Critical evaluation of 1H NMR metabonomics of serum as a methodology for disease risk assessment and diagnostics

Clinical and biochemical associations of urinary metabolites: quantitative epidemiological approach on renal-cardiometabolic biomarkers

BACKGROUND

Urinary metabolomics has demonstrated considerable potential to assess kidney function and its metabolic corollaries in health and disease. However, applications in epidemiology remain sparse due to technical challenges.

METHODS

We added 17 metabolites to an open-access urinary nuclear magnetic resonance metabolomics platform, extending the panel to 61 metabolites (n = 994). We also introduced automated quantification for 11 metabolites, extending the panel to 12 metabolites (+creatinine). Epidemiological associations between these 12 metabolites and 49 clinical measures were studied in three independent cohorts (up to 5989 participants). Detailed regression analyses with various confounding factors are presented for body mass index (BMI) and smoking.

RESULTS

Sex-specific population reference concentrations and distributions are provided for 61 urinary metabolites (419 men and 575 women), together with methodological intra-assay metabolite variations as well as the biological intra-individual and epidemiological population variations. For the 12 metabolites, 362 associations were found. These are mostly novel and reflect potential molecular proxies to estimate kidney function, as the associations cannot be simply explained by estimated glomerular filtration rate. Unspecific renal excretion results in leakage of amino acids (and glucose) to urine in all individuals. Seven urinary metabolites associated with smoking, providing questionnaire-independent proxy measures of smoking status in epidemiological studies. Common confounders did not affect metabolite associations with smoking, but insulin had a clear effect on most associations with BMI, including strong effects on 2-hydroxyisobutyrate, valine, alanine, trigonelline and hippurate.

CONCLUSIONS

Urinary metabolomics provides new insight on kidney function and related biomarkers on the renal-cardiometabolic system, supporting large-scale applications in epidemiology.

Metabolite Analyses Using Nuclear Magnetic Resonance (NMR) Spectroscopy in Plasma of Patients with Prostate Cancer

Nuclear magnetic resonance (NMR) spectroscopy enables the detection and the quantification of a large range of molecules, including low-molecular-weight metabolites and lipids. NMR spectroscopy is a powerful approach when applied to the high-throughput analysis of plasma or serum samples allowing, in addition, the detection of total proteins, lipoproteins, and signals arising from the glycosylation of circulating acute-phase proteins. Here, we describe the usage of NMR spectroscopy for profiling the plasma or serum of patients with prostate cancer.

Lipoprotein Subclasses Independently Contribute to Subclinical Variance of Microvascular and Macrovascular Health

Lipoproteins are important cardiovascular (CV) risk biomarkers. This study aimed to investigate the associations of lipoprotein subclasses with micro- and macrovascular biomarkers to better understand how these subclasses relate to atherosclerotic CV diseases. One hundred and fifty-eight serum samples from the EXAMIN AGE study, consisting of healthy individuals and CV risk patients, were analysed with nuclear magnetic resonance (NMR) spectroscopy to quantify lipoprotein subclasses. Microvascular health was quantified by measuring retinal arteriolar and venular diameters. Macrovascular health was quantified by measuring carotid-to-femoral pulse wave velocity (PWV). Nineteen lipoprotein subclasses showed statistically significant associations with retinal vessel diameters and nine with PWV. These lipoprotein subclasses together explained up to 26% of variation (R2 = 0.26, F(29,121) = 2.80, p < 0.001) in micro- and 12% (R2 = 0.12, F(29,124) = 1.70, p = 0.025) of variation in macrovascular health. High-density (HDL-C) and low-density lipoprotein cholesterol (LDL-C) as well as triglycerides together explained up to 13% (R2 = 0.13, F(3143) = 8.42, p < 0.001) of micro- and 8% (R2 = 0.08, F(3145) = 5.46, p = 0.001) of macrovascular variation. Lipoprotein subclasses seem to reflect micro- and macrovascular end organ damage more precisely as compared to only measuring HDL-C, LDL-C and triglycerides. Further studies are needed to analyse how the additional quantification of lipoprotein subclasses can improve CV risk stratification and CV disease prediction.

NMR and Metabolomics-A Roadmap for the Future

Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021-the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.

Bibliometric and Visual Analysis on Metabolomics in Coronary Artery Disease Research

Background

Metabolomics has immense research value in coronary artery disease and has drawn increasing attention over the past decades. Many articles have been published in this field, which may challenge researchers aiming to investigate all the available information. However, bibliometrics can provide deep insights into this research field.

Objective

We aimed to qualitatively and quantitatively study metabolomics and coronary artery disease research, visually analyse the development status, trends, research hotspots, and frontiers of this field, and provide a reference for research on coronary artery disease.

Methods

Articles were acquired from the Web of Science Core Collection. VOSviewer and CiteSpace software were used to analyse publication growth, country/region, institution, journal distribution, author, reference, and keywords, and detected the keywords with strong citation burstness to identify emerging topics.

Results

A total of 1121 references were obtained, and the annual number of publications increased over the past 16 years. Metabolomics research has shown a gradual upward trend in coronary artery disease. The United States of America and China ranked at the top in terms of percentage of articles. The institution with the highest number of research publications in this field was Harvard University, followed by the University of California System and Brigham Women's Hospital. The most frequently cited authors included Hazen SL, Tang WH, and Wang ZN. Ala-Korpela M was the most productive author, followed by Clish CB and Adamski J. The journal with the most publications in this field was Scientific Reports, followed by PLoS One and the Journal of Proteome Research. The keywords used at a high frequency were "risk," "biomarkers," "insulin resistance," and "atherosclerosis." Burst detection analysis of top keywords showed that "microbiota," "tryptophan," and "diabetes" are the current research frontiers in this field.

Conclusion

This study provides useful information for acquiring knowledge on metabolomics and coronary artery diseases. Metabolomics research has shown a gradual upward trend in coronary artery disease studies over the past 16 years. Research on tryptophan metabolism regulated by intestinal flora will become an emerging academic trend in this field, which can offer guidance for more extensive and in-depth studies in the future.

Integrative analysis of metabolomic, genomic, and imaging-based phenotypes identify very-low-density lipoprotein as a potential risk factor for lumbar Modic changes

PURPOSE

Modic changes (MC) on magnetic resonance imaging (MRI) have been associated with the development and severity of low back pain (LBP). The etiology of MC remains elusive, but it has been suggested that altered metabolism may be a risk factor. As such, this study aimed to identify metabolomic biomarkers for MC phenotypes of the lumbar spine via a combined metabolomic-genomic approach.

