cause of stroke, specifically in the younger population. Diagnosis is based on only radiological features as no other clinical data are available. The purpose of this study was to identify novel biomarker candidate proteins differentially expressed in the cerebrospinal fluid (CSF) of patients with MMD using proteomic analysis.

Methods

For detection of biomarkers, CSF samples were obtained from 20 patients with MMD and 12 control patients. Mass spectral data were generated by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) with an anion exchange chip in three different buffer conditions. After expression difference mapping was undertaken using the obtained protein profiles, a comparative analysis was performed.

Results

A statistically significant number of proteins (34) were recognized as single biomarker candidate proteins which were differentially detected in the CSF of patients with MMD, compared to the control patients (p < 0.05). All peak intensity profiles of the biomarker candidates underwent classification and regression tree (CART) analysis to produce prediction models. Two important biomarkers could successfully classify the patients with MMD and control patients.

Conclusions

In this study, several novel biomarker candidate proteins differentially expressed in the CSF of patients with MMD were identified by a recently developed proteomic approach. This is a pilot study of CSF proteomics for MMD using SELDI technology. These biomarker candidates have the potential to shed light on the underlying pathogenesis of MMD.

     

Intensive determination of storage condition effects on human plasma metabolomics

Introduction

Human plasma metabolomics offer powerful tools for understanding disease mechanisms and identifying clinical biomarkers for diagnosis, efficacy prediction and patient stratification. Although storage conditions can affect the reliability of data from metabolites, strict control of these conditions remains challenging, particularly when clinical samples are included from multiple centers. Therefore, it is necessary to consider stability profiles of each analyte.

Objectives

The purpose of this study was to extract unstable metabolites from vast metabolome data and identify factors that cause instability.

Method

Plasma samples were obtained from five healthy volunteers, were stored under ten different conditions of time and temperature and were quantified using leading-edge metabolomics. Instability was evaluated by comparing quantitation values under each storage condition with those obtained after ?80 °C storage.

Result

Stability profiling of the 992 metabolites showed time- and temperature-dependent increases in numbers of significantly changed metabolites. This large volume of data enabled comparisons of unstable metabolites with their related molecules and allowed identification of causative factors, including compound-specific enzymatic activity in plasma and chemical reactivity. Furthermore, these analyses indicated extreme instability of 1-docosahexaenoylglycerol, 1-arachidonoylglycerophosphate, cystine, cysteine and N⁶-methyladenosine.

Conclusion

A large volume of data regarding storage stability was obtained. These data are a contribution to the discovery of biomarker candidates without misselection based on unreliable values and to the establishment of suitable handling procedures for targeted biomarker quantification.

     

Finite element analysis of mechanics of neovessels with intraplaque hemorrhage in carotid atherosclerosis

Background

Intraplaque hemorrhage is a widely known factor facilitating plaque instability. Neovascularization of plaque can be regarded as a compensatory response to the blood supply in the deep intimal and medial areas of the artery. Due to the physiological function, the deformation of carotid atherosclerotic plaque would happen under the action of blood pressure and blood flow. Neovessels are subject to mechanical loading and likely undergo deformation. The rupture of neovessels may deteriorate the instability of plaque. This study focuses on the local mechanical environments around neovessels and investigates the relationship between the biomechanics and the morphological specificity of neovessels.

Methods

Stress and stretch were used to evaluate the rupture risk of the neovessels in plaque. Computational structural analysis was performed based on two human carotid plaque slice samples. Two-dimensional models containing neovessels and other components were built according to the plaque slice samples. Each component was assumed to be non-linear isotropic, piecewise homogeneous and incompressible. Different mechanical boundary conditions, i.e. static pressures, were imposed in the carotid lumen and neovessels lumen respectively. Finite element method was used to simulate the mechanical conditions in the atherosclerotic plaque.

Results

Those neovessels closer to the carotid lumen undergo larger stress and stretch. With the same distance to the carotid lumen, the longer the perimeter of neovessels is, the larger stress and the deformation of the neovessels will be. Under the same conditions, the neovessels with larger curvature suffer greater stress and stretch. Neovessels surrounded by red blood cells undergo a much larger stretch.

