Plasma Metabolome and Lipidome Associations with Type 2 Diabetes and Diabetic Nephropathy

Traditional Chinese medicine improved diabetic kidney disease through targeting gut microbiota

CONTEXT

Diabetic kidney disease (DKD) affects nearly 40% of diabetic patients, often leading to end-stage renal disease that requires renal replacement therapies, such as dialysis and transplantation. The gut microbiota, an integral aspect of human evolution, plays a crucial role in this condition. Traditional Chinese medicine (TCM) has shown promising outcomes in ameliorating DKD by addressing the gut microbiota.

OBJECTIVE

This review elucidates the modifications in gut microbiota observed in DKD and explores the impact of TCM interventions on correcting microbial dysregulation.

METHODS

We searched relevant articles from databases including Web of Science, PubMed, ScienceDirect, Wiley, and Springer Nature. The following keywords were used: diabetic kidney disease, diabetic nephropathy, gut microbiota, natural product, TCM, Chinese herbal medicine, and Chinese medicinal herbs. Rigorous criteria were applied to identify high-quality studies on TCM interventions against DKD.

RESULTS

Dysregulation of the gut microbiota, including Lactobacillus, Streptococcus, and Clostridium, has been observed in individuals with DKD. Key indicators of microbial dysregulation include increased uremic solutes and decreased short-chain fatty acids. Various TCM therapies, such as formulas, tablets, granules, capsules, and decoctions, exhibit unique advantages in regulating the disordered microbiota to treat DKD.

CONCLUSION

This review highlights the importance of targeting the gut-kidney axis to regulate microbial disorders, their metabolites, and associated signaling pathways in DKD. The Qing-Re-Xiao-Zheng formula, the Shenyan Kangfu tablet, the Huangkui capsule, and the Bekhogainsam decoction are potential candidates to address the gut-kidney axis. TCM interventions offer a significant therapeutic approach by targeting microbial dysregulation in patients with DKD.

Assessing the Inhibitory Potential of Pregnenolone Sulfate on Pentraxin 3 in Diabetic Kidney Disease: A Molecular Docking and Simulation Study

Diabetic Kidney Disease (DKD), a frequent consequence of diabetes, has substantial implications for both morbidity and mortality rates, prompting the exploration of new metabolic biomarkers due to limitations in current methods like creatinine and albumin measurements. Pentraxin 3 (PTX3) shows promise for assessing renal inflammation in DKD. This study investigates how DKD metabolites could influence PTX3 expression through molecular docking, ADMET profiling, and dynamic simulation. Network and pathway analyses were conducted to explore metabolite interactions with DKD genes and their contributions to DKD pathogenesis. Thirty-three DKD-associated metabolites were screened, using pentoxifylline (PEN) as a reference. The pharmacokinetic properties of these compounds were evaluated through molecular docking and ADMET profiling. Molecular dynamics simulations over 200 ns assessed the stability of PTX3 (apo), the PRE-PTX3 complex, and PEN-PTX3 across multiple parameters. Cytoscape identified 1082 nodes and 1381 edges linking metabolites with DKD genes. KEGG pathway analysis underscored PTX3's role in inflammation. Molecular docking revealed pregnenolone sulfate (PRE) with the highest binding affinity (-6.25 kcal/mol), followed by hydrocortisone (-6.03 kcal/mol) and 2-arachidonoylglycerol (-5.92 kcal/mol), compared to PEN (-5.35 kcal/mol). ADMET profiling selected PRE for dynamic simulation alongside PEN. Analysis of RMSD, RMSF, RG, SASA, H-bond, PCA, FEL, and MM-PBSA indicated stable complex behavior over time. Our findings suggest that increasing PRE levels could be beneficial in managing DKD, potentially through isolating PRE from fungal sources, synthesizing it as dietary supplements, or enhancing endogenous PRE synthesis within the body.

Metabolomics in diabetic nephropathy: Unveiling novel biomarkers for diagnosis (Review)

Diabetic nephropathy (DN) also known as diabetic kidney disease, is a major microvascular complication of diabetes and a leading cause of end‑stage renal disease (ESRD), which affects the morbidity and mortality of patients with diabetes. Despite advancements in diabetes care, current diagnostic methods, such as the determination of albuminuria and the estimated glomerular filtration rate, are limited in sensitivity and specificity, often only identifying kidney damage after considerable morphological changes. The present review discusses the potential of metabolomics as an approach for the early detection and management of DN. Metabolomics is the study of metabolites, the small molecules produced by cellular processes, and may provide a more sensitive and specific diagnostic tool compared with traditional methods. For the purposes of this review, a systematic search was conducted on PubMed and Google Scholar for recent human studies published between 2011 and 2023 that used metabolomics in the diagnosis of DN. Metabolomics has demonstrated potential in identifying metabolic biomarkers specific to DN. The ability to detect a broad spectrum of metabolites with high sensitivity and specificity may allow for earlier diagnosis and better management of patients with DN, potentially reducing the progression to ESRD. Furthermore, metabolomics pathway analysis assesses the pathophysiological mechanisms underlying DN. On the whole, metabolomics is a potential tool in the diagnosis and management of DN. By providing a more in‑depth understanding of metabolic alterations associated with DN, metabolomics could significantly improve early detection, enable timely interventions and reduce the healthcare burdens associated with this condition.

Cross-Validation of Lipid Structure Assignment Using Orthogonal Ion Activation Modalities on the Same Mass Spectrometer

The onset and progression of cancer is associated with changes in the composition of the lipidome. Therefore, better understanding of the molecular mechanisms of these disease states requires detailed structural characterization of the individual lipids within the complex cellular milieu. Recently, changes in the unsaturation profile of membrane lipids have been observed in cancer cells and tissues, but assigning the position(s) of carbon-carbon double bonds in fatty acyl chains carried by membrane phospholipids, including the resolution of lipid regioisomers, has proven analytically challenging. Conventional tandem mass spectrometry approaches based on collision-induced dissociation of ionized glycerophospholipids do not yield spectra that are indicative of the location(s) of carbon-carbon double bonds. Ozone-induced dissociation (OzID) and ultraviolet photodissociation (UVPD) have emerged as alternative ion activation modalities wherein diagnostic product ions can enable de novo assignment of position(s) of unsaturation based on predictable fragmentation behaviors. Here, for the first time, OzID and UVPD (193 nm) mass spectra are acquired on the same mass spectrometer to evaluate the relative performance of the two modalities for lipid identification and to interrogate the respective fragmentation pathways under comparable conditions. Based on investigations of lipid standards, fragmentation rules for each technique are expanded to increase confidence in structural assignments and exclude potential false positives. Parallel application of both methods to unsaturated phosphatidylcholines extracted from isogenic colorectal cancer cell lines provides high confidence in the assignment of multiple double bond isomers in these samples and cross-validates relative changes in isomer abundance.

Advancements, Challenges, and clinical implications of integration of metabolomics technologies in diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN), a severe complication of diabetes, involves a range of renal abnormalities driven by metabolic derangements. Metabolomics, revealing dynamic metabolic shifts in diseases like DN and offering insights into personalized treatment strategies, emerges as a promising tool for improved diagnostics and therapies.

METHODS

We conducted an extensive literature review to examine how metabolomics contributes to the study of DN and the challenges associated with its implementation in clinical practice. We identified and assessed relevant studies that utilized metabolomics methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to assess their efficacy in diagnosing DN.

RESULTS

Metabolomics unveils key pathways in DN progression, highlighting glucose metabolism, dyslipidemia, and mitochondrial dysfunction. Biomarkers like glycated albumin and free fatty acids offer insights into DN nuances, guiding potential treatments. Metabolomics detects small-molecule metabolites, revealing disease-specific patterns for personalized care.

CONCLUSION

Metabolomics offers valuable insights into the molecular mechanisms underlying DN progression and holds promise for personalized medicine approaches. Further research in this field is warranted to elucidate additional metabolic pathways and identify novel biomarkers for early detection and targeted therapeutic interventions in DN.

Advanced Mass Spectrometry-Based Biomarker Identification for Metabolomics of Diabetes Mellitus and Its Complications

Over the years, there has been notable progress in understanding the pathogenesis and treatment modalities of diabetes and its complications, including the application of metabolomics in the study of diabetes, capturing attention from researchers worldwide. Advanced mass spectrometry, including gas chromatography-tandem mass spectrometry (GC-MS/MS), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOF-MS), etc., has significantly broadened the spectrum of detectable metabolites, even at lower concentrations. Advanced mass spectrometry has emerged as a powerful tool in diabetes research, particularly in the context of metabolomics. By leveraging the precision and sensitivity of advanced mass spectrometry techniques, researchers have unlocked a wealth of information within the metabolome. This technology has enabled the identification and quantification of potential biomarkers associated with diabetes and its complications, providing new ideas and methods for clinical diagnostics and metabolic studies. Moreover, it offers a less invasive, or even non-invasive, means of tracking disease progression, evaluating treatment efficacy, and understanding the underlying metabolic alterations in diabetes. This paper summarizes advanced mass spectrometry for the application of metabolomics in diabetes mellitus, gestational diabetes mellitus, diabetic peripheral neuropathy, diabetic retinopathy, diabetic nephropathy, diabetic encephalopathy, diabetic cardiomyopathy, and diabetic foot ulcers and organizes some of the potential biomarkers of the different complications with the aim of providing ideas and methods for subsequent in-depth metabolic research and searching for new ways of treating the disease.

Association between gut microbiota and diabetic nephropathy: a mendelian randomization study

Background

The correlation between diabetic nephropathy (DN) and gut microbiota (GM) has been suggested in numerous animal experiments and cross-sectional studies. However, a causal association between GM and DN has not been ascertained.

Methods

This research adopted MR analysis to evaluate the causal link between GM and DN derived from data acquired through publicly available genome-wide association studies (GWAS). The study utilized the inverse variance weighted (IVW) approach to assess causal association between GM and DN. Four additional methods including MR-Egger, weighted median, weighted mode, and simple mode were employed to ensure comprehensive analysis and robust results. The Cochran's Q test and the MR-Egger method were conducted to identify heterogeneity and horizontal pleiotropy, respectively. The leave-one-out approach was utilized to evaluate the stability of MR results. Finally, a reverse MR was performed to identify the reverse causal association between GM and DN.

Results

According to IVW analysis, Class Verrucomicrobiae (p = 0.003), Order Verrucomicrobiales (p = 0.003), Family Verrucomicrobiaceae (p = 0.003), Genus Akkermansia (p = 0.003), Genus Catenibacterium (p = 0.031), Genus Coprococcus 1 (p = 0.022), Genus Eubacterium hallii group (p = 0.018), and Genus Marvinbryantia (p = 0.023) were associated with a higher risk of DN. On the contrary, Class Actinobacteria (p = 0.037), Group Eubacterium ventriosum group (p = 0.030), Group Ruminococcus gauvreauii group (p = 0.048), Order Lactobacillales (p = 0.045), Phylum Proteobacteria (p = 0.017) were associated with a lower risk of DN. The sensitivity analysis did not identify any substantial pleiotropy or heterogeneity in the outcomes. We found causal effects of DN on 11 GM species in the reverse MR analysis. Notably, Phylum Proteobacteria and DN are mutually causalities.

Conclusion

This study identified the causal association between GM and DN with MR analysis, which may enhance the understanding of the intestinal-renal axis and provide novel potential targets for early non-invasive diagnosis and treatment of DN.

Gut microbiota-dependent phenylacetylglutamine in cardiovascular disease: current knowledge and new insights

Phenylacetylglutamine (PAGln) is an amino acid derivate that comes from the amino acid phenylalanine. There are increasing studies showing that the level of PAGln is associated with the risk of different cardiovascular diseases. In this review, we discussed the metabolic pathway of PAGln production and the quantitative measurement methods of PAGln. We summarized the epidemiological evidence to show the role of PAGln in diagnostic and prognostic value in several cardiovascular diseases, such as heart failure, coronary heart disease/atherosclerosis, and cardiac arrhythmia. The underlying mechanism of PAGln is now considered to be related to the thrombotic potential of platelets via adrenergic receptors. Besides, other possible mechanisms such as inflammatory response and oxidative stress could also be induced by PAGln. Moreover, since PAGln is produced across different organs including the intestine, liver, and kidney, the cross-talk among multiple organs focused on the function of this uremic toxic metabolite. Finally, the prognostic value of PAGln compared to the classical biomarker was discussed and we also highlighted important gaps in knowledge and areas requiring future investigation of PAGln in cardiovascular diseases.

Lipidomics Profiling Reveals Serum Phospholipids Associated with Albuminuria in Early Type 2 Diabetic Kidney Disease

Early screening and administration of DKD are beneficial for renal outcomes of type 2 diabetic patients. However, the current early diagnosis using the albuminuria/creatine ratio (ACR) contains limitations. This study aimed to compare serum lipidome variation between type 2 diabetes and early DKD patients with increased albuminuria through an untargeted lipidomics method to explore the potential lipid biomarkers for DKD identification. 92 type 2 diabetic patients were enrolled and divided into two groups: DM group (ACR < 3 mg/mmol, n = 49) and early DKD group (3 mg/mmol ≤ ACR < 30 mg/mmol, n = 43). Fasting serum was analyzed through an ultraperformance liquid mass spectrometry tandem chromatography system (LC-MS). Orthogonal partial least-squares discriminant analysis (OPLS-DA) and univariate and multivariate analysis were performed to filter differentially depressed lipids. Receiver operating characteristic (ROC) curves were used to estimate the diagnostic capability of potential lipid biomarkers. We found that serum phospholipids including phosphatidylserine (PS), sphingomyelin (SM), and phosphatidylcholine (PC) were significantly upregulated in the DKD group and were highly correlated with the ACR. In addition, a panel of two phospholipids including PS(27:0)-H and PS(30:2e)-H showed good performance to help clinical lipids in early DKD identification, which increased the area under the curve (AUC) from 0.568 to 0.954. The study exhibited the serum lipidome variation in early DKD patients, and the increased phospholipids might participate in the development of albuminuria. The panel of PS(27:0)-H and PS(30:2e)-H could be a potential biomarker for DKD diagnosis.

Cadmium influence on lipid metabolism in Sprague-Dawley rats through linoleic acid and glycerophospholipid metabolism pathways

Cadmium (Cd) is widely distributed in the environment and easy adsorbed by living organisms with adverse effects. Exposure to Cd-contaminated food may disrupt lipid metabolism and increase human health risk. To study the perturbation effect of Cd on lipid metabolism in vivo, 24 male Sprague-Dawley (SD) rats were randomly assigned four groups and treated by Cd chloride solution (0, 1.375 mg/kg, 5.5 mg/kg, 22 mg/kg) for 14 days. The characteristic indexes of serum lipid metabolism were analyzed. Afterwards, untargeted metabolomics analysis was applied to explore the adverse effects of Cd on rats by liquid chromatography coupled with mass spectrometry (LC-MS). The results revealed that Cd exposure obviously decreased the average serum of triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C) and caused an imbalance of endogenous compounds in the 22 mg/kg Cd-exposed group. Compared with the control group, 30 metabolites with significant differences were identified in the serum. Our results indicated that Cd caused lipid metabolic disorders in rats by disrupting linoleic acid and glycerophospholipid metabolism pathways. Furthermore, there were three kinds of remarkable differential metabolites-9Z,12Z-octadecadienoic acid, PC(20:4(8Z,11Z,14Z,17Z)/0:0), and PC(15:0/18:2(9Z,12Z)), which enriched the two significant metabolism pathways and could be the potential biomarkers.

Phenylacetylglutamine as a novel biomarker of type 2 diabetes with distal symmetric polyneuropathy by metabolomics

PURPOSE

Type 2 diabetes mellitus (T2DM) with distal symmetric polyneuropathy (DSPN) is a disease involving the nervous system caused by metabolic disorder, while the metabolic spectrum and key metabolites remain poorly defined.

METHODS

Plasma samples of 30 healthy controls, 30 T2DM patients, and 60 DSPN patients were subjected to nontargeted metabolomics. Potential biomarkers of DSPN were screened based on univariate and multivariate statistical analyses, ROC curve analysis, and logistic regression. Finally, another 22 patients with T2DM who developed DSPN after follow-up were selected for validation of the new biomarker based on target metabolomics.

RESULTS

Compared with the control group and the T2DM group, 6 metabolites showed differences in the DSPN group (P < 0.05; FDR < 0.1; VIP > 1) and a rising step trend was observed. Among them, phenylacetylglutamine (PAG) and sorbitol displayed an excellent discriminatory ability and associated with disease severity. The verification results demonstrated that when T2DM progressed to DSPN, the phenylacetylglutamine content increased significantly (P = 0.004).

CONCLUSION

The discovered and verified endogenous metabolite PAG may be a novel potential biomarker of DSPN and involved in the disease pathogenesis.

Insulin resistance in Alzheimer's disease: The genetics and metabolomics links

Alzheimer's disease (AD) is a neurodegenerative disease with significant socioeconomic burden worldwide. Although genetics and environmental factors play a role, AD is highly associated with insulin resistance (IR) disorders such as metabolic syndrome (MS), obesity, and type two diabetes mellitus (T2DM). These findings highlight a shared pathogenesis. The use of metabolomics as a downstream systems' biology (omics) approach can help to identify these shared metabolic traits and assist in the early identification of at-risk groups and potentially guide therapy. Targeting the shared AD-IR metabolic trait with lifestyle interventions and pharmacological treatments may offer promising AD therapeutic approach. In this narrative review, we reviewed the literature on the AD-IR pathogenic link, the shared genetics and metabolomics biomarkers between AD and IR disorders, as well as the lifestyle interventions and pharmacological treatments which target this pathogenic link.

Metabolomics-based study of potential biomarkers of sepsis

The purpose of our study was to explore potential characteristic biomarkers in patients with sepsis. Peripheral blood specimens from sepsis patients and normal human volunteers were processed by liquid chromatography-mass spectrometry-based analysis. Outlier data were excluded by principal component analysis and orthogonal partial least squares-discriminant analysis using the metabolomics R software package metaX and MetaboAnalyst 5.0 ( https://www.metaboanalyst.ca/home.xhtml ) online analysis software, and differential metabolite counts were identified by using volcano and heatmaps. The obtained differential metabolites were combined with KEGG (Kyoto Gene and Kyoto Encyclopedia) analysis to screen out potential core differential metabolites, and ROC curves were drawn to analyze the changes in serum metabolites in sepsis patients and to explore the potential value of the metabolites in the diagnosis of sepsis patients. By metabolomic analysis, nine differential metabolites were screened for their significance in guiding the diagnosis and differential diagnosis of sepsis namely: 3-phenyl lactic acid, N-phenylacetylglutamine, phenylethylamine, traumatin, xanthine, methyl jasmonate, indole, l-tryptophan and 1107116. In this study, nine metabolites were finally screened based on metabolomic analysis and used as potential characteristic biomarkers for the diagnosis of sepsis.

Indolepropionic Acid, a Gut Bacteria-Produced Tryptophan Metabolite and the Risk of Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease

An intricate relationship between gut microbiota, diet, and the human body has recently been extensively investigated. Gut microbiota and gut-derived metabolites, especially, tryptophan derivatives, modulate metabolic and immune functions in health and disease. One of the tryptophan derivatives, indolepropionic acid (IPA), is increasingly being studied as a marker for the onset and development of metabolic disorders, including type 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD). The IPA levels heavily depend on the diet, particularly dietary fiber, and show huge variations among individuals. We suggest that these variations could partially be explained using genetic variants known to be associated with specific diseases such as T2D. In this narrative review, we elaborate on the beneficial effects of IPA in the mitigation of T2D and NAFLD, and further study the putative interactions between IPA and well-known genetic variants (TCF7L2, FTO, and PPARG), known to be associated with the risk of T2D. We have investigated the long-term preventive value of IPA in the development of T2D in the Finnish prediabetic population and the correlation of IPA with phytosterols in obese individuals from an ongoing Kuopio obesity surgery study. The diversity in IPA-linked mechanisms affecting glucose metabolism and liver fibrosis makes it a unique small metabolite and a promising candidate for the reversal or management of metabolic disorders, mainly T2D and NAFLD.

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.

Ginsenoside Compound K Ameliorates Development of Diabetic Kidney Disease through Inhibiting TLR4 Activation Induced by Microbially Produced Imidazole Propionate

Diabetic kidney disease (DKD) is a common and devastating complication in diabetic patients, which is recognized as a large and growing problem leading to end-stage kidney disease. As dietary-mediated therapies are gradually becoming more acceptable to patients with DKD, we planned to find active compounds on preventing DKD progression from dietary material. The present paper reports the renoprotective properties and underlying mechanisms of ginsenoside compound K (CK), a major metabolite in serum after oral administration of ginseng. CK supplementation for 16 weeks could improve urine microalbumin, the ratio of urinary albumin/creatinine and renal morphological abnormal changes in db/db mice. In addition, CK supplementation reshaped the gut microbiota by decreasing the contents of Bacteroides and Paraprevotella and increasing the contents of Lactobacillu and Akkermansia at the genus level, as well as reduced histidine-derived microbial metabolite imidazole propionate (IMP) in the serum. We first found that IMP played a significant role in the progression of DKD through activating toll-like receptor 4 (TLR4). We also confirmed CK supplementation can down-regulate IMP-induced protein expression of the TLR4 signaling pathway in vivo and in vitro. This study suggests that dietary CK could offer a better health benefit in the early intervention of DKD. From a nutrition perspective, CK or dietary material containing CK can possibly be developed as new adjuvant therapy products for DKD.

Efficacy and mechanism of Jiedu Tongluo Tiaogan Formula in treating type 2 diabetes mellitus combined with non-alcoholic fatty liver disease: Study protocol for a parallel-armed, randomized controlled trial

Background: The incidence of Type 2 diabetes mellitus (T2DM) combined with non-alcoholic fatty liver disease (NAFLD) has risen over the years. This comorbid condition significantly increases the probability of cirrhosis, liver cancer, and mortality compared to the disease alone. The multi-targeted, holistic treatment efficacy of traditional Chinese medicine (TCM) plays a vital role in the treatment of T2DM and NAFLD. Jiedu Tongluo Tiaogan Formula (JTTF), based on TCM theory, is widely used in clinical treatment, and its effectiveness in lowering glucose, regulating lipids, improving insulin resistance, and its pathways of action have been demonstrated in previous studies. However, the mechanism of this formula has not been investigated from a metabolomics perspective. Moreover, high-quality clinical studies on T2DM combined with NAFLD are lacking. Therefore, we aim to conduct a clinical trial to investigate the clinical efficacy, safety, and possible pathways of JTTF in the treatment of T2DM combined with NAFLD using metabolomics techniques.

Methods: A total of 98 participants will be recruited to this clinical trial and randomly assigned to either a treatment group (JTTF + conventional basic treatment) or control group (conventional basic treatment) in a 1:1 ratio. Both groups will have received the same lifestyle interventions in the preceding 12 weeks. The primary outcome will be change in visceral fat area and total score on the TCM syndromes efficacy score scale. The secondary outcome will include changes in ultrasound steatosis grade, fibrosis 4 score (FIB-4), metabolic parameters, anthropometric parameters, visceral fat area. In addition, serum and urine samples collected at baseline and at the end of 12 weeks of treatment will be sequentially tested for untargeted and targeted metabolomics.

Discussion: This study will evaluate the efficacy and safety of JTTF, as well as investigate the differential metabolites and possible mechanisms of JTTF treatment in T2DM combined with NAFLD. We hypothesize that patients will benefit from JTTF, which may provide strong evidence for the clinical use of JTTF in the treatment of T2DM and NAFLD, leading to the possibility of further mechanistic exploration.

Clinical Trial Registration: This clinical trial has been registered in China Clinical Trial Registry (ChiCTR 2100051174).

A rapid LC-MS/MS assay for detection and monitoring of underivatized branched-chain amino acids in maple syrup urine disease

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Branched chain amino acid (BCAA) testing is crucial in the diagnosis and monitoring of maple syrup urine disease (MSUD).

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Mixed mode chromatography can be applied to separate BCAAs without requiring sample derivatization.

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A rapid, clinically validated LC-MS/MS-based assay for analysis of underivatized BCAA in human plasma was developed.

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The assay involves minimal sample preparation without derivatization, rapid chromatographic separation, and requires only 20 µL of sample.

Introduction

Quantitation of the isomeric branched-chain amino acids (BCAA; valine, alloisoleucine, isoleucine, leucine) is a challenging task that typically requires derivatization steps or long runtimes if a traditional chromatographic method involving a ninhydrin ion pairing reagent is used.

Objectives

To develop and perform clinical validation of a rapid, LC-MS/MS-based targeted metabolomics assay for detection and monitoring of underivatized BCAA in human plasma.

Methods:

Various columns and modes of chromatography were tested. The final optimized method utilized mixed mode chromatography with an Intrada column under isocratic condition. Sample preparation utilized the 96-well format. Briefly, extraction solvent containing the internal standard is added to 20 uL of sample, followed by shaking and positive pressure filtering, and the resulting extracted sample is analyzed. The assay was validated based on accepted quality standards (e.g., CLIA and CLSI) for clinical assays.

Results

The method is linear over a wide range of concentrations, 2.0–1500 µM, with LOD of 0.60 µM and LOQ of 2.0 µM. The precision of the assay was 4–10% across analytes. The method was also validated against reference laboratories via blinded split-sample analysis and demonstrated good agreement with accuracy: 89–95% relative to the external group mean.

Conclusion

We have developed a method that is accurate, rapid, and reliable for routine clinical testing of patient sample BCAA, which is used in the diagnosis and management of maple syrup urine disease (MSUD). The assay also has desirable characteristics, such as short run time, small sample volume requirement, simple sample preparation without the need for derivatization, and high throughput.

What we know about protein gut metabolites: Implications and insights for human health and diseases

Gut microbiota is a complex ecosystem of symbiotic bacteria that contribute to human metabolism and supply intestinal metabolites, whose production is mainly influenced by the diet. Dietary patterns characterized by a high intake of protein promotes the growth of proteolytic bacteria's, which produce metabolites from undigested protein fermentation. Microbioal protein metabolites can regulate immune, metabolic and neuronal responses in different target organs. Metabolic pathways of these compounds and their mechanisms of action on different pathologies can lead to the discovery of new diagnostic techniques, drugs and the potential use as functional ingredients in food. This review discusses the potential mechanisms by which amino acid catabolism is involved in microbial protein metabolites. In addition, results from several studies on the association of products from the intestinal metabolism of indigestible proteins and the state of health or disease of the host are revised.

Kidney and plasma metabolomics provide insights into the molecular mechanisms of urate nephropathy in a mouse model of hyperuricemia

Hyperuricemia (HUA) is closely associated with kidney damage and kidney diseases in humans; however, the underlying mechanisms of HUA-induced kidney diseases remain unknown. In the present study, we examined the kidney and plasma metabolic profiles in a HUA mouse model constructed by knocking out (Ko) the urate oxidase (Uox) gene. The Uox-Ko mice were characterized by an increase in uric acid, glycine, 3'-adenosine monophosphate, citrate, N-acetyl-l-glutamate, l-kynurenine, 5-hydroxyindoleacetate, xanthurenic acid, cortisol, and (-)-prostaglandin e2 together with a decrease of inosine in the kidneys. These altered metabolites confirmed disturbances of purine metabolism, amino acid biosynthesis, tryptophan metabolism, and neuroactive ligand-receptor interaction in Uox-Ko mice. Betaine and biotin were related to kidney function and identified as the potential plasma metabolic biomarker for predicting urate nephropathy (UN). Taken together, these results revealed the underlying pathogenic mechanisms of UN. Investigating these pathways might provide novel targets for the therapeutic intervention of UN and can potentially lead to new treatment strategies.