METHODS

A population cohort of 3,584 southern Chinese underwent lumbar spine MRI. Blood samples were genotyped with single-nucleotide polymorphisms (SNP) arrays (n = 2,482) and serum metabolomics profiling using magnetic resonance spectroscopy (n = 757), covering 130 metabolites representing three molecular windows, were assessed. Genome-wide association studies (GWAS) were performed on each metabolite, to construct polygenic scores for predicting metabolite levels in subjects who had GWAS but not metabolomic data. Associations between predicted metabolite levels and MC phenotypes were assessed using linear/logistic regression and least absolute shrinkage and selection operator (LASSO). Two-sample Mendelian randomization analysis tested for causal relationships between metabolic biomarkers and MC.

RESULTS

20.4% had MC (10.6% type 1, 67.2% type 2, 22.2% mixed types). Significant MC metabolomic biomarkers were mean diameter of very-low-density lipoprotein (VLDL)/low-density lipoprotein (LDL) particles and cholesterol esters/phospholipids in large LDL. Mendelian randomization indicated that decreased VLDL mean diameter may lead to MC.

CONCLUSIONS

This large-scale study is the first to address metabolomics in subject with/without lumbar MC. Causality studies implicate VLDL related to MC, noting a metabolic etiology. Our study substantiates the field of "spino-metabolomics" and illustrates the power of integrating metabolomics-genomics-imaging phenotypes to discover biomarkers for spinal disorders, paving the way for more personalized spine care for patients.

The Effect of Partly Replacing Vegetable Fat with Bovine Milk Fat in Infant Formula on Postprandial Lipid and Energy Metabolism: A Proof-of-principle Study in Healthy Young Male Adults

SCOPE

Infant formula (IF) uses besides vegetable fats also bovine milk fat, which differs in triacylglycerol (TAG) structure. Furthermore, it differs in fatty acid (FA) composition. Whether changing fat source in IF affects postprandial energy metabolism, lipemic response, and blood lipid profile is unknown.

METHODS AND RESULTS

A proof-of-principle study, with a randomized controlled double-blind cross-over design, is conducted. Twenty healthy male adults consumed drinks with either 100% vegetable fat (VEG) or 67% bovine milk fat and 33% vegetable fat (BOV), on 2 separate days. For a detailed insight in the postprandial responses, indirect calorimetry is performed continuously, and venous blood samples are taken every 30 min, until 5 h postprandially. No differences in postprandial energy metabolism, serum lipids, lipoprotein, or chylomicron concentrations are observed between drinks. After consumption of VEG-drink, C18:2n-6 in serum increased. Observed differences in chylomicron FA profile reflect differences in initial FA profile of test drinks. Serum ketone bodies concentrations increase following consumption of BOV-drink.

CONCLUSIONS

The use of bovine milk fat in IF does neither affect postprandial energy metabolism nor lipemic response in healthy adults, but alters postprandial FA profiles and ketone metabolism. Whether the exact same effects occur in infants requires experimental verification.

Importance of lipids for upper motor neuron health and disease

Building evidence reveals the importance of maintaining lipid homeostasis for the health and function of neurons, and upper motor neurons (UMNs) are no exception. UMNs are critically important for the initiation and modulation of voluntary movement as they are responsible for conveying cerebral cortex' input to spinal cord targets. To maintain their unique cytoarchitecture with a prominent apical dendrite and a very long axon, UMNs require a stable cell membrane, a lipid bilayer. Lipids can act as building blocks for many biomolecules, and they also contribute to the production of energy. Therefore, UMNs require sustained control over the production, utilization and homeostasis of lipids. Perturbations of lipid homeostasis lead to UMN vulnerability and progressive degeneration in diseases such as hereditary spastic paraplegia (HSP) and primary lateral sclerosis (PLS). Here, we discuss the importance of lipids, especially for UMNs.

Nuclear magnetic resonance spectroscopy of human body fluids and in vivo magnetic resonance spectroscopy: Potential role in the diagnosis and management of prostate cancer

Prostate cancer is the most common solid organ cancer in men, and the second most common cause of male cancer-related mortality. It has few effective therapies, and is difficult to diagnose accurately. Prostate-specific antigen (PSA), which is currently the most effective diagnostic tool available, cannot reliably discriminate between different pathologies, and in fact only around 30% of patients found to have elevated levels of PSA are subsequently confirmed to actually have prostate cancer. As such, there is a desperate need for more reliable diagnostic tools that will allow the early detection of prostate cancer so that the appropriate interventions can be applied. Nuclear magnetic resonance (NMR) spectroscopy and magnetic resonance spectroscopy (MRS) are 2 high throughput, noninvasive analytical procedures that have the potential to enable differentiation of prostate cancer from other pathologies using metabolomics, by focusing specifically on certain metabolites which are associated with the development of prostate cancer cells and its progression. The value that this type of approach has for the early detection, diagnosis, prognosis, and personalized treatment of prostate cancer is becoming increasingly apparent. Recent years have seen many promising developments in the fields of NMR spectroscopy and MRS, with improvements having been made to hardware as well as to techniques associated with the acquisition, processing, and analysis of related data. This review focuses firstly on proton NMR spectroscopy of blood serum, urine, and expressed prostatic secretions in vitro, and then on 1- and 2-dimensional proton MRS of the prostate in vivo. Major advances in these fields and methodological principles of data collection, acquisition, processing, and analysis are described along with some discussion of related challenges, before prospects that proton MRS has for future improvements to the clinical management of prostate cancer are considered.

NMR Based Metabolomics Comparison of Different Blood Sampling Techniques in Awake and Anesthetized Rats

The composition of body fluids has become one of the most commonly used methods for diagnosing various diseases or monitoring the drug responses, especially in serum/plasma. It is therefore vital for investigators to find an appropriate way to collect blood samples from laboratory animals. This study compared blood samples collected from different sites using the NMR based metabolomics approach. Blood samples were collected from the saphenous vein (awake state), tail vein (awake and anesthetized states after administration of sevoflurane or pentobarbital) and the inferior thoracic vena cava (ITVC, anesthetized state). These approaches from the saphenous and tail veins have the potential to enable the collection of multiple samples, and the approach from ITVC is the best method for the collection of blood for the terminate state. The compositions of small molecules in the serum were determined using the 1H-NMR method, and the data were analyzed with traditional correlation analysis, principle component analysis (PCA) and OPLS-DA methods. The results showed that acute anesthesia significantly influenced the composition of serum in a very short period, such as the significant increase in glucose, and decrease in lactate. This indicates that it is better to obtain blood samples under the awake state. From the perspective of animal welfare and multiple sampling, the current study shows that the saphenous vein and tail vein are the best locations to collect multiple blood samples for a reduced risk of injury in the awake state. Furthermore, it is also suitable for investigating pharmacokinetics and the effects of drug intervention on animals.

