To improve the interpretation and utilisation of blood lipids, ketones and acylcarnitine concentrations as biomarkers in clinical assessments, more information is needed on their dynamic alterations in response to dietary intake and fasting. The aim of this intervention study was to characterise the changes in serum lipid, ketone and acylcarnitine concentrations 24 h after a standardised breakfast meal. Thirty-four healthy subjects (eighteen males and sixteen females) aged 20–30 years were served a breakfast meal (∼500 kcal, 36 E% fat, 46 E% carbohydrates, 16 E% protein, 2E% fibre), after which they consumed only water for 24 h. Blood samples were drawn before and at thirteen standardised timepoints after the meal. Metabolite concentrations were plotted as a function of time since the completion of the breakfast meal. Results demonstrated that concentrations of HDL-cholesterol and LDL-cholesterol decreased until ∼2 h (–4 % for both), while TAG concentrations peaked at 3 h (+27 %). Acetoacetate and β-hydroxybutyrate were highest 24 h after the meal (+433 and +633 %, respectively). Acetylcarnitine, butyrylcarnitine, hexanoylcarnitine, octanoylcarnitine, decanoylcarnitine and dodecanoylcarnitine reached the lowest values at 60 min (decreases ranging from –47 to –70 %), before increasing and peaking at 24 h after the meal (increases ranging from +86 to +120 %). Our findings suggest that distinguishing between fasting and non-fasting blood samples falls short of capturing the dynamics in lipid, ketone, carnitine and acylcarnitine concentrations. To enhance the utility of serum acylcarnitine analyses, we strongly recommend accounting for the specific time since the last meal at the time of blood sampling.

Calorie restriction plays a role in reducing food intake and weight gain, and improving health and lifespan. We hypothesized that calorie restriction would affect body weight (BW), serum indices, gut microbiota, metabolites and short-chain fatty acids of finishing pigs. Castrated male (Landrace × Yorkshire) pigs (86.13 ± 3.50 kg) were randomly assigned into two groups indicated as control (Con) and calorie restriction (CR) (eight pigs/group), respectively. Pigs in the Con group consumed feed ad libitum, whereas pigs in the CR group were fed 70% of the amount of feed in the Con group. The trial lasted for 38 days. Blood and colonic contents were collected for serum parameters, and microbiota and metabolome analysis, respectively. Main effects were tested by Student’s t-test. We found that for finishing pigs, calorie restriction reduced the cumulative food intake, BW gain, serum total cholesterol, triglyceride, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, alanine aminotransferase and aspartate aminotransferase levels. Calorie restriction did not change the α and β diversity of intestinal microbiota. However, calorie restriction significantly increased the abundance of Romboutsia and unclassified_c_Bacilli, and significantly reduced the abundance of Lachnospiraceae_XPB1014_group, Candidatus_Saccharimonas, Escherichia-–Shigella and Gastranaerophilales. Calorie restriction also simultaneously changed the structure of intestinal metabolites and increased the concentration of isobutyric acid, isovaleric acid and valeric acid. In conclusion, calorie restriction may affect metabolism, reduce obesity and improve intestinal microbiota, which may be a healthy diet treatment that can reduce obesity and improve metabolism.