Conclusions

Local mechanical conditions may result in the hemorrhage of neovessels and accelerate the rupture of plaque. The mechanical environments of the neovessel are related to its shape, curvature, distance to the carotid lumen and the material properties of plaque.

     

Immunohistochemical investigation of prognostic biomarkers in resected colorectal liver metastases: a systematic review and meta-analysis

Background

Many studies have investigated the prognostic role of biomarkers in colorectal liver metastases (CRLM). However, no biomarker has been established in routine clinical practice. The aim of this study was to scrutinize the current literature for biomarkers evaluated by immunohistochemistry as prognostic markers in patients with resected CRLM.

Methods

A systematic review was performed according to the PRISMA guidelines. Articles were identified in the PubMed database with selected search terms and by cross-references search. The REMARK quality criteria were applied. Markers were included if they reported the prognostic impact of immunohistochemical markers in a multivariable setting in relation to overall survival (OS). A meta-analysis was conducted when more than one original article provided survival data of a marker.

Results

In total, 26 biomarkers were identified as independent significant markers for OS in resected CRLM. These biomarkers were found to be involved in multiple oncogenic signalling pathways that control cell growth, apoptosis, angiogenesis and evasion of immune detection. Among these biomarker candidates were Ki-67, EGFR, p53, hTERT, CD34, TSP-1, KISS1, Aurora kinase A and CDX2. CD34 and TSP-1 were reported as significantly associated with survival by more than one study and where therefore pooled in a meta-analysis.

Conclusion

A number of independent prognostic biomarkers for resected CRLM were identified. However, most markers were evaluated in a retrospective setting with small patient cohorts, without external validation. Large, prospective, multicentre studies with standardised methods are needed before biomarkers can translated into the clinic.

     

Metabolomic profiling of maternal hair suggests rapid development of intrahepatic cholestasis of pregnancy

Introduction

Intrahepatic cholestasis of pregnancy (ICP) is a common maternal liver disease; development can result in devastating consequences, including sudden fetal death and stillbirth. Currently, recognition of ICP only occurs following onset of clinical symptoms.

Objective

Investigate the maternal hair metabolome for predictive biomarkers of ICP.

Methods

The maternal hair metabolome (gestational age of sampling between 17 and 41 weeks) of 38 Chinese women with ICP and 46 pregnant controls was analysed using gas chromatography–mass spectrometry.

Results

Of 105 metabolites detected in hair, none were significantly associated with ICP.

Conclusion

Hair samples represent accumulative environmental exposure over time. Samples collected at the onset of ICP did not reveal any metabolic shifts, suggesting rapid development of the disease.

     

Network-based logistic regression integration method for biomarker identification

Background

Many mathematical and statistical models and algorithms have been proposed to do biomarker identification in recent years. However, the biomarkers inferred from different datasets suffer a lack of reproducibilities due to the heterogeneity of the data generated from different platforms or laboratories. This motivates us to develop robust biomarker identification methods by integrating multiple datasets.

Methods

In this paper, we developed an integrative method for classification based on logistic regression. Different constant terms are set in the logistic regression model to measure the heterogeneity of the samples. By minimizing the differences of the constant terms within the same dataset, both the homogeneity within the same dataset and the heterogeneity in multiple datasets can be kept. The model is formulated as an optimization problem with a network penalty measuring the differences of the constant terms. The L₁ penalty, elastic penalty and network related penalties are added to the objective function for the biomarker discovery purpose. Algorithms based on proximal Newton method are proposed to solve the optimization problem.

Results

We first applied the proposed method to the simulated datasets. Both the AUC of the prediction and the biomarker identification accuracy are improved. We then applied the method to two breast cancer gene expression datasets. By integrating both datasets, the prediction AUC is improved over directly merging the datasets and MetaLasso. And it’s comparable to the best AUC when doing biomarker identification in an individual dataset. The identified biomarkers using network related penalty for variables were further analyzed. Meaningful subnetworks enriched by breast cancer were identified.

Conclusion

A network-based integrative logistic regression model is proposed in the paper. It improves both the prediction and biomarker identification accuracy.

