Urine tricarboxylic acid cycle signatures of early-stage diabetic kidney disease

Lunyera, Joseph; Diamantidis, Clarissa J.; Bosworth, Hayden B.; Patel, Uptal D.; Bain, James R.; Muehlbauer, Michael J.; Nguyen, Maggie; Sharma, Binu; Jennie, Z.; Shah, Svati H.; Scialla, Julia J.

doi:10.1007/s11306-021-01858-4

Cited by 9 publications

(6 citation statements)

References 21 publications

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“…In this study, TCA cycle‐related metabolites, including citrate, isocitrate, and succinate, were significantly altered in children with poor outcomes, suggesting that dysregulation of aerobic energy metabolism is associated with the prognosis of SE. Dysregulation of the TCA cycle has been associated with several diseases related to oxidative stress, such as atrial fibrillation, heart failure, 32 diabetic kidney disease, 33 and neurodegenerative disease 34 . In an in vitro model of amyotrophic lateral sclerosis, oxidative stress disrupted the concentrations of succinate and malate in motor neurons, indicating an impaired TCA cycle in disease physiology 35 .…”

Section: Discussionmentioning

confidence: 99%

Metabolomics of cerebrospinal fluid reveals prognostic biomarkers in pediatric status epilepticus

Wang

et al. 2023

CNS Neurosci Ther

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AimsStatus epilepticus (SE) is the most common neurological emergency in pediatric patients. This study aimed to screen for prognostic biomarkers of SE in the cerebrospinal fluid (CSF) using metabolomics.MethodsUltra‐performance liquid chromatography quadrupole time‐of‐flight tandem mass spectrometry (UPLC‐QTOF‐MS) was conducted to identify prognostic biomarkers in CSF metabolomics by comparing the poor outcome group (N = 13) with the good outcome group (N = 15) of children with SE. Differentially expressed metabolites were identified using Mann–Whitney U test corrected by Benjamini‐Hochberg and partial least squares discriminant analysis (PLS‐DA).ResultsThe PLS‐DA model identified and validated significant metabolic differences between the poor and good outcome groups of children with SE (PLS‐DA with R2Y = 0.992 and Q2 = 0.798). A total of 49 prognosis‐related metabolites were identified. Of these metabolites, 20 including glutamyl‐glutamine, 3‐iodothyronamine, and L‐fucose had an area under the curve (AUC) ≥ 80% in prognostic prediction of SE. The logistic regression model combining glutamyl‐glutamine and 3‐iodothyronamine produced an AUC value of 0.976, with a sensitivity of 0.863 and specificity of 0.956. Pathway analysis revealed that dysregulation of the citrate cycle (TCA) and arginine biosynthesis may contribute to poor SE prognosis.ConclusionsThis study highlighted the prognosis‐related metabolomic disturbances in the CSF of children with SE and identified potential prognostic biomarkers. A prognostic prediction model combining glutamyl‐glutamine and 3‐iodothyronamine with high predictive value was established.

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Section: Discussionmentioning

confidence: 99%

Metabolomics of cerebrospinal fluid reveals prognostic biomarkers in pediatric status epilepticus

Wang

et al. 2023

CNS Neurosci Ther

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“…Metabolic pathway enrichment analysis showed that the differential metabolites of DKD patients were mainly involved in butyric acid metabolism, tricarboxylic acid cycle, alanine, aspartic acid and glutamic acid metabolism, β‐alanine metabolism, and ketone synthesis and degradation. Some studies have pointed out that urinary tricarboxylic acid cycle is abnormal in patients with early DKD [ 23 ]. In addition, amino acid metabolism in serum and plasma of patients with diabetes was abnormal, including leucine metabolite, isoleucine metabolite, and valine metabolite [ 6 , 24 ].…”

Section: Discussionmentioning

confidence: 99%

Serum metabolomics detected by LDI‐TOF‐MS can be used to distinguish between diabetic patients with and without diabetic kidney disease

et al. 2023

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Diabetic kidney disease (DKD) is an important cause of end‐stage renal disease with changes in metabolic characteristics. The objective of this study was to study changes in serum metabolic characteristics in patients with DKD and to examine metabolite panels to distinguish DKD from diabetes with matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry(MALDI‐TOF‐MS). We recruited 40 type II diabetes mellitus (T2DM) patients with or without DKD from a single center for a cross‐sectional study. Serum metabolic profiling was performed with MALDI‐TOF‐MS using a vertical silicon nanowire array. Differential metabolites between DKD and diabetes patients were selected, and their relevance to the clinical parameters of DKD was analyzed. We applied machine learning methods to the differential metabolite panels to distinguish DKD patients from diabetes patients. Twenty‐four differential serum metabolites between DKD patients and diabetes patients were identified, which were mainly enriched in butyrate metabolism, TCA cycle, and alanine, aspartate, and glutamate metabolism. Among the metabolites, l‐kynurenine was positively correlated with urinary microalbumin, urinary microalbumin/creatinine ratio (UACR), creatinine, and urea nitrogen content. l‐Serine, pimelic acid, 5‐methylfuran‐2‐carboxylic acid, 4‐methylbenzaldehyde, and dihydrouracil were associated with the estimated glomerular filtration rate (eGFR). The panel of differential metabolites could be used to distinguish between DKD and diabetes patients with an AUC value reaching 0.9899–0.9949. Among the differential metabolites, l‐kynurenine was related to the progression of DKD. The differential metabolites exhibited excellent performance at distinguishing between DKD and diabetes. This study provides a novel direction for metabolomics‐based clinical detection of DKD.

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“…Metabolites in the TCA cycle include malate, fumarate, pyruvate, alpha-ketoglutarate, succinate and citrate/isocitrate, while low urinary TCA circulating organic anions, particularly low methylmalonic acid, ethyl malonic acid and citrate/isocitrate, are potential biomarkers of renal damage in early diabetic nephropathy ( 135 ). By reducing Cu 2+ to Cu + , ferredoxin-1 promotes the abnormal oligomerization of copper-dependent fatty acylated proteins in the TCA cycle and reduces Fe-S cluster protein levels, leading to protein aggregation, toxic stress, and ultimately, to cuproptosis ( 136 ).…”

Section: Oxalate Metabolism and The Tricarboxylic Acid Cycle In Cell ...mentioning

confidence: 99%

Cell death‑related molecules and targets in the progression of urolithiasis (Review)

Wu,

Xue,

et al. 2024

Int J Mol Med

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Urolithiasis is a high-incidence disease caused by calcium oxalate (mainly), uric acid, calcium phosphate, struvite, apatite, cystine and other stones. The development of kidney stones is closely related to renal tubule cell damage and crystal adhesion and aggregation. Cell death, comprising the core steps of cell damage, can be classified into various types (i.e., apoptosis, ferroptosis, necroptosis and pyroptosis). Different crystal types, concentrations, morphologies and sizes cause tubular cell damage via the regulation of different forms of cell death. Oxidative stress caused by high oxalate or crystal concentrations is considered to be a precursor to a variety of types of cell death. In addition, complex crosstalk exists among numerous signaling pathways and their key molecules in various types of cell death. Urolithiasis is considered a metabolic disorder, and tricarboxylic acid cycle-related molecules, such as citrate and succinate, are closely related to cell death and the inhibition of stone development. However, a literature review of the associations between kidney stone development, metabolism and various types of cell death is currently lacking, at least to the best of our knowledge. Thus, the present review summarizes the major advances in the understanding of regulated cell death and urolithiasis progression.

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Urine tricarboxylic acid cycle signatures of early-stage diabetic kidney disease

Cited by 9 publications

References 21 publications

Metabolomics of cerebrospinal fluid reveals prognostic biomarkers in pediatric status epilepticus

Metabolomics of cerebrospinal fluid reveals prognostic biomarkers in pediatric status epilepticus

Serum metabolomics detected by LDI‐TOF‐MS can be used to distinguish between diabetic patients with and without diabetic kidney disease

Cell death‑related molecules and targets in the progression of urolithiasis (Review)

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