An NMR-based lipidomic approach to identify Parkinson's disease-stage specific lipoprotein-lipid signatures in plasma

Disturbances in lipid composition and lipoproteins metabolism can play a crucial role in the pathogenesis of Parkinson's disease (PD) and other neurodegenerative diseases. The lipidomic strategy proposed here involves lipoprotein profiling using NMR spectroscopy and multivariate data pre-processing and analysis tools on 94 plasma samples (belonging to 38 early-stage PD patients, 10 PD-related dementia patients, 23 persons with Alzheimer's dementia, and 23 healthy control subjects) to firstly differentiate PD patients (irrespective of the stage of the disease) from persons with Alzheimer's disease (AD) as well as from controls, and then to discriminate among PD patients according to disease severity. The whole data set was subdivided into 86 training and 8 external test samples for validation purposes. A two-step classification scheme, based on linear discriminant analysis with variable selection accomplished by a stepwise orthogonalisation procedure, was proposed to optimise classification performance. Careful pre-processing of NMR signals was crucial to ensure data set quality. A total of 30 chemical shift buckets enabled differentiation between PD patients (regardless of disease severity), AD and control subjects, providing classification, cross-validation and external prediction rates of 100% in all cases. Only 15 variables were required to further discriminate between early-stage PD and PD-related dementia, again with 100% correct classifications, and internal/external predictions. The simplicity and effectiveness of the classification methodology proposed support the use of NMR spectroscopy, in combination with chemometrics, as a viable alternative diagnostic tool to conventional PD clinical diagnosis.

Metabolomics and Heart Diseases: From Basic to Clinical Approach

BACKGROUND

The field of metabolomics has been steadily increasing in size for the last 15 years. Advances in analytical and statistical methods have allowed metabolomics to flourish in various areas of medicine. Cardiovascular diseases are some of the main research targets in metabolomics, due to their social and medical relevance, and also to the important role metabolic alterations play in their pathogenesis and evolution. Metabolomics has been applied to the full spectrum of cardiovascular diseases: from patient risk stratification to myocardial infarction and heart failure. However - despite the many proof-ofconcept studies describing the applicability of metabolomics in the diagnosis, prognosis and treatment evaluation in cardiovascular diseases - it is not yet used in routine clinical practice. Recently, large phenome centers have been established in clinical environments, and it is expected that they will provide definitive proof of the applicability of metabolomics in clinical practice. But there is also room for small and medium size centers to work on uncommon pathologies or to resolve specific but relevant clinical questions.

OBJECTIVES

In this review, we will introduce metabolomics, cover the metabolomic work done so far in the area of cardiovascular diseases.

CONCLUSION

The cardiovascular field has been at the forefront of metabolomics application and it should lead the transfer to the clinic in the not so distant future.

Metabolomics in early detection and prognosis of acute coronary syndrome

Acute coronary syndrome (ACS) is one of the most dangerous types of coronary heart disease (CHD) and contributes to significant mortality and morbidity worldwide. Outcomes in these patients remain a challenge despite improvements in diagnosis and treatment. Risk stratification continues to be problematic and the identification of novel predictors is crucial for improved outcomes. As such, there is a strong need for the development of novel analytical methods as well as the characterization of better predictive and prognostic biomarkers to enable more personalized treatment. Metabolite profile analysis may greatly assist in interpreting altered pathway dynamics, especially when combined with other 'omics' technologies such as transcriptomics and proteomics. In this review, we describe ACS pathophysiology and recent advances in the role of metabolomics in the diagnosis and the molecular pathogenesis of ACS. We briefly describe key technologies used in metabolomics research and statistical approaches for data reduction and pathway analysis and discuss their application to CHD.

Sex differences in the 1H NMR metabolic profile of serum in cardiovascular risk patients

Personalized diagnosis and risk stratification of cardiovascular diseases would allow optimizing therapeutic strategies and lifestyle changes. Metabolomics is a promising technique for personalized diagnosis and prognosis; however, various physiological parameters, including sex, influence the metabolic profile thus hampering its translation to the clinic. Knowledge of the variation in the metabolic profile associated with sex would facilitate metabolomic translation to the clinic. The objective of the present work was to investigate the possible differences in the metabolic 1H NMR profile associated to sex beyond lipoproteins. 1H NMR spectra from whole serum and methanol deproteinized samples from 39 patients (22 males, 17 females) between 55-70 years old with suspected coronary artery disease that underwent a stress test that was considered negative where included. Deproteinized serum could be used to differentiate sex based on higher levels of lactate and glucose in women. Lipoprotein region was the most variable area of the spectra between individuals, but spectra of whole serum were able to differentiate sex based on lipoproteins. There are sex-related differences in the 1H NMR metabolic profile of individuals with suspected cardiovascular disease beyond lipoproteins. These findings may help the translation of metabolomics to the clinic.

Explore potential plasma biomarkers of acute respiratory distress syndrome (ARDS) using GC-MS metabolomics analysis

OBJECTIVES

The aim of this study was to analyse the metabolomics of patients with acute respiratory distress syndrome (ARDS) for the identification of metabolic markers with potential diagnostic and prognostic value.

METHODS

The enrolled subjects included adult patients with ARDS that met the Berlin definition and healthy controls matched based on age, gender, and body mass index (BMI). Plasma samples were collected from 37 patients with ARDS and 28 healthy controls. The plasma metabolites were detected with gas chromatography-mass spectrometry (GC-MS), and the relevant metabolic pathways were predicted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

RESULTS

A total of 222 metabolites were identified in our study, of which 128 were significantly altered in patients with ARDS compared with healthy controls. Phenylalanine, aspartic acid, and carbamic acid levels were significantly different between all groups of patients with ARDS classified from mild to severe. Furthermore, four metabolites, ornithine, caprylic acid, azetidine, and iminodiacetic acid, could serve as biomarkers to potentially predict the severity of ARDS. We discovered 92 pathways that were significantly different between ARDS and control groups, including 57 pathways linked to metabolism.

CONCLUSIONS

Plasma metabolomics may improve our understanding of ARDS biology. Specific products related to hypoxia may serve as early biomarkers for ARDS prediction, while the metabolites with significant correlations with partial pressure of arterial oxygen (PaO₂)/percentage of inspired oxygen (FiO₂) may play a role in determining ARDS severity. This study suggests that metabolomic analysis in patients at risk of ARDS or those with early ARDS may provide new insight into disease pathogenesis or prognosis.

Phlebotomy tube interference with nuclear magnetic resonance (NMR) lipoprotein subclass analysis

BACKGROUND

Lipoprotein subclass analysis by nuclear magnetic resonance (NMR) can be used in risk assessment of atherosclerotic cardiovascular disease (ASCVD). There is little information in the literature regarding phlebotomy tube interferences with NMR testing.