     

Metabotyping reveals distinct metabolic alterations in ketotic cows and identifies early predictive serum biomarkers for the risk of disease

Introduction

Ketosis is a prevalent metabolic disease of transition dairy cows that affects milk yield and the development of other periparturient diseases.

Objectives

The objective of this study was to retrospectively metabotype the serum of dairy cows affected by ketosis before clinical signs of disease, during the diagnosis of ketosis, and after the diagnosis of disease and identify potential predictive and diagnostic serum metabolite biomarkers for the risk of ketosis.

Methods

Targeted metabolomics was used to identify and quantify 128 serum metabolites in healthy (CON, n?=?20) and ketotic (n?=?6) cows by DI/LC-MS/MS at ?8 and ?4 weeks prepartum, during the disease week, and at +4 and +8 weeks after parturition.

Results

Significant changes were detected in the levels of several metabolite groups including amino acids, glycerophospholipids, sphingolipids, acylcarnitines, and biogenic amines in the serum of ketotic cows during all time points studied.

Conclusions

Results of this study support the idea that ketosis is preceded and associated and followed by alterations in multiple metabolite groups. Moreover, two sets of predictive biomarker models and one set of diagnostic biomarker model with very high sensitivity and specificity were identified. Overall, these findings throw light on the pathobiology of ketosis and some of the metabolites identified might serve as predictive biomarkers for the risk of ketosis. The data must be considered as preliminary given the lower number of ketotic cows in this study and more research with a larger cohort of cows is warranted to validate the results.

     

Carotid plaque rather than intima-media thickness as a predictor of recurrent vascular events in patients with acute ischemic stroke

Background

To investigate the impacts of carotid plaque and intima-media thickness (IMT) on future vascular events (VEs) in the patients with acute ischemic stroke.

Methods

A total of 479 consecutive Korean patients with acute ischemic stroke were divided into 2 groups according to development of VEs; VE group (65.4 ± 10.9 years) vs no VE group (62.8 ± 13.2 years). VEs were defined as the development of recurrent stroke, coronary events, peripheral arterial disease, and death. Clinical, laboratory, and imaging findings were compared between the groups.

Results

During 105.5 ± 29.0 months of follow up, VEs were developed in 142 patients (29.6%). In univariate analysis, VEs were significantly associated with age, gender, diabetes, renal function, lipid levels, left ventricular function, carotid plaque or IMT. In multivariate analysis, the presence of carotid plaque, diabetes, renal function and male gender were independent predictors of future VEs in the patients with ischemic stroke, but carotid IMT was not a predictor of future VEs. Event free survival was significantly lower in patients with carotid plaque than without carotid plaque on Kaplan-Meier analysis (log rank p < 0.001).

Conclusion

The present study demonstrated that diabetes, impaired renal function, male gender, and the presence of carotid plaque rather than IMT were independent predictors of future VEs in Korean patients with acute ischemic stroke. Active medical management and careful monitoring for the development of recurrent VEs are strongly recommended in patients with acute ischemic stroke and carotid plaque.

     

Crop metabolomics: from diagnostics to assisted breeding

Background

Until recently, plant metabolomics have provided a deep understanding on the metabolic regulation in individual plants as experimental units. The application of these techniques to agricultural systems subjected to more complex interactions is a step towards the implementation of translational metabolomics in crop breeding.

Aim of Review

We present here a review paper discussing advances in the knowledge reached in the last years derived from the application of metabolomic techniques that evolved from biomarker discovery to improve crop yield and quality.

Key Scientific Concepts of Review

Translational metabolomics applied to crop breeding programs.

     

Minimal variation of the plasma lipidome after delayed processing of neonatal cord blood

Background

Cord blood lipids are potential disease biomarkers. We aimed to determine if their concentrations were affected by delayed blood processing.

Method

Refrigerated cord blood from six healthy newborns was centrifuged every 12 h for 4 days. Plasma lipids were analysed by liquid chromatography/mass spectroscopy.

Results

Of 262 lipids identified, only eight varied significantly over time. These comprised three dihexosylceramides, two phosphatidylserines and two phosphatidylethanolamines whose relative concentrations increased and one sphingomyelin that decreased.

Conclusion

Delay in separation of plasma from refrigerated cord blood has minimal effect overall on the plasma lipidome.