Multilevel data analysis of a crossover designed human nutritional intervention study

Chronic alcohol and nicotine consumption as catalyst for systemic inflammatory storm and bone destruction in apical periodontitis

AIM

To assess the periapical alveolar bone pattern and the serum levels of proinflammatory cytokines, biochemical markers and metabolites in rats subjected to chronic alcohol and nicotine consumption and induced apical periodontitis.

METHODOLOGY

Twenty-eight male Wistar rats were divided into four groups: Control, Alcohol, Nicotine and Alcohol+Nicotine. The alcohol groups were exposed to self-administration of a 25% alcohol solution, while the other groups were given only filtered water. The nicotine groups received daily intraperitoneal injections of a nicotine solution (0.19 μL of nicotine/mL), whereas the other groups received saline solution. Periapical lesions were induced by exposing the pulps of the left mandibular first molars for 28 days. After euthanasia, the mandibles were removed and the percentage bone volume, bone mineral density, trabecular thickness, trabecular separation and trabecular number of the periapical bone were measured using micro-computed tomography images. Serum samples were collected for analysis of proinflammatory cytokines (IL-1β, IL-4, IL-6 and TNF-α), biochemical and metabolomic analysis. Statistical analysis was performed with a significance level of 5%. Nonparametric data were analysed using the Kruskal-Wallis test followed by Dunn's test, while one-way anova followed by Tukey's test was performed for parametric data.

RESULTS

The groups exposed to alcohol or nicotine consumption exhibited an altered bone pattern indicating lower bone density and higher levels of IL-1β, IL-6 and TNF-α compared to the Control group (p < .05). Significant differences were observed among the groups in the levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase, cholesterol, triglycerides, urea, creatinine, albumin, uric acid, bilirubin and calcium. Metabolomic analysis revealed significant differences in glycine, phosphocholine, lysine, lactate, valine, pyruvate and lipids (CH2 CH2 CO), n(CH2 ) and n(CH3 ). Most of these parameters were even more altered in the simultaneous consumption of both substances compared to single consumption.

CONCLUSION

Alcohol and nicotine chronic consumption altered several metabolic markers, impaired liver and kidney function, increased the production of systemic proinflammatory mediators and harmed the periapical bone microarchitecture in the presence of apical periodontitis. The simultaneous consumption of alcohol and nicotine intensified these detrimental effects.

Exploring Human Metabolome after Wine Intake-A Review

Wine has a rich history dating back to 2200 BC, originally recognized for its medicinal properties. Today, with the aid of advanced technologies like metabolomics and sophisticated analytical techniques, we have gained remarkable insights into the molecular-level changes induced by wine consumption in the human organism. This review embarks on a comprehensive exploration of the alterations in human metabolome associated with wine consumption. A great number of 51 studies from the last 25 years were reviewed; these studies systematically investigated shifts in metabolic profiles within blood, urine, and feces samples, encompassing both short-term and long-term studies of the consumption of wine and wine derivatives. Significant metabolic alterations were observed in a wide variety of metabolites belonging to different compound classes, such as phenolic compounds, lipids, organic acids, and amino acids, among others. Within these classes, both endogenous metabolites as well as diet-related metabolites that exhibited up-regulation or down-regulation following wine consumption were included. The up-regulation of short-chain fatty acids and the down-regulation of sphingomyelins after wine intake, as well as the up-regulation of gut microbial fermentation metabolites like vanillic and syringic acid are some of the most important findings reported in the reviewed literature. Our results confirm the intact passage of certain wine compounds, such as tartaric acid and other wine acids, to the human organism. In an era where the health effects of wine consumption are of growing interest, this review offers a holistic perspective on the metabolic underpinnings of this centuries-old tradition.

NMR-Based Metabolomics Demonstrates a Metabolic Change during Early Developmental Stages from Healthy Infants to Young Children

The present study aims to identify the salivary metabolic profile of healthy infants and young children, and to correlate this with age, salivary gland maturation, and dentition. Forty-eight children were selected after clinical evaluation in which all intraoral structures were examined. Total unstimulated saliva was collected, and salivary metabolites were analyzed by 1H Nuclear Magnetic Resonance (NMR) at 25 °C. Partial least squares discriminant analysis (PLS-DA), orthogonal PLS-DA (O-PLS-DA), and univariate analysis were used, adopting a 95% confidence interval. The study showed a distinct salivary metabolomic profile related to age and developmental phase. The saliva of children in the pre-eruption teeth period showed a different metabolite profile than that of children after the eruption. However, more evident changes were observed in the saliva profile of children older than 30 months. Alanine, choline, ethanol, lactate, and sugar region were found in higher levels in the saliva of patients before 30 months old. Acetate, N-acetyl sugar, butyrate, caproate, creatinine, leucine, phenylalanine, propionate, valine, succinate, and valerate were found to be more abundant in the saliva of children after 30 months old. The saliva profile is a result of changes in age and dental eruption, and these findings can be useful for monitoring the physiological changes that occur in infancy.

Effect of high-fat diet and morning or evening exercise on lipoprotein subfraction profiles: secondary analysis of a randomised trial

We investigated the effect of a high-fat diet (HFD) on serum lipid subfractions in men with overweight/obesity and determined whether morning or evening exercise affected these lipid profiles. In a three-armed randomised trial, 24 men consumed an HFD for 11 days. One group of participants did not exercise (n = 8, CONTROL), one group trained at 06:30 h (n = 8, EXam), and one group at 18:30 h (n = 8, EXpm) on days 6-10. We assessed the effects of HFD and exercise training on circulating lipoprotein subclass profiles using NMR spectroscopy. Five days of HFD induced substantial perturbations in fasting lipid subfraction profiles, with changes in 31/100 subfraction variables (adjusted p values [q] < 0.05). Exercise training induced a systematic change in lipid subfraction profiles, with little overall difference between EXam and EXpm. Compared with CONTROL, exercise training reduced serum concentrations of > 20% of fasting lipid subfractions. EXpm reduced fasting cholesterol concentrations in three LDL subfractions by ⁓30%, while EXam only reduced concentration in the largest LDL particles by 19% (all q < 0.05). Lipid subfraction profiles changed markedly after 5 days HFD in men with overweight/obesity. Both morning and evening exercise training impacted subfraction profiles compared with no exercise.

Salivary Metabolomic Analysis Reveals Amino Acid Metabolism Shift in SARS-CoV-2 Virus Activity and Post-Infection Condition

The SARS-CoV-2 virus primarily infects salivary glands suggesting a change in the saliva metabolite profile; this shift may be used as a monitoring instrument during SARS-CoV-2 infection. The present study aims to determine the salivary metabolomic profile of patients with and post-SARS-CoV-19 infection. Patients were without (PCR-), with SARS-CoV-2 (PCR+), or post-SARS-CoV-2 infection. Unstimulated whole saliva was collected, and the ¹H spectra were acquired in a 500 MHz Bruker nuclear magnetic resonance spectrometer at 25 °C. They were subjected to multivariate analysis using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), as well as univariate analysis through t-tests (SPSS 20.0, IL, USA), with a significance level of p < 0.05. A distinction was found when comparing PCR- subjects to those with SARS-CoV-2 infection. When comparing the three groups, the PLS-DA cross-validation presented satisfactory accuracy (ACC = 0.69, R2 = 0.39, Q2 = 0.08). Seventeen metabolites were found in different proportions among the groups. The results suggested the downregulation of major amino acid levels, such as alanine, glutamine, histidine, leucine, lysine, phenylalanine, and proline in the PCR+ group compared to the PCR- ones. In addition, acetate, valerate, and capronic acid were higher in PCR- patients than in PCR+. Sucrose and butyrate were higher in post-SARS-CoV-2 infection compared to PCR-. In general, a reduction in amino acids was observed in subjects with and post-SARS-CoV-2 disease. The salivary metabolomic strategy NMR-based was able to differentiate between non-infected individuals and those with acute and post-SARS-CoV-19 infection.