Reliable dietary assessment is key to our understanding of diet-health interactions(1). Most current dietary assessment methods are self-reported, making them prone to a range of biases(2). Objective markers, such as certain metabolite concentrations in human tissue, may prove a more reliable method of dietary assessment in the future while reducing participant burden(2,3). An example of these metabolite markers are alkylresorcinols, a family of compounds found in the bran layer of common grains. High concentrations of alkylresorcinols reflect high wholegrain intakes, very low levels indicate only refined grain intake, while no alkylresorcinols would indicate a gluten-free diet. We conducted a randomised crossover feeding study of three interventions (one standardisation day followed by a feeding day where known amounts were consumed under observation). Each feeding day differed by which food groups were provided (e.g., chicken, legumes, and fruit) or entirely absent (e.g., grains, fish, and dairy). Participants provided 24-hour urine samples on all six days (standardisation days and feeding days), as well as a 24-recalls and fasted blood samples the morning after each day. Known metabolite markers of dietary intake (approximately 70) were identified in blood and urine with LC-MSqToF, and semi-quantified with a known standard. Twenty-four hour urine sodium content was also measured as the current best known objective marker of dietary intake(4). Twenty-four participants (74% female, age (SD) 24.8 (6.1), BMI 24.1 (4.0)) commenced the study, with 21 (88%) completing all three interventions. Mean energy intake on feeding days (11720 kJ (2943.01)) was higher than self-reported energy intake on standardisation days (9243.57 kJ (3582.92)). Meals were well tolerated, with mean (range) intakes of 9.8 (6.3 – 16.1) serves whole grains, 2.4 (1.6 – 4.8) serves fish, 3.1 (1.9 – 5.5) serves dairy, 5.6 (4.5 – 9.1) serves chicken, 8.2 (7.0 – 14.1) serves legumes, 3.1 (1.3 – 4.6) serves fruit, 3.9 (2.6 – 6.4) serves red meat, 1.7 (1.35 – 2.6) serves nuts and seeds, or 13.4 (9.4 – 19.5) serves vegetables on their respective feeding days. The three feeding days provided clearly identifiable clusters when assessing the overall metabolic profile, both in terms of what was measured on the feeding days, and the difference in metabolite concentrations between standardisation day and feeding day. The relative correlations between self-reported intakes and individual metabolite concentrations reflecting specific foods or food groups with the known dietary intakes from feeding days will be presented first at the conference.

We aim to explore the association between caffeine and its metabolites and bone mineral density (BMD) in postmenopausal women. Data of 4286 postmenopausal women were extracted from the National Health and Nutrition Examination Survey (NHANES) database in 2009–14 in this cross-sectional study. Weighted linear regression and stepwise regression analyses were used to screen the covariates. Weighted univariate and multivariate linear regression analyses were used to explore the associations between caffeine and its metabolites and BMD. The evaluation index was estimated value (β) with 95 % confidence intervals (CIs). We also explored these relationships in age subgroups. The median BMD level among the eligible women was 0⋅7 gm/cm2. After adjusting for covariates including age, body mass index (BMI), fat intake, Calcium (Ca) supplements, diabetes mellitus (DM), angina pectoris, parental history of osteoporosis (OP), anti-osteoporosis therapy, poverty income ratio (PIR), vitamin D (VD) supplements, coronary heart disease (CHD), and previous fracture, we found that caffeine intake was not significantly related to the BMD reduction (β = 0, P = 0⋅135). However, caffeine metabolites, including MethyluricAcid3, MethyluricAcid7, MethyluricAcid37, Methylxanthine3, and Methylxanthine37, were negatively associated with the BMD (all P < 0⋅05). In addition, MethyluricAcid37 and Methylxanthine37 were negatively associated with BMD in females aged <65 years old, while MethyluricAcid3 and Methylxanthine3 were noteworthy in those who aged ≥65 years old. The roles of caffeine and its metabolites in BMD reduction and OP in postmenopausal women needed further exploration.

Metabolomics has been utilised in epidemiological studies to investigate biomarkers of nutritional status and metabolism in relation to non-communicable diseases. However, little is known about the effect of prandial status on several biomarker concentrations. Therefore, the aim of this intervention study was to investigate the effect of a standardised breakfast meal followed by food abstinence for 24 h on serum concentrations of amino acids, one-carbon metabolites and B-vitamin biomarkers. Thirty-four healthy subjects (eighteen males and sixteen females) aged 20–30 years were served a breakfast meal (∼500 kcal) after which they consumed only water for 24 h. Blood samples were drawn before and at thirteen standardised timepoints after the meal. Circulating concentrations of most amino acids and metabolites linked to one-carbon metabolism peaked within the first 3 h after the meal. The branched-chain amino acids steadily increased from 6 or 8 hours after the meal, while proline decreased in the same period. Homocysteine and cysteine concentrations immediately decreased after the meal but steadily increased from 3 and 4 hours until 24 h. FMN and riboflavin fluctuated immediately after the meal but increased from 6 h, while folate increased immediately after the meal and remained elevated during the 24 h. Our findings indicate that accurate reporting of time since last meal is crucial when investigating concentrations of certain amino acids and one-carbon metabolites. Our results suggest a need for caution when interpretating studies, which utilise such biomarkers, but do not strictly control for time since the last meal.

