Targeted metabolomics as a tool for the diagnosis of kidney disease in Type II diabetes mellitus

Biliary atresia and cholestasis plasma non-targeted metabolomics unravels perturbed metabolic pathways and unveils a diagnostic model for biliary atresia

The clinical diagnosis of biliary atresia (BA) poses challenges, particularly in distinguishing it from cholestasis (CS). Moreover, the prognosis for BA is unfavorable and there is a dearth of effective non-invasive diagnostic models for detection. Therefore, the aim of this study is to elucidate the metabolic disparities among children with BA, CS, and normal controls (NC) without any hepatic abnormalities through comprehensive metabolomics analysis. Additionally, our objective is to develop an advanced diagnostic model that enables identification of BA. The plasma samples from 90 children with BA, 48 children with CS, and 47 NC without any liver abnormalities children were subjected to metabolomics analysis, revealing significant differences in metabolite profiles among the 3 groups, particularly between BA and CS. A total of 238 differential metabolites were identified in the positive mode, while 89 differential metabolites were detected in the negative mode. Enrichment analysis revealed 10 distinct metabolic pathways that differed, such as lysine degradation, bile acid biosynthesis. A total of 18 biomarkers were identified through biomarker analysis, and in combination with the exploration of 3 additional biomarkers (LysoPC(18:2(9Z,12Z)), PC (22:5(7Z,10Z,13Z,16Z,19Z)/14:0), and Biliverdin-IX-α), a diagnostic model for BA was constructed using logistic regression analysis. The resulting ROC area under the curve was determined to be 0.968. This study presents an innovative and pioneering approach that utilizes metabolomics analysis to develop a diagnostic model for BA, thereby reducing the need for unnecessary invasive examinations and contributing to advancements in diagnosis and prognosis for patients with BA.

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.

Altered metabolic profiles and targets relevant to the protective effect of acteoside on diabetic nephropathy in db/db mice based on metabolomics and network pharmacology studies

ETHNOPHARMACOLOGICAL RELEVANCE

Diabetic nephropathy (DN) was a major cause of end-stage renal failure and a common microvascular complication in patients with diabetes mellitus (DM). Acteoside (ACT) was the main ingredient extracted from the leaves of Rehmannia glutinosa, which had the functions of entering the lung, moisturizing the skin and relieving itching, nourishing yin and tonifying the kidney, cooling blood, and stopping bleeding. ACT had attracted worldwide interest because of its therapeutic effects on DM and its complications.

AIM OF THE STUDY

To clarify the metabolic profiles and targets of ACT in db/db mice based on metabolomics and network pharmacology studies.

MATERIALS AND METHODS

Db/db mice were used to observe the biochemical indices and histopathological changes in the kidney to evaluate the pharmacological effects of ACT on DN. Untargeted metabolomics studies were performed to investigate by UHPLC-LTQ-Orbitrap MS on urine, serum, and kidney samples. The key targets and pathways were analyzed by network pharmacology. For the pathways enriched by untargeted metabolomics, targeted metabolomics by UHPLC-QQQ-MS/MS was performed in kidney samples for validation. Sensitive biomarkers in kidney samples were evaluated. The effect of ACT on the improvement of DN from the perspective of metabolism of small molecules in vivo was described.

RESULTS

ACT could delay the progression of DN and improve the degree of histopathological damage to the kidney. The pathways were focused on amino acid metabolism by untargeted metabolomics. Through network pharmacology analysis, the effect pathways were related to signal transduction, carbohydrate, lipid, amino acid metabolism and mainly affected the endocrine and immune systems. Amino acid metabolism was disturbed in the kidney of db/db mice, which could be callback by ACT, such as tryptophan, glutamine, cysteine, leucine, threonine, proline, phenylalanine, histidine, serine, arginine, asparagine by targeted metabolomics.

CONCLUSIONS

In conclusion, this study provided strong support for ACT on DN treatment in clinics. Meanwhile, the Rehmannia glutinosa was used fully to raise the income level of farmers economically, while achieving the social benefit of empowering rural revitalization.

The association and prediction value of acylcarnitine on diabetic nephropathy in Chinese patients with type 2 diabetes mellitus

BACKGROUND

Acylcarnitines play a role in type 2 diabetes mellitus (T2DM), but the relationship between acylcarnitine and diabetic nephropathy was unclear. We aimed to explore the association of acylcarnitine metabolites with diabetic nephropathy and estimate the predictive value of acylcarnitine for diabetic nephropathy.

METHODS

A total of 1032 (mean age: 57.24 ± 13.82) T2DM participants were derived from Liaoning Medical University First Affiliated Hospital. Mass Spectrometry was utilized to measure levels of 25 acylcarnitine metabolites in fasting plasma. Diabetic nephropathy was ascertained based on the medical records. Factor analysis was used to reduce the dimensions and extract factors of the 25 acylcarnitine metabolites. Logistic regression was used to estimate the relationship between factors extracted from the 25 acylcarnitine metabolites and diabetic nephropathy. Receiver operating characteristic curves were used to test the predictive values of acylcarnitine factors for diabetic nephropathy.

RESULTS

Among all T2DM participants, 138 (13.37%) patients had diabetic nephropathy. Six factors were extracted from 25 acylcarnitines, which account for 69.42% of the total variance. In multi-adjusted logistic regression models, the odds ratio (OR, 95% confidence interval [CI]) of diabetic nephropathy on factor 1 (including butyrylcarnitine/glutaryl-carnitine/hexanoylcarnitine/octanoylcarnitine/decanoylcarnitine/lauroylcarnitine/tetradecenoylcarnitine), factor 2 (including propionylcarnitine/palmitoylcarnitine/hydroxypalmitoleyl-carnitine/octadecanoylcarnitine/arachidiccarnitine), and factor 3 (including tetradecanoyldiacylcarnitine/behenic carnitine/tetracosanoic carnitine/hexacosanoic carnitine) were 1.33 (95%CI 1.12-1.58), 0.76 (95%CI 0.62-0.93), and 1.24 (95%CI 1.05-1.47), respectively. The area under the curve for diabetic nephropathy prediction was significantly increased after the complement of factors 1, 2, and 3 in traditional factors model (P < 0.01).

CONCLUSIONS

Some plasma acylcarnitine metabolites extracted in factors 1 and 3 were higher in diabetic nephropathy, while factor 2 was lower in diabetic nephropathy among T2DM patients. The addition of acylcarnitine to traditional factors model improved the predictive value for diabetic nephropathy.

Amino acid analysis as a method of discovering biomarkers for diagnosis of diabetes and its complications

Diabetes mellitus (DM) is a severe chronic diseases with a global prevalence of 9%, leading to poor health and high health care costs, and is a direct cause of millions of deaths each year. The rising epidemic of diabetes and its complications, such as retinal and peripheral nerve disease, is a huge burden globally. A better understanding of the molecular pathways involved in the development and progression of diabetes and its complications can facilitate individualized prevention and treatment. High diabetes mellitus incidence rate is caused mainly by lack of non-invasive and reliable methods for early diagnosis, such as plasma biomarkers. The incidence of diabetes and its complications in the world still grows so it is crucial to develop a new, faster, high specificity and more sensitive diagnostic technologies. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner, and effective biomarkers can greatly improve the efficiency of diabetes and its complications. By providing information on potential metabolic pathways, metabolomics can further define the mechanisms underlying the progression of diabetes and its complications, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The application of amino acid metabolomics in epidemiological studies has identified new biomarkers of diabetes mellitus (DM) and its complications, such as branched-chain amino acids, phenylalanine and arginine metabolites. This study focused on the analysis of metabolic amino acid profiling as a method for identifying biomarkers for the detection and screening of diabetes and its complications. The results presented are all from recent studies, and in all cases analyzed, there were significant changes in the amino acid profile of patients in the experimental group compared to the control group. This study demonstrates the potential of amino acid profiles as a detection method for diabetes and its complications.

The value of blood and urine metabolomics in differential diagnosis of cholestasis in infants

Background

Early detection of biliary atresia (BA) is a great challenge providing the main useful way to improve its clinical consequence. Promising metabolomics provides an effective method for determining innovative biomarkers and biochemical ways for improving early diagnosis. This study aimed to determine the benefit of serum and urinary potential bile acid metabolites in the differentiation of BA from non-biliary atresia (non-BA) cases using tandem mass spectrometry (MS/MS). Fourteen bile acids metabolites were measured quantitively by MS/MS in serum and urine samples from 102 cholestatic infants and 102 control infants, in addition to the assay of the total serum bile acid enzymatically.

Results

After the diagnostic clinical and laboratory workflow, cholestatic infants were divided into BA (37 infants) and non-BA (65 infants) subgroups. Remarkably on analysis of serum individual bile acid concentrations, there were significant differences between cholestatic BA and non-BA regarding serum (glycocenodeoxycholic acid (GCDCA), taurochenodeoxycholic acid (TCDCA), taurocholic acid (TCA), and GCDCA/chenodeoxycholic acid (CDCA) ratio) (p < 0.001, for all), while there was no significant difference between the two groups regarding serum level of (cholic acid (CA), glycocholic (GCA), or TCDCA/CDCA ratio). There were no significant differences in either the urinary individual bile acids or urinary primary bile acids (conjugated or unconjugated) between BA and non-BA. Further principal component analysis (PCA) analysis was done and receiver operating characteristic (ROC) analysis was performed using score plots of the positive factors in the first two principal components PC1 (CA, GCA, GCDCA, TCA, TCDCA) and PC2 (CA, CDCA, lithocholic (LCA), ursodeoxycholic acid (UDCA)) for establishing the differences between the two diseased groups and revealed that the area under the curve (AUC) for PC1 was (0.770) higher than AUC for PC2 (0.583) indicating that the positive components of PC1 may be potential biomarkers for differentiation between the two cholestatic groups.

Conclusions

Metabolomics of serum bile acid levels using tandem mass spectrometry might change the paradigm differentiating BA from non-BA saving patients from unnecessary invasive procedures.

Omics and Artificial Intelligence in Kidney Diseases

Omics applications in nephrology may have relevance in the future to improve clinical care of kidney disease patients. In a short term, patients will benefit from specific measurement and computational analyses around biomarkers identified at various omics-levels. In mid term and long term, these approaches will need to be integrated into a holistic representation of the kidney and all its influencing factors for individualized patient care. Research demonstrates robust data to justify the application of omics for better understanding, risk stratification, and individualized treatment of kidney disease patients. Despite these advances in the research setting, there is still a lack of evidence showing the combination of omics technologies with artificial intelligence and its application in clinical diagnostics and care of patients with kidney disease.

Potential Value of TNF-α (-376 G/A) Polymorphism and Cystatin C (CysC) in the Diagnosis of Sepsis Associated Acute Kidney Injury (S-AK I) and Prediction of Mortality in Critically Ill patients

Sepsis Associated Kidney Injury represents a major health concern as it is frequently associated with increased risk of mortality and morbidity. We aimed to evaluate the potential value of TNF-α (-376 G/A) and cystatin C in the diagnosis of S-AKI and prediction of mortality in critically ill patients. This study included 200 critically ill patients and 200 healthy controls. Patients were categorized into 116 with acute septic shock and 84 with sepsis, from which 142 (71%) developed S-AKI. Genotyping of TNF-α (-376 G/A) was performed by RT-PCR and serum CysC was assessed by Enzyme Linked Immunosorbent Assay. Our results showed a highly significant difference in the genotype frequencies of TNF-α (-376 G/A) SNP between S-AKI and non-AKI patients (p < 0.001). Additionally, sCysC levels were significantly higher in the S-AKI group (p = 0.011). The combination of both sCysC and TNF-α (-376 G/A) together had a better diagnostic ability for S-AKI than sCysC alone (AUC = 0.610, 0.838, respectively). Both GA and AA genotypes were independent predictors of S-AKI (p= < 0.001, p = 0.002 respectively). Additionally, sCysC was significantly associated with the risk of S-AKI development (Odds Ratio = 1.111). Both genotypes and sCysC were significant predictors of non-survival (p < 0.001), suggesting their potential role in the diagnosis of S-AKI and prediction of mortality.

Identification of volatile organic compounds in the urine of patients with cervical cancer. Test concept for timely screening

The objective of this research was to identify a global chemical pattern of volatile organic compounds (VOCs) in urine capable of discriminating between women with cervical cancer (CC) and control women using an electronic nose and to elucidate potential biomarkers by gas chromatography-mass spectrometry (GC-MS). A cross-sectional study was performed, with 12 control women, 5 women with CIN (Cervical Intraepithelial Neoplasia) and 12 women with CC. Global VOCs in urine were assessed using an electronic nose and specific by GC-MS. Multivariate analysis was performed: Principal Component Analysis (PCA), Canonical Principal Coordinate Analysis (CAP) and Partial Least Squares Discriminant Analysis (PLS-DA) and the test's diagnostic power was evaluated through ROC (Receiver Operating Characteristic) curves. Results from the PCA between the control group compared to the CC present variability of 98.4% (PC₁ = 93.9%, PC₂ = 2.3% and PC₃ = 2.1%). CAP model shows a separation between the overall VOCs profile of the control and CC group with a correct classification of 94.7%. PLS-DA indicated that 8 sensors have a higher contribution in the CC group. The sensitivity, specificity, value reached 91.6% (61.5%-99.7%) and 100% (73.5%-100%) respectively, according to the ROC curve. GC-MS analysis indicated that 33 compounds occur only in the CC group and some of them have been found in other types of cancer. In all, this study provides the basis for the development of an accessible, non-invasive, sensitive and specific screening platform for cervical cancer through the application of electronic nose and chemometric analysis.