Untargeted serum metabolomics and tryptophan metabolism profiling in type 2 diabetic patients with diabetic glomerulopathy

Research on disease diagnosis based on teacher-student network and Raman spectroscopy

Diabetic nephropathy is a serious complication of diabetes, and primary Sjögren's syndrome is a disease that poses a major threat to women's health. Therefore, studying these two diseases is of practical significance. In the field of spectral analysis, although common Raman spectral feature selection models can effectively extract features, they have the problem of changing the characteristics of the original data. The teacher-student network combined with Raman spectroscopy can perform feature selection while retaining the original features, and transfer the performance of the complex deep neural network structure to another lightweight network structure model. This study selects five flow learning models as the teacher network, builds a neural network as the student network, uses multi-layer perceptron for classification, and selects the optimal features based on the evaluation indicators accuracy, precision, recall, and F1-score. After five-fold cross-validation, the research results show that in the diagnosis of diabetic nephropathy, the optimal accuracy rate can reach 98.3%, which is 14.02% higher than the existing research; in the diagnosis of primary Sjögren's syndrome, the optimal accuracy rate can be reached 100%, which is 10.48% higher than the existing research. This study proved the feasibility of Raman spectroscopy combined with teacher-student network in the field of disease diagnosis by producing good experimental results in the diagnosis of diabetic nephropathy and primary Sjögren's syndrome.

Serum metabolic alterations in peritoneal dialysis patients with excessive daytime sleepiness

Excessive daytime sleepiness (EDS) is associated with quality of life and all-cause mortality in the end-stage renal disease population. This study aims to identify biomarkers and reveal the underlying mechanisms of EDS in peritoneal dialysis (PD) patients. A total of 48 nondiabetic continuous ambulatory peritoneal dialysis patients were assigned to the EDS group and the non-EDS group according to the Epworth Sleepiness Scale (ESS). Ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) was used to identify the differential metabolites. Twenty-seven (male/female, 15/12; age, 60.1 ± 16.2 years) PD patients with ESS ≥ 10 were assigned to the EDS group, while twenty-one (male/female, 13/8; age, 57.9 ± 10.1 years) PD patients with ESS < 10 were defined as the non-EDS group. With UHPLC-Q-TOF/MS, 39 metabolites with significant differences between the two groups were found, 9 of which had good correlations with disease severity and were further classified into amino acid, lipid and organic acid metabolism. A total of 103 overlapping target proteins of the differential metabolites and EDS were found. Then, the EDS-metabolite-target network and the protein-protein interaction network were constructed. The metabolomics approach integrated with network pharmacology provides new insights into the early diagnosis and mechanisms of EDS in PD patients.

Metabolomic epidemiology offers insights into disease aetiology

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

Abnormal tryptophan catabolism in diabetes mellitus and its complications: Opportunities and challenges

In recent years, the incidence rate of diabetes mellitus (DM), including type 1 diabetes mellitus(T1DM), type 2 diabetes mellitus(T2DM), and gestational diabetes mellitus (GDM), has increased year by year and has become a major global health problem. DM can lead to serious complications of macrovascular and microvascular. Tryptophan (Trp) is an essential amino acid for the human body. Trp is metabolized in the body through the indole pathway, kynurenine (Kyn) pathway and serotonin (5-HT) pathway, and is regulated by intestinal microorganisms to varying degrees. These three metabolic pathways have extensive regulatory effects on the immune, endocrine, neural, and energy metabolism systems of the body, and are related to the physiological and pathological processes of various diseases. The key enzymes and metabolites in the Trp metabolic pathway are also deeply involved in the pathogenesis of DM, playing an important role in pancreatic function, insulin resistance (IR), intestinal barrier, and angiogenesis. In DM and its complications, there is a disruption of Trp metabolic balance. Several therapy approaches for DM and complications have been proven to modify tryptophan metabolism. The metabolism of Trp is becoming a new area of focus for DM prevention and care. This paper reviews the impact of the three metabolic pathways of Trp on the pathogenesis of DM and the alterations in Trp metabolism in these diseases, expecting to provide entry points for the treatment of DM and its complications.

Sacubitril/Valsartan contributes to improving the diabetic kidney disease and regulating the gut microbiota in mice

Background

Diabetic kidney disease (DKD), as a serious microvascular complication of diabetes, has limted treatment options. It is reported that the Sacubitril/Valsartan (Sac/Val) can improve kidney function, and the disordered gut microbiota and part of its metabolites are related to the development of DKD. Therefore, we aim to explore whether the effect of Sac/Val on DKD is associated with the gut microbiota and related plasma metabolic profiles.

Methods

Male C57BL/6J mice were randomly divided into 3 groups: Con group (n = 5), DKD group (n = 6), and Sac/Val group (n = 6) . Sac/Val group was treated with Sac/Val solution. The intervention was given once every 2 days for 6 weeks. We measured the blood glucose and urine protein level of mice at different times. We then collected samples at the end of experiment for the 16s rRNA gene sequencing analysis and the untargeted plasma metabonomic analysis.

Results

We found that the plasma creatinine concentration of DKD-group mice was significantly higher than that of Con-group mice, whereas it was reduced after the Sac/Val treatment. Compared with DKD mice, Sac/Val treatment could decrease the expression of indicators related to EndMT and renal fibrosis like vimentin, collagen IV and fibronectin in kidney. According to the criteria of LDA ≥ 2.5 and p<0.05, LefSe analysis of gut microbiota identified 13 biomarkers in Con group, and 33 biomarkers in DKD group, mainly including Prevotella, Escherichia_Shigella and Christensenellaceae_R_7_group, etc. For the Sac/Val group, there were 21 biomarkers, such as Bacteroides, Rikenellaceae_RC9_gut_group, Parabacteroides, Lactobacillus, etc. Plasma metabolomics analysis identified a total of 648 metabolites, and 167 important differential metabolites were screened among groups. KEGG pathway of tryptophan metabolism: M and bile secretion: OS had the highest significance of enrichment.

Conclusions

Sac/Val improves the renal function of DKD mice by inhibiting renal fibrosis. This drug can also regulate gut microbiota in DKD mice.

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.

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).

Metabolomics of Type 1 and Type 2 Diabetes: Insights into Risk Prediction and Mechanisms

PURPOSE OF REVIEW

Metabolomics enables rapid interrogation of widespread metabolic processes making it well suited for studying diabetes. Here, we review the current status of metabolomic investigation in diabetes, highlighting its applications for improving risk prediction and mechanistic understanding.

RECENT FINDINGS

Findings of metabolite associations with type 2 diabetes risk have confirmed experimental observations (e.g., branched-chain amino acids) and also pinpointed novel pathways of diabetes risk (e.g., dimethylguanidino valeric acid). In type 1 diabetes, abnormal metabolite patterns are observed prior to the development of autoantibodies and hyperglycemia. Diabetes complications display specific metabolite signatures that are distinct from the metabolic derangements of diabetes and differ across vascular beds. Lastly, metabolites respond acutely to pharmacologic treatment, providing opportunities to understand inter-individual treatment responses. Metabolomic studies have elucidated biological mechanisms underlying diabetes development, complications, and therapeutic response. While not yet ready for clinical translation, metabolomics is a powerful and promising precision medicine tool.

Creation of a collection of blood samples of patients with multiple myeloma

Aim. To create a collection of samples of blood components of patients with multiple myeloma for potential fundamental and applied biomedical research.

Material and methods. The material was collected according to the developed algorithm, including the collection of clinical information, biological material, sample preparation, quality control and storage in the biobank of the National Medical Research Center of Oncology.

Results. As of August 2021, the cryostorage of the National Medical Research Center of Oncology biobank contains a collection of 175 samples of blood serum, plasma and mononuclear cell fraction of patients with multiple myeloma. Samples were obtained from 32 patients of both sexes, the mean age of which was 59,5±1,65 years. To create an electronic catalog, personal, clinical and laboratory data about patients were collected, after which each sample was assigned its own unique identification number. Written informed consent was obtained from all patients for the storage of their biomaterial in a biobank with possible subsequent use for scientific purposes. Freezing of the obtained samples was carried out in accordance with low-temperature storage protocol. The electronic catalog contains a wide range of systematized clinical and laboratory information on samples.

Conclusion. The collection of multiple myeloma samples is a unique resource for potential research on its pathophysiology, the development of diagnostic biomarkers, and the search for targeted agents.

Exploration study on serum metabolic profiles of Chinese male patients with artificial stone silicosis, silicosis, and coal worker's pneumoconiosis

Long-term exposure to inhaled silica dust induces pneumoconiosis, which remains a heavy burden in developing countries. Modern industry provides new resources of occupational SiO₂ leading to artificial stone silicosis especially in developed countries. This study aimed to characterize the serum metabolic profile of pneumoconiosis and artificial stone silicosis patients. Our case-control study recruited 46 pairs of pneumoconiosis patients and dust-exposed workers. Nontargeted metabolomics and lipidomics by ultra-high-performance liquid chromatography-tandem mass spectrometry platform were conducted to characterize serum metabolic profile in propensity score-matched (PSM) pilot study. 54 differential metabolites were screened, 24 of which showed good screening efficiency through receiver operating characteristics (ROC) in pilot study and validation study (both AUC > 0.75). 4 of the 24 metabolites can predict pneumoconiosis stages, which are 1,2-dioctanoylthiophosphatidylcholine, phosphatidylcholine(O-18:1/20:1), indole-3-acetamide and l-homoarginine. Kynurenine, N-tetradecanoylsphingosine 1-phosphate, 5-methoxytryptophol and phosphatidylethanolamine(22:6/18:1) displayed the potential as specific biomarkers for artificial stone silicosis. Taken together, our results confirmed that tryptophan metabolism is closely related to pneumoconiosis and may be related to disease progression. Hopefully, our results could supplement the biomarkers of pneumoconiosis and provide evidence for the discovery of artificial stone silicosis-specific biomarkers.