METHODS

Pooled human serum was exposed to phlebotomy tubes manufactured by Becton Dickinson (BD), Greiner Bio-One, or Sarstedt. Serum was analyzed on the Axinon lipoFIT by NMR assay and by conventional lipid assays performed on a Roche Cobas 8000 system. The effect of incomplete fill volume was also assessed.

RESULTS

Analytical interference in NMR lipoprotein subclass results was observed across many different tube types. The 5 mL Greiner Bio-One Z Serum Sep Clot Activator tube correlated the best with non-gel containing serum tubes from BD and Greiner Bio-One. BD Serum Separator Tubes (SSTs) displayed strong interferences across several NMR analytes that were enhanced with decreased tube fill volumes. Interferences were also observed with different sizes of Greiner Bio-One Z Serum Sep Clot Activator tubes. Interference was generally not observed with conventional lipid testing, although minor interference was found for some tubes with lipoprotein(a) [Lp(a)].

CONCLUSIONS

NMR lipoprotein subclass analysis should be standardized by both tube type and tube size to prevent risk of analytical interference.

Quantitative ¹H-NMR Spectroscopy for Profiling Primary Metabolites in Mulberry Leaves

The primary metabolites in aqueous extract of mulberry (Morus alba L.) leaves were characterized by using proton nuclear magnetic resonance (¹H-NMR) spectroscopy. With the convenience of resonance assignment, GABA together with the other 10 primary metabolites was simultaneously identified and quantified in one ¹H-NMR spectrum. In this study, external calibration curves for metabolites were employed to calculate the concentrations of interests. The proposed quantitative approach was demonstrated with good linearity (r² ranged in the interval of 0.9965–0.9999), precision, repeatability, stability (RSD values in the ranges of 0.35–4.89%, 0.77–7.13% and 0.28–2.33%, respectively) and accuracy (recovery rates from 89.2% to 118.5%). The established ¹H-NMR method was then successfully applied to quantify 11 primary metabolites in mulberry leaves from different geographical regions within a rapid analysis time and a simple sample preparation procedure.

Hemoglobin level and lipoprotein particle size

Background

Alterations in lipoprotein size are associated with increased cardiovascular disease risk. Higher hemoglobin levels may indicate a higher risk of atherosclerosis and was previously associated with obesity, metabolic syndrome, and insulin resistance. No previous studies have investigated an association between hemoglobin concentration and lipoprotein particle size.

Methods

We conducted a population-based, cross-sectional study of 766 Caucasian, middle-aged subjects (341 men and 425 women) born in Pieksämäki, Finland, who were categorized into five age groups. The concentrations and sizes of lipoprotein subclass particles were analyzed by high-throughput nuclear magnetic resonance (NMR) spectroscopy.

Results

Larger very low density lipoprotein (VLDL) particle diameter was associated with higher hemoglobin concentrations in men (p = 0.003). There was a strong relationship between smaller high density lipoprotein (HDL) particle size and higher hemoglobin concentration in both men and women as well as with smaller low density lipoprotein (LDL) particle size and higher hemoglobin concentration in men and women (p < 0.001; p = 0.009, p = 0.008). VLDL particle concentration had a moderate positive correlation with hemoglobin concentration (r = 0.15; p < 0.001). LDL particle concentration showed a statistical trend suggesting increasing particle concentration with increasing hemoglobin levels (r = 0.08; p = 0.05).

Conclusion

Higher hemoglobin levels are associated with larger VLDL, smaller LDL, and smaller HDL particle sizes and increasing amounts of larger VLDL and smaller LDL particles. This suggests that a higher hemoglobin concentration is associated with an unfavorable lipoprotein particle profile that is part of states that increase cardiovascular disease risk like diabetes and metabolic syndrome.

Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies

Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.

Dietary fatty acids were not independently associated with lipoprotein subclasses in elderly women

Dietary fatty acids are known to affect serum lipoproteins; however, little is known about the associations between consumption of dietary fatty acids and lipoprotein subclasses. In this study, we hypothesized that there is an association between dietary fatty acids and lipoprotein subclasses and investigated the cross-sectional association of dietary fat intake with subclasses of lipoproteins in elderly women. Altogether, 547 women (aged ≥65 years) who were part of OSTPRE cohort participated. Dietary intake was assessed by 3-day food records, lifestyle, and health information obtained through self-administrated questionnaires, and lipoprotein subclasses were determined by nuclear magnetic resonance spectroscopy. To analyze the associations between fatty acids and lipoprotein subclasses, we used Pearson and Spearman correlation coefficients and the analysis of covariance (ANCOVA) test with, adjustment for physical activity, body mass index, age, smoking status, and intake of lipid-lowering drugs. There were significant correlations between saturated fatty acids (SFA; % of energy) and concentrations of large, medium, and small low-density lipoproteins (LDL); total cholesterol in large, medium, and small LDL; and phospholipids in large, medium, and small LDL, after correction for multiple testing. After adjustment for covariates, the higher intake of SFA was associated with smaller size of LDL particles (P = .04, ANCOVA) and lower amount of triglycerides in small very low-density lipoproteins (P = .046, ANCOVA). However, these associations did not remain significant after correction for multiple testing. In conclusion, high intake of SFA may be associated with the size of LDL particles, but the results do not support significant, independent associations between dietary fatty acids and lipoprotein subclasses.

Metabolic profiling of polycystic ovary syndrome reveals interactions with abdominal obesity

Background:

Polycystic ovary syndrome (PCOS) is a common reproductive disorder associated with metabolic disturbances including obesity, insulin resistance and diabetes mellitus. Here we investigate whether changes in the metabolic profile of PCOS women are driven by increased tendency to obesity or are specific features of PCOS related to increased testosterone levels.

Design and methods:

We conducted an NMR metabolomics association study of PCOS cases (n=145) and controls (n=687) nested in a population-based birth cohort (n=3127). Subjects were 31 years old at examination. The main analyses were adjusted for waist circumference (WC) as a proxy measure of central obesity. Subsequently, metabolite concentrations were compared between cases and controls within pre-defined WC strata. In each stratum, additional metabolomics association analyses with testosterone levels were conducted separately among cases and controls.

Results:

Overall, women with PCOS showed more adverse metabolite profiles than the controls. Four lipid fractions in different subclasses of very low density lipoprotein (VLDL) were associated with PCOS, after adjusting for WC and correction for multiple testing (P<0.002). In stratified analysis the PCOS women within large WC strata (⩾98 cm) had significantly lower high density lipoprotein (HDL) levels, Apo A1 and albumin values compared with the controls. Testosterone levels were significantly associated with VLDL and serum lipids in PCOS cases with large WC but not in the controls. The higher testosterone levels, adjusted for WC, associated adversely with insulin levels and HOMA IR in cases but not in the controls.