Postoperative Metabolic Phenoreversion in Clear Cell Renal Cell Carcinoma

The ultimate goal of surgical treatment in cancer is to remove the tumor mass for restoring a healthy state. A 16-lipid panel that discriminated healthy controls from clear cell renal cell carcinoma (ccRCC) patients in a prior study was evaluated in the present work in paired-serum samples collected from patients (n = 41) before and after nephrectomy. Changes in the lipid and metabolite fingerprints from ccRCC patients were investigated and compared with fingerprints from healthy individuals obtained by means of ultra-performance liquid chromatography-high-resolution mass spectrometry. The lipid panel differentiated phenotypes associated with metabolic restoration after surgery, representing a serum signature of phenoreversion to a healthy metabolic state. In particular, PC 16:0/0:0, PC 18:2/18:2, and linoleic acid allowed discriminating serum samples from ccRCC patients with poor prognosis from those with an improved outcome during the follow-up period. Ratios of PC 16:0/0:0 and PC 18:2/18:2 with linoleic acid levels may contribute as prognostic tools to support decision-making during the patient follow-up care. The preliminary character of these results should be validated with larger cohorts, including subjects with different ethnicities, life style, and diets. MetaboLights study references: MTBLS1839, MTBLS3838, and MTBLS4629.

Flavor Profiling Using Comprehensive Mass Spectrometry Analysis of Metabolites in Tomato Soups

Trained sensory panels are regularly used to rate food products but do not allow for data-driven approaches to steer food product development. This study evaluated the potential of a molecular-based strategy by analyzing 27 tomato soups that were enhanced with yeast-derived flavor products using a sensory panel as well as LC-MS and GC-MS profiling. These data sets were used to build prediction models for 26 different sensory attributes using partial least squares analysis. We found driving separation factors between the tomato soups and metabolites predicting different flavors. Many metabolites were putatively identified as dipeptides and sulfur-containing modified amino acids, which are scientifically described as related to umami or having "garlic-like" and "onion-like" attributes. Proposed identities of high-impact sensory markers (methionyl-proline and asparagine-leucine) were verified using MS/MS. The overall results highlighted the strength of combining sensory data and metabolomics platforms to find new information related to flavor perception in a complex food matrix.

Impact of Phenol-Enriched Olive Oils on Serum Metabonome and Its Relationship with Cardiometabolic Parameters: A Randomized, Double-Blind, Cross-Over, Controlled Trial

Phenol-rich foods consumption such as virgin olive oil (VOO) has been shown to have beneficial effects on cardiovascular diseases. The broader biochemical impact of VOO and phenol-enriched OOs remains, however, unclear. A randomized, double-blind, cross-over, controlled trial was performed with thirty-three hypercholesterolemic individuals who ingested for 3-weeks (25 mL/day): (1) an OO enriched with its own olive oil phenolic compounds (PCs) (500 ppm; FOO); (2) an OO enriched with its own olive oil PCs (250 ppm) plus thyme PCs (250 ppm; FOOT); and (3) a VOO with low phenolic content (80 ppm). Serum lipid and glycemic profiles, serum ¹H-NMR spectroscopy-based metabolomics, endothelial function, blood pressure, and cardiovascular risk were measured. We combined OPLS-DA with machine learning modelling to identify metabolites discrimination of the treatment groups. Both phenol-enriched OO interventions decreased the levels of glutamine, creatinine, creatine, dimethylamine, and histidine in comparison to VOO one. In addition, FOOT decreased the plasma levels of glycine and DMSO2 compared to VOO, while FOO decreased the circulating alanine concentrations but increased the plasma levels of acetone and 3-HB compared to VOO. Based on these findings, phenol-enriched OOs were shown to result in a favorable shift in the circulating metabolic phenotype, inducing a reduction in metabolites associated with cardiometabolic diseases.

Effect of Supplementation with Olive Leaf Extract Enriched with Oleuropein on the Metabolome and Redox Status of Athletes’ Blood and Urine—A Metabolomic Approach

Oleuropein (OE) is a secoiridoid glycoside occurring mostly in the Oleaceae family and presenting several pharmacological properties, including hypolipidemic and antioxidant properties. Based on these, several dietary supplements containing olive leaf extracts enriched with OE are commercially available in many countries. The current study aimed to examine the effect of supplementation with such an extract on the serum and urine metabolome of young healthy male athletes. For this purpose, applying a randomized, balanced, double-blind study, nine young, healthy males (physical education students) received either a commercially prepared extract or placebo for one week, followed by a two-week washout period; then, they were subsequently dosed with the alternate scheme (crossover design). Urine and serum samples were analyzed using UHPLC-HRMS, followed by evaluation with several multivariate methods of data analysis. The data were interpreted using a multilevel metabolomic approach (multilevel-sPLSDA) as it was found to be the most efficient approach for the study design. Metabolic pathway analysis of the most affected metabolites revealed that tryptophan and acylcarnitine’s biochemistries were most influenced. Furthermore, several metabolites connected to indole metabolism were detected, which may indicate enhanced serotonin turnover. Phenylethylamine and related metabolites, as well as estrone, were connected to enhanced performance. In addition, possible changes to the lipidemic profile and the blood and urine redox statuses were investigated.

Differences between Arterial and Venous Umbilical Cord Plasma Metabolome and Association with Parity

Umbilical cord blood is frequently used in health monitoring of the neonate. Results may be affected by the proportion of arterial and venous cord blood, the venous blood coming from the mother to supply oxygen and nutrients to the infant, and the arterial carrying waste products from the fetus. Here, we sampled arterial and venous umbilical cords separately from 48 newly delivered infants and examined plasma metabolomes using GC-MS/MS metabolomics. We investigated differences in metabolomes between arterial and venous blood and their associations with gestational length, birth weight, sex, and whether the baby was the first born or not, as well as maternal age and BMI. Using multilevel random forest analysis, a classification rate of 79% was achieved for arteriovenous differences (p = 0.004). Several monosaccharides had higher concentrations in the arterial cord plasma while amino acids were higher in venous plasma, suggesting that the main differences in the measured arterial and venous plasma metabolomes are related to amino acid and energy metabolism. Venous cord plasma metabolites related to energy metabolism were positively associated with parity (77% classification rate, p = 0.004) while arterial cord plasma metabolites were not. This underlines the importance of defining cord blood type for metabolomic studies.

Multivariate analysis of NMR-based metabolomic data

Nuclear magnetic resonance (NMR) spectroscopy allows for simultaneous detection of a wide range of metabolites and lipids. As metabolites act together in complex metabolic networks, they are often highly correlated, and optimal biological insight is achieved when using methods that take the correlation into account. For this reason, latent-variable-based methods, such as principal component analysis and partial least-squares discriminant analysis, are widely used in metabolomic studies. However, with increasing availability of larger population cohorts, and a shift from analysis of spectral data to using quantified metabolite levels, both more traditional statistical approaches and alternative machine learning methods have become more widely used. This review aims at providing an overview of the current state-of-the-art multivariate methods for the analysis of NMR-based metabolomic data as well as alternative methods, highlighting their strengths and limitations.

Quantitative GC-TCD Measurements of Major Flatus Components: A Preliminary Analysis of the Diet Effect

The impact of diet and digestive disorders in flatus composition remains largely unexplored. This is partially due to the lack of standardized sampling collection methods, and the easy atmospheric contamination. This paper describes a method to quantitatively determine the major gases in flatus and their application in a nutritional intervention. We describe how to direct sample flatus into Tedlar bags, and simultaneous analysis by gas chromatography–thermal conductivity detection (GC–TCD). Results are analyzed by univariate hypothesis testing and by multilevel principal component analysis. The reported methodology allows simultaneous determination of the five major gases with root mean measurement errors of 0.8% for oxygen (O₂), 0.9% for nitrogen (N₂), 0.14% for carbon dioxide (CO₂), 0.11% for methane (CH₄), and 0.26% for hydrogen (H₂). The atmospheric contamination was limited to 0.86 (95% CI: [0.7–1.0])% for oxygen and 3.4 (95% CI: [1.4–5.3])% for nitrogen. As an illustration, the method has been successfully applied to measure the response to a nutritional intervention in a reduced crossover study in healthy subjects.

Exploring Serum NMR-Based Metabolomic Fingerprint of Colorectal Cancer Patients: Effects of Surgery and Possible Associations with Cancer Relapse

Background: Colorectal cancer (CRC) is the fourth most commonly diagnosed and third most deadly cancer worldwide. Surgery is the main treatment option for early disease; however, a relevant proportion of CRC patients relapse. Here, variations among preoperative and postoperative serum metabolomic fingerprint of CRC patients were studied, and possible associations between metabolic variations and cancer relapse were explored. Methods: A total of 41 patients with stage I-III CRC, planned for radical resection, were enrolled. Serum samples, collected preoperatively (t0) and 4–6 weeks after surgery before the start of any treatment (t1), were analyzed via NMR spectroscopy. NMR data were analyzed using multivariate and univariate statistical approaches. Results: Serum metabolomic fingerprints show differential clustering between t0 and t1 (82–85% accuracy). Pyruvate, HDL-related parameters, acetone, and 3-hydroxybutyrate appear to be the major players in this discrimination. Eight out of the 41 CRC patients enrolled developed cancer relapse. Postoperative, relapsed patients show an increase of pyruvate and HDL-related parameters, and a decrease of Apo-A1 Apo-B100 ratio and VLDL-related parameters. Conclusions: Surgery significantly alters the metabolomic fingerprint of CRC patients. Some metabolic changes seem to be associated with the development of cancer relapse. These data, if validated in a larger cohort, open new possibilities for risk stratification in patients with early-stage CRC.

Circulating Structurally Related (-)-Epicatechin Metabolite Species and Levels after Sustained Intake of a Cocoa Powder High in Polyphenols Are Comparable to Those Achieved after a Single Dose

BACKGROUND

While the bioavailability of cocoa polyphenols, particularly of the monomer (-)-epicatechin, has been investigated after a single-dose intake, the effect of sustained cocoa consumption on the metabolic profile of the structurally related (-)-epicatechin metabolites (SREMs) has not been investigated.

METHODS

A randomized, controlled crossover clinical trial in healthy young adults (18-40 year) was conducted to evaluate SREMs after consumption of a single-dose and after daily consumption of 1.3 g of polyphenol-rich cocoa powder for 28 days. The circulating SREMs were measured by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS).

RESULTS

Twenty subjects (eleven males and nine females) were enrolled. The SREMs concentrations increased to 1741 ± 337 nM after a single-dose and to 1445 ± 270 nM after sustained supplementation. Sulfate conjugates showed higher levels in females (p < 0.05). The epicatechin-3'-glucuronide (E3'G) and epicatechin-3'-sulfate (E3'S) were the most abundant metabolites in all subjects. A high intra-individual correlation (r = 0.72, p < 0.001) between SREMs concentrations after single-dose and sustained supplementation was observed. The antioxidant capacity of plasma did not change in response to the intervention and was not correlated with any of the SREMs.

CONCLUSION

The individual SREMs profile and concentrations after a 28-day supplementation are comparable to those after a single dose.

Linking the FTO obesity rs1421085 variant circuitry to cellular, metabolic, and organismal phenotypes in vivo

Variants in FTO have the strongest association with obesity; however, it is still unclear how those noncoding variants mechanistically affect whole-body physiology. We engineered a deletion of the rs1421085 conserved cis-regulatory module (CRM) in mice and confirmed in vivo that the CRM modulates Irx3 and Irx5 gene expression and mitochondrial function in adipocytes. The CRM affects molecular and cellular phenotypes in an adipose depot-dependent manner and affects organismal phenotypes that are relevant for obesity, including decreased high-fat diet-induced weight gain, decreased whole-body fat mass, and decreased skin fat thickness. Last, we connected the CRM to a genetically determined effect on steroid patterns in males that was dependent on nutritional challenge and conserved across mice and humans. Together, our data establish cross-species conservation of the rs1421085 regulatory circuitry at the molecular, cellular, metabolic, and organismal level, revealing previously unknown contextual dependence of the variant's action.

Freitas-Fernandes L, Fidalgo TKS, Tadeu Lopes R, Valente AP, R. Arnold R, de Aguiar Ribeiro A. The bacterial microbiome and metabolome in caries progression and arrest

Aim: This in vivo experimental study investigated bacterial microbiome and metabolome longitudinal changes associated with enamel caries lesion progression and arrest. Methods: We induced natural caries activity in three caries-free volunteers prior to four premolar extractions for orthodontic reasons. The experimental model included placement of a modified orthodontic band on smooth surfaces and a mesh on occlusal surfaces. We applied the caries-inducing protocol for 4- and 6-weeks, and subsequently promoted caries lesion arrest via a 2-week toothbrushing period. Lesions were verified clinically and quantitated via micro-CT enamel density measurements. The biofilm microbial composition was determined via 16S rRNA gene Illumina sequencing and NMR spectrometry was used for metabolomics. Results: Biofilm maturation and caries lesion progression were characterized by an increase in Gram-negative anaerobes, including Veillonella and Prevotella. Streptococcus was associated caries lesion progression, while a more equal distribution of Streptococcus, Bifidobacterium, Atopobium, Prevotella, Veillonella, and Saccharibacteria (TM7) characterized arrest. Lactate, acetate, pyruvate, alanine, valine, and sugars were more abundant in mature biofilms compared to newly formed biofilms. Conclusions: These longitudinal bacterial microbiome and metabolome results provide novel mechanistic insights into the role of the biofilm in caries progression and arrest and offer promising candidate biomarkers for validation in future studies.

Effect of antihistamine-containing syrup on salivary metabolites: an in vitro and in vivo study

This study tested the null hypothesis that antihistamine-containing syrup does not change salivary metabolites in vitro and in vivo. For the in vitro experiments, saliva from 10 volunteers was mixed with a syrup or pill suspension of loratadine (1 mg/ml Claritin®, Schering-Plough, Rio de Janeiro, Brazil). For the in vivo experiment, 10 volunteers performed a mouth rinse with 10 mL of antihistamine syrup (Claritin®; Schering-Plough, Rio de Janeiro, Brazil) for 20 seconds and then discarded the rinse water. After 20 seconds, 5 mL of unstimulated whole saliva was spit into a plastic tube kept on ice. The protein profile of in vitro and in vivo experiments was analyzed using 12% sodium dodecyl sulfate-polyacrylamide gel electrophoresis. The samples were also analyzed by nuclear magnetic resonance (NMR) spectroscopy, followed by Principal Component Analysis and Wilcoxon test (p < 0.05). There were differences in salivary metabolites after syrup interaction. The salivary concentrations of acetate, n-caproate, arginine, glutamate, and lysine among other metabolites were reduced with the syrup in both in vivo and in vitro experiments (p < 0.05), but no differences were observed when the pill suspension was used (p > 0.05). Similar changes in metabolite profiles were observed in both in vitro and in vivo experiments. Electrophoresis revealed no difference in the salivary protein pattern. The null hypothesis was rejected because the intake of syrup medicine changes the salivary composition and influences oral homeostasis and susceptibility to oral diseases.

Salivary metabolome of children and adolescents under peritoneal dialysis

OBJECTIVE

To study the influence of peritoneal dialysis (PD) on the salivary metabolite profile of children and adolescents with renal failure.

MATERIALS AND METHODS

Healthy children/adolescents (n = 31; mean age: 12.18 ± 3.76) and children/adolescents subjected to PD (n = 12; mean age: 10.10 ± 4.25) were recruited. Oral health status assessed by the dmft/DMFT and Volpe-Manhold calculus indices. The ¹H spectra were acquired in a 600-MHz Bruker nuclear magnetic resonance spectrometer and were subjected to multivariate analysis using partial least squares discriminant analysis (PLS-DA), orthogonal PLS-DA (O-PLS-DA), and univariate analysis through chi-square and t tests (SPSS 20.0, IL, USA), with a significance level of p < 0.05.

