Metabolomic prediction of severe maternal and newborn complications in preeclampsia.

INTRODUCTION: Preeclampsia (PreE) remains a major source of maternal and newborn complications. Prenatal prediction of these complications could significantly improve pregnancy management. OBJECTIVES: Using metabolomic analysis we investigated the prenatal prediction of maternal and newborn complications in early and late PreE and investigated the pathogenesis of such complications. METHODS: Serum samples from 76 cases of PreE (36 early-onset and 40 late-onset), and 40 unaffected controls were collected. Direct Injection Liquid Chromatography-Mass Spectrometry combined with Nuclear Magnetic Resonance (NMR) spectroscopy was performed. Logistic regression analysis was used to generate models for prediction of adverse maternal and neonatal outcomes in patients with PreE. Metabolite set enrichment analysis (MSEA) was used to identify the most dysregulated metabolites and pathways in PreE. RESULTS: Forty-three metabolites were significantly altered (p < 0.05) in PreE cases with maternal complications and 162 metabolites were altered in PreE cases with newborn adverse outcomes. The top metabolite prediction model achieved an area under the receiver operating characteristic curve (AUC) = 0.806 (0.660-0.952) for predicting adverse maternal outcomes in early-onset PreE, while the AUC for late-onset PreE was 0.843 (0.712-0.974). For the prediction of adverse newborn outcomes, regression models achieved an AUC = 0.828 (0.674-0.982) in early-onset PreE and 0.911 (0.828-0.994) in late-onset PreE. Profound alterations of lipid metabolism were associated with adverse outcomes. CONCLUSION: Prenatal metabolomic markers achieved robust prediction, superior to conventional markers for the prediction of adverse maternal and newborn outcomes in patients with PreE. We report for the first-time the prediction and metabolomic basis of adverse maternal and newborn outcomes in patients with PreE.

Bile salt hydrolase acyltransferase activity expands bile acid diversity.

Bile acids (BAs) are steroid detergents in bile that contribute to the absorption of fats and fat-soluble vitamins while shaping the gut microbiome because of their antimicrobial properties1-4. Here we identify the enzyme responsible for a mechanism of BA metabolism by the gut microbiota involving amino acid conjugation to the acyl-site of BAs, thus producing a diverse suite of microbially conjugated bile acids (MCBAs). We show that this transformation is mediated by acyltransferase activity of bile salt hydrolase (bile salt hydrolase/transferase, BSH/T). Clostridium perfringens BSH/T rapidly performed acyl transfer when provided various amino acids and taurocholate, glycocholate or cholate, with an optimum at pH 5.3. Amino acid conjugation by C. perfringens BSH/T was diverse, including all proteinaceous amino acids except proline and aspartate. MCBA production was widespread among gut bacteria, with strain-specific amino acid use. Species with similar BSH/T amino acid sequences had similar conjugation profiles and several bsh/t alleles correlated with increased conjugation diversity. Tertiary structure mapping of BSH/T followed by mutagenesis experiments showed that active site structure affects amino acid selectivity. These MCBA products had antimicrobial properties, where greater amino acid hydrophobicity showed greater antimicrobial activity. Inhibitory concentrations of MCBAs reached those measured natively in the mammalian gut. MCBAs fed to mice entered enterohepatic circulation, in which liver and gallbladder concentrations varied depending on the conjugated amino acid. Quantifying MCBAs in human faecal samples showed that they reach concentrations equal to or greater than secondary and primary BAs and were reduced after bariatric surgery, thus supporting MCBAs as a significant component of the BA pool that can be altered by changes in gastrointestinal physiology. In conclusion, the inherent acyltransferase activity of BSH/T greatly diversifies BA chemistry, creating a set of previously underappreciated metabolites with the potential to affect the microbiome and human health.

Non-targeted LC-MS/MS metabolomic profiling of human plasma uncovers a novel Mediterranean diet biomarker panel.