Prostate cancer (PC) presents great challenges in early diagnosis and often leads to unnecessary invasive procedures as well as over diagnosis and treatment, thus highlighting the need for promising early diagnostic biomarkers. The aim of this review is to provide an up-to-date summary of chronologically existing metabolomics PC biomarkers, their potential to improve clinical PC diagnosis and to reduce the proliferation and monitoring of PC. The systematic research was conducted on PubMed in accordance with PRISMA guidelines to report PC biomarkers. The majority of the studies distinguished malignant from benign prostate and few explored the biomarkers associated with the progression of PC. The present review summarises the primary outcomes of most significant studies to extend our knowledge of PC metabolomics biomarkers. We observed divergent inter-laboratory technical procedures employing different statistical approaches produced abundant information regarding PC metabolites perturbation. Since PC metabolomics is still in its early phase, it is vital that we dig out the most specific, sensitive and accurate metabolic signatures and conduct more studies with milestone findings with comparable sample sizes to validate and corroborate the findings.

Low birth weight (LBW) neonates show impaired growth compared with normal birth weight (NBW) neonates. Glutamine (Gln) supplementation benefits growth of weaning piglets, while the effect on neonates is not sufficiently clear. We examined the effect of neonatal Gln supplementation on piglet growth, milk intake and metabolic parameters. Sow-reared pairs of newborn LBW (0·8–1·2 kg) and NBW (1·4–1·8 kg) male piglets received Gln (1 g/kg body mass (BM)/d; Gln-LBW, Gln-NBW; n 24/group) or isonitrogenous alanine (1·22 g/kg BM/d; Ala-LBW; Ala-NBW; n 24/group) supplementation at 1–5 or 1–12 d of age (daily in three equal portions at 07:00, 12:00 and 17:00 by syringe feeding). We measured piglet BM, milk intake (1, 11–12 d), plasma metabolite, insulin, amino acid (AA) and liver TAG concentrations (5, 12 d). The Gln-LBW group had higher BM (+7·5%, 10 d, P = 0·066; 11–12 d, P < 0·05) and milk intake (+14·7%, P = 0·015) than Ala-LBW. At 5 d, Ala-LBW group had higher plasma TAG (+34·7%, P < 0·1) and lower carnosine (–22·5%, P < 0·05) than Ala-NBW and Gln-LBW, and higher liver TAG (+66·9%, P = 0·029) than Ala-NBW. At 12 d, plasma urea was higher (+37·5%, P < 0·05) with Gln than Ala supplementation. Several proteinogenic AA in plasma were lower (P < 0·05) in Ala-NBW v. Gln-NBW. Plasma arginine was higher (P < 0·05) in Gln-NBW v Ala-NBW piglets (5, 12 d). Supplemental Gln moderately improved growth and milk intake and affected lipid metabolism in LBW piglets and AA metabolism in NBW piglets, suggesting effects on intestinal and liver function.

Life-course experiences have been postulated to program hypothalamus–pituitary–adrenal (HPA) axis activity, suggesting that HPA axis activity is, at least partially, stable over time. Yet, there is paucity of data on the long-term stability of cortisol production and metabolism. We performed a prospective follow-up study in twins recruited from a nationwide register to estimate the stability of cortisol production and metabolism over time, and the contribution of genetic and environmental factors to this stability. In total, 218 healthy mono- and dizygotic twins were included. At the ages of 9, 12 and 17 years, morning urine samples were collected for assessment (by gas chromatography-tandem mass spectrometry) of cortisol metabolites, enabling the calculation of cortisol metabolite excretion rate and cortisol metabolism activity. Our results showed a low stability for both cortisol metabolite excretion rate (with correlations <.20) and cortisol metabolism activity indices (with correlations of .25 to .46 between 9 and 12 years, −.02 to .15 between 12 and 17 years and .09 to .28 between 9 and 17 years). Because of the low stability over time, genetic and environmental contributions to this stability were difficult to assess, although it seemed to be mostly determined by genetic factors. The low stability in both cortisol production and metabolism between ages 9 and 17 years reflects the dynamic nature of the HPA axis.

Knowing the biological signals associated with appetite control is crucial for understanding the regulation of food intake. Biomarkers of appetite have been defined as physiological measures that relate to subjective appetite ratings, measured food intake, or both. Several metabolites including amino acids, lipids and glucose were proposed as key molecules associated with appetite control over 60 years ago, and along with bile acids are all among possible appetite biomarker candidates. Additional metabolites that have been associated with appetite include endocannabinoids, lactate, cortisol and β-hydroxybutyrate. However, although appetite is a complex integrative process, studies often investigated a limited number of markers in isolation. Metabolomics involves the study of small molecules or metabolites present in biological samples such as urine or blood, and may present a powerful approach to further the understanding of appetite control. Using multiple analytical techniques allows the characterisation of molecules, such as carbohydrates, lipids, amino acids, bile acids and fatty acids. Metabolomics has proven successful in identifying markers of consumption of certain foods and biomarkers implicated in several diseases. However, it has been underexploited in appetite control or obesity. The aim of the present narrative review is to: (1) provide an overview of existing metabolites that have been identified in human biofluids and associated with appetite control; and (2) discuss the potential of metabolomics to deepen understanding of appetite control in humans.

In the livestock husbandry compensatory growth may be explored as a means to improve nutrient utilization, to reduce gut health problems due to excess protein intake, to simplify feeding strategies and thus to improve production efficiencies. This study investigated the effects of early protein restriction (EPR) and early antibiotic intervention (EAI) on growth performance, intestinal morphology, colonic bacteria, metabolites and mucosal gene expressions during the restriction phase and re-alimentation phase. A total of 64 piglets (10.04 ± 0.73 kg) were randomly divided into four treatment groups according to a 2 × 2 factorial arrangement with two levels of proteins (14% v. 20%) and two levels of antibiotics (0 v. 50 mg/kg kitasamycin and 20 mg/kg colistin sulphate). After a 30-day restriction phase with four kinds of diets, all groups were fed the same diets for another 74 days. The results showed that EPR decreased BW, average daily gain (ADG), average daily feed intake in the restriction phase (P < 0.01) and increased ADG on days 66 to 104 of the late re-alimentation phase. Early protein restriction could decrease the villus height in the jejunum (P < 0.05), while shifting to the same diets restored the villus height. Meanwhile, during the re-alimentation phase, pigs in the protein restriction groups had increased concentrations of total short chain fatty acids (P < 0.05), and modified the abundances of Firmicutes and Bacteroidetes in the colon. Furthermore, the lower microbial diversity caused by EPR was improved, and gene expression analysis indicated a better barrier function in the colon. During the whole trial, EAI had no interaction with EPR and played a dispensable role in compensatory growth. Collectively, the retardation of growth caused by EPR can be compensated for in the later stages of pig raising, and accompanied by altered intestinal morphology, microbial composition.

Although common beans (Phaseolus vulgaris L.) are consumed worldwide, studies on the metabolic fate of phenolic compounds from common beans are still very scarce. The present work aimed to study the bioavailability of phenolic compounds in human plasma and urine, after acute consumption of a single meal of cooked common beans. Blood and urine of seven volunteers were collected before (0 h) and at different time points (1, 2, 4, 6 and 8 h for plasma and 0–2, 2–4, 4–6, 6–8 and 8–24 h for urine) after beans’ intake. Ultra-high performance liquid chromatography-quadrupole-time of flight-MS (UPLC-Q-TOF-MS) was used for quantification. After beans’ intake, 405 (sd 3) g, containing 188 mg of phenolic compounds (expressed as gallic acid equivalents), there was a significant increase (P < 0·05) in the plasma concentration of six metabolites and in the urinary excretion of eleven metabolites. After 1 h post-consumption, metabolites, such as kaempferol-3-O-glucuronide, showed a significant increase in plasma concentration, suggesting kaempferol’s glucuronidation in the upper gastrointestinal tract. More than 50 % of the total amount of metabolites, such as 4-methylcatechol-O-sulphate and dihydrocaffeic acid-3-O-sulphate, were excreted after 8 h post-consumption, indicating colonic bacterial metabolism of the phenolic compounds. Partial least square-discriminant analysis models clearly showed clusters of metabolites, which contributed to extend the list of compounds related to cooked common beans’ human intake at different time points and showed the human inter-individual variability in plasma concentration as well as in urinary excreted metabolites, after cooked common beans’ intake.

The BW has been largely used as a selection criterion in genetic selection programmes; however, increases in BW can affect animal metabolism and metabolites. The knowledge of how genetic potential for growth affects the metabolites can give a footprint of growth metabolism. This research aimed to evaluate the effect of genetic potential for post-weaning growth (GG) on performance, carcass traits and serum metabolome of non-castrated Nellore males during the finishing phase. Forty-eight Nellore non-castrated males, with divergent potential for post-weaning growth, were selected and divided into two groups: high potential for post-weaning growth (HG; n = 24) and low potential for post-weaning growth (LG; n = 24). Animals were kept and fed for 90 days where performance and ultrasound carcass traits were evaluated. Blood samples were collected at the beginning and end of feeding period to analyse serum metabolites concentration. The hot carcass weight and dressing percentage were recorded at slaughter. The feedlot performance and carcass traits were not affected by genetic potential. The HG animals had a lower glucose (P = 0.039), glutamate (P = 0.038), glutamine (P = 0.004), greater betaine (P = 0.039) and pyruvate (P = 0.039) compared to the LG group at the beginning of feedlot. In addition, higher creatine phosphate concentrations were observed at the beginning of feeding period, compared to final, for both groups (P = 0.039). In conclusion, the genetic potential for post-weaning growth does not affect performance and carcass traits during the finishing period. Differences in metabolite concentrations can be better found at the beginning of feedlot, providing a footprint of growth metabolism, but similar metabolite concentration at the end of finishing period.

Puberty attainment in dairy heifers has been widely studied from a hormonal point of view, but few studies have focussed on puberty–blood profile relationships during growth. We led experiments to determine the effects of feeding treatments on growth parameters, age at puberty and plasma biochemical profiles, and the relationships between age at puberty and metabolic profiles at 6, 9, 12 and 15 months (mo) of age. Blood samples were collected from 67 Holstein heifers, born between September 2011 and February 2012, every 10 days from 5.5 mo of age until heifers were considered pubertal (plasma progesterone concentration greater than 1.0 ng/ml) or oestrus synchronisation (November 2012; 11 to 15 mo of age). Heifers born before 30 November were fed either a standard diet (SD, n=27) or an intensive-plane diet (ID1, n=27) from 0 to 6 mo of age. This strategy aimed to reach 190 to 200 kg (SD) or 220 to 230 kg (ID1) BW at 6 mo of age. All heifers born after 1 December received an intensive-plane diet (ID2, n=13) from birth until oestrus synchronisation, in order to reach a similar BW at first insemination as heifers born before 1 December. Only 56 heifers reached puberty before oestrus synchronisation, at an average age of 10.3±2.2 mo (6.2 to 14.4 mo) and a BW of 296±40 kg (224 to 369 kg). There was no difference among the three feeding treatments until 6 mo, but at 9, 12 and 15 mo of age, ID2 (n=11) heifers weighed 37, 52 and 30 kg more than SD (n=22) and ID1 (n=23) heifers (P<0.001), respectively. Glucose, non-esterified fatty acids, albumin, aspartate aminotransferase, alkaline phosphatase and tartrate-resistant acid phosphatase, calcium, phosphorus, potassium and iron decreased with age, whereas β-hydroxybutyric acid, total cholesterol, creatinine, the creatinine : albumin ratio, alanine aminotransferase and chloride increased. The feeding treatment significantly affected creatinine, the creatinine : albumin ratio, and phosphorus and sodium levels, which were higher for ID2 heifers compared with SD and ID1. A logistic regression based on plasma metabolites at 6 mo of age to explain puberty attainment before or at 12 mo of age showed a positive relationship with plasma cholesterol (odds ratio=9.05). In conclusion, the feeding treatment had minor consequences on plasma metabolites, but it did affect growth performance.

The consumption of berries has been linked to decreased risk of degenerative disease. Berries are regularly processed into juices. It is largely unknown how the juicing process affects the bioavailability of metabolites. As metabolomics has shown to be a valuable nutritional tool to study global metabolite differences, the aim of this study was to investigate the effect of juicing on the relative appearance of blueberry metabolites in humans using metabolomics. Nine healthy subjects consumed 250 g of fresh blueberries either as the whole fruit or after juicing, and provided blood and urine samples before and 2 h after intake in a cross-over design. Samples underwent metabolite profiling using LCMS, and data were mined with multivariate analysis. Overall, <12 % of all ions detected were significantly influenced by blueberry treatment (P<0·05). Partial least-squared discriminant analysis models of post-treatment samples revealed good discrimination. In urinary samples, whole blueberry treatment resulted in 108 ions that were significantly higher compared with juiced treatment (positive and negative mode combined), whereas only eight were significantly higher after juiced treatment. Examples of putative annotations included metabolites of ferulic and caffeic acids, several phenolic metabolites conjugated to sulphate, glycoside or glucuronide and fatty acyl derivatives, which were of higher intensity after whole blueberry treatment. In conclusion, consumption of whole blueberries resulted in a higher range of phenolic and other metabolites in plasma and urine samples 2 h after consumption. Both whole and juiced blueberries resulted in very similar metabolite profiles at 2 h, although this was the only time point measured.

The supplementing of sow diets with lipids during pregnancy and lactation has been shown to reduce sow condition loss and improve piglet performance. The aim of this study was to determine the effects of supplemental palm oil (PO) on sow performance, plasma metabolites and hormones, milk profiles and pre-weaning piglet development. A commercial sow ration (C) or an experimental diet supplemented with 10% extra energy in the form of PO, were provided from day 90 of gestation until weaning (24 to 28 days postpartum) in two groups of eight multiparous sows. Gestation length of PO sows increased by 1 day (P<0.05). Maternal BW changes were similar throughout the trial, but loss of backfat during lactation was reduced in PO animals (C: −3.6±0.8 mm; PO: −0.1±0.8 mm; P<0.01). Milk fat was increased by PO supplementation (C day 3: 8.0±0.3% fat; PO day 3: 9.1±0.3% fat; C day 7: 7.8±0.5% fat; PO day 7: 9.9±0.5% fat; P<0.05) and hence milk energy yield of PO sows was also elevated (P<0.05). The proportion of saturated fatty acids was greater in colostrum from PO sows (C: 29.19±0.31 g/100 g of fat; PO: 30.77±0.36 g/100 g of fat; P<0.01). Blood samples taken on 105 days of gestation, within 24 h of farrowing, day 7 of lactation and at weaning (28±3 days post-farrowing) showed there were no differences in plasma concentrations of triacylglycerol, non-esterified fatty acids, insulin or IGF-1 throughout the trial. However, circulating plasma concentrations of both glucose and leptin were elevated during lactation in PO sows (P<0.05 and P<0.005, respectively) and thyroxine was greater at weaning in PO sows (P<0.05). Piglet weight and body composition were similar at birth, as were piglet growth rates throughout the pre-weaning period. A period of 7 days after birth, C piglets contained more body fat, as indicated by their lower fat-free mass per kg (C: 66.4±0.8 arbitrary units/kg; PO: 69.7±0.8 arbitrary unit/kg; P<0.01), but by day 14 of life this situation was reversed (C: 65.8±0.6 arbitrary units/kg; PO: 63.6±0.6 arbitrary units/kg; P<0.05). Following weaning, PO sows exhibited an increased ratio of male to female offspring at their subsequent farrowing (C: 1.0±0.3; PO: 2.2±0.2; P<0.05). We conclude that supplementation of sow diets with PO during late gestation and lactation appears to increase sow milk fat content and hence energy supply to piglets. Furthermore, elevated glucose concentrations in the sow during lactation may be suggestive of impaired glucose homoeostasis.

(−)-Hydroxycitric acid (HCA), a major component of Garcinia cambogia extracts, has been shown to suppress BW gain and fat accumulation in animals and humans. However, the mechanism remains unknown. In this study, gas chromatography-mass spectrometry was used to analyse serum metabolites, and principal component analysis and partial least-squares-discriminant analysis models were generated to analyse serum metabolite changes in broiler chickens after the administration of (−)-HCA at 0, 1000, 2000 and 3000 mg/kg diets for 28 days. Metabolites showing significant changes were screened by ‘variable importance in the projection’ plots. The results showed that 20 metabolites in the 1000 mg/kg (−)-HCA treatment group and 16 metabolites in 3000 mg/kg (−)-HCA treatment group were significantly altered. Metabolites pathway enrichment analysis indicated that these metabolites were mainly associated with metabolism of amino acids, protein synthesis, citric acid cycle, and uric acid and fatty acid synthesis. The data indicated that (−)-HCA promoted protein synthesis by regulating the metabolic directions of amino acids. At the same time, (−)-HCA treatment inhibited fatty acid synthesis by promoting the citric acid cycle, resulting in reduced cytosolic acetyl-CoA content in broiler chickens. The present study identified global changes in metabolites and analysed the main canonical metabolic pathways in broiler chickens supplemented with (−)-HCA. These results will deepen our understanding of the mechanism of (−)-HCA’s effects in animals.

Generalised anxiety disorder (GAD) is a common psychiatric illness characterised by selective morpho-functional brain alterations. The breath of neuroimaging studies investigating the neural basis of GAD is extensive; however, its pathophysiology is still largely unknown. Specifically for proton Magnetic Resonance Spectroscopy (¹H MRS) investigations, which have the aim of identifying differences in metabolite levels between conditions in key brain areas, often showed contrasting results. Indeed, there are selected ¹H MRS studies reporting deficits of key metabolites in GAD patients; however, collectively the literature remains mixed with respect to consistency of major findings. In this review, we evaluate published ¹H MRS studies on GAD with the final aim of providing a comprehensive overview of the extent of neurometabolic dysfunctions associated with GAD. Interestingly, the majority of the studies reviewed showed altered metabolite levels in the dorsolateral prefrontal cortex and hippocampus suggesting regional specificity. These results also provide evidence of the utility of ¹H MRS not only for elucidating the pathophysiology of neuropsychiatric diseases, but also for the identification of more beneficial and targeted pharmacological interventions. Additionally, future studies are warranted to overcome methodological differences observed across the studies.

The fate of foliarly applied 14C diclofop-methyl {methyl 2-[4-(2,3-dichlorophenoxy)phenoxy] propanoate} was determined in intact plants of barnyardgrass [Echinochloa crus-galli (L.) Beauv.], a susceptible (S) grass; proso millet (Panicum miliaceum L.), a moderately susceptible (MS) grass; longspine sandbur [Cenchrus longispinus (Hack.) Fern.], a tolerant (T) grass; soybean [Glycine max (L.) Merr. ‘Hark’], and cucumber (Cucumis sativus L. ‘Green Star’), both T broadleaf plants, 1,3, and 5 days after treatment (DAT). Diclofop-methyl accounted for 94% of the radioactivity washed from the leaf surfaces of all species. Plant extracts contained diclofop-methyl, diclofop, and water-soluble conjugates. Barnyardgrass (S), proso millet (MS), and soybean (T) had 75, 68, and 66% of the extracted radioactivity as diclofop and diclofop-methyl. Longspine sandbur (T) and cucumber (T) had 71 and 84% of the extracted radioactivity as water-soluble conjugates. Acid hydrolysis of the water-soluble conjugates yielded 84 and 71% diclofop in barnyardgrass (S) and proso millet (MS). Cucumber (T), soybean (T), and longspine sandbur (T) had 43, 23, and 25% ring-OH diclofop. Alkaline hydrolysis of the non-extracted plant residue yielded diclofop as the major component in all species.

Vegetative filter strips (VFS) potentially reduce herbicide transport from agricultural fields by increasing herbicide mass infiltrated (M inf) and herbicide mass adsorbed (M as) compared with bare field soil. However, there are conflicting reports in the literature concerning the contribution of Mas to herbicide trapping efficiency (TE). Moreover, no study has evaluated TE among metolachlor and metolachlor metabolites in a VFS. This experiment was conducted to compare TE, M inf, and M as among metolachlor, metolachlor oxanilic acid (OA), and metolachlor ethanesulfonic acid (ESA) in buffalograss filter strips. Runoff was applied as a point source upslope of a 1- × 3-m microwatershed at a rate of 750 L h−1. The point source was fortified with metolachlor, metolachlor OA, and metolachlor ESA, each at 0.12 μg ml−1. After moving through the plot, water samples were collected at 5-min intervals and stored at 5 C until analysis. Water samples were extracted using solid-phase extraction and analyzed by high-performance liquid chromatography–photodiode array detection. TE was significantly greater for metolachlor (25.3%) as compared with the OA (15.5%) and ESA metabolites (14.2%). The average M inf was 8.5% and was not significantly different among compounds. Significantly more metolachlor (17.3%) was retained as Mas compared with either metolachlor OA (7.0%) or metolachlor ESA (5.5%). Moreover, M as accounted for 68 and 42% of the total TE for metolachlor and metolachlor metabolites, respectively. These results demonstrate that adsorption to the VFS grass, grass thatch, or soil surface (or all) is an important retention mechanism for metolachlor and metolachlor metabolites, especially under saturated conditions. Moreover, the Mas data indicate that metolachlor is preferentially retained by the VFS grass, grass thatch, or soil surface (or all) compared with the OA and ESA metabolites. Greater metolachlor retention in VFS compared with the OA and ESA metabolites may partially explain why metolachlor metabolites are frequently measured at higher concentrations than metolachlor is in surface water.

In pigs, many production traits are known to vary among breeds or lines. These traits can be considered end phenotypes or external traits as they are the final results of complex biological interactions and processes whose fine biological mechanisms are still largely unknown. This study was designed to compare plasma and serum metabolomic profiles between animals of two heavy pig breeds (12 Italian Large White and 12 Italian Duroc), testing indirectly the hypothesis that different genetic backgrounds might be the determining factors of differences observed on the level of metabolites in the analyzed biofluids between breeds. We used a targeted metabolomic approach based on mass spectrometric detection of about 180 metabolites and applied a statistical validation pipeline to identify differences in the metabolomic profiles of the two heavy pig breeds. Blood samples were collected after jugulation at the slaughterhouse and prepared for metabolomics analysis that was carried out using the Biocrates AbsoluteIDQ p180 Kit, covering five different biochemical classes: glycerophospholipids, amino acids, biogenic amines, hexoses and acylcarnitines. A statistical pipeline that included the selection of the most relevant metabolites differentiating the two breeds by sparse Partial Least Squares Discriminant Analysis (sPLS-DA) was coupled with a stability test and significance test determined with leave one out and permutation procedures. sPLS-DA plots clearly separated the pigs of the two investigated breeds. A few metabolites (a total of five metabolites considering the two biofluids) involved in key metabolic pathways largely contributed to these differences between breeds. In particular, a higher level of the sphingomyelins SM (OH) C14:1 (both in plasma and serum), SM (OH) C16:1 (in serum) and SM C16:0 (in serum) were observed in Italian Duroc than in Italian Large White pigs and the inverse was for the biogenic amine kynurenine (in plasma). The level of another biogenic amine (acetylornithine) was higher in Italian Large White than in Italian Duroc pigs in both analysed biofluids. These results provided biomarkers that could be important to understand the biological differences between these two heavy pig breeds. In particular, according to the functional role played by sphingomyelins in obesity-induced inflammatory responses, it could be possible to speculate that a higher level of sphingomyelins in Italian Duroc might be related to the higher interrmuscular fat deposition of this breed compared with the Italian Large White. Additional studies will be needed to evaluate the relevance of these biomarkers for practical applications in pig breeding and nutrition.