RESULTS

A similar caries pattern (p = 0.57; chi-square test) was observed between the healthy (dmft = 0.72 ± 1.28 and DMFT 0.93 ± 2.30) and PD groups (dmft = 2.14 ± 3.67, DMFT 0.33 ± 0.71) and dental calculus (p > 0.05, t test). PLS-DA and O-PLS-DA were able to distinguish both groups (ACC = 0.85, R² = 0.80, Q² = 0.15). Salivary metabolites decrease in creatine, propionate, and sugar levels in the PD group and an increase in creatinine, butyrate, and lactate levels when compared with the healthy group.

CONCLUSIONS

Children and adolescents subjected to PD have a different salivary metabolic profile from that of their healthy subjects.

CLINICAL RELEVANCE

Complications of peritoneal dialysis procedures could be monitored by proper knowledge of saliva characteristics as predictors of peritonitis-related outcome. The use of metabolomics in pediatric nephrology may be an innovative methodology for the early diagnosis and monitoring of kidney diseases.

Three-level simultaneous component analysis for analyzing the near-infrared spectra of aqueous solutions under multiple perturbations

Quantitative analysis under various perturbations is a difficult problem because the analytical signal changes with different factors. In this work, three-level simultaneous component analysis (3-MSCA) was used for analyzing the near-infrared (NIR) spectra of aqueous solutions under different perturbations. The spectral data of aqueous proline solutions at different pH, concentration and temperature were measured, and a three-level model was built to describe the effects of the three perturbations on the spectra, respectively. The first level model describes the change of the spectra with pH, from which significant aggregation of proline was observed around the isoelectric point. The second and third level model show the spectral change with concentration and temperature, respectively, and the spectral feature has a very good linear relationship with the corresponding influencing factors. Therefore, the pH and concentration scores can be used as the calibration curve for quantitative analysis of the pH and the content of proline, and the temperature scores can be used to predict the temperature of the solutions. In addition, the structural change of water molecules under different conditions is obtained from the loadings. A decline of the bulk water was found with the increase of concentration, implying an ascending trend of the bonded water due to the interaction of proline and water. The dissociation of water clusters with the increase of temperature is also displayed.

Cytocompatibility of filling pastes by primary teeth root simulating model

Evaluate the cytocompatibility of Calen^®/ZO, Calcicur^®, Vitapex^®, Endoflas^®, and zinc oxide/eugenol-based (ZOE) root canal pastes (RCP) to human primary osteoblasts (HPO) through a simplified model for primary teeth. The model employed pipette tips filled with 0.037 g of paste, exposed to 185 µL of culture medium for 24 h (n = 6). Release of components was analysed by Proton Nuclear Magnetic Resonance Spectroscopy (¹H-NMR). HPO were exposed to conditioned media for 24 h. Cell viability was assessed by cell density and metabolic activity, and release of interleukin 6 (IL-6), vascular endothelial growth factor (VEGF) and fibroblast growth factor (bFGF) by immunological assay. Physicochemical properties and antimicrobial efficacy were also evaluated. ¹H-NMR spectra analysis showed similarity between ZOE, Endoflas^®, Calcicur^®, and Vitapex^® compared to Calen^®/ZO and positive control, which showed distinct released components. Calen^®/ZO and Calcicur^® exhibited high alkaline pH in all periods and showed similar solubility. Calen^®/ZO, ZOE, and Vitapex^® showed similar flow rate. Calen^®/ZO, Calcicur^®, and Vitapex^® did not exhibit antimicrobial efficacy. Calen^®/ZO presented cytotoxicity (p < 0.05). Pastes did not increase IL-6 release compared to control. Apart from Vitapex^®, all pastes significantly induced VEGF/bFGF release. Interactive effects among released products may affect biological response to filling pastes. Calcicur^®, ZOE, Endoflas^® and Calen^®/ZO presented good to moderate cytocompatibility, with low impact on pro-inflammatory cytokine release and induction of growth factors of interest to tissue repair. This simplified model, specific for the evaluation of the cytocompatibility of RCPs on primary teeth, suggests how these pastes might contribute to bone repair in clinical situations of apical periodontitis in children.

Changes in the blood plasma lipidome associated with effective or poor response to atypical antipsychotic treatments in schizophrenia patients

Atypical antipsychotics are widely used to manage schizophrenia symptoms. However, these drugs can induce deleterious side effects, such as MetS, which are associated with an increased cardiovascular risk to patients. Lipids play a central role in this context, and changes in lipid metabolism have been implicated in schizophrenia's pathobiology. Furthermore, recent evidence suggests that lipidome changes may be related to antipsychotic treatment response. The aim of this study was to evaluate the lipidome changes in blood plasma samples of schizophrenia patients before and after 6 weeks of treatment with either risperidone, olanzapine, or quetiapine. Liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis showed changes in the levels of ceramides (Cer), glycerophosphatidic acids (PA), glycerophosphocholines (PC), phosphatidylethanolamines (PE), phosphatidylinositols (PI), glycerophosphoglycerols (PG), and phosphatidylserines (PS) for all treatments. However, the treatment with risperidone also affected diacylglycerides (DG), ceramide 1-phosphates (CerP), triglycerides (TG), sphingomyelins (SM), and ceramide phosphoinositols (PI-Cer). Moreover, specific lipid profiles were observed that could be used to distinguish poor and good responders to the different antipsychotics. As such, further work in this area may lead to lipid-based biomarkers that could be used to improve the clinical management of schizophrenia patients.

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.

Chemical Fingerprints of Emotional Body Odor

Chemical communication is common among animals. In humans, the chemical basis of social communication has remained a black box, despite psychological and neural research showing distinctive physiological, behavioral, and neural consequences of body odors emitted during emotional states like fear and happiness. We used a multidisciplinary approach to examine whether molecular cues could be associated with an emotional state in the emitter. Our research revealed that the volatile molecules transmitting different emotions to perceivers also have objectively different chemical properties. Chemical analysis of underarm sweat collected from the same donors in fearful, happy, and emotionally neutral states was conducted using untargeted two-dimensional (GC×GC) coupled with time of flight (ToF) MS-based profiling. Based on the multivariate statistical analyses, we find that the pattern of chemical volatiles (N = 1655 peaks) associated with fearful state is clearly different from that associated with (pleasant) neutral state. Happy sweat is also significantly different from the other states, chemically, but shows a bipolar pattern of overlap with fearful as well as neutral state. Candidate chemical classes associated with emotional and neutral sweat have been identified, specifically, linear aldehydes, ketones, esters, and cyclic molecules (5 rings). This research constitutes a first step toward identifying the chemical fingerprints of emotion.

Implementation of liquid chromatography-high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial

Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. By its nature, this is a highly interdisciplinary field of research, and mastering all of the steps comprised in the pipeline can be a challenging task, especially for those researchers new to the topic. In this tutorial, we aim to provide an overview of the most widely adopted methods of performing LC-HRMS-based untargeted metabolomics of biological samples. A detailed protocol is provided in the Supplementary Information for rapidly implementing a basic screening workflow in a laboratory setting. This tutorial covers experimental design, sample preparation and analysis, signal processing and data treatment, and, finally, data analysis and its biological interpretation. Each section is accompanied by up-to-date literature to guide readers through the preparation and optimization of such a workflow, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls.

NMR-Based Metabolomics for the Assessment of Inhaled Pharmacotherapy in Chronic Obstructive Pulmonary Disease Patients

The aim of this proof-of-concept, pilot study was the evaluation of the effects of steroid administration and suspension of an inhaled corticosteroid (ICS)-long-acting β₂-agonist (LABA) extrafine fixed dose combination (FDC) on metabolomic fingerprints in subjects with chronic obstructive pulmonary disease (COPD). We hypothesized that a comprehensive metabolomics approach discriminates across inhaled pharmacotherapies and that their effects on metabolomic signatures depend on the biological fluids analyzed. We performed metabolomics via nuclear magnetic resonance (NMR) spectroscopy in exhaled breath condensate (EBC), sputum supernatants, serum, and urine. Fourteen patients suffering from COPD who were on regular inhaled fluticasone propionate/salmeterol therapy (visit 1) were consecutively treated with 2-week beclomethasone dipropionate/formoterol (visit 2), 4-week formoterol alone (visit 3), and 4-week beclomethasone/formoterol (visit 4). The comprehensive NMR-based metabolomics approach showed differences across all pharmacotherapies and that different biofluids provided orthogonal information. Serum formate was lower at visits 1 versus 3 (P = 0.03), EBC formate was higher at visit 1 versus 4 (P = 0.03), and urinary 1-methyl-nicotinamide was lower at 3 versus 4 visit (P = 0.002). NMR-based metabolomics of different biofluids distinguishes across inhaled pharmacotherapies, provides complementary information on the effects of an extrafine ICS/LABA FDC on metabolic fingerprints in COPD patients, and might be useful for elucidating the ICS mechanism of action.

Identification of Cannabis sativa L. (hemp) Retailers by Means of Multivariate Analysis of Cannabinoids

In this work, the concentration of nine cannabinoids, six neutral cannabinoids (THC, CBD, CBC, CBG, CBN and CBDV) and three acidic cannabinoids (THCA CBGA and CBDA), was used to identify the Italian retailers of Cannabis sativa L. (hemp), reinforcing the idea that the practice of categorizing hemp samples only using THC and CBD is inadequate. A high-performance liquid chromatography/high-resolution mass spectrometry (HPLC-MS/MS) method was developed for screening and simultaneously analyzing the nine cannabinoids in 161 hemp samples sold by four retailers located in different Italian cities. The hemp samples dataset was analyzed by univariate and multivariate analysis with the aim to identify the hemp retailers without any other information on the hemp samples like Cannabis strains, seeds, soil and cultivation characteristics, geographical origin, product storage, etc. The univariate analysis highlighted that the hemp samples could not be differentiated by using any of the nine cannabinoids analyzed. To evaluate the real efficiency of the discrimination among the four hemp retailers a partial least squares discriminant analysis (PLS-DA) was applied. The PLS-DA results showed a very good discrimination between the four hemp retailers with an explained variance of 100% and low classification errors in both calibration (5%) and cross validation (6%). A total of 92% of the hemp samples were correctly classified by the cannabinoid variables in both fitting and cross validation. This work contributed to show that an analytical method coupled with multivariate analysis can be used as a powerful tool for forensic purposes.

Metabolic profiles of serum samples from ground glass opacity represent potential diagnostic biomarkers for lung cancer

Background

Lung cancer is a leading cause of cancer deaths worldwide. Low-dose computed tomography (LDCT) screening trials indicated that LDCT is effective for the early detection of lung cancer, but the findings were accompanied by high false positive rates. Therefore, the detection of lung cancer needs complementary blood biomarker tests to reduce false positive rates.

Methods

In order to evaluate the potential of metabolite biomarkers for diagnosing lung cancer and increasing the effectiveness of clinical interventions, serum samples from subjects participating in a low-dose CT-scan screening were analyzed by using untargeted liquid chromatography-hybrid quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). Samples were acquired from 34 lung patients with ground glass opacity diagnosed lung cancer and 39 healthy controls.

Results

In total, we identified 9 metabolites in electron spray ionization (ESI)(+) mode and 7 metabolites in ESI(-) mode. L-(+)-gulose, phosphatidylethanolamine (PE)(22:2(13Z,16Z)/15:0), cysteinyl-glutamine, S-japonin, threoninyl-glutamine, chlorate, 3-oxoadipic acid, dukunolide A, and malonic semialdehyde levels were observed to be elevated in serum samples of lung cancer cases when compared to those of healthy controls. By contrast, 1-(2-furanylmethyl)-1H-pyrrole, 2,4-dihydroxybenzoic acid, monoethyl carbonate, guanidinosuccinic acid, pseudouridine, DIMBOA-Glc, and 4-feruloyl-1,5-quinolactone levels were lower in serum samples of lung cancer cases compared with those of healthy controls.

Conclusions

This study demonstrates evidence of early metabolic alterations that can possibly distinguish malignant ground glass opacity from benign ground glass opacity. Further studies in larger pools of samples are warranted.

Data analysis of MS-based clinical lipidomics studies with crossover design: A tutorial mini-review of statistical methods

Clinical lipidomics using mass spectrometry (MS) is important to support discovery of biomarkers for diagnosis and understanding the pathophysiology of diseases. Frequently, lipidomics data from clinical studies have large variations among individuals because the human metabolome/lipidome is strongly influenced by genotype, daily activity, diet and gut flora. This inter-personal variability makes data analysis more complex and normally requires a large cohort for robust statistical analysis. Crossover designed experiments treat each subject as his or her own control, thereby reducing the between-subject variability, such that the effects of exposure/treatment are more likely to be identified when using a relatively small number of subjects. This design repeatedly samples an individual when crossing over from one treatment/exposure to another during the course of the study. The acquired datasets have a distinct data structure resulting from repeated longitudinal measurements. A variety of statistical methods are used in published crossover studies, but many appear to ignore the data structure inherent in the experimental design. An appropriate data analysis approach is critical to discovering robust clinical biomarkers. Hereby, we summarize the statistical methodologies suitable for clinical lipidomics studies using crossover design. To help understand and apply these methods to practical cases, we focused on the general concepts of statistical models in the context of analysis of metabolomics data without spending too much effort on mathematical details. Importantly, we aim to evaluate these methods and provide suggestions for data analysis and biomarker discovery. We applied the discussed methods on a MS-based lipidomics dataset from a double-blind random crossover designed clinical dietary intervention study. The strength and potential pitfalls of each method are briefly discussed and a suggestion for analytic workflow proposed.

The effect of sampling procedures and day-to-day variations in metabolomics studies of biofluids

Metabolomics analysis of biofluids is a feasible tool for disease characterization and monitoring due to its minimally invasive nature. To reduce unwanted variation in biobanks and clinical studies, it is important to determine the effect of external factors on metabolic profiles of biofluids. In this study we examined the effect of sample collection and sample processing procedures on NMR measured serum lipoproteins and small-molecule metabolites in serum and urine, using a cohort of men diagnosed with either prostate cancer or benign prostatic hyperplasia. We determined day-to-day reliability of metabolites by systematic sample collection at two different days, in both fasting and non-fasting conditions. Study participants received prostate massage the first day to assess the differences between urine with and without prostate secretions. Further, metabolic differences between first-void and mid-stream urine samples, and the effect of centrifugation of urine samples before storage were assessed. Our results show that day-to-day reliability is highly variable between metabolites in both serum and urine, while lipoprotein subfractions possess high reliability. Further, fasting status clearly influenced the metabolite concentrations, demonstrating the importance of keeping this condition constant within a study cohort. Day-to-day reliabilities were however comparable in fasting and non-fasting samples. Urine sampling procedures such as sampling of first-void or mid-stream urine, and centrifugation or not before sample storage, were shown to only have minimal effect on the overall metabolic profile, and is thus unlikely to constitute a confounder in clinical studies utilizing NMR derived metabolomics.

The 1H NMR serum metabolomics response to a two meal challenge: a cross-over dietary intervention study in healthy human volunteers

BACKGROUND

Metabolomics represents a powerful tool for exploring modulation of the human metabolome in response to food intake. However, the choice of multivariate statistical approach is not always evident, especially for complex experimental designs with repeated measurements per individual. Here we have investigated the serum metabolic responses to two breakfast meals: an egg and ham based breakfast and a cereal based breakfast using three different multivariate approaches based on the Projections to Latent Structures framework.

METHODS

In a cross over design, 24 healthy volunteers ate the egg and ham breakfast and cereal breakfast on four occasions each. Postprandial serum samples were subjected to metabolite profiling using ¹H nuclear magnetic resonance spectroscopy and metabolites were identified using 2D nuclear magnetic resonance spectroscopy. Metabolic profiles were analyzed using Orthogonal Projections to Latent Structures with Discriminant Analysis and Effect Projections and ANOVA-decomposed Projections to Latent Structures.

RESULTS

The Orthogonal Projections to Latent Structures with Discriminant Analysis model correctly classified 92 and 90% of the samples from the cereal breakfast and egg and ham breakfast, respectively, but confounded dietary effects with inter-personal variability. Orthogonal Projections to Latent Structures with Effect Projections removed inter-personal variability and performed perfect classification between breakfasts, however at the expense of comparing means of respective breakfasts instead of all samples. ANOVA-decomposed Projections to Latent Structures managed to remove inter-personal variability and predicted 99% of all individual samples correctly. Proline, tyrosine, and N-acetylated amino acids were found in higher concentration after consumption of the cereal breakfast while creatine, methanol, and isoleucine were found in higher concentration after the egg and ham breakfast.

CONCLUSIONS

Our results demonstrate that the choice of statistical method will influence the results and adequate methods need to be employed to manage sample dependency and repeated measurements in cross-over studies. In addition, ¹H nuclear magnetic resonance serum metabolomics could reproducibly characterize postprandial metabolic profiles and identify discriminatory metabolites largely reflecting dietary composition.

TRIAL REGISTRATION

Registered with ClinicalTrials.gov, identifier: NCT02039596 . Date of registration: January 17, 2014.

Metabolic Signature of a Functional High-Catechin Tea after Acute and Sustained Consumption in Healthy Volunteers through 1H NMR Based Metabolomics Analysis of Urine

Functional tea beverages have emerged as a novel approach to achieving health benefits associated with tea. The use of metabolomics may improve the evaluation of their consumption and their effects. The current study aimed at exploring the urinary signature of exposure to a functional high-catechin tea (HCT) using untargeted NMR-based metabolomics. Ten volunteers participated in a crossover intervention study. Individuals consumed an HCT or a control beverage over a period of 28 days. Multilevel partial least-squares discriminant analysis (ML-PLS-DA) was used for paired comparisons. A further crossover model was performed to assess the significant changes. The consumption of the HCT resulted in the excretion of theanine, epicatechin, pyrogallol sulfate, and higher levels of 3-methyl-2-oxovalerate and succinate, as well as unknown compounds. In conclusion, the present work established novel urinary signatures of a functional drink. Such signatures may be potential biomarkers and/or reflect certain benefits of functional tea beverages.

Variable selection and validation in multivariate modelling

MOTIVATION

Validation of variable selection and predictive performance is crucial in construction of robust multivariate models that generalize well, minimize overfitting and facilitate interpretation of results. Inappropriate variable selection leads instead to selection bias, thereby increasing the risk of model overfitting and false positive discoveries. Although several algorithms exist to identify a minimal set of most informative variables (i.e. the minimal-optimal problem), few can select all variables related to the research question (i.e. the all-relevant problem). Robust algorithms combining identification of both minimal-optimal and all-relevant variables with proper cross-validation are urgently needed.

RESULTS

We developed the MUVR algorithm to improve predictive performance and minimize overfitting and false positives in multivariate analysis. In the MUVR algorithm, minimal variable selection is achieved by performing recursive variable elimination in a repeated double cross-validation (rdCV) procedure. The algorithm supports partial least squares and random forest modelling, and simultaneously identifies minimal-optimal and all-relevant variable sets for regression, classification and multilevel analyses. Using three authentic omics datasets, MUVR yielded parsimonious models with minimal overfitting and improved model performance compared with state-of-the-art rdCV. Moreover, MUVR showed advantages over other variable selection algorithms, i.e. Boruta and VSURF, including simultaneous variable selection and validation scheme and wider applicability.

AVAILABILITY AND IMPLEMENTATION

Algorithms, data, scripts and tutorial are open source and available as an R package ('MUVR') at https://gitlab.com/CarlBrunius/MUVR.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comparison of adipose‑ and bone marrow‑derived stem cells in protecting against ox‑LDL‑induced inflammation in M1‑macrophage‑derived foam cells

Adipose‑derived stem cells (ADSCs) and bone marrow‑derived stem cells (BMSCs) are considered to be prospective sources of mesenchymal stromal cells (MSCs), that can be used in cell therapy for atherosclerosis. The present study investigated whether ADSCs co‑cultured with M1 foam macrophages via treatment with oxidized low‑density lipoprotein (ox‑LDL) would lead to similar or improved anti‑inflammatory effects compared with BMSCs. ADSCs, peripheral blood monocytes, BMSCs and ox‑LDL were isolated from ten coronary heart disease (CHD) patients. After three passages, the supernatants of the ADSCs and BMSCs were collected and systematically analysed by liquid chromatography‑quadrupole time‑of‑flight‑mass spectrometry (6530; Agilent Technologies, Inc., Santa Clara, CA, USA). Cis‑9, trans‑11 was deemed to be responsible for the potential differences in the metabolic characteristics of ADSCs and BMSCs. These peripheral blood monocytes were characterized using flow cytometry. Following peripheral blood monocytes differentiation into M1 macrophages, the formation of M1 foam macrophages was achieved through treatment with ox‑LDL. Overall, 2x106 ADSCs, BMSCs or BMSCs+cis‑9, trans‑11 were co‑cultured with M1 foam macrophages. Anti‑inflammatory capability, phagocytic activity, anti‑apoptotic capability and cell viability assays were compared among these groups. It was demonstrated that the accumulation of lipid droplets decreased following ADSCs, BMSCs or BMSCs+cis‑9, trans‑11 treatment in M1 macrophages derived from foam cells. Consistently, ADSCs exhibited great advantageous anti‑inflammatory capabilities, phagocytic activity, anti‑apoptotic capability activity and cell viability over BMSCs or BMSCs+cis‑9, trans‑11. Additionally, BMSCs+cis‑9, trans‑11 also demonstrated marked improvement in anti‑inflammatory capability, phagocytic activity, anti‑apoptotic capability activity and cell viability in comparison with BMSCs. The present results indicated that ADSCs would be more appropriate for transplantation to treat atherosclerosis than BMSCs alone or BMSCs+cis‑9, trans‑11. This may be an important mechanism to regulate macrophage immune function.

Processing and Analysis of Untargeted Multicohort NMR Data

NMR data from large studies combining multiple cohorts is becoming common in large-scale metabolomics. The data size and combination of cohorts with diverse properties leads to special problems for data processing and analysis. These include alignment, normalization, detection and removal of outliers, presence of strong correlations, and the identification of unknowns. Nonetheless, these challenges can be addressed with suitable algorithms and techniques, leading to enhanced data sets ripe for further data mining.

Metabolite and lipoprotein responses and prediction of weight gain during breast cancer treatment

Background

Breast cancer treatment has metabolic side effects, potentially affecting risk of cardiovascular disease (CVD) and recurrence. We aimed to compare alterations in serum metabolites and lipoproteins during treatment between recipients and non-recipients of chemotherapy, and describe metabolite profiles associated with treatment-related weight gain.

Methods

This pilot study includes 60 stage I/II breast cancer patients who underwent surgery and were treated according to national guidelines. Serum sampled pre-surgery and after 6 and 12 months was analysed by MR spectroscopy and mass spectrometry. In all, 170 metabolites and 105 lipoprotein subfractions were quantified.

Results

The metabolite and lipoprotein profiles of chemotherapy recipients and non-recipients changed significantly 6 months after surgery (p < 0.001). Kynurenine, the lipid signal at 1.55–1.60 ppm, ADMA, 2 phosphatidylcholines (PC aa C38:3, PC ae C42:1), alpha-aminoadipic acid, hexoses and sphingolipids were increased in chemotherapy recipients after 6 months. VLDL and small dense LDL increased after 6 months, while HDL decreased, with triglyceride enrichment in HDL and LDL. At baseline, weight gainers had less acylcarnitines, phosphatidylcholines, lyso-phosphatidylcholines and sphingolipids, and showed an inflammatory lipid profile.

Conclusion

Chemotherapy recipients exhibit metabolic changes associated with inflammation, altered immune response and increased risk of CVD. Altered lipid metabolism may predispose for treatment-related weight gain.

Effect of non-surgical periodontal therapy on salivary metabolic fingerprint of generalized chronic periodontitis using nuclear magnetic resonance spectroscopy

OBJECTIVE

Metabolomic analysis of saliva proved its accuracy in discriminating patients with generalized chronic periodontitis (GCP) from healthy subjects by identifying specific molecular signatures of the disease. There is lack of investigations concerning the effect of periodontal treatment on individual metabolic fingerprints. Therefore, the aim of this study was to determine whether non-surgical periodontal therapy could change salivary metabolomic profile in GCP to one more similar to periodontal health.

DESIGN

Unstimulated whole saliva of 32 controls and 19 GCP patients were obtained prior to and 3 months after conventional staged non-surgical periodontal therapy. Metabolic profiling was performed using Nuclear Magnetic Resonance (NMR) spectroscopy, followed by univariate and multivariate paired approaches to assess the changes introduced by the therapy.

RESULTS

In GCP group, periodontal treatment led to an improvement in all clinical parameters (p < 0.001). The accuracy of the multivariate model in discriminating the metabolomic profile of each GCP patient at two time points was 92.5%. Despite the almost perfect separation of the spectra in the metabolic space, the univariate analysis failed to identify significant variations in single metabolite content. The post-treatment metabolic profile of GCP patients could not be assimilated to that of healthy controls who exhibited different levels of lactate, pyruvate, valine, proline, tyrosine, and formate.

CONCLUSIONS

Based on these data, NMR-spectroscopic analysis revealed that, despite significant changes in the overall metabolomic fingerprint after non-surgical therapy, GCP patients maintained a distinctive metabolic profile compared to healthy individuals.

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized. A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state-of-the-art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful "tips and tricks" along the analytical workflow.

Breathomics for Assessing the Effects of Treatment and Withdrawal With Inhaled Beclomethasone/Formoterol in Patients With COPD

Background: Prospective pharmacological studies on breathomics profiles in COPD patients have not been previously reported. We assessed the effects of treatment and withdrawal of an extrafine inhaled corticosteroid (ICS)-long-acting β₂-agonist (LABA) fixed dose combination (FDC) using a multidimensional classification model including breathomics.

Methods: A pilot, proof-of-concept, pharmacological study was undertaken in 14 COPD patients on maintenance treatment with inhaled fluticasone propionate/salmeterol (500/50 μg b.i.d.) for at least 8 weeks (visit 1). Patients received 2-week treatment with inhaled beclomethasone dipropionate/formoterol (100/6 μg b.i.d.) (visit 2), 4-week treatment with formoterol alone (6 μg b.i.d.) (visit 3), and 4-week treatment with beclomethasone/formoterol (100/6 μg b.i.d.) (visit 4). Exhaled breath analysis with two e-noses, based on different technologies, and exhaled breath condensate (EBC) NMR-based metabolomics were performed. Sputum cell counts, sputum supernatant and EBC prostaglandin E₂ (PGE₂) and 15-F_2t-isoprostane, fraction of exhaled nitric oxide, and spirometry were measured.

Results: Compared with formoterol alone, EBC acetate and sputum PGE₂, reflecting airway inflammation, were reduced after 4-week beclomethasone/formoterol. Three independent breathomics techniques showed that extrafine beclomethasone/formoterol short-term treatment was associated with different breathprints compared with regular fluticasone propionate/salmeterol. Either ICS/LABA FDC vs. formoterol alone was associated with increased pre-bronchodilator FEF_25−75% and FEV₁/FVC (P = 0.008–0.029). The multidimensional model distinguished fluticasone propionate/salmeterol vs. beclomethasone/formoterol, fluticasone propionate/salmeterol vs. formoterol, and formoterol vs. beclomethasone/formoterol (accuracy > 70%, P < 0.01).

Conclusions: Breathomics could be used for assessing ICS treatment and withdrawal in COPD patients. Large, controlled, prospective pharmacological trials are required to clarify the biological implications of breathomics changes. EUDRACT number: 2012-001749-42.

Plasma metabolites associated with type 2 diabetes in a Swedish population: a case-control study nested in a prospective cohort

AIMS/HYPOTHESIS

The aims of the present work were to identify plasma metabolites that predict future type 2 diabetes, to investigate the changes in identified metabolites among individuals who later did or did not develop type 2 diabetes over time, and to assess the extent to which inclusion of predictive metabolites could improve risk prediction.

METHODS

We established a nested case-control study within the Swedish prospective population-based Västerbotten Intervention Programme cohort. Using untargeted liquid chromatography-MS metabolomics, we analysed plasma samples from 503 case-control pairs at baseline (a median time of 7 years prior to diagnosis) and samples from a subset of 187 case-control pairs at 10 years of follow-up. Discriminative metabolites between cases and controls at baseline were optimally selected using a multivariate data analysis pipeline adapted for large-scale metabolomics. Conditional logistic regression was used to assess associations between discriminative metabolites and future type 2 diabetes, adjusting for several known risk factors. Reproducibility of identified metabolites was estimated by intra-class correlation over the 10 year period among the subset of healthy participants; their systematic changes over time in relation to diagnosis among those who developed type 2 diabetes were investigated using mixed models. Risk prediction performance of models made from different predictors was evaluated using area under the receiver operating characteristic curve, discrimination improvement index and net reclassification index.

RESULTS

We identified 46 predictive plasma metabolites of type 2 diabetes. Among novel findings, phosphatidylcholines (PCs) containing odd-chain fatty acids (C19:1 and C17:0) and 2-hydroxyethanesulfonate were associated with the likelihood of developing type 2 diabetes; we also confirmed previously identified predictive biomarkers. Identified metabolites strongly correlated with insulin resistance and/or beta cell dysfunction. Of 46 identified metabolites, 26 showed intermediate to high reproducibility among healthy individuals. Moreover, PCs with odd-chain fatty acids, branched-chain amino acids, 3-methyl-2-oxovaleric acid and glutamate changed over time along with disease progression among diabetes cases. Importantly, we found that a combination of five of the most robustly predictive metabolites significantly improved risk prediction if added to models with an a priori defined set of traditional risk factors, but only a marginal improvement was achieved when using models based on optimally selected traditional risk factors.

CONCLUSIONS/INTERPRETATION

Predictive metabolites may improve understanding of the pathophysiology of type 2 diabetes and reflect disease progression, but they provide limited incremental value in risk prediction beyond optimal use of traditional risk factors.

Investigating the Impact of Hearing Aid Use and Auditory Training on Cognition, Depressive Symptoms, and Social Interaction in Adults With Hearing Loss: Protocol for a Crossover Trial

Background

Sensorineural hearing loss is the most common sensory deficit among older adults. Some of the psychosocial consequences of this condition include difficulty in understanding speech, depression, and social isolation. Studies have shown that older adults with hearing loss show some age-related cognitive decline. Hearing aids have been proven as successful interventions to alleviate sensorineural hearing loss. In addition to hearing aid use, the positive effects of auditory training—formal listening activities designed to optimize speech perception—are now being documented among adults with hearing loss who use hearing aids, especially new hearing aid users. Auditory training has also been shown to produce prolonged cognitive performance improvements. However, there is still little evidence to support the benefits of simultaneous hearing aid use and individualized face-to-face auditory training on cognitive performance in adults with hearing loss.

Objective

This study will investigate whether using hearing aids for the first time will improve the impact of individualized face-to-face auditory training on cognition, depression, and social interaction for adults with sensorineural hearing loss. The rationale for this study is based on the hypothesis that, in adults with sensorineural hearing loss, using hearing aids for the first time in combination with individualized face-to-face auditory training will be more effective for improving cognition, depressive symptoms, and social interaction rather than auditory training on its own.

Methods

This is a crossover trial targeting 40 men and women between 50 and 90 years of age with either mild or moderate symmetric sensorineural hearing loss. Consented, willing participants will be recruited from either an independent living accommodation or via a community database to undergo a 6-month intensive face-to-face auditory training program (active control). Participants will be assigned in random order to receive hearing aid (intervention) for either the first 3 or last 3 months of the 6-month auditory training program. Each participant will be tested at baseline, 3, and 6 months using a neuropsychological battery of computer-based cognitive assessments, together with a depression symptom instrument and a social interaction measure. The primary outcome will be cognitive performance with regard to spatial working memory. Secondary outcome measures include other cognition performance measures, depressive symptoms, social interaction, and hearing satisfaction.

Results

Data analysis is currently under way and the first results are expected to be submitted for publication in June 2018.

Conclusions

Results from the study will inform strategies for aural rehabilitation, hearing aid delivery, and future hearing loss intervention trials.

Trial Registration

ClinicalTrials.gov NCT03112850; https://clinicaltrials.gov/ct2/show/NCT03112850 (Archived by WebCite at http://www.webcitation.org/6xz12fD0B).

Characterization of metabolic responses to healthy diets and association with blood pressure: application to the Optimal Macronutrient Intake Trial for Heart Health (OmniHeart), a randomized controlled study

Background

Interindividual variation in the response to diet is common, but the underlying mechanism for such variation is unclear.

Objective

The objective of this study was to use a metabolic profiling approach to identify a panel of urinary metabolites representing individuals demonstrating typical (homogeneous) metabolic responses to healthy diets, and subsequently to define the association of these metabolites with improvement of risk factors for cardiovascular diseases (CVDs).

Design

24-h urine samples from 158 participants with pre-hypertension and stage 1 hypertension, collected at baseline and following the consumption of a carbohydrate-rich, a protein-rich, and a monounsaturated fat-rich healthy diet (6 wk/diet) in a randomized, crossover study, were analyzed by proton (1H) nuclear magnetic resonance (NMR) spectroscopy. Urinary metabolite profiles were interrogated to identify typical and variable responses to each diet. We quantified the differences in absolute excretion of metabolites, distinguishing between dietary comparisons within the typical response groups, and established their associations with CVD risk factors using linear regression.

Results

Globally all 3 diets induced a similar pattern of change in the urinary metabolic profiles for the majority of participants (60.1%). Diet-dependent metabolic variation was not significantly associated with total cholesterol or low-density lipoprotein (LDL) cholesterol concentration. However, blood pressure (BP) was found to be significantly associated with 6 urinary metabolites reflecting dietary intake [proline-betaine (inverse), carnitine (direct)], gut microbial co-metabolites [hippurate (direct), 4-cresyl sulfate (inverse), phenylacetylglutamine (inverse)], and tryptophan metabolism [N-methyl-2-pyridone-5-carboxamide (inverse)]. A dampened clinical response was observed in some individuals with variable metabolic responses, which could be attributed to nonadherence to diet (≤25.3%), variation in gut microbiome activity (7.6%), or a combination of both (7.0%).

Conclusions

These data indicate interindividual variations in BP in response to dietary change and highlight the potential influence of the gut microbiome in mediating this relation. This approach provides a framework for stratification of individuals undergoing dietary management. The original OmniHeart intervention study and the metabolomics study were registered at www.clinicaltrials.gov as NCT00051350 and NCT03369535, respectively.

Multivariate Analysis of Multiple Datasets: a Practical Guide for Chemical Ecology

Chemical ecology has strong links with metabolomics, the large-scale study of all metabolites detectable in a biological sample. Consequently, chemical ecologists are often challenged by the statistical analyses of such large datasets. This holds especially true when the purpose is to integrate multiple datasets to obtain a holistic view and a better understanding of a biological system under study. The present article provides a comprehensive resource to analyze such complex datasets using multivariate methods. It starts from the necessary pre-treatment of data including data transformations and distance calculations, to the application of both gold standard and novel multivariate methods for the integration of different omics data. We illustrate the process of analysis along with detailed results interpretations for six issues representative of the different types of biological questions encountered by chemical ecologists. We provide the necessary knowledge and tools with reproducible R codes and chemical-ecological datasets to practice and teach multivariate methods.

Orange juice affects acylcarnitine metabolism in healthy volunteers as revealed by a mass-spectrometry based metabolomics approach

Citrus juices, especially orange juice, constitute rich sources of bioactive compounds with a wide range of health-promoting activities. Data from epidemiological and in vitro studies suggest that orange juice (OJ) may have a positive impact on lipid metabolism. However, the effect of orange juice intake on blood lipid profile is still poorly understood. We have used two different blood samples, Dried Blood Spots (DBS) and plasma, to assess the effect of two-week orange juice consumption in healthy volunteers by a mass-spectrometry based metabolomics approach. DBS were analysed by liquid chromatography mass spectrometry (LC-MS) and plasma samples were analysed by the gas chromatography mass spectrometry (GC-MS). One hundred sixty-nine lipids including acylcarnitines (AC), lysophosphatidylcholines (LysoPC), (diacyl- and acyl-alkyl-) phosphatidylcholines (PC aa and PC ae) and sphingomyelins (SM) were identified and quantified in DBS. Eighteen fatty acids were identified and quantified in plasma. Multivariate analysis allowed to identify an increase in C3:1, C5-DC(C6-OH), C5-M-DC, C5:1-DC, C8, C12-DC, lysoPC18:3, myristic acid, pentadecanoic acid, palmitoleic and palmitic acid and a decrease in nervonic acid, C0, C2, C10, C10:1, C16:1, C16-OH, C16:1-OH, C18-OH, PC aa C40:4, PC ae C38:4, PC ae C42:3, PC ae C42:4 and cholesterol levels after orange juice intake. A two-week period of orange juice intake could affect fatty acids β-oxidation through mitochondrial and peroxisomal pathways, leading to an increase of short-chain acylcarnitines and a decrease of medium and long-chain acylcarnitines. This is the first report analyzing the effect of orange juice intake in healthy volunteers using a dried blood spot-based metabolomics approach.

Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration

Metabolomics aims to quantitatively measure small-molecule metabolites in biological samples, such as bodily fluids (e.g., urine, blood, and saliva), tissues, and breathe exhalation, which reflects metabolic responses of a living system to pathophysiological stimuli or genetic modification. In the past decade, metabolomics has made notable progresses in providing useful systematic insights into the underlying mechanisms and offering potential biomarkers of many diseases. Metabolomics is a complementary manner of genomics and transcriptomics, and bridges the gap between genotype and phenotype, which reflects the functional output of a biological system interplaying with environmental factors. Recently, the technology of metabolomics study has been developed quickly. This review will discuss the whole pipeline of metabolomics study, including experimental design, sample collection and preparation, sample detection and data analysis, as well as mechanism interpretation, which can help understand metabolic effects and metabolite function for living organism in system level.

Prediction of Clinical Endpoints in Breast Cancer Using NMR Metabolic Profiles

Metabolic profiles reflect biological conditions as a result of biochemical changes within a living system. It is therefore possible to associate metabolic signatures with clinical endpoints of diseases, such as breast cancer. Nuclear magnetic resonance (NMR) spectroscopy is one of the most common techniques used for metabolic profiling, and produces high dimensional datasets from which meaningful biological information can be extracted. Here, we present an overview of data analysis techniques used to achieve this, describing key steps in the procedure. Moreover, examples of clinical endpoints of interest are provided. Although these are specific for breast cancer, the procedures for the analysis of NMR spectra as described here are applicable to any type of cancer and to other diseases.

The abundance of metabolites related to protein methylation correlates with the metastatic capacity of human melanoma xenografts

Metabolic reprogramming is a major factor in transformation, and particular metabolic phenotypes correlate with oncogenotype, tumor progression, and metastasis. By profiling metabolites in 17 patient-derived xenograft melanoma models, we identified durable metabolomic signatures that correlate with biological features of the tumors. BRAF mutant tumors had metabolomic and metabolic flux features of enhanced glycolysis compared to BRAF wild-type tumors. Tumors that metastasized efficiently from their primary sites had elevated levels of metabolites related to protein methylation, including trimethyllysine (TML). TML levels correlated with histone H3 trimethylation at Lys⁹ and Lys²⁷, and methylation at these sites was also enhanced in efficiently metastasizing tumors. Erasing either of these marks by genetically or pharmacologically silencing the histone methyltransferase SETDB1 or EZH2 had no effect on primary tumor growth but reduced cellular invasiveness and metastatic spread. Thus, metabolite profiling can uncover targetable epigenetic requirements for the metastasis of human melanoma cells.