Plasma Metabolite Profiling Identifies Nondiabetic Chronic Pancreatitis Patients With Metabolic Alterations Progressing to Prediabetes Before HbA1c.

INTRODUCTION: Diabetes (T3cDM) secondary to chronic pancreatitis (CP) arises due to endocrine dysfunction and metabolic dysregulations. Currently, diagnostic tests are not available to identify patients who may progress from normoglycemia to hyperglycemia in CP. We conducted plasma metabolomic profiling to diagnose glycemic alterations early in the course of disease. METHODS: Liquid chromatography-tandem mass spectrometry was used to generate untargeted, targeted plasma metabolomic profiles in patients with CP, controls (n = 445) following TRIPOD guidelines. Patients were stratified based on glucose tolerance tests following ADA guidelines. Multivariate analysis was performed using partial least squares discriminant analysis to assess discriminatory ability of metabolites among stratified groups. COMBIROC and logistic regression were used to derive biomarker signatures. AI-ML tool (Rapidminer) was used to verify these preliminary results. RESULTS: Ceramide, lysophosphatidylethanolamine, phosphatidylcholine, lysophosphatidic acid (LPA), phosphatidylethanolamine, carnitine, and lysophosphatidylcholine discriminated T3cDM CP patients from healthy controls with AUC 93% (95% CI 0.81-0.98, P < 0.0001), and integration with pancreatic morphology improved AUC to 100% (95% CI 0.93-1.00, P < 0.0001). LPA, phosphatidylinositol, and ceramide discriminated nondiabetic CP with glycemic alterations (pre-diabetic CP); AUC 66% (95% CI 0.55-0.76, P = 0.1), and integration enhanced AUC to 74% (95% CI 0.55-0.88, P = 0.86). T3cDM was distinguished from prediabetic by LPA, phosphatidylinositol, and sphinganine (AUC 70%; 95% CI 0.54-0.83, P = 0.08), and integration improved AUC to 83% (95% CI 0.68-0.93, P = 0.05). CombiROC cutoff identified 75% and 78% prediabetes in validation 1 and 2 cohorts. Random forest algorithm assessed performance of integrated panel demonstrating AUC of 72% in predicting glycemic alterations. DISCUSSION: We report for the first time that a panel of metabolites integrated with pancreatic morphology detects glycemia progression before HbA1c in patients with CP.

Deliverables from Metabolomics in Kidney Disease: Adenine, New Insights and Implication for Clinical Decision Making.

BACKGROUND: Chronic kidney disease (CKD) presents a persistent global health challenge, characterized by complex pathophysiology and diverse progression patterns. Metabolomics has emerged as a valuable tool in unraveling the intricate molecular mechanisms driving CKD progression. SUMMARY: This comprehensive review provides a summary of recent progress in the field of metabolomics in kidney disease with a focus on spatial metabolomics to shed important insights to enhancing our understanding of CKD progression, emphasizing its transformative potential in early disease detection, refined risk assessment, and the development of targeted interventions to improve patient outcomes. KEY MESSAGE: Through an extensive analysis of metabolic pathways and small molecule fluctuations, bulk and spatial metabolomics offer unique insights spanning the entire spectrum of CKD, from early stages to advanced disease states. Recent advances in metabolomics technology have enabled spatial identification of biomarkers to provide breakthrough discoveries in predicting CKD trajectory and enabling personalized risk assessment. Furthermore, metabolomics can help decipher the complex molecular intricacies associated with kidney diseases for exciting novel therapeutic approaches. A recent example is the identification of adenine as a key marker of kidney fibrosis for diabetic kidney disease using both untargeted and targeted bulk and spatial metabolomics. The metabolomics studies were critical to identify a new biomarker for kidney failure and to guide new therapeutics for diabetic kidney disease. Similar approaches are being pursued for acute kidney injury and other kidney diseases to enhance precision medicine decision making.

Evaluation of Exploratory Fluid Biomarker Results from a Phase 1 Senolytic Trial in Mild Alzheimer's Disease.

Senescent cell accumulation contributes to the progression of age-related disorders including Alzheimer's disease (AD). Clinical trials evaluating senolytics, drugs that clear senescent cells, are underway, but lack standardized outcome measures. Our team recently published data from the first open-label trial to evaluate senolytics (dasatinib plus quercetin) in AD. After 12-weeks of intermittent treatment, we reported brain exposure to dasatinib, favorable safety and tolerability, and modest post-treatment changes in cerebrospinal fluid (CSF) inflammatory and AD biomarkers using commercially available assays. Herein, we present more comprehensive exploratory analyses of senolytic associated changes in AD relevant proteins, metabolites, lipids, and transcripts measured across blood, CSF, and urine. These analyses included mass spectrometry for precise quantification of amyloid beta (Aß) and tau in CSF; immunoassays to assess senescence associated secretory factors in plasma, CSF, and urine; mass spectrometry analysis of urinary metabolites and lipids in blood and CSF; and transcriptomic analyses relevant to chronic stress measured in peripheral blood cells. Levels of Aß and tau species remained stable. Targeted cytokine and chemokine analyses revealed treatment-associated increases in inflammatory plasma fractalkine and MMP-7 and CSF IL-6. Urinary metabolites remained unchanged. Modest treatment-associated lipid profile changes suggestive of decreased inflammation were observed both peripherally and centrally. Blood transcriptomic analysis indicated downregulation of inflammatory genes including FOS, FOSB, IL1ß, IL8, JUN, JUNB, PTGS2. These data provide a foundation for developing standardized outcome measures across senolytic studies and indicate distinct biofluid-specific signatures that will require validation in future studies. ClinicalTrials.gov: NCT04063124.

Screening DNA Damage in the Rat Kidney and Liver by Untargeted DNA Adductomics.

Air pollution, tobacco smoke, and red meat are associated with renal cell cancer (RCC) risk in the United States and Western Europe; however, the chemicals that form DNA adducts and initiate RCC are mainly unknown. Aristolochia herbaceous plants are used for medicinal purposes in Asia and worldwide. They are a significant risk factor for upper tract urothelial carcinoma (UTUC) and RCC to a lesser extent. The aristolochic acid (AA) 8-methoxy-6-nitrophenanthro-[3,4-d]-1,3-dioxolo-5-carboxylic acid (AA-I), a component of Aristolochia herbs, contributes to UTUC in Asian cohorts and in Croatia, where AA-I exposure occurs from ingesting contaminated wheat flour. The DNA adduct of AA-I, 7-(2'-deoxyadenosin-N6-yl)-aristolactam I, is often detected in patients with UTUC, and its characteristic A:T-to-T:A mutational signature occurs in oncogenes and tumor suppressor genes in AA-associated UTUC. Identifying DNA adducts in the renal parenchyma and pelvis caused by other chemicals is crucial to gaining insights into unknown RCC and UTUC etiologies. We employed untargeted screening with wide-selected ion monitoring tandem mass spectrometry (wide-SIM/MS2) with nanoflow liquid chromatography/Orbitrap mass spectrometry to detect DNA adducts formed in rat kidneys and liver from a mixture of 13 environmental, tobacco, and dietary carcinogens that may contribute to RCC. Twenty DNA adducts were detected. DNA adducts of 3-nitrobenzanthrone (3-NBA), an atmospheric pollutant, and AA-I were the most abundant. The nitrophenanthrene moieties of 3-NBA and AA-I undergo reduction to their N-hydroxy intermediates to form 2'-deoxyguanosine (dG) and 2'-deoxyadenosine (dA) adducts. We also discovered a 2'-deoxycytidine AA-I adduct and dA and dG adducts of 10-methoxy-6-nitro-phenanthro-[3,4-d]-1,3-dioxolo-5-carboxylic acid (AA-III), an AA-I isomer and minor component of the herbal extract assayed, signifying AA-III is a potent kidney DNA-damaging agent. The roles of AA-III, other nitrophenanthrenes, and nitroarenes in renal DNA damage and human RCC warrant further study. Wide-SIM/MS2 is a powerful scanning technology in DNA adduct discovery and cancer etiology characterization.

Characterization of isomeric acetyl amino acids and di-acetyl amino acids by LC/MS/MS.

Acetylation of amino acids is important in the molecular biology and biochemistry because they are part of several metabolic pathways. N-acetyl amino acids can form through degradation of N-acetyl proteins or direct acetylation of amino acids by specific enzymes. Acetylation of α-amino acids can be either on the alpha -NH2 or on the side-chain functional group, where both the acetyl products are isomeric and can show different biological roles. Theoretically, all proteinogenic α-amino acids are expected to undergo acetylation and they can be a part of metabolome. Thus, it is essential to detect and identify all the possible acetylated products of α-amino acids for untargeted metabolomics studies. In this study, it is aimed to synthesize and characterize all acetylated products of natural α-amino acids. A total of 20 Nα -acetyl amino acids (1-20), six side-chain acetyl amino acids (21-26), and six diacetyl amino acids (27-32) were synthesized and characterized by liquid chromatography-electrospray ionizationtandem mass spectrometry (LC-ESI-MS/MS). The [M + H]+ ions of all the acetyl amino acids were subjected to MS/MS experiments to obtain their structural information. Apart from the expected loss of (H2 O + CO) (immonium ions), most of the acetyl amino acids specifically showed loss of H2 O and loss of a ketene (C2 H2 O) from [M+H]+ ions. The side-chain acetyl amino acids showed a clear-cut structure specific fragment ions that enabled easy differentiation from their isomeric Nα -acetyl amino acids. The other isomeric/isobaric acetyl amino acids could also be easily distinguished by their MS/MS spectra. The MS/MS of immonium ions of the acetyl amino acids were also studied, and they included characteristic products reflecting the structures of parent Nα -acetyl and side-chain acetyl amino acids.

Evaluation of amino acids and other related metabolites levels in end-stage renal disease (ESRD) patients on hemodialysis by LC/MS/MS and GC/MS.

End-stage renal disease (ESRD) is a rapidly increasing health problem, and every year, about 2 million ESRD cases are reported worldwide. Hemodialysis (HD) is the vital renal reinstatement therapy for ESRD, and HD patterns play a crucial role in patients' health. Plasma metabolomics is the potential approach to understanding the HD process, effectiveness, and patterns. The lack of protein vitality is a primary problem for HD patients, and the quantities of amino acids intracellularly and in the blood are considered to be a symbolic index of protein metabolism and nutrition conditions. In the current study, LC/MS/MS and GC/MS methods were developed for 29 targeted plasma metabolites and validated as per ICH bioanalytical method validation M10 guidelines. The 29 metabolites included 20 proteinogenic amino acids and nine other related metabolites. The methods were employed to measure the absolute quantities (µM) of the targeted metabolites in HD patients (n=60) before and after dialysis (PRE-HD and POST-HD), and compared with the healthy control (HC) group (n=60). Phenylacetylglutamine was found to be higher in both PRE-HD (72.88±14.5 µM) and POST-HD (26.62±7.9 µM), when compared to HC (1.61±0.6 µM). On the other hand, glutamic acid was lower in PRE-HD (14.90±6.5 µM), and POST-HD (13.6±6.1 µM) than that of HC (245.4±37.8 µM). The dialytic loss was found to be 52-45% for arginine, lysine, and histidine, while it was 38-26% for glycine, cysteine, proline, alanine, threonine, glutamine, valine, and methionine. The dialytic loss was low (≤12%) for aspartic acid, glutamic acid, asparagine, leucine, tyrosine, tryptophan, and isoleucine. Graphical abstract adapted from mass spectrometry templates by Biorender.com retrieved from https://app.biorender.com/biorender-templates .

Assessment of uremic toxins in advanced chronic kidney disease patients on maintenance hemodialysis by LC-ESI-MS/MS.

INTRODUCTION: In the advanced stage of chronic kidney disease (CKD), electrolytes, fluids, and metabolic wastes including various uremic toxins, accumulate at high concentrations in the patients' blood. Hemodialysis (HD) is the conventional procedure used worldwide to remove metabolic wastes. The creatinine and urea levels have been routinely monitored to estimate kidney function and effectiveness of the HD process. This study, first from in Indian perspective, aimed at the identification and quantification of major uremic toxins in CKD patients on maintenance HD (PRE-HD), and compared with the healthy controls (HC) as well as after HD (POST-HD). OBJECTIVES: The study mainly focused on the identification of major uremic toxins in Indian perspective and the quantitative analysis of indoxyl sulfate and p-cresol sulfate (routinely targeted uremic toxins), and phenyl sulfate, catechol sulfate, and guaiacol sulfate (targeted for the first time), apart from creatinine and urea in PRE-HD, POST-HD, and HC groups. METHODS: Blood samples were collected from 90 HD patients (both PRE-HD and POST-HD), and 74 HCs. The plasma samples were subjected to direct ESI-HRMS and LC/HRMS for untargeted metabolomics and LC-MS/MS for quantitative analysis. RESULTS: Various known uremic toxins, and a few new and unknown peaks were detected in PRE-HD patients. The p-cresol sulfate and indoxyl sulfate were dominant in PRE-HD, the concentrations of phenyl sulfate, catechol sulfate, and guaiacol sulfate were about 50% of that of indoxyl sulfate. Statistical evaluation on the levels of targeted uremic toxins in PRE-HD, POST-HD, and HC groups showed a significant difference among the three groups. The dialytic clearance of indoxyl sulfate and p-cresol sulfate was found to be < 35%, while that of the other three sulfates was 50-58%. CONCLUSION: LC-MS/MS method was developed and validated to evaluate five major uremic toxins in CKD patients on HD. The levels of the targeted uremic toxins could be used to assess kidney function and the effectiveness of HD.