Acyl ethanolamides in Diabetes and Diabetic Nephropathy: Novel targets from untargeted plasma metabolomic profiles of South Asian Indian men

Untargeted Maternal Plasma Metabolomics in Hirschsprung Disease: A Pilot Study

Background and Aims

Hirschsprung disease (HSCR) is a congenital disorder of unknown etiology affecting the enteric nervous system (ENS). Since the early gestational development of the ENS is dependent on the prenatal maternal metabolic environment, the objective of this pilot study was to explore the role of specific maternal plasma metabolites in the etiology of HSCR.

Methods

In this cross-sectional study, postnatal (as a surrogate for prenatal) plasma samples were obtained from mothers of children diagnosed with HSCR (n = 7) and age-matched mothers of normal children (n = 6). The plasma metabolome was analyzed by ultra-high-pressure liquid chromatography and mass spectrometry. Metabolites were identified by mzCloud using Compound Discoverer software. Using an untargeted metabolomics workflow, metabolites with case versus control group differences were identified.

Results

A total of 268 unique plasma metabolites were identified and annotated in maternal plasma. Of these, 57 were significantly different between case and control groups (P < 0.05, t-test). Using a false discovery rate corrected cutoff of 10% to adjust for multiple comparisons, 19 metabolites were significantly different in HSCR cases, including carnitines, medium-chain fatty acids, and glutamic acid. Pathways affected were for amino acid and lipid metabolism.

Conclusion

Disordered prenatal metabolic pathways may be involved in the etiopathogenesis of HSCR in the developing fetus. This is the first study to assess maternal plasma metabolomics in HSCR.

Evaluation of the expression of genes associated with iron metabolism in peripheral blood mononuclear cells from Type 2 diabetes mellitus patients

AIMS

Type 2 Diabetes (T2DM) has been linked to ferroptosis. This study aimed to assess expression levels of genes linked with iron metabolism in peripheral blood mononuclear cells (PBMCs) from T2DM patients and to investigate the association of these expression levels with anthropometric and clinical parameters.

METHODS

Gene expression of iron metabolism genes (Ferritin Light Chain, FTL; Ferritin Heavy Chain, FTH1; Transferrin Receptor, TFRC; Divalent Metal Transporter 1, SLC11A2; Ferroportin, SLC40A1) in archival PBMCs was assessed using quantitative real-time PCR assays. Correlations of gene expression with anthropometric/biochemical patient data were evaluated.

RESULTS

The study included 36 (18 male/18 female) T2DM patients and 45 (28 male/17 female) normoglycemic (NGT) subjects with a mean age of 38.1 ± 6.8 years and 47.6 ± 8.6 years respectively. Relative expression of FTL was significantly lower in T2DM females compared to that in NGT females (P = 0.027). Relative expression of SLC40A1 was significantly lower in the T2DM group (P = 0.043) and in the T2DM females (P = 0.021). Relative expression of SLC11A2 was negatively correlated with systolic blood pressure in T2DM male patients. Relative expression of SLC40A1 was negatively associated with serum phosphorous and positively associated with serum thyroid stimulating hormone in male T2DM patients.

CONCLUSIONS

Our findings indicate a reduction in the expression of FTL in perimenopausal T2DM females. Also, in male T2DM patients and NGT subjects, biochemical markers are significantly correlated with the expression of FTL, FTH1, SLC11A2, and SLC40A1 in PBMCs.

Machine learning for predicting diabetic metabolism in the Indian population using polar metabolomic and lipidomic features

AIMS

To identify metabolite and lipid biomarