Dodecanoylcarnitine

Whole-blood metabolomics of dementia patients reveal classes of disease-linked metabolites

Dementia is caused by factors that damage neurons. We quantified small molecular markers in whole blood of dementia patients, using nontargeted liquid chromatography-mass spectroscopy (LC-MS). Thirty-three metabolites, classified into five groups (A to E), differed significantly in dementia patients, compared with healthy elderly subjects. Seven A metabolites present in plasma, including quinolinic acid, kynurenine, and indoxyl-sulfate, increased. Possibly they act as neurotoxins in the central nervous system (CNS). The remaining 26 compounds (B to E) decreased, possibly causing a loss of support or protection of the brain in dementia. Six B metabolites, normally enriched in red blood cells (RBCs), all contain trimethylated ammonium moieties. These metabolites include ergothioneine and structurally related compounds that have scarcely been investigated as dementia markers, validating the examination of RBC metabolites. Ergothioneine, a potent antioxidant, is significantly decreased in various cognition-related disorders, such as mild cognitive impairment and frailty. C compounds also include some oxidoreductants and are normally abundant in RBCs (NADP+, glutathione, adenosine triphosphate, pantothenate, S-adenosyl-methionine, and gluconate). Their decreased levels in dementia patients may also contribute to depressed brain function. Twelve D metabolites contains plasma compounds, such as amino acids, glycerophosphocholine, dodecanoyl-carnitine, and 2-hydroxybutyrate, which normally protect the brain, but their diminution in dementia may reduce that protection. Seven D compounds have been identified previously as dementia markers. B to E compounds may be critical to maintain the CNS by acting directly or indirectly. How RBC metabolites act in the CNS and why they diminish significantly in dementia remain to be determined.

Multi-omics reveals specific host metabolism-microbiome associations in intracerebral hemorrhage

Intracerebral hemorrhage (ICH) is the most devastating subtype of stroke, but effective prevention and treatment strategies are lacking. Recently, gut microbiome and its metabolitesis are considered to be an influencing factor of stroke. However, little is known about the effects of the gut microbiome on ICH and host metabolic activity. Therefore, we used 16S sequencing, macrogenomics sequencing and untargeted metabolomics to explore the differences in gut microbial-metabolome interactions between patients with intracerebral hemorrhage and healthy control populations. We found a significant decrease in the phylum of Firmicutes and a significant increase of Bacteroidetes in ICH patients. At the genus level, Streptococcus, Bifidobacterium, Akkermansia, and Lactobacillus were more abundant in ICH patients. Macrogenomic analysis revealed active glycosaminoglycan degradation, heme synthesis, galactose degradation, lipopolysaccharide core region synthesis, and beta-Lactam resistance in ICH patients. Serum untargeted metabolomic analysis combined with ROC curves showed that octanoylcarnitine, decanoylcarnitine, dodecanoylcarnitine, glyceric acid, pyruvic acid, aspartic acid, methylcysteine, pyroglutamic acid, 9E-tetradecenoic acid, N-Acetylneuraminic acid, and aconitic acid were the best markers for the diagnosis of ICH. Correlation analysis showed that microbiome enriched in the gut of ICH patients were significantly correlated with serum metabolites, revealing a close correlation between the gut microbiome of ICH patients and the host metabolome, and significant differences from the healthy population. microbiota-host co-metabolites including pyruvic acid and 9E-tetradecenoic acid is associated with the the National Institutes of Health Stroke Scale (NIHSS) scores. In conclusion, microbiome-related metabolites in ICH patients was associated with the severity of ICH, the microbiota-host co-metabolites may be a potential may be potential therapeutic targets.

Associations of Gut Microbiota and Fatty Metabolism With Immune Thrombocytopenia

Objective: To determine whether gut microbiota, fatty metabolism and cytokines were associated with immune thrombocytopenia (ITP).
Methods: In total, 29 preliminarily diagnosed ITP patients and 33 healthy volunteers were enrolled. Fecal bacterial were analyzed based on 16S rRNA sequencing. Plasma cytokines and motabolites were analyzed using flow cytometry and liquid chromatography-mass spectrometry (LC-MS), respectively.
Results: Bacteroides, Phascolarctobacterium, and Lactobacillus were enriched at the genus level in ITP patients, while Ruminococcaceae UCG-002, Eubacterium coprostanoligeues, Megamonas, and Lachnospiraceae NC2004 were depleted. At the phylum level, the relative abundance of Proteobacteria and Chloroflexi increased in ITP patients, while Firmicutes, Actinobacteria, and the Firmicutes/Bacteroidetes ratio decreased. Plasma levels of 5-hydroxyeicosatetraenoic acid (5-HETE), 6-trans-12-epi-leukotriene B4 (6t,12e-LTB₄), and resolvin D2 (RvD₂) were upregulated, and stachydrine, dowicide A, dodecanoylcarnitine were downregulated in ITP patients. Furthermore, RvD₂ is positively correlated with order Bacteroidetes VC2.1 Bac22, 5-HETE is positively correlated with genus Azospirillum, and 6t,12e-LTB₄ is positively correlated with genus Cupriavidus. In addition, stachydrine is positively correlated with family Planococcaceae, dowicide A is positively correlated with class MVP-15, and dodecanoylcarnitine is positively correlated with order WCHB1-41. Plasma levels of interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) were upregulated in ITP patients.
Conclusion: Our study revealed a relationship between microbiota and fatty metabolism in ITP. Gut microbiota may participate in the pathogenesis of ITP through affecting cytokine secretion, interfering with fatty metabolism.

