The increasing prevalence of diabetes and its complications, such as cardiovascular and kidney disease, remains a huge burden globally. Identification of biomarkers for the screening, diagnosis, and prognosis of diabetes and its complications and better understanding of the molecular pathways involved in the development and progression of diabetes can facilitate individualized prevention and treatment. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner. Providing information on underlying metabolic pathways, metabolomics can further identify mechanisms of diabetes and its progression. The application of metabolomics in epidemiological studies have identified novel biomarkers for type 2 diabetes (T2D) and its complications, such as branched-chain amino acids, metabolites of phenylalanine, metabolites involved in energy metabolism, and lipid metabolism. Metabolomics have also been applied to explore the potential pathways modulated by medications. Investigating diabetes using a systems biology approach by integrating metabolomics with other omics data, such as genetics, transcriptomics, proteomics, and clinical data can present a comprehensive metabolic network and facilitate causal inference. In this regard, metabolomics can deepen the molecular understanding, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The current review focused on metabolomic biomarkers for kidney and cardiovascular disease in T2D identified from epidemiological studies, and will also provide a brief overview on metabolomic investigations for T2D.

Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies.

The tryptophan–kynurenine pathway (Trp–KYN) is the major route for tryptophan conversion in the brain and in the periphery. Kynurenines display a wide range of biological actions (which are often contrasting) such as cytotoxic/cytoprotective, oxidant/antioxidant or pro-/anti-inflammatory. The net effect depends on their local concentration, cellular environment, as well as a complex positive and negative feedback loops. The imbalance between beneficial and harmful kynurenines was implicated in the pathogenesis of various neurodegenerative disorders, psychiatric illnesses and metabolic disorders, including diabetes mellitus (DM). Despite available therapies, DM may lead to serious macro- and microvascular complications including cardio- and cerebrovascular disease, peripheral vascular disease, chronic renal disease, diabetic retinopathy, autonomic neuropathy or cognitive impairment. It is well established that low-grade inflammation, which often coincides with DM, can affect the function of KP and, conversely, that kynurenines may modulate the immune response. This review provides a detailed summary of findings concerning the status of the Trp–KYN pathway in DM based on available animal, human and microbiome studies. We highlight the importance of the molecular interplay between the deranged (functionally and qualitatively) conversion of Trp to kynurenines in the development of DM and insulin resistance. The Trp–KYN pathway emerges as a novel target in the search for preventive and therapeutic interventions in DM.

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Kynurenine Pathway in Diabetes Mellitus—Novel Pharmacological Target?

Background Emerging evidence indicates that gut dysbiosis is involved in the occurrence and development of diabetic kidney diseases (DKD). However, the key microbial taxa closely related to DKD have not been determined. Methods PubMed, Web of Science, Cochrane, Chinese Biomedical Databases, China National Knowledge Internet, and Embase were searched for case-control or cross-sectional studies comparing the gut microbiota of patients with DKD and healthy controls (HC) from inception to February 8, 2022, and random/fixed-effects meta-analysis on the standardized mean difference (SMD) were performed for alpha diversity indexes between DKD and HC, and beta diversity indexes and the relative abundance of gut microbiota were extracted and summarized qualitatively. Results A total of 16 studies (578 patients with DKD and 444 HC) were included. Compared to HC, the bacterial richness of patients with DKD was significantly decreased, and the diversity indexes were decreased but not statistically, companying with a distinct beta diversity. The relative abundance of phylum Proteobacteria, Actinobacteria, and Bacteroidetes, family Coriobacteriaceae, Enterobacteriaceae, and Veillonellaceae, genus Enterococcus, Citrobacter, Escherichia, Klebsiella, Akkermansia, Sutterella, and Acinetobacter, and species E. coli were enriched while that of phylum Firmicutes, family Lachnospiraceae, genus Roseburia, Prevotella, and Bifidobacterium were depleted in patients with DKD. Conclusions The gut microbiota of patients with DKD may possess specific features characterized by expansion of genus Escherichia, Citrobacter, and Klebsiella, and depletion of Roseburia, which may contribute most to the alterations of their corresponding family and phylum taxa, as well as the bacterial diversity and composition. These microbial taxa may be closely related to DKD and serve as promising targets for the management of DKD. Systematic Review Registration https://www.crd.york.ac.uk/prospero/, identifier CRD42021289863.

/pdf/the-specific-alteration-of-gut-microbiota-in-diabetic-kidney-2aot0tvb.pdf

The Specific Alteration of Gut Microbiota in Diabetic Kidney Diseases—A Systematic Review and Meta-Analysis

Background The kynurenine pathway (KP) is the major catabolic pathway for tryptophan degradation. The KP plays an important role as the sole de novo nicotinamide adenine dinucleotide (NAD+) biosynthetic pathway in normal human physiology and functions as a counter-regulatory mechanism to mitigate immune responses during inflammation. Although the KP has been implicated in a variety of disorders including Huntington's disease, seizures, cardiovascular disease, and osteoporosis, its role in renal diseases is seldom discussed. Summary This review summarizes the roles of the KP and its metabolites in acute kidney injury (AKI) and chronic kidney disease (CKD) based on current literature evidence. Metabolomics studies demonstrated that the KP metabolites were significantly altered in patients and animal models with AKI or CKD. The diagnostic and prognostic values of the KP metabolites in AKI and CKD were highlighted in cross-sectional and longitudinal human observational studies. The biological impact of the KP on the pathophysiology of AKI and CKD has been studied in experimental models of different etiologies. In particular, the activation of the KP was found to confer protection in animal models of glomerulonephritis, and its immunomodulatory mechanism may involve the regulation of T cell subsets such as Th17 and regulatory T cells. Manipulation of the KP to increase NAD+ production or diversion toward specific KP metabolites was also found to be beneficial in animal models of AKI. Key Messages: KP metabolites are reported to be dysregulated in human observational and animal experimental studies of AKI and CKD. In AKI, the magnitude and direction of changes in the KP depend on the etiology of the damage. In CKD, KP metabolites are altered with the onset and progression of CKD all the way to advanced stages of the disease, including uremia and its related vascular complications. The activation of the KP and diversion to specific sub-branches are currently being explored as therapeutic strategies in these diseases, especially with regards to the immunomodulatory effects of certain KP metabolites. Further elucidation of the KP may hold promise for the development of biomarkers and targeted therapies for these kidney diseases.

The Kynurenine Pathway in Acute Kidney Injury and Chronic Kidney Disease.

Disturbance of circulating metabolites and disorders of the gut microbiota are involved in the progression of diabetic kidney disease (DKD). However, there is limited research on the relationship between serum metabolites and gut microbiota, and their involvement in DKD. In this study, using an experimental DKD rat model induced by combining streptozotocin injection and unilateral nephrectomy, we employed untargeted metabolomics and 16S rRNA gene sequencing to explore the relationship between the metabolic profile and the structure and function of gut microbiota. Striking alterations took place in 140 serum metabolites, as well as in the composition and function of rat gut microbiota. These changes were mainly associated with carbohydrate, lipid, and amino acid metabolism. In these pathways, isomaltose, D-mannose, galactonic acid, citramalic acid, and prostaglandin B2 were significantly upregulated. 3-(2-Hydroxyethyl)indole, 3-methylindole, and indoleacrylic acid were downregulated and were the critical metabolites in the DKD model. Furthermore, the levels of these three indoles were restored after treatment with the traditional Chinese herbal medicine Tangshen Formula. At the genera level, g_Eubacterium_nodatum_group, g_Lactobacillus, and g_Faecalibaculum were most involved in metabolic disorders in the progression of DKD. Notably, the circulating lipid metabolites had a strong relationship with DKD-related parameters and were especially negatively related to the mesangial matrix area. Serum lipid indices (TG and TC) and UACR were directly associated with certain microbial genera. In conclusion, the present research verified the anomalous circulating metabolites and gut microbiota in DKD progression. We also identified the potential metabolic and microbial targets for the treatment of DKD.

Characteristics of Serum Metabolites and Gut Microbiota in Diabetic Kidney Disease

