New Advances in Rapid Pretreatment for Small Dense LDL Cholesterol Measurement Using Shear Horizontal Surface Acoustic Wave (SH-SAW) Technology.

Atherosclerosis is an inflammatory disease of the arteries associated with alterations in lipid and other metabolism and is a major cause of cardiovascular disease (CVD). LDL consists of several subclasses with different sizes, densities, and physicochemical compositions. Small dense LDL (sd-LDL) is a subclass of LDL. There is growing evidence that sd-LDL-C is associated with CVD risk, metabolic dysregulation, and several pathophysiological processes. In this study, we present a straightforward membrane device filtration method that can be performed with simple laboratory methods to directly determine sd-LDL in serum without the need for specialized equipment. The method consists of three steps: first, the precipitation of lipoproteins with magnesium harpin; second, the collection of effluent from a 100 nm filter; and third, the quantification of sd-LDL-ApoB in the effluent with an SH-SAW biosensor. There was a good correlation between ApoB values obtained using the centrifugation (y = 1.0411x + 12.96, r = 0.82, n = 20) and filtration (y = 1.0633x + 15.13, r = 0.88, n = 20) methods and commercially available sd-LDL-C assay values. In addition to the filtrate method, there was also a close correlation between sd-LDL-C and ELISA assay values (y = 1.0483x - 4489, r = 0.88, n = 20). The filtration treatment method also showed a high correlation with LDL subfractions and NMR spectra ApoB measurements (y = 2.4846x + 4.637, r = 0.89, n = 20). The presence of sd-LDL-ApoB in the effluent was also confirmed by ELISA assay. These results suggest that this filtration method is a simple and promising pretreatment for use with the SH-SAW biosensor as a rapid in vitro diagnostic (IVD) method for predicting sd-LDL concentrations. Overall, we propose a very sensitive and specific SH-SAW biosensor with the ApoB antibody in its sensitive region to monitor sd-LDL levels by employing a simple delay-time phase shifted SH-SAW device. In conclusion, based on the demonstration of our study, the SH-SAW biosensor could be a strong candidate for the future measurement of sd-LDL.

NMR-Based Analysis of Plasma Lipoprotein Subclass and Lipid Composition Demonstrate the Different Dietary Effects in ApoE-Deficient Mice.

Plasma lipid levels are commonly measured using traditional methods such as triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and cholesterol (CH). However, the use of newer technologies, such as nuclear magnetic resonance (NMR) with post-analysis platforms, has made it easier to assess lipoprotein profiles in research. In this study involving ApoE-deficient mice that were fed high-fat diets, significant changes were observed in TG, CH, free cholesterol (FC), and phospholipid (PL) levels within the LDL fraction. The varied proportions of TG in wild-type mice and CH, FC, and PL in ApoE-/- mice were strikingly different in very low-density lipoproteins (VLDL), LDL, intermediate-density lipoprotein (IDL), and HDL. This comprehensive analysis expands our understanding of lipoprotein subfractions and the impacts of the APOE protein and high-fat diet in mouse models. The new testing method allows for a complete assessment of plasma lipids and their correlation with genetic background and diet in mice.

Exploring the aging process of cognitively healthy adults by analyzing cerebrospinal fluid metabolomics using liquid chromatography-tandem mass spectrometry.

BACKGROUND: During biological aging, significant metabolic dysregulation in the central nervous system may lead to cognitive decline and neurodegeneration. However, the metabolomics of the aging process in cerebrospinal fluid (CSF) has not been thoroughly explored. METHODS: In this cohort study of CSF metabolomics using liquid chromatography-mass spectrometry (LC-MS), fasting CSF samples collected from 92 cognitively unimpaired adults aged 20-87 years without obesity or diabetes were analyzed. RESULTS: We identified 37 metabolites in these CSF samples with significant positive correlations with aging, including cysteine, pantothenic acid, 5-hydroxyindoleacetic acid (5-HIAA), aspartic acid, and glutamate; and two metabolites with negative correlations, asparagine and glycerophosphocholine. The combined alterations of asparagine, cysteine, glycerophosphocholine, pantothenic acid, sucrose, and 5-HIAA showed a superior correlation with aging (AUC = 0.982). These age-correlated changes in CSF metabolites might reflect blood-brain barrier breakdown, neuroinflammation, and mitochondrial dysfunction in the aging brain. We also found sex differences in CSF metabolites with higher levels of taurine and 5-HIAA in women using propensity-matched comparison. CONCLUSIONS: Our LC-MS metabolomics of the aging process in a Taiwanese population revealed several significantly altered CSF metabolites during aging and between the sexes. These metabolic alterations in CSF might provide clues for healthy brain aging and deserve further exploration.

Plasma acylcarnitine in elderly Taiwanese: as biomarkers of possible sarcopenia and sarcopenia.

BACKGROUND: Sarcopenia is defined as the disease of muscle loss and dysfunction. The prevalence of sarcopenia is strongly age-dependent. It could bring about disability, hospitalization, and mortality. The purpose of this study was to identify plasma metabolites associated with possible sarcopenia and muscle function to improve disease monitoring and understand the mechanism of muscle strength and function decline. METHODS: The participants were a group of healthy older adult who live in retirement homes in Asia (Taiwan) and can manage their daily lives without assistance. The participants were enrolled and divided into four groups: control (Con, n = 57); low physical function (LPF, n = 104); sarcopenia (S, n = 63); and severe sarcopenia (SS, n = 65) according to Asian countries that used Asian Working Group for Sarcopenia (AWGS) criteria. The plasma metabolites were used and the results were calculated as the difference between the control and other groups. RESULTS: Clinical parameters, age, gender, body mass index (BMI), hand grip strength (HGS), gait speed (GS), blood urea nitrogen (BUN), hemoglobin, and hematocrit were significantly different between the control and LPF groups. Metabolite patterns of LPF, S, and SS were explored in our study. Plasma kynurenine (KYN) and acylcarnitines (C0, C4, C6, and C18:1-OH) were identified with higher concentrations in older Taiwanese adults with possible sarcopenia and S compared to the Con group. After multivariable adjustment, the data indicate that age, BMI, and butyrylcarnitine (C4) are more important factors to identify individuals with low physical function and sarcopenia. CONCLUSION: This metabolomic study raises the importance of acylcarnitines on muscle mass and function. It suggests that age, BMI, BUN, KYN, and C4/Cr can be important evaluation markers for LPF (AUC: 0.766), S (AUC: 0.787), and SS (AUC: 0.919).

Metabolomic differences of exhaled breath condensate among children with and without asthma.

BACKGROUND: There remains an unmet need in objective tests for diagnosing asthma in children. The objective of this study was to investigate the potential of metabolomic profiles of exhaled breath condensate (EBC) to discriminate stable asthma in Asian children in the community. METHODS: One hundred and sixty-five Asian children (92 stable asthma and 73 non-asthmatic controls) participating in a population-based cohort were enrolled and divided into training and validation sets. Nuclear magnetic resonance-based metabolomic profiles of EBC samples were analyzed by using orthogonal partial least squares discriminant analysis. RESULTS: EBC metabolomic signature (lactate, formate, butyrate, and isobutyrate) had an area under the receiver operator characteristic curve (AUC) of 0.826 in discriminating children with and without asthma in the training set, which significantly outperformed FeNO (AUC = 0.574; P < .001) and FEV1 /FVC % predicted (AUC = 0.569; P < .001). The AUC for EBC metabolomic signature was 0.745 in the validation set, which was slightly but not significantly lower than in the testing set (P = .282). We further extrapolated two potentially involved metabolic pathways, including pyruvate (P = 1.67 × 10-3 ; impact: 0.14) and methane (P = 1.89 × 10-3 ; impact: 0.15), as the most likely divergent metabolisms between children with and without asthma. CONCLUSION: This study provided evidence supporting the role of EBC metabolomic signature to discriminate stable asthma in Asian children in the community, with a discriminative property outperforming conventional clinical tests such as FeNO or spirometry.

The Effect of Calcium and Halide Ions on the Gramicidin A Molecular State and Antimicrobial Activity.

Gramicidin A (gA) forms several convertible conformations in different environments. In this study, we investigated the effect of calcium halides on the molecular state and antimicrobial activity of gramicidin A. The molecular state of gramicidin A is highly affected by the concentration of calcium salt and the type of halide anion. Gramicidin A can exist in two states that can be characterized by circular dichroism (CD), mass, nuclear magnetic resonance (NMR) and fluorescence spectroscopy. In State 1, the main molecular state of gramicidin A is as a dimer, and the addition of calcium salt can convert a mixture of four species into a single species, which is possibly a left-handed parallel double helix. In State 2, the addition of calcium halides drives gramicidin A dissociation and denaturation from a structured dimer into a rapid equilibrium of structured/unstructured monomer. We found that the abilities of dissociation and denaturation were highly dependent on the type of halide anion. The dissociation ability of calcium halides may play a vital role in the antimicrobial activity, as the structured monomeric form had the highest antimicrobial activity. Herein, our study demonstrated that the molecular state was correlated with the antimicrobial activity.

Metabolomic biomarkers in cervicovaginal fluid for detecting endometrial cancer through nuclear magnetic resonance spectroscopy.

INTRODUCTION: Endometrial cancer (EC) is one of the most common gynecologic neoplasms in developed countries but lacks screening biomarkers. OBJECTIVES: We aim to identify and validate metabolomic biomarkers in cervicovaginal fluid (CVF) for detecting EC through nuclear magnetic resonance (NMR) spectroscopy. METHODS: We screened 100 women with suspicion of EC and benign gynecological conditions, and randomized them into the training and independent testing datasets using a 5:1 study design. CVF samples were analyzed using a 600-MHz NMR spectrometer equipped with a cryoprobe. Four machine learning algorithms-support vector machine (SVM), partial least squares discriminant analysis (PLS-DA), random forest (RF), and logistic regression (LR), were applied to develop the model for identifying metabolomic biomarkers in cervicovaginal fluid for EC detection. RESULTS: A total of 54 women were eligible for the final analysis, with 21 EC and 33 non-EC. From 29 identified metabolites in cervicovaginal fluid samples, the top-ranking metabolites chosen through SVM, RF and PLS-DA which existed in independent metabolic pathways, i.e. phosphocholine, malate, and asparagine, were selected to build the prediction model. The SVM, PLS-DA, RF, and LR methods all yielded area under the curve values between 0.88 and 0.92 in the training dataset. In the testing dataset, the SVM and RF methods yielded the highest accuracy of 0.78 and the specificity of 0.75 and 0.80, respectively. CONCLUSION: Phosphocholine, asparagine, and malate from cervicovaginal fluid, which were identified and independently validated through models built using machine learning algorithms, are promising metabolomic biomarkers for the detection of EC using NMR spectroscopy.

Combined Plasma DHA-Containing Phosphatidylcholine PCaa C38:6 and Tetradecanoyl-Carnitine as an Early Biomarker for Assessing the Mortality Risk among Sarcopenic Patients.

The coming of the hyper-aged society in Taiwan prompts us to investigate the relationship between the metabolic status of sarcopenic patients and their most adverse outcome-death. We studied the association between any plasma metabolites and the risk for mortality among older Taiwanese sarcopenic patients. We applied a targeted metabolomic approach to study the plasma metabolites of adults aged ≥65 years, and identified the metabolic signature predictive of the mortality of sarcopenic patients who died within a 5.5-year follow-up period. Thirty-five sarcopenic patients who died within the follow-up period (Dead cohort) had shown a specific plasma metabolic signature, as compared with 54 patients who were alive (Alive cohort). Only 10 of 116 non-sarcopenic individuals died during the same period. After multivariable adjustment, we found that sex, hypertension, tetradecanoyl-carnitine (C14-carnitine), and docosahexaenoic acid (DHA)-containing phosphatidylcholine diacyl (PCaa) C38:6 and C40:6 were important risk factors for the mortality of sarcopenic patients. Low PCaa C38:6 levels and high C14-carnitine levels correlated with an increased mortality risk; this was even the same for those patients with hypertension (HTN). Our findings suggest that plasma PCaa C38:6 and acylcarnitine C14-carnitine, when combined, can be a better early biomarker for evaluating the mortality risk of sarcopenia patients.

Rapid and Direct Detection of Trimethylamine N-oxide Using an Off-Chip Capacitance Biosensor with Readout SoC for Early-Stage Thrombosis and Cardiovascular Disease.

A demonstration of an off-chip capacitance array sensor with a limit of detection of 1 µM trimethylamine N-oxide (TMAO) to diagnose a chronic metabolism disease in urine is presented. The improved Cole-Cole model is employed to determine the parameters of R_catalyzed, C_catalyzed, and Rp_catalyzed, enabling the prediction of the catalytic resistance of enzyme, reduction effects of the analyte, and characterize the small signal alternating current properties of ionic strength caused by catalysis. Based on the standard solutions, we investigate the effects of pixel geometry parameters, driving electrode width, and sensing electrode width on the electrical field change of the off-chip capacitance sensor; the proposed off-chip sensor with readout system-on-chip exhibits a high sensitivity of 21 analog-to-digital converter counts/µM TMAO (or 2.5 mV/µM TMAO), response time of 1 s, repetition of 98.9%, and drift over time of 0.5 mV. The proposed off-chip sensor effectively discriminates TMAO in a phosphate-buffered saline solution based on minute changes in capacitance induced by the TorA enzyme, resulting in a discernible 2.15% distinction. These measurements have been successfully corroborated using the conventional cyclic voltammetry method, demonstrating a mere 0.024% variance. The off-chip sensor is crafted with a specific focus on detecting TMAO, achieved by excluding any reduction reactions between the TMAO-specific enzyme TorA and the compounds creatine and creatinine present in urine. This deliberate omission ensures that the sensor's attention remains solely on TMAO, thereby enhancing its precision in achieving accurate and reliable TMAO detection.

Do low skeletal muscle bulk and disturbed body fat mass impact tumor recurrence in stage I/II HCC undergoing surgery? An observational cohort study.

INTRODUCTION: The influence of deranged body composition on stage I/II HCC after surgery remains undetermined. The current study aimed to investigate the impact of low skeletal muscle bulk and disturbed body fat mass on the recurrence outcome of stage I/II HCC patients undergoing liver resection. The associated metabolomic alterations were also assessed. METHODS: From 2012 to 2021, stage I and II HCC patients who underwent liver resection at our institute were retrospectively reviewed. Their preoperative body composition including skeletal muscle mass and body fat volume was measured by computed tomography (CT). The recurrence outcome was recorded and analyzed. The preoperative serum was collected and subjected to metabolomic analysis. RESULTS: A total of 450 stage I and II HCC patients were included in the current study. Among them, 76% were male and around 60% had HBV infection. After stratified by normal cutoff values obtained from a healthy cohort, 6.4% of stage I/II HCC patients were found to have a low psoas muscle index (PMI), 17.8% a high subcutaneous adipose tissue (SAT) index, and 27.8% a high visceral adipose tissue (VAT) index. Cox regression multivariate analysis further demonstrated that low PMI and high SAT index were independent prognostic factors for time-to-recurrence (TTR) after surgery. Metabolomic analysis discovered that free fatty acid ß-oxidation was enhanced in with low PMI or high SAT index. CONCLUSION: The current study demonstrated that reduced psoas muscle mass may impair while elevated SAT may prolong the TTR of stage I/II HCC patients undergoing liver resections. VAT, on the other hand, was not associated with recurrence outcome after surgery. Further studies are warranted to validate our findings.

Alternations of Lipoprotein Profiles in the Plasma as Biomarkers of Huntington's Disease.

Alterations in lipid composition and disturbed lipoprotein metabolism are involved in the pathomechanism of Huntington's disease (HD). Here, we measured 112 lipoprotein subfractions and components in the plasma of 20 normal controls, 24 symptomatic (sympHD) and 9 presymptomatic (preHD) HD patients. Significant changes were found in 30 lipoprotein subfractions and components in all HD patients. Plasma levels of total cholesterol (CH), apolipoprotein (Apo)B, ApoB-particle number (PN), and components of low-density lipoprotein (LDL) were lower in preHD and sympHD patients. Components of LDL4, LDL5, LDL6 and high-density lipoprotein (HDL)4 demonstrated lower levels in preHD and sympHD patients compared with controls. Components in LDL3 displayed lower levels in sympHD compared with the controls, whereas components in very low-density lipoprotein (VLDL)5 were higher in sympHD patients compared to the controls. The levels of components in HDL4 and VLDL5 demonstrated correlation with the scores of motor assessment, independence scale or functional capacity of Unified Huntington's Disease Rating Scale. These findings indicate the potential of components of VLDL5, LDL3, LDL4, LDL5 and HDL4 to serve as the biomarkers for HD diagnosis and disease progression, and demonstrate substantial evidence of the involvement of lipids and apolipoproteins in HD pathogenesis.

Metabolomic Signature of Diabetic Kidney Disease in Cerebrospinal Fluid and Plasma of Patients with Type 2 Diabetes Using Liquid Chromatography-Mass Spectrometry.

Diabetic kidney disease (DKD) is the major cause of end stage renal disease in patients with type 2 diabetes mellitus (T2DM). The subtle metabolic changes in plasma and cerebrospinal fluid (CSF) might precede the development of DKD by years. In this longitudinal study, CSF and plasma samples were collected from 28 patients with T2DM and 25 controls, during spinal anesthesia for elective surgery in 2017. These samples were analyzed using liquid chromatography-mass spectrometry (LC-MS) in 2017, and the results were correlated with current DKD in 2017, and the development of new-onset DKD, in 2021. Comparing patients with T2DM having new-onset DKD with those without DKD, revealed significantly increased CSF tryptophan and plasma uric acid levels, whereas phosphatidylcholine 36:4 was lower. The altered metabolites in the current DKD cases were uric acid and paraxanthine in the CSF and uric acid, L-acetylcarnitine, bilirubin, and phosphatidylethanolamine 38:4 in the plasma. These metabolic alterations suggest the defective mitochondrial fatty acid oxidation and purine and phospholipid metabolism in patients with DKD. A correlation analysis found CSF uric acid had an independent positive association with the urine albumin-to-creatinine ratio. In conclusion, these identified CSF and plasma biomarkers of DKD in diabetic patients, might be valuable for monitoring the DKD progression.

Kynurenine Pathway of Tryptophan Metabolism Is Associated with Hospital Mortality in Patients with Acute Respiratory Distress Syndrome: A Prospective Cohort Study.

Acute respiratory distress syndrome (ARDS) involves dysregulated immune-inflammatory responses, characterized by severe oxidative stress and high mortality. Metabolites modulating the inflammatory and immune responses may play a central role in the pathogenesis of ARDS. Most biogenic amines may induce the production of reactive oxygen species, oxidative stress, mitochondrial dysfunction, and programmed cell death. We conducted a prospective study on metabolic profiling specific to the amino acids and biogenic amines of 69 patients with ARDS. Overall, hospital mortality was 52.2%. Between day 1 and day 7 after ARDS onset, plasma kynurenine levels and the kynurenine/tryptophan ratio were significantly higher among non-survivors than in survivors (all p < 0.05). Urine metabolic profiling revealed a significantly higher prevalence of tryptophan degradation and higher concentrations of metabolites downstream of the kynurenine pathway among non-survivors than among survivors upon ARDS onset. Cox regression models revealed that plasma kynurenine levels and the plasma kynurenine/tryptophan ratio on day 1 were independently associated with hospital mortality. The activation of the kynurenine pathway was associated with mortality in patients with ARDS. Metabolic phenotypes and modulating metabolic perturbations of the kynurenine pathway could perhaps serve as prognostic markers or as a target for therapeutic interventions aimed at reducing oxidative stress and mortality in ARDS.

Pharmacometabolomic study of drug response to antihypertensive medications for hypertension marker identification in Han Chinese individuals in Taiwan.

Various groups of antihypertensive drugs targeting different pathways have been developed; however, the pharmacometabolic responses to these drugs have rarely been compared to elucidate the common pathway of blood pressure regulation. Here, we performed a comparative multi-dimensional pharmacometabolic study on the four major lines of antihypertensive drugs, namely angiotensin-converting enzyme inhibitors (ACEis), angiotensin receptor blockers (ARBs), calcium channel blockers (CCBs), and diuretics (DIURs), through ultra-performance liquid chromatography coupled to quantum time-of-flight mass spectrometry. Two hundred fifty patients with young-onset hypertension, who were equally divided among five study groups: non-medicated, ACEi, ARB, CCB, and DIUR groups, were recruited. In a metabolome-wide association study conducted through analysis of covariance, 37 molecular features significantly associated with pharmacometabolic responses to antihypertensive drugs were identified. One-third of these features were shared by multiple medications. ACEis, ARBs, and DIURs shared more features than CCB, partially reflecting that ACEis, ARBs, and DIURs affect the renin-angiotensin-aldosterone system. Thirteen molecular features were consistently identified by all four models of the analysis of covariance. A tandem mass spectrometry (or MS/MS) experiment was performed to decipher the chemical structure of these 13 molecular features, including ARB-associated lysophosphatidylcholine (P4135), CCB-associated diacylglycerol(15:0/18:2) (P1175), and DIUR-associated oleamide (P1516). In addition, diacylglycerol(15:0/14:2) (P408) was significantly associated with the pharmacometabolic response to all four antihypertensive drugs. The identified metabolites provide insights into the mechanisms of blood pressure regulation and potential predictive markers of pharmacometabolic responses to antihypertensive drugs.

Gut Microbiota and Bile Acids Mediate the Clinical Benefits of YH1 in Male Patients with Type 2 Diabetes Mellitus: A Pilot Observational Study.

Our previous clinical trial showed that a novel concentrated herbal extract formula, YH1 (Rhizoma coptidis and Shen-Ling-Bai-Zhu-San), improved blood glucose and lipid control. This pilot observational study investigated whether YH1 affects microbiota, plasma, and fecal bile acid (BA) compositions in ten untreated male patients with type 2 diabetes (T2D), hyperlipidemia, and a body mass index ≥ 23 kg/m2. Stool and plasma samples were collected for microbiome, BA, and biochemical analyses before and after 4 weeks of YH1 therapy. As previous studies found, the glycated albumin, 2-h postprandial glucose, triglycerides, total cholesterol, and low-density lipoprotein cholesterol levels were significantly improved after YH1 treatment. Gut microbiota revealed an increased abundance of the short-chain fatty acid-producing bacteria Anaerostipes and Escherichia/Shigella. Furthermore, YH1 inhibited specific phylotypes of bile salt hydrolase-expressing bacteria, including Parabacteroides, Bifidobacterium, and Bacteroides caccae. Stool tauro-conjugated BA levels increased after YH1 treatment. Plasma total BAs and 7α-hydroxy-4-cholesten-3-one (C4), a BA synthesis indicator, were elevated. The reduced deconjugation of BAs and increased plasma conjugated BAs, especially tauro-conjugated BAs, led to a decreased glyco- to tauro-conjugated BA ratio and reduced unconjugated secondary BAs. These results suggest that YH1 ameliorates T2D and hyperlipidemia by modulating microbiota constituents that alter fecal and plasma BA compositions and promote liver cholesterol-to-BA conversion and glucose homeostasis.

Discovering a trans-omics biomarker signature that predisposes high risk diabetic patients to diabetic kidney disease.

Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively. Three models are developed. In model 1, the top 20 features selected by AI give an accuracy rate of 0.83 and an area under curve (AUC) of 0.89 when differentiating DM and non-DM individuals. In model 2, among DM patients, a biomarker signature of 10 AI-selected features gives an accuracy rate of 0.70 and an AUC of 0.76 when identifying subjects at high risk of renal impairment. In model 3, among non-DM patients, a biomarker signature of 25 AI-selected features gives an accuracy rate of 0.82 and an AUC of 0.76 when pinpointing subjects at high risk of chronic kidney disease. In addition, the performance of the three models is rigorously verified using an independent validation cohort. Intriguingly, analysis of the protein-protein interaction network of the genes containing the identified SNPs (RPTOR, CLPTM1L, ALDH1L1, LY6D, PCDH9, B3GNTL1, CDS1, ADCYAP and FAM53A) reveals that, at the molecular level, there seems to be interconnected factors that have an effect on the progression of renal impairment among DM patients. In conclusion, our findings reveal the potential of employing machine learning algorithms to augment traditional methods and our findings suggest what molecular mechanisms may underlie the complex interaction between DM and chronic kidney disease. Moreover, the development of our AI-assisted models will improve precision when diagnosing renal impairment in predisposed patients, both DM and non-DM. Finally, a large prospective cohort study is needed to validate the clinical utility and mechanistic implications of these biomarker signatures.

Aß40(L17A/F19A) mutant diminishes the aggregation and neurotoxicity of Aß40.

Aggregated ß-amyloid peptides (Aß) are neurotoxic and responsible for neuronal death both in vitro and in vivo. From the structural point of view, Aß self-aggregation involves a conformational change in the peptide. Here, we investigated the relationship between conformational changes and amino acid residues of Aß(40). Urea unfolding in combination with NMR spectroscopy was applied to probe the stabilization of Aß(40) conformation. L17 and F19 residues were found more sensitive to environmental changes than the other residues. Replacement of these two residues with alanine could stabilize the conformation of Aß(40). Further analysis indicated that the Aß(40)(L17A/F19A) mutant could diminish the aggregation and reduce the neurotoxicity. These results suggest that L17 and F19 are the critical residues responsible for conformational changes which may trigger neurotoxic cascade of Aß(40).

7-Ketocholesterol Induces Lipid Metabolic Reprogramming and Enhances Cholesterol Ester Accumulation in Cardiac Cells.

7-Ketocholesterol (7KCh) is a major oxidized cholesterol product abundant in lipoprotein deposits and atherosclerotic plaques. Our previous study has shown that 7KCh accumulates in erythrocytes of heart failure patients, and further investigation centered on how 7KCh may affect metabolism in cardiomyocytes. We applied metabolomics to study the metabolic changes in cardiac cell line HL-1 after treatment with 7KCh. Mevalonic acid (MVA) pathway-derived metabolites, such as farnesyl-pyrophosphate and geranylgeranyl-pyrophosphate, phospholipids, and triacylglycerols levels significantly declined, while the levels of lysophospholipids, such as lysophosphatidylcholines (lysoPCs) and lysophosphatidylethanolamines (lysoPEs), considerably increased in 7KCh-treated cells. Furthermore, the cholesterol content showed no significant change, but the production of cholesteryl esters was enhanced in the treated cells. To explore the possible mechanisms, we applied mRNA-sequencing (mRNA-seq) to study genes differentially expressed in 7KCh-treated cells. The transcriptomic analysis revealed that genes involved in lipid metabolic processes, including MVA biosynthesis and cholesterol transport and esterification, were differentially expressed in treated cells. Integrated analysis of both metabolomic and transcriptomic data suggests that 7KCh induces cholesteryl ester accumulation and reprogramming of lipid metabolism through altered transcription of such genes as sterol O-acyltransferase- and phospholipase A2-encoding genes. The 7KCh-induced reprogramming of lipid metabolism in cardiac cells may be implicated in the pathogenesis of cardiovascular diseases.

1H NMR metabolomic profiling of human cerebrospinal fluid in aging process.

The molecular process of biological aging might be accompanied by significant metabolic derangement, especially in the central nervous system (CNS), since the brain has an enormous energy demand. However, the metabolic signature of the aging process in cerebrospinal fluid (CSF) has not been thoroughly investigated, especially in the Asian population. In this prospective cohort study on CSF metabolomics using proton nuclear magnetic resonance (NMR) spectroscopy, fasting CSF samples from 75 cognitively unimpaired patients aged 20-92 years without diabetes or obesity, undergoing spinal anesthesia for elective surgery were analyzed. Several metabolites in CSF samples were identified as having a significant association with the aging process in cerebral circulation; among the metabolites, the levels of alanine, citrate, creatinine, lactate, leucine, tyrosine, and valine significantly increased in old patients compared to those in young patients. The combined CSF metabolite alterations in citrate, lactate, leucine, tyrosine, and valine had a superior correlation with the aging process in all age groups. In conclusion, our pilot study of aging CSF metabolomics in the Taiwanese population presents significantly altered CSF metabolites with potential relevance to the aging process. These metabolic alterations in CSF samples might imply increasing anaerobic glycolysis, mitochondrial dysfunction, and decreasing glucose utilization in cerebral circulation in aged patients.

A panel of biomarkers in the prediction for early allograft dysfunction and mortality after living donor liver transplantation.

Early allograft dysfunction (EAD) is associated with graft failure and mortality after living donor liver transplantation (LDLT). In this study, we report biomarkers superior to other conventional clinical markers in the prediction of EAD and all-cause in-hospital mortality in LDLT patient cohort. Blood samples of living donor liver transplant recipients were collected on postoperative day 1 and analyzed by liquid chromatography coupled with mass spectrometry (LC-MS). Significant metabolites associated with the prediction of EAD were identified using orthogonal projection to latent structures-discriminant analysis (OPLS-DA). A few lipids, more specifically, lysoPC (16:0), PC (18:0/20:5), betaine and palmitic acid (C16:0) were found to effectively differentiate EAD from non-EAD on postoperative day 1. A combination of these four metabolites showed an AUC of 0.821, which was further improved to 0.846 by the addition of a clinical parameter, total bilirubin. The panel exhibits a high prognostic accuracy in prediction of all-cause in-hospital mortality and mortality within 7 postoperative days with AUCs of 0.843 and 0.954. These results show the combination of metabolomics-derived biomarkers and clinical parameters demonstrates the power of panels in diagnostic and prognostic evaluation of LDLT.