Serum Metabolomic Signatures for Knee Cartilage Volume Loss over 10 Years in Community-Dwelling Older Adults

Osteoarthritis (OA) is the most prevalent joint disorder characterized by joint structural pathological changes with the loss of articular cartilage as its hallmark. Tools that can predict cartilage loss would help identify people at high risk, thus preventing OA development. The recent advance of the metabolomics provides a new avenue to systematically investigate metabolic alterations in disease and identify biomarkers for early diagnosis. Using a metabolomics approach, the current study aimed to identify serum metabolomic signatures for predicting knee cartilage volume loss over 10 years in the Tasmania Older Adult Cohort (TASOAC). Cartilage volume was measured in the medial, lateral, and patellar compartments of the knee by MRI at baseline and follow-up. Changes in cartilage volume over 10 years were calculated as percentage change per year. Fasting serum samples collected at 2.6-year follow-up were metabolomically profiled using the TMIC Prime Metabolomics Profiling Assay and pairwise metabolite ratios as the proxies of enzymatic reaction were calculated. Linear regression was used to identify metabolite ratio(s) associated with change in cartilage volume in each of the knee compartments with adjustment for age, sex, and BMI. The significance level was defined at α = 3.0 × 10^-6 to control multiple testing. A total of 344 participants (51% females) were included in the study. The mean age was 62.83 ± 6.13 years and the mean BMI was 27.48 ± 4.41 kg/m² at baseline. The average follow-up time was 10.84 ± 0.66 years. Cartilage volume was reduced by 1.34 ± 0.72%, 1.06 ± 0.58%, and 0.98 ± 0.46% per year in the medial, lateral, and patellar compartments, respectively. Our data showed that the increased ratios of hexadecenoylcarnitine (C16:1) to tetradecanoylcarnitine (C14) and C16:1 to dodecanoylcarnitine (C12) were associated with 0.12 ± 0.02% reduction per year in patellar cartilage volume (both p < 3.03 × 10^-6). In conclusion, our data suggested that alteration of long chain fatty acid β-oxidation was involved in patellar cartilage loss. While confirmation is needed, the ratios of C16:1 to C14 and C12 might be used to predict long-term cartilage loss.

First-trimester preterm preeclampsia prediction with metabolite biomarkers: differential prediction according to maternal body mass index

Background: Prediction of preeclampsia risk is key to informing effective maternal care. Current screening for preeclampsia at 11 to 13 weeks of gestation using maternal demographic characteristics and medical history with measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor can identify approximately 75% of women who develop preterm preeclampsia with delivery at <37 weeks of gestation. Further improvements to preeclampsia screening tests will likely require integrating additional biomarkers. Recent research suggests the existence of distinct maternal risk profiles. Therefore, biomarker evaluation should account for the possibility that a biomarker only predicts preeclampsia in a specific maternal phenotype.
Objective: This study aimed to verify metabolite biomarkers as preterm preeclampsia predictors early in pregnancy in all women and across body mass index groups.
Study design: Observational case-control study drawn from a large prospective study on the early prediction of pregnancy complications in women attending their routine first hospital visit at King's College Hospital, London, United Kingdom, in 2010 to 2015. Pregnant women underwent a complete first-trimester assessment, including the collection of blood samples for biobanking. In 11- to 13-week plasma samples of 2501 singleton pregnancies, the levels of preselected metabolites implicated in the prediction of pregnancy complications were analyzed using a targeted liquid chromatography-mass spectrometry method, yielding high-quality quantification data on 50 metabolites. The ratios of amino acid levels involved in arginine biosynthesis and nitric oxide synthase pathways were added to the list of biomarkers. Placental growth factor and pregnancy-associated plasma protein A were also available for all study subjects, serving as comparator risk predictors. Data on 1635 control and 106 pregnancies complicated by preterm preeclampsia were considered for this analysis, normalized using multiples of medians. Prediction analyses were performed across the following patient strata: all subjects and the body mass index classes of <25, 25 to <30, and ≥30 kg/m². Adjusted median levels were compared between cases and controls and between each body mass index class group. Odds ratios and 95% confidence intervals were calculated at the mean ±1 standard deviation to gauge clinical prediction merits.
Results: The levels of 13 metabolites were associated with preterm preeclampsia in the entire study population (P<.05) with particularly significant (P<.01) associations found for 6 of them, namely, 2-hydroxy-(2/3)-methylbutyric acid, 25-hydroxyvitamin D3, 2-hydroxybutyric acid, alanine, dodecanoylcarnitine, and 1-(1Z-octadecenyl)-2-oleoyl-sn-glycero-3-phosphocholine. Fold changes in 7 amino acid ratios, all involving glutamine or ornithine, were also significantly different between cases and controls (P<.01). The predictive performance of some metabolites and ratios differed according to body mass index classification; for example, ornithine (P<.001) and several ornithine-related ratios (P<.0001 to P<.01) were only strongly associated with preterm preeclampsia in the body mass index of <25 kg/m² group, whereas dodecanoylcarnitine and 3 glutamine ratios were particularly predictive in the body mass index of ≥30 kg/m² group (P<.01).
Conclusion: Single metabolites and ratios of amino acids related to arginine bioavailability and nitric oxide synthase pathways were associated with preterm preeclampsia risk at 11 to 13 weeks of gestation. Differential prediction was observed according to body mass index classes, supporting the existence of distinct maternal risk profiles. Future studies in preeclampsia prediction should account for the possibility of different maternal risk profiles to improve etiologic and prognostic understanding and, ultimately, clinical utility of screening tests.