Conclusions:

Our findings show that both abdominal obesity and hyperandrogenism contribute to the dyslipidaemia and other metabolic traits of PCOS which all may negatively contribute to the long-term health of women with PCOS.

Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics

Metabolomics is becoming common in epidemiology due to recent developments in quantitative profiling technologies and appealing results from their applications for understanding health and disease. Our team has developed an automated high-throughput serum NMR metabolomics platform that provides quantitative molecular data on 14 lipoprotein subclasses, their lipid concentrations and composition, apolipoprotein A-I and B, multiple cholesterol and triglyceride measures, albumin, various fatty acids as well as on numerous low-molecular-weight metabolites, including amino acids, glycolysis related measures and ketone bodies. The molar concentrations of these measures are obtained from a single serum sample with costs comparable to standard lipid measurements. We have analyzed almost 250 000 samples from around 100 epidemiological cohorts and biobanks and the new international set-up of multiple platforms will allow an annual throughput of more than 250 000 samples. The molecular data have been used to study type 1 and type 2 diabetes etiology as well as to characterize the molecular reflections of the metabolic syndrome, long-term physical activity, diet and lipoprotein metabolism. The results have revealed new biomarkers for early atherosclerosis, type 2 diabetes, diabetic nephropathy, cardiovascular disease and all-cause mortality. We have also combined genomics and metabolomics in diverse studies. We envision that quantitative high-throughput NMR metabolomics will be incorporated as a routine in large biobanks; this would make perfect sense both from the biological research and cost point of view - the standard output of over 200 molecular measures would vastly extend the relevance of the sample collections and make many separate clinical chemistry assays redundant.

Metabolic profiling of synovial fluid in a unilateral ovine model of anterior cruciate ligament reconstruction of the knee suggests biomarkers for early osteoarthritis

Joint injuries and subsequent osteoarthritis (OA) are the leading causes of chronic joint disease. In this work, we explore the possibility of applying magnetic resonance spectroscopy-based metabolomics to detect host responses to an anterior cruciate ligament (ACL) reconstruction injury in synovial fluid in an ovine model. Using multivariate statistical analysis, we were able to distinguish post-injury joint samples (ACL and sham surgery) from the uninjured control samples, and as well the ACL surgical samples from sham surgery. In all samples there were 65 metabolites quantified, of which six could be suggested as biomarkers for early post-injury degenerative changes in the knee joints: isobutyrate, glucose, hydroxyproline, asparagine, serine, and uridine. Our results raise a cautionary note indicating that surgical interventions into the knee can result in metabolic alterations that need to be distinguished from those caused by the early onset of OA. Our findings illustrate the potential application of metabolomics as a diagnostic and prognostic tool for detection of injuries to the knee joint. The ability to detect a unique pattern of metabolic changes in the synovial fluid of sheep offers the possibility of extending the approach to precision medicine protocols in patient populations in the future.

Epidemiology: disease associations and modulators of HDL-related biomarkers

Epidemiological studies have shown an inverse association between high-density lipoprotein cholesterol (HDL-C) levels and risk of ischemic heart disease. In addition, a low level of HDL-C has been shown to be a risk factor for other diseases not related to atherosclerosis. However, recent studies have not supported a causal effect of HDL-C in the development of atherosclerosis. Furthermore, new drugs markedly elevating HDL-C levels have been disappointing with respect to clinical endpoints. Earlier, most studies have focused almost exclusively on the total HDL-C without regard to the chemical composition or multiple subclasses of HDL particles. Recently, there have been efforts to dissect the HDL fraction into as many well-defined subfractions and individual molecules of HDL particles as possible. On the other hand, the focus is shifting from the structure and composition to the function of HDL particles. Biomarkers and mechanisms that could potentially explain the beneficial characteristics of HDL particles unrelated to their cholesterol content have been sought with sophisticated methods such as proteomics, lipidomics, metabonomics, and function studies including efflux capacity. These new approaches have been used in order to resolve the complex effects of diseases, conditions, environmental factors, and genes in relation to the protective role of HDL but high-throughput methods are still needed for large-scale epidemiological studies.

Lipoprotein subclass metabolism in nonalcoholic steatohepatitis

Nonalcoholic steatohepatitis (NASH) is associated with increased synthesis of triglycerides and cholesterol coupled with increased VLDL synthesis in the liver. In addition, increased cholesterol content in the liver associates with NASH. Here we study the association of lipoprotein subclass metabolism with NASH. To this aim, liver biopsies from 116 morbidly obese individuals [age 47.3 ± 8.7 (mean ± SD) years, BMI 45.1 ± 6.1 kg/m², 39 men and 77 women] were used for histological assessment. Proton NMR spectroscopy was used to measure lipid concentrations of 14 lipoprotein subclasses in native serum samples at baseline and after obesity surgery. We observed that total lipid concentration of VLDL and LDL subclasses, but not HDL subclasses, associated with NASH [false discovery rate (FDR) < 0.1]. More specifically, total lipid and cholesterol concentration of VLDL and LDL subclasses associated with inflammation, fibrosis, and cell injury (FDR < 0.1), independent of steatosis. Cholesterol concentration of all VLDL subclasses also correlated with total and free cholesterol content in the liver. All NASH-related changes in lipoprotein subclasses were reversed by obesity surgery. High total lipid and cholesterol concentration of serum VLDL and LDL subclasses are linked to cholesterol accumulation in the liver and to liver cell injury in NASH.

Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping

Proton nuclear magnetic resonance (NMR)-based metabolic phenotyping of urine and blood plasma/serum samples provides important prognostic and diagnostic information and permits monitoring of disease progression in an objective manner. Much effort has been made in recent years to develop NMR instrumentation and technology to allow the acquisition of data in an effective, reproducible, and high-throughput approach that allows the study of general population samples from epidemiological collections for biomarkers of disease risk. The challenge remains to develop highly reproducible methods and standardized protocols that minimize technical or experimental bias, allowing realistic interlaboratory comparisons of subtle biomarker information. Here we present a detailed set of updated protocols that carefully consider major experimental conditions, including sample preparation, spectrometer parameters, NMR pulse sequences, throughput, reproducibility, quality control, and resolution. These results provide an experimental platform that facilitates NMR spectroscopy usage across different large cohorts of biofluid samples, enabling integration of global metabolic profiling that is a prerequisite for personalized healthcare.