INTRODUCTION: Consumption of a Mediterranean diet (MD) has established health benefits, and the identification of novel biomarkers could enable objective monitoring of dietary pattern adherence. OBJECTIVES: The present investigation performed untargeted metabolomics on blood plasma from a controlled study of MD adherence, to identify novel blood-based metabolite biomarkers associated with the MD pattern, and to build a logistic regression model that could be used to characterise MD adherence. METHODS: A hundred and thirty-five plasma samples from n = 58 patients collected at different time points were available. Using a 14-point scale MD Score (MDS) subjects were divided into 'high' or 'low' MDS adherence groups and liquid chromatography-mass spectrometry (LC-MS/MS) was applied for analysis. RESULTS: The strongest association with MDS was pectenotoxin 2 seco acid (r = 0.53; ROC = 0.78), a non-toxic marine xenobiotic metabolite. Several lipids were useful biomarkers including eicosapentaenoic acid, the structurally related lysophospholipid (20:5(5Z,8Z,11Z,14Z,17Z)/0:0), a phosphatidylcholine (P-18:1(9Z)/16:0) and also xi-8-hydroxyhexadecanedioic acid. Two metabolites negatively correlated with MDS, these were the monoacylglycerides (0:0/16:1(9Z)/0:0) and (0:0/20:3(5Z,8Z,11Z)/0:0). By stepwise elimination we selected a panel of 3 highly discriminatory metabolites and developed a linear regression model which identified 'high MDS' individuals with high sensitivity and specificity [AUC (95% CI) 0.83 (0.76-0.97)]. CONCLUSION: Our study highlights the utility of metabolomics as an approach for developing novel panels of dietary biomarkers. Quantitative profiling of these metabolites is required to validate their utility for evaluating dietary adherence.

Metabolomics: An Emerging "Omics" Platform for Systems Biology and Its Implications for Huntington Disease Research.

Huntington's disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.

The longitudinal biochemical profiling of TBI in a drop weight model of TBI.

Traumatic brain injury (TBI) is a major cause of mortality and disability worldwide, particularly among individuals under the age of 45. It is a complex, and heterogeneous disease with a multifaceted pathophysiology that remains to be elucidated. Metabolomics has the potential to identify metabolic pathways and unique biochemical profiles associated with TBI. Herein, we employed a longitudinal metabolomics approach to study TBI in a weight drop mouse model to reveal metabolic changes associated with TBI pathogenesis, severity, and secondary injury. Using proton nuclear magnetic resonance (1H NMR) spectroscopy, we biochemically profiled post-mortem brain from mice that suffered mild TBI (N = 25; 13 male and 12 female), severe TBI (N = 24; 11 male and 13 female) and sham controls (N = 16; 11 male and 5 female) at baseline, day 1 and day 7 following the injury. 1H NMR-based metabolomics, in combination with bioinformatic analyses, highlights a few significant metabolites associated with TBI severity and perturbed metabolism related to the injury. We report that the concentrations of taurine, creatinine, adenine, dimethylamine, histidine, N-Acetyl aspartate, and glucose 1-phosphate are all associated with TBI severity. Longitudinal metabolic observation of brain tissue revealed that mild TBI and severe TBI lead distinct metabolic profile changes. A multi-class model was able to classify the severity of injury as well as time after TBI with estimated 86% accuracy. Further, we identified a high degree of correlation between respective hemisphere metabolic profiles (r > 0.84, p < 0.05, Pearson correlation). This study highlights the metabolic changes associated with underlying TBI severity and secondary injury. While comprehensive, future studies should investigate whether: (a) the biochemical pathways highlighted here are recapitulated in the brain of TBI sufferers and (b) if the panel of biomarkers are also as effective in less invasively harvested biomatrices, for objective and rapid identification of TBI severity and prognosis.

Integrative Analysis Unveils the Correlation of Aminoacyl-tRNA Biosynthesis Metabolites with the Methylation of the SEPSECS Gene in Huntington's Disease Brain Tissue.

The impact of environmental factors on epigenetic changes is well established, and cellular function is determined not only by the genome but also by interacting partners such as metabolites. Given the significant impact of metabolism on disease progression, exploring the interaction between the metabolome and epigenome may offer new insights into Huntington's disease (HD) diagnosis and treatment. Using fourteen post-mortem HD cases and fourteen control subjects, we performed metabolomic profiling of human postmortem brain tissue (striatum and frontal lobe), and we performed DNA methylome profiling using the same frontal lobe tissue. Along with finding several perturbed metabolites and differentially methylated loci, Aminoacyl-tRNA biosynthesis (adj p-value = 0.0098) was the most significantly perturbed metabolic pathway with which two CpGs of the SEPSECS gene were correlated. This study improves our understanding of molecular biomarker connections and, importantly, increases our knowledge of metabolic alterations driving HD progression.

Induced endometrial inflammation compromises conceptus development in dairy cattle†.

Endometrial inflammation is associated with reduced pregnancy per artificial insemination (AI) and increased pregnancy loss in cows. It was hypothesized that induced endometritis alters histotroph composition and induces inflammatory signatures on conceptus that compromise development. In Experiment 1, lactating cows were assigned to control (CON; n = 23) or to an intrauterine infusion of Escherichia coli and Trueperella pyogenes (ENDO; n = 34) to induce endometritis. Cows received AI 26 days after treatment, and the uterine fluid and conceptuses were collected on day 16 after AI. In Experiment 2, Holstein heifers were assigned to CON (n = 14) or ENDO (n = 14). An embryo was transferred on day 7 of the estrous cycle, and uterine fluid and conceptuses were recovered on day 16. Composition of histotroph and trophoblast and embryonic disc gene expression were assessed. Bacterial-induced endometritis in lactating cows altered histotroph composition and pathways linked to phospholipid synthesis, cellular energy production, and the Warburg effect. Also, ENDO reduced conceptus length in cows and altered expression of genes involved in pathogen recognition, nutrient uptake, cell growth, choline metabolism, and conceptus signaling needed for maternal recognition of pregnancy. The impact of ENDO was lesser on conceptuses from heifers receiving embryo transfer; however, the affected genes and associated pathways involved restricted growth and increased immune response similar to the observed responses to ENDO in conceptuses from lactating cows. Bacterial-induced endometrial inflammation altered histotroph composition, reduced conceptus growth, and caused embryonic cells to activate survival rather than anabolic pathways that could compromise development.

Urinary Cytokines as Potential Biomarkers of Mild Cognitive Impairment and Alzheimer's Disease: A Pilot Study.

Background: Alzheimer's disease (AD) is the most common form of dementia, accounting for 80% of all cases. Mild cognitive impairment (MCI) is a transitional state between normal aging and AD. Early detection is crucial, as irreversible brain damage occurs before symptoms manifest. Objective: This study aimed to identify potential biomarkers for early detection of AD by analyzing urinary cytokine concentrations. We investigated 37 cytokines in AD, MCI, and cognitively normal individuals (NC), assessing their associations with AD development. Methods: Urinary cytokine concentrations were measured in AD (n = 25), MCI (n = 25), and NC (n = 26) patients. IL6ST and MMP-2 levels were compared between AD and NC, while TNFRSF8, IL6ST, and IL-19 were assessed in AD versus MCI. Diagnostic models distinguished AD from NC, and in-silico analysis explored molecular mechanisms related to AD. Results: Significant perturbations in IL6ST and MMP-2 concentrations were observed in AD urine compared to NC, suggesting their potential as biomarkers. TNFRSF8, IL6ST, and IL-19 differed significantly between AD and MCI, implicating them in disease progression. Diagnostic models exhibited promising performance (AUC: 0.59-0.79, sensitivity: 0.72-0.80, specificity: 0.56-0.78) in distinguishing AD from NC. In-silico analysis revealed molecular insights, including relevant non-coding RNAs, microRNAs, and transcription factors. Conclusion: This study establishes significant associations between urinary cytokine concentrations and AD and MCI. IL6ST, MMP-2, TNFRSF8, IL6ST, and IL-19 emerge as potential biomarkers for early detection of AD. In-silico analysis enhances understanding of molecular mechanisms in AD. Further validation and exploration of these biomarkers in larger cohorts are warranted to assess their clinical utility.

Pre-estrus progesterone does not affect post-estrus luminal metabolome in cross-bred beef cows.

In brief: The concentration of progesterone through the estrous cycle modulates uterine function to affect the luminal metabolome. This paper reports that the dynamic changes in the bovine uterine luminal metabolome during diestrus are independent of the concentration of progesterone in the previous cycle. Abstract: In cattle, the concentration of sex steroids modulates uterine function, which is reflected in the composition of the luminal metabolome. Ultimately, the uterine luminal metabolome influences embryonic growth and development. Our objectives were (i) to compare the luminal metabolome 4, 7, and 14 days after estrus of cows that were exposed to greater (HP4; n = 16) vs lower (LP4; n = 24) concentrations of progesterone before displaying estrus and ovulating spontaneously and (ii) to identify changes in the luminal concentration of metabolites across these time points. Luminal epithelial cells and fluid were collected using a cytology brush, and gene expression and metabolite concentrations were assessed by RNAseq and targeted mass spectrometry, respectively. Metabolome profile was similar between treatments within each of days 4, 7, and 14 (false discovery rate (FDR): ≥ 0.1). Concentrations of 53 metabolites changed, independent of treatment, across the diestrus. Metabolites were mostly lipids (40 out 53) and the greatest concentrations were at day 14 (FDR: ≤ 0.1). On day 7, the concentration of putrescine and the gene expression of ODC1, PAOX, SLC3A2, and SAT1 increased (P ≤ 0.05). On day 14, the concentration of 3 ceramides, 4 glucosylceramides, and 12 sphingomyelins and the expression of SGMS2 were increased, in addition to the concentration of choline and 20 phosphatidylcholines. Collectively, the post-estrus concentration of luminal metabolites changed dynamically, independent of the concentration of sex steroids on the previous cycle, and the greatest magnitude changes were on day 14 when lipid metabolism was the most enriched pathway.

Association of Metabolomic Biomarkers with Sleeve Gastrectomy Weight Loss Outcomes.

This prospective observational study aimed to evaluate the association of metabolomic alterations with weight loss outcomes following sleeve gastrectomy (SG). We evaluated the metabolomic profile of serum and feces prior to SG and three months post-SG, along with weight loss outcomes in 45 adults with obesity. The percent total weight loss for the highest versus the lowest weight loss tertiles (T3 vs. T1) was 17.0 ± 1.3% and 11.1 ± 0.8%, p < 0.001. Serum metabolite alterations specific to T3 at three months included a decrease in methionine sulfoxide concentration as well as alterations to tryptophan and methionine metabolism (p < 0.03). Fecal metabolite changes specific to T3 included a decrease in taurine concentration and perturbations to arachidonic acid metabolism, and taurine and hypotaurine metabolism (p < 0.002). Preoperative metabolites were found to be highly predictive of weight loss outcomes in machine learning algorithms, with an average area under the curve of 94.6% for serum and 93.4% for feces. This comprehensive metabolomics analysis of weight loss outcome differences post-SG highlights specific metabolic alterations as well as machine learning algorithms predictive of weight loss. These findings could contribute to the development of novel therapeutic targets to enhance weight loss outcomes after SG.

Fetal effects of mild maternal COVID-19 infection: metabolomic profiling of cord blood.

INTRODUCTION: The impact of maternal coronavirus disease 2019 (COVID-19) infection on fetal health remains to be precisely characterized. OBJECTIVES: Using metabolomic profiling of newborn umbilical cord blood, we aimed to investigate the potential fetal biological consequences of maternal COVID-19 infection. METHODS: Cord blood plasma samples from 23 mild COVID-19 cases (mother infected/newborn negative) and 23 gestational age-matched controls were analyzed using nuclear magnetic spectroscopy and liquid chromatography coupled with mass spectrometry. Metabolite set enrichment analysis (MSEA) was used to evaluate altered biochemical pathways due to COVID-19 intrauterine exposure. Logistic regression models were developed using metabolites to predict intrauterine exposure. RESULTS: Significant concentration differences between groups (p-value < 0.05) were observed in 19 metabolites. Elevated levels of glucocorticoids, pyruvate, lactate, purine metabolites, phenylalanine, and branched-chain amino acids of valine and isoleucine were discovered in cases while ceramide subclasses were decreased. The top metabolite model including cortisol and ceramide (d18:1/23:0) achieved an Area under the Receiver Operating Characteristics curve (95% CI) = 0.841 (0.725-0.957) for detecting fetal exposure to maternal COVID-19 infection. MSEA highlighted steroidogenesis, pyruvate metabolism, gluconeogenesis, and the Warburg effect as the major perturbed metabolic pathways (p-value < 0.05). These changes indicate fetal increased oxidative metabolism, hyperinsulinemia, and inflammatory response. CONCLUSION: We present fetal biochemical changes related to intrauterine inflammation and altered energy metabolism in cases of mild maternal COVID-19 infection despite the absence of viral infection. Elucidation of the long-term consequences of these findings is imperative considering the large number of exposures in the population.

Alzheimer's Precision Neurology: Epigenetics of Cytochrome P450 Genes in Circulating Cell-Free DNA for Disease Prediction and Mechanism.

Precision neurology combines high-throughput technologies and statistical modeling to identify novel disease pathways and predictive biomarkers in Alzheimer's disease (AD). Brain cytochrome P450 (CYP) genes are major regulators of cholesterol, sex hormone, and xenobiotic metabolism, and they could play important roles in neurodegenerative disorders. Increasing evidence suggests that epigenetic factors contribute to AD development. We evaluated cytosine ('CpG')-based DNA methylation changes in AD using circulating cell-free DNA (cfDNA), to which neuronal cells are known to contribute. We investigated CYP-based mechanisms for AD pathogenesis and epigenetic biomarkers for disease detection. We performed a case-control study using 25 patients with AD and 23 cognitively healthy controls using the cfDNA of CYP genes. We performed a logistic regression analysis using the MetaboAnalyst software computer program and a molecular pathway analysis based on epigenetically altered CYP genes using the Cytoscape program. We identified 130 significantly (false discovery rate correction q-value < 0.05) differentially methylated CpG sites within the CYP genes. The top two differentially methylated genes identified were CYP51A1 and CYP2S1. The significant molecular pathways that were perturbed in AD cfDNA were (i) androgen and estrogen biosynthesis and metabolism, (ii) C21 steroid hormone biosynthesis and metabolism, and (iii) arachidonic acid metabolism. Existing evidence suggests a potential role of each of these biochemical pathways in AD pathogenesis. Next, we randomly divided the study group into discovery and validation sub-sets, each consisting of patients with AD and control patients. Regression models for AD prediction based on CYP CpG methylation markers were developed in the discovery or training group and tested in the independent validation group. The CYP biomarkers achieved a high predictive accuracy. After a 10-fold cross-validation, the combination of cg17852385/cg23101118 + cg14355428/cg22536554 achieved an AUC (95% CI) of 0.928 (0.787~1.00), with 100% sensitivity and 92.3% specificity for AD detection in the discovery group. The performance remained high in the independent validation or test group, achieving an AUC (95% CI) of 0.942 (0.905~0.979) with a 90% sensitivity and specificity. Our findings suggest that the epigenetic modification of CYP genes may play an important role in AD pathogenesis and that circulating CYP-based cfDNA biomarkers have the potential to accurately and non-invasively detect AD.

Dementia with Lewy bodies post-mortem brains reveal differentially methylated CpG sites with biomarker potential.

Dementia with Lewy bodies (DLB) is a common form of dementia with known genetic and environmental interactions. However, the underlying epigenetic mechanisms which reflect these gene-environment interactions are poorly studied. Herein, we measure genome-wide DNA methylation profiles of post-mortem brain tissue (Broadmann area 7) from 15 pathologically confirmed DLB brains and compare them with 16 cognitively normal controls using Illumina MethylationEPIC arrays. We identify 17 significantly differentially methylated CpGs (DMCs) and 17 differentially methylated regions (DMRs) between the groups. The DMCs are mainly located at the CpG islands, promoter and first exon regions. Genes associated with the DMCs are linked to "Parkinson's disease" and "metabolic pathway", as well as the diseases of "severe intellectual disability" and "mood disorders". Overall, our study highlights previously unreported DMCs offering insights into DLB pathogenesis with the possibility that some of these could be used as biomarkers of DLB in the future.

Artificial Intelligence and Circulating Cell-Free DNA Methylation Profiling: Mechanism and Detection of Alzheimer's Disease.

Background: Despite extensive efforts, significant gaps remain in our understanding of Alzheimer's disease (AD) pathophysiology. Novel approaches using circulating cell-free DNA (cfDNA) have the potential to revolutionize our understanding of neurodegenerative disorders. Methods: We performed DNA methylation profiling of cfDNA from AD patients and compared them to cognitively normal controls. Six Artificial Intelligence (AI) platforms were utilized for the diagnosis of AD while enrichment analysis was used to elucidate the pathogenesis of AD. Results: A total of 3684 CpGs were significantly (adj. p-value < 0.05) differentially methylated in AD versus controls. All six AI algorithms achieved high predictive accuracy (AUC = 0.949−0.998) in an independent test group. As an example, Deep Learning (DL) achieved an AUC (95% CI) = 0.99 (0.95−1.0), with 94.5% sensitivity and specificity. Conclusion: We describe numerous epigenetically altered genes which were previously reported to be differentially expressed in the brain of AD sufferers. Genes identified by AI to be the best predictors of AD were either known to be expressed in the brain or have been previously linked to AD. We highlight enrichment in the Calcium signaling pathway, Glutamatergic synapse, Hedgehog signaling pathway, Axon guidance and Olfactory transduction in AD sufferers. To the best of our knowledge, this is the first reported genome-wide DNA methylation study using cfDNA to detect AD.

Methylated Cytochrome P450 and the Solute Carrier Family of Genes Correlate With Perturbations in Bile Acid Metabolism in Parkinson's Disease.

Parkinson's disease (PD) is second most prevalent neurodegenerative disorder following Alzheimer's disease. Parkinson's disease is hypothesized to be caused by a multifaceted interplay between genetic and environmental factors. Herein, and for the first time, we describe the integration of metabolomics and epigenetics (genome-wide DNA methylation; epimetabolomics) to profile the frontal lobe from people who died from PD and compared them with age-, and sex-matched controls. We identified 48 metabolites to be at significantly different concentrations (FDR q < 0.05), 4,313 differentially methylated sites [5'-C-phosphate-G-3' (CpGs)] (FDR q < 0.05) and increased DNA methylation age in the primary motor cortex of people who died from PD. We identified Primary bile acid biosynthesis as the major biochemical pathway to be perturbed in the frontal lobe of PD sufferers, and the metabolite taurine (p-value = 5.91E-06) as being positively correlated with CpG cg14286187 (SLC25A27; CYP39A1) (FDR q = 0.002), highlighting previously unreported biochemical changes associated with PD pathogenesis. In this novel multi-omics study, we identify regulatory mechanisms which we believe warrant future translational investigation and central biomarkers of PD which require further validation in more accessible biomatrices.

Metabolomic identification of placental alterations in fetal growth restriction.

INTRODUCTION: Fetal growth restriction (FGR), viz., birth weight <10th percentile is a common pregnancy complication which increases the risk of adverse fetal and newborn outcomes. The placenta is the key organ for fetal growth as it controls oxygen and nutrient availability. This study aims to elucidate the mechanisms of and identify putative placental biomarkers for FGR using high-resolution metabolomics. METHODS: Placenta samples from 19 FGR cases and 30 controls were analyzed using proton magnetic resonance (1H NMR) spectroscopy and direct flow injection mass spectrometry with reverse-phase liquid-chromatography mass spectrometry (DI-LC-MS/MS). Significant concentration differences (p-value <.05) in 179 of the 220 metabolites were measured. RESULTS: Of the 179 metabolites, 176 (98.3%) had reduced placental levels in FGR cases. The best performing metabolite model: 3-hydroxybutyrate, glycine and PCaaC42:0 achieved an AUC (95% CI) = 0.912 (0.814-1.000) with a sensitivity of 86.7% and specificity of 84.2% for FGR detection. Metabolite set enrichment analysis (MSEA) revealed significant (p < .05) perturbation of multiple placental metabolite pathways including urea metabolism, ammonia recycling, porphyrin metabolism, bile acid biosynthesis, galactose metabolism and perturbed protein biosynthesis. CONCLUSION: The placental metabolic pathway analysis revealed abnormalities that are consistent with fetal hepatic dysfunction in FGR. Near global reduction of metabolite concentrations was found in the placenta from FGR cases and metabolites demonstrated excellent diagnostic accuracy for FGR detection.

Urine metabolomic biomarkers for prediction of isolated fetal congenital heart defect.

OBJECTIVE: To identify maternal second and third trimester urine metabolomic biomarkers for the detection of fetal congenital heart defects (CHDs). STUDY DESIGN: This was a prospective study. Metabolomic analysis of randomly collected maternal urine was performed, comparing pregnancies with isolated, non-syndromic CHDs versus unaffected controls. Mass spectrometry (liquid chromatography and direct injection and tandem mass spectrometry, LC-MS-MS) as well as nuclear magnetic resonance spectrometry, 1H NMR, were used to perform the analyses between 14 0/7 and 37 0/7 weeks gestation. A total of 36 CHD cases and 41 controls were compared. Predictive algorithms using urine markers alone or combined with, clinical and ultrasound (US) (four-chamber view) predictors were developed and compared. RESULTS: A total of 222 metabolites were identified, of which 16 were overlapping between the two platforms. Twenty-three metabolite concentrations were found in significantly altered in CHD gestations on univariate analysis. The concentration of methionine was most significantly altered. A predictive algorithm combining metabolites (histamine, choline, glucose, formate, methionine, and carnitine) plus US four-chamber view achieved an AUC = 0.894; 95% CI, 0814-0.973 with a sensitivity of 83.8% and specificity of 87.8%. Enrichment pathway analysis identified several lipid related pathways that are dysregulated in CHD, including phospholipid biosynthesis, phosphatidylcholine biosynthesis, phosphatidylethanolamine biosynthesis, and fatty acid metabolism. This could be consistent with the increased risk of CHD in diabetic pregnancies. CONCLUSIONS: We report a novel, noninvasive approach, based on the analysis of maternal urine for isolated CHD detection. Further, the dysregulation of lipid- and folate metabolism appears to support prior data on the mechanism of CHD.

Lipid Profiling of Alzheimer's Disease Brain Highlights Enrichment in Glycerol(phospho)lipid, and Sphingolipid Metabolism.

Alzheimer's disease (AD) is reported to be closely linked with abnormal lipid metabolism. To gain a more comprehensive understanding of what causes AD and its subsequent development, we profiled the lipidome of postmortem (PM) human brains (neocortex) of people with a range of AD pathology (Braak 0-6). Using high-resolution mass spectrometry, we employed a semi-targeted, fully quantitative lipidomics profiling method (Lipidyzer) to compare the biochemical profiles of brain tissues from persons with mild AD (n = 15) and severe AD (AD; n = 16), and compared them with age-matched, cognitively normal controls (n = 16). Univariate analysis revealed that the concentrations of 420 lipid metabolites significantly (p < 0.05; q < 0.05) differed between AD and controls. A total of 49 lipid metabolites differed between mild AD and controls, and 439 differed between severe AD and mild AD. Interestingly, 13 different subclasses of lipids were significantly perturbed, including neutral lipids, glycerolipids, glycerophospholipids, and sphingolipids. Diacylglycerol (DAG) (14:0/14:0), triacylglycerol (TAG) (58:10/FA20:5), and TAG (48:4/FA18:3) were the most notably altered lipids when AD and control brains were compared (p < 0.05). When we compare mild AD and control brains, phosphatidylethanolamine (PE) (p-18:0/18:1), phosphatidylserine (PS) (18:1/18:2), and PS (14:0/22:6) differed the most (p < 0.05). PE (p-18:0/18:1), DAG (14:0/14:0), and PS (18:1/20:4) were identified as the most significantly perturbed lipids when AD and mild AD brains were compared (p < 0.05). Our analysis provides the most extensive lipid profiling yet undertaken in AD brain tissue and reveals the cumulative perturbation of several lipid pathways with progressive disease pathology. Lipidomics has considerable potential for studying AD etiology and identifying early diagnostic biomarkers.

The relevance of the LEADS framework during the COVID-19 pandemic.

COVID-19 has created a unique context for the practice of leadership in healthcare. Given the significant use of the LEADS in a Caring Environment capabilities framework (LEADS) in Canada's health system, it is important to document the relevancy of LEADS. The authors reviewed literature, conducted research, and reflected on their own experience to identify leadership practices during the pandemic and related them to LEADS. Findings are presented in three sections: Hindsight (before), Insight (during), and Foresight (post). We profile the issue of improving long-term Care to provide an example of how LEADS can be applied in crisis times. Our analysis suggests that while LEADS appears to specify the leadership capabilities needed, it requires adaptation to context. The vision Canada has for healthcare will dictate how LEADS will be used as a guide to leadership practice in the current context or to shape a bolder vision of healthcare's future.

Artificial intelligence and leukocyte epigenomics: Evaluation and prediction of late-onset Alzheimer's disease.

We evaluated the utility of leucocyte epigenomic-biomarkers for Alzheimer's Disease (AD) detection and elucidates its molecular pathogeneses. Genome-wide DNA methylation analysis was performed using the Infinium MethylationEPIC BeadChip array in 24 late-onset AD (LOAD) and 24 cognitively healthy subjects. Data were analyzed using six Artificial Intelligence (AI) methodologies including Deep Learning (DL) followed by Ingenuity Pathway Analysis (IPA) was used for AD prediction. We identified 152 significantly (FDR p<0.05) differentially methylated intragenic CpGs in 171 distinct genes in AD patients compared to controls. All AI platforms accurately predicted AD with AUCs ≥0.93 using 283,143 intragenic and 244,246 intergenic/extragenic CpGs. DL had an AUC = 0.99 using intragenic CpGs, with both sensitivity and specificity being 97%. High AD prediction was also achieved using intergenic/extragenic CpG sites (DL significance value being AUC = 0.99 with 97% sensitivity and specificity). Epigenetically altered genes included CR1L & CTSV (abnormal morphology of cerebral cortex), S1PR1 (CNS inflammation), and LTB4R (inflammatory response). These genes have been previously linked with AD and dementia. The differentially methylated genes CTSV & PRMT5 (ventricular hypertrophy and dilation) are linked to cardiovascular disease and of interest given the known association between impaired cerebral blood flow, cardiovascular disease, and AD. We report a novel, minimally invasive approach using peripheral blood leucocyte epigenomics, and AI analysis to detect AD and elucidate its pathogenesis.