Abnormal gut microbiota and blood metabolome profiles have been reported both in children and adults with uncomplicated type 1 diabetes as well as in adults with type 1 diabetes and advanced stages of diabetic nephropathy. In this study we aimed to investigate the gut microbiota and a panel of targeted plasma metabolites in individuals with type 1 diabetes of long duration without and with different levels of albuminuria. In a cross-sectional study we included 161 individuals with type 1 diabetes and 50 healthy control individuals. Individuals with type 1 diabetes were categorised into three groups according to historically measured albuminuria: (1) normoalbuminuria (<3.39 mg/mmol); (2) microalbuminuria (3.39–33.79 mg/mmol); and (3) macroalbuminuria (≥33.90 mg/mmol). From faecal samples, the gut microbiota composition at genus level was characterised by 16S rRNA gene amplicon sequencing and in plasma a targeted profile of 31 metabolites was analysed with ultra HPLC coupled to MS/MS. Study participants were aged 60 ± 11 years (mean ± SD) and 42% were women. The individuals with type 1 diabetes had had diabetes for a mean of 42 ± 15 years and had an eGFR of 75 ± 25 ml min−1 (1.73 m)−2. Measures of the gut microbial beta diversity differed significantly between healthy controls and individuals with type 1 diabetes, either with micro- or macroalbuminuria. Taxonomic analyses showed that 79 of 324 genera differed in relative abundance between individuals with type 1 diabetes and healthy controls and ten genera differed significantly among the three albuminuria groups with type 1 diabetes. For the measured plasma metabolites, 11 of 31 metabolites differed significantly between individuals with type 1 diabetes and healthy controls. When individuals with type 1 diabetes were stratified by the level of albuminuria, individuals with macroalbuminuria had higher plasma concentrations of indoxyl sulphate and l-citrulline than those with normo- or microalbuminuria and higher plasma levels of homocitrulline and l-kynurenine compared with individuals with normoalbuminuria. Whereas plasma concentrations of tryptophan were lower in individuals with macroalbuminuria compared with those with normoalbuminuria. We demonstrate that individuals with type 1 diabetes of long duration are characterised by aberrant profiles of gut microbiota and plasma metabolites. Moreover, individuals with type 1 diabetes with initial stages of diabetic nephropathy show different gut microbiota and plasma metabolite profiles depending on the level of albuminuria.

/pdf/gut-microbiota-profile-and-selected-plasma-metabolites-in-2j6uqr584w.pdf

Gut microbiota profile and selected plasma metabolites in type 1 diabetes without and with stratification by albuminuria.

OBJECTIVE: Our aim was to apply state-of-the-art machine learning algorithms to predict the risk of future progression to diabetes complications, including diabetic kidney disease (≥30% decline in eGFR) and diabetic retinopathy (mild, moderate or severe).

RESEARCH DESIGN AND METHODS: Using data in a cohort of 537 adults with type 1 diabetes we predicted diabetes complications emerging during a median follow-up of 5.4 years. Prediction models were computed first with clinical risk factors at baseline (17 measures) and then with clinical risk factors and blood-derived metabolomics and lipidomics data (965 molecular features) at baseline. Participants were first classified into two groups: type 1 diabetes stable (n=195) or type 1 diabetes with progression to diabetes complications (n=190). Furthermore, progression of diabetic kidney disease (≥30% decline in eGFR; n=79) and diabetic retinopathy (mild, moderate or severe; n=111) were predicted in two complication-specific models. Models were compared by 5-fold cross-validated area under the receiver operating characteristic (AUROC) curves. The Shapley additive explanations algorithm was used for feature selection and for interpreting the models. Accuracy, precision, recall, and F-score were used to evaluate clinical utility. 

RESULTS: During a median follow-up of 5.4 years, 79 (21 %) of the participants (mean+-SD: age 54.8 +- 13.7 years) progressed in diabetic kidney disease and 111 (29 %) of the participants progressed to diabetic retinopathy. The predictive models for diabetic kidney disease progression were highly accurate with clinical risk factors: the accuracy of 0.95 and AUROC of 0.92 (95% CI 0.857;0.995) was achieved, further improved to the accuracy of 0.98 and AUROC of 0.99 (95% CI 0.876;0.997) when omics-based predictors were included. The predictive panel composition was: albuminuria, retinopathy, estimated glomerular filtration rate, hemoglobin A1c, and six metabolites (five identified as ribitol, ribonic acid, myo-inositol, 2,4- and 3,4-dihydroxybutanoic acids).
Models for diabetic retinopathy progression were less predictive with clinical risk predictors at, AUROC of 0.81 (95% CI 0.754;0.958) and with omics included at AUROC of 0.87 (95% CI 0.781;0.996) curve. The final retinopathy-panel included: hemoglobin A1c, albuminuria, mild degree of retinopathy, and seven metabolites, including one ceramide and the 3,4-dihydroxybutanoic acid). 

CONCLUSIONS: Here we demonstrate the application of machine learning to effectively predict five-year progression of complications, in particular diabetic kidney disease, using a panel of known clinical risk factors in combination with blood small molecules. Further replication of this machine learning tool in a real-world context or a clinical trial will facilitate its implementation in the clinic.

/pdf/a-machine-learning-approach-to-predict-5-year-risk-for-1tl2giij9i.pdf

A Machine Learning Approach to Predict 5-year Risk for Complications in Type 1 Diabetes

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Gut microbiota profile and selected plasma metabolites in type 1 diabetes without and with stratification by albuminuria.

A Machine Learning Approach to Predict 5-year Risk for Complications in Type 1 Diabetes