Evidence for altered metabolic pathways during environmental stress: (1)H-NMR spectroscopy based metabolomics and clinical studies on subjects of sea-voyage and Antarctic-stay

The Antarctic context is an analogue of space travel, with close similarity in ambience of extreme climate, isolation, constrained living spaces, disrupted sleep cycles, and environmental stress. The present study examined the impact of the harsh habitat of Antarctica on human physiology and its metabolic pathways, by analyzing human serum samples, using (1)H-NMR spectroscopy for identification of metabolites; and quantifying other physiological and clinical parameters for correlation between expression data and metabolite data. Sera from seven adult males (of median age 36years) who participated in this study, from the 28th Indian Expeditionary group to the Antarctica station Maitri, were collected in chronological sequence. These included: i) baseline control; ii) during ship journey; iii) at Antarctica, in the months of March, May, August and November; to enable study of temporal evolution of monitored physiological states. 29 metabolites in serum were identified from the 400MHz (1)H-NMR spectra. Out of these, 19 metabolites showed significant variations in levels, during the ship journey and the stay at Maitri, compared to the base-line levels. Further biochemical analysis also supported these results, indicating that the ship journey, and the long-term Antarctic exposure, affected kidney and liver functioning. Our metabolite data highlights for the first time the effect of environmental stress on the patho-physiology of the human system. Multivariate analysis tools were employed for this metabonomics study, using (1)H-NMR spectroscopy.

Cross-sectional and longitudinal associations of circulating omega-3 and omega-6 fatty acids with lipoprotein particle concentrations and sizes: population-based cohort study with 6-year follow-up

BACKGROUND

Cross-sectional studies have suggested that serum omega-3 (n-3) and omega-6 (n-6) polyunsaturated fatty acids (PUFAs) are related to favorable lipoprotein particle concentrations. We explored the associations of serum n-3 and n-6 PUFAs with lipoprotein particle concentrations and sizes in a general population cohort at baseline and after 6 years.

FINDINGS

The cohort included 665 adults (274 men) with a 6-year follow-up. Nutritional counseling was given at baseline. Serum n-3 and n-6 PUFAs and lipoprotein particle concentrations and the mean particle sizes of VLDL, LDL, and HDL were quantified by nuclear magnetic resonance (NMR) spectroscopy for all baseline and follow-up samples at the same time. Concentrations of n-3 and n-6 PUFAs were expressed relative to total fatty acids. At baseline, n-3 PUFAs were not associated with lipoprotein particle concentrations. A weak negative association was observed for VLDL (P = 0.021) and positive for HDL (P = 0.011) particle size. n-6 PUFA was negatively associated with VLDL particle concentration and positively with LDL (P < 0.001) and HDL particle size (P < 0.001). The 6-year change in n-3 PUFA correlated positively with the change in particle size for HDL and LDL lipoproteins but negatively with VLDL particle size. An increase in 6-year levels of n-6 PUFAs was negatively correlated with the change in VLDL particle concentration and size, and positively with LDL particle size.

CONCLUSION

Change in circulating levels of both n-3 and n-6 PUFAs, relative to total fatty acids, during 6 years of follow-up are associated with changes in lipoprotein particle size and concentrations at the population level.

Association between serum fatty acids and lipoprotein subclass profile in healthy young adults: exploring common genetic and environmental factors

OBJECTIVE

Little is known about the associations of serum fatty acids with lipoprotein profile and the underlying genetic and environmental etiology of these relationships. We aimed to analyze the phenotypic association of serum n-6 and n-3 polyunsaturated (PUFAs), monounsaturated (MUFAs) and saturated (SFAs) fatty acids (relative proportion to total fatty acids) with lipids and lipoproteins, and to quantify common genetic and environmental factors determining their covariation.

METHODS

Two cohorts of healthy Finnish twins were assessed in young adulthood. Data were available for 1269 individual twins including 561 complete pairs. Serum metabolites were measured by nuclear magnetic resonance spectroscopy. Bivariate quantitative genetic models were used to decompose the phenotypic covariance between the pairs of traits into genetic and environmental components.

RESULTS

Among the strongest correlations observed, serum total n-6 PUFAs and linoleic acid were inversely (max. r=-0.65) and MUFAs positively (max. r=0.63) correlated with triglycerides and very low-density lipoprotein (VLDL) particle concentration, particularly with large VLDL (for n-6 PUFAs) and medium VLDL (for MUFAs). Genetic factors significantly contributed to their covariance with bivariate heritability estimates ranging from 44% to 56% for n-6 PUFAs and 58% to 66% for MUFAs. Genetic correlations with lipid traits were moderate to high (max. rA=-0.59 and 0.70 for n-6 PUFAs and MUFAs, respectively). Statistically significant, but substantially weaker phenotypic correlations of total n-3 PUFAs, docosahexaenoic acid (DHA) and SFAs with lipoprotein profile were not decomposed into their genetic and environmental components.

CONCLUSION

Shared genetic factors are important in explaining why higher concentrations of serum n-6 PUFAs and lower concentrations of serum MUFAs strongly associate with lower triglyceride and VLDL particle concentrations.

Untargeted LC-MS metabolomics of bronchoalveolar lavage fluid differentiates acute respiratory distress syndrome from health

Acute respiratory distress syndrome (ARDS) remains a significant hazard to human health and is clinically challenging because there are no prognostic biomarkers and no effective pharmacotherapy. The lung compartment metabolome may detail the status of the local environment that could be useful in ARDS biomarker discovery and the identification of drug target opportunities. However, neither the utility of bronchoalveolar lavage fluid (BALF) as a biofluid for metabolomics nor the optimal analytical platform for metabolite identification is established. To address this, we undertook a study to compare metabolites in BALF samples from patients with ARDS and healthy controls using a newly developed liquid chromatography (LC)-mass spectroscopy (MS) platform for untargeted metabolomics. Following initial testing of three different high-performance liquid chromatography (HPLC) columns, we determined that reversed phase (RP)-LC and hydrophilic interaction chromatography (HILIC) were the most informative chromatographic methods because they yielded the most and highest quality data. Following confirmation of metabolite identification, statistical analysis resulted in 37 differentiating metabolites in the BALF of ARDS compared with health across both analytical platforms. Pathway analysis revealed networks associated with amino acid metabolism, glycolysis and gluconeogenesis, fatty acid biosynthesis, phospholipids, and purine metabolism in the ARDS BALF. The complementary analytical platforms of RPLC and HILIC-LC generated informative, insightful metabolomics data of the ARDS lung environment.

Association between habitual dietary intake and lipoprotein subclass profile in healthy young adults

BACKGROUND AND AIMS

Nutritional epidemiology is increasingly shifting its focus from studying single nutrients to the exploration of the whole diet utilizing dietary pattern analysis. We analyzed associations between habitual diet (including macronutrients, dietary patterns, biomarker of fish intake) and lipoprotein particle subclass profile in young adults.

METHODS AND RESULTS

Complete dietary data (food-frequency questionnaire) and lipoprotein subclass profile (via nuclear magnetic resonance spectroscopy) were available for 663 subjects from the population-based FinnTwin12 study (57% women, age: 21-25 y). The serum docosahexaenoic to total fatty acid ratio was used as a biomarker of habitual fish consumption. Factor analysis identified 5 dietary patterns: "Fruit and vegetables", "Meat", "Sweets and desserts", "Junk food" and "Fish". After adjustment for sex, age, body mass index, waist circumference, physical activity, smoking status and alcohol intake, the "Junk food" pattern was positively related to serum triglycerides (r = 0.12, P = 0.002), a shift in the subclass distribution of VLDL toward larger particles (r = 0.12 for VLDL size, P < 0.001) and LDL toward smaller particles (r = -0.15 for LDL size, P < 0.001). In addition, higher scores on this pattern were positively correlated with concentrations of small, dense HDL (r = 0.16, P < 0.001). Habitual fish intake associated negatively with VLDL particle diameter ("Fish" pattern and biomarker) and positively with HDL particle diameter (biomarker).

CONCLUSIONS

Our results suggest that in young adults, higher habitual fish consumption is related to favorable subclass distributions of VLDL and HDL, while junk food intake is associated with unfavorable alterations in the distribution of all lipoprotein subclasses independent of adiposity and other lifestyle factors.

Gender-dependent associations of metabolite profiles and body fat distribution in a healthy population with central obesity: towards metabolomics diagnostics

Obesity is a risk factor for cardiovascular diseases and type 2 diabetes especially when the fat is accumulated to central depots. Novel biomarkers are crucial to develop diagnostics for obesity and related metabolic disorders. We evaluated the associations between metabolite profiles (136 lipid components, 12 lipoprotein subclasses, 17 low-molecular-weight metabolites, 12 clinical markers) and 28 phenotype parameters (including different body fat distribution parameters such as android (A), gynoid (G), abdominal visceral (VAT), subcutaneous (SAT) fat) in 215 plasma/serum samples from healthy overweight men (n=32) and women (n=83) with central obesity. (Partial) correlation analysis and partial least squares (PLS) regression analysis showed that only specific metabolites were associated to A:G ratio, VAT, and SAT, respectively. These association patterns were gender dependent. For example, insulin, cholesterol, VLDL, and certain triacylglycerols (TG 54:1-3) correlated to VAT in women, while in men VAT was associated with TG 50:1-5, TG 55:1, phosphatidylcholine (PC 32:0), and VLDL ((X)L). Moreover, multiple regression analysis revealed that waist circumference and total fat were sufficient to predict VAT and SAT in women. In contrast, only VAT but not SAT could be predicted in men and only when plasma metabolites were included, with PC 32:0 being most strongly associated with VAT. These findings collectively highlight the potential of metabolomics in obesity and that gender differences need to be taken into account for novel biomarker and diagnostic discovery for obesity and metabolic disorders.

Association of height and pubertal timing with lipoprotein subclass profile: exploring the role of genetic and environmental effects

OBJECTIVES

Little is known about the relationship between growth and lipoprotein profile. We aimed to analyze common genetic and environmental factors in the association of height from late childhood to adulthood and pubertal timing with serum lipid and lipoprotein subclass profile.

METHODS

A longitudinal cohort of Finnish twin pairs (FinnTwin12) was analyzed using self-reported height at 11-12, 14, 17 years and measured stature at adult age (21-24 years). Data were available for 719 individual twins including 298 complete pairs. Serum lipids and lipoprotein subclasses were measured by proton nuclear magnetic resonance spectroscopy. Multivariate variance component models for twin data were fitted. Cholesky decomposition was used to partition the phenotypic covariation among traits into additive genetic and unique environmental correlations.

RESULTS

In men, the strongest associations for both adult height and puberty were observed with total cholesterol, low-density lipoprotein cholesterol, intermediate-density lipoprotein cholesterol, and low-density lipoprotein particle subclasses (max. r = -0.19). In women, the magnitude of the correlations was weaker (max. r = -0.13). Few associations were detected between height during adolescence and adult lipid profile. Early onset of puberty was related to an adverse lipid profile, but delayed pubertal development in girls was associated with an unfavorable profile, as well. All associations were mediated mainly by additive genetic factors, but unique environmental effects cannot be disregarded.

CONCLUSIONS

Early puberty and shorter adult height relate to higher concentrations of atherogenic lipids and lipoprotein particles in early adulthood. Common genetic effects behind these phenotypes substantially contribute to the observed associations.

Triglyceride-cholesterol imbalance across lipoprotein subclasses predicts diabetic kidney disease and mortality in type 1 diabetes: the FinnDiane Study

BACKGROUND

Circulating cholesterol (C) and triglyceride (TG) levels are associated with vascular injury in type 1 diabetes (T1DM). Lipoproteins are responsible for transporting lipids, and alterations in their subclass distributions may partly explain the increased mortality in individuals with T1DM.

DESIGN AND SUBJECTS

A cohort of 3544 individuals with T1DM was recruited by the nationwide multicentre FinnDiane Study Group. At baseline, six very low-density lipoprotein VLDL, one intermediate-density lipoprotein IDL, three low-density lipoprotein LDL and four higher high-density lipoprotein HDL subclasses were quantified by proton nuclear magnetic resonance spectroscopy. At follow-up, the baseline data were analysed for incident micro- or macroalbuminuria (117 cases in 5.3 years), progression from microalbuminuria (63 cases in 6.1 years), progression from macroalbuminuria (109 cases in 5.9 years) and mortality (385 deaths in 9.4 years). Univariate associations were tested by age-matched cases and controls and multivariate lipoprotein profiles were analysed using the self-organizing map (SOM).

RESULTS

TG and C levels in large VLDL were associated with incident albuminuria, TG and C in medium VLDL were associated with progression from microalbuminuria, and TG and C in all VLDL subclasses were associated with mortality. Large HDL-C was inversely associated with mortality. Three extreme phenotypes emerged from SOM analysis: (i) low C (<3% mortality), (ii) low TG/C ratio (6% mortality), and (iii) high TG/C ratio (40% mortality) in all subclasses.

CONCLUSIONS

TG-C imbalance is a general lipoprotein characteristic in individuals with T1DM and high vascular disease risk.

Integration of datasets from different analytical techniques to assess the impact of nutrition on human metabolome

Bacteria colonizing the human intestinal tract exhibit a high phylogenetic diversity that reflects their immense metabolic potentials. The catalytic activity of gut microbes has an important impact on gastrointestinal (GI) functions and host health. The microbial conversion of carbohydrates and other food components leads to the formation of a large number of compounds that affect the host metabolome and have beneficial or adverse effects on human health. Metabolomics is a metabolic-biology system approach focused on the metabolic responses understanding of living systems to physio-pathological stimuli by using multivariate statistical data on human body fluids obtained by different instrumental techniques. A metabolomic approach based on an analytical platform could be able to separate, detect, characterize and quantify a wide range of metabolites and its metabolic pathways. This approach has been recently applied to study the metabolic changes triggered in the gut microbiota by specific diet components and diet variations, specific diseases, probiotic and synbiotic food intake. This review describes the metabolomic data obtained by analyzing human fluids by using different techniques and particularly Gas Chromatography Mass Spectrometry Solid-phase Micro Extraction (GC-MS/SPME), Proton Nuclear Magnetic Resonance (¹H-NMR) Spectroscopy and Fourier Transform Infrared (FTIR) Spectroscopy. This instrumental approach has a good potential in the identification and detection of specific food intake and diseases biomarkers.

Lipoprotein subclass profiles in individuals with varying degrees of glucose tolerance: a population-based study of 9399 Finnish men

OBJECTIVES

We investigated serum concentrations of lipoprotein subclass particles and their lipid components determined by proton nuclear magnetic resonance spectroscopy in a population-based study.

DESIGN AND METHODS

A total of 9399 Finnish men were included in the study: 3034 men with normal fasting glucose and normal glucose tolerance; 4345 with isolated impaired fasting glucose (IFG); 312 with isolated impaired glucose tolerance (IGT); 1058 with both IFG and IGT; and 650 with newly diagnosed type 2 diabetes (New DM). Lipoprotein subclasses included chylomicrons (CM) and largest VLDL particles, other VLDL particles (five subclasses), intermediate-density lipoprotein (IDL), LDL (three subclasses) and HDL (four subclasses). The phospholipid, triglyceride (TG), cholesterol, free cholesterol and cholesterol ester levels of the lipoprotein particles were measured.

RESULTS

Abnormal glucose tolerance (especially IGT and New DM) was significantly associated with increased concentrations of VLDL subclass particles and their components (with the exception of very small VLDL particles). After further adjustment for total TGs and HDL cholesterol, increased lipid concentrations in the CM/largest VLDL particles and in most of the other VLDL particles remained significant in individuals with isolated IGT, IFG+IGT and New DM. There was a consistent trend towards a decrease in large and an increase in small HDL particle concentrations in individuals with hyperglycaemia even after adjustment for serum total TGs and HDL cholesterol.

CONCLUSIONS

Abnormal glucose tolerance modifies the concentrations of lipoprotein subclass particles and their lipid components in the circulation and is also related to compositional changes in these particles.

Mild cognitive impairment associates with concurrent decreases in serum cholesterol and cholesterol-related lipoprotein subclasses

BACKGROUND AND OBJECTIVE

Accumulating evidence suggests that serum lipids are associated with cognitive decline and dementias. However, majority of the existing information concerns only serum total cholesterol (TC) and data at the level of lipoprotein fractions and subclasses is limited. The aim of this study was to explore the levels and trends of main cholesterol and triglyceride measures and eight lipoprotein subclasses during normal aging and the development of mild cognitive impairment by following a group of elderly for six years.

DESIGN

Longitudinal.

SETTING

City of Kuopio, Finland.

PARTICIPANTS

45 elderly individuals of which 20 developed mild cognitive impairment (MCI) during the follow-up.

MEASUREMENTS

On each visit participants underwent an extensive neuropsychological and clinical assessment. Lipoprotein levels were measured via 1H NMR from native serum samples.

RESULTS

Serum cholesterol and many primarily cholesterol-associated lipoprotein measures clearly decreased in MCI while the trends were increasing for those elderly people who maintained normal cognition.

CONCLUSION

These findings suggest that a decreasing trend in serum cholesterol measures in elderly individuals may suffice as an indication for more detailed inspection for potential signs of cognitive decline.

Weight change and lipoprotein particle concentration and particle size: a cohort study with 6.5-year follow-up

OBJECTIVE

Obesity and overweight are related to unfavourable lipoprotein subclass profiles. Here we studied the relation between weight change and lipoprotein particle concentrations and sizes in a general population cohort in a longitudinal setting.

METHODS

The cohort included 683 adults with a 6.5-year follow-up. Lipoprotein particle subclasses and mean particle sizes of VLDL, LDL, and HDL were measured by nuclear magnetic resonance spectroscopy.

RESULTS

During the follow-up period, a weight loss of at least 5% was associated with decreased particle concentrations of all apoB-containing subclasses and increased concentrations of large HDL particles. Coherently, weight gain (≥5%) was associated with increases in all apoB-containing subclasses and decreases in total and medium HDL particle concentrations. The relatively largest increase occurred for large HDL particle concentration (24.1%, 95% CI 15.8-32.5) in weight loss and for large VLDL particle concentration (33.0%, 19.6-46.4) in weight gain. Weight change correlated positively with changes in apoB-containing lipoprotein particle concentrations and also with the change in average VLDL particle size. Negative correlations were found between weight change and the change in average LDL (r = -0.10) and HDL (r = -0.32) particle size, but not between weight change and total HDL particle concentration.

CONCLUSION

Moderate weight loss is related to favourable and weight gain to unfavourable changes in lipoprotein subclass profiles. These population level findings underline the importance of weight control as a modifier of cardiovascular risk factors.

Gram-negative and Gram-positive bacterial infections give rise to a different metabolic response in a mouse model

Metabolomics has become an important tool to study host-pathogen interactions and to discover potential novel therapeutic targets. In an attempt to develop a better understanding of the process of pathogenesis and the associated host response we have used a quantitative ¹H NMR approach to study the metabolic response to different bacterial infections. Here we describe that metabolic changes found in serum of mice that were infected with Staphylococcus aureus, Streptococcus pneumoniae, Escherichia coli and Pseudomonas aeruginosa can distinguish between infections caused by Gram-positive and Gram-negative bacterial strains. By combining the results of the mouse study with those of bacterial footprinting culture experiments, bacterially secreted metabolites could be identified as potential bacterium-specific biomarkers for P. aeruginosa infections but not for the other strains. Multivariate statistical analysis revealed correlations between metabolic, cytokine and physiological responses. In TLR4 and TLR2 knockout mice, host-response pathway correlated metabolites could be identified and allowed us for the first time to distinguish between bacterial- and host-induced metabolic changes. Since Gram-positive and Gram-negative bacteria activate different receptor pathways in the host, our results suggest that it may become possible in the future to use a metabolomics approach to improve on current clinical microbiology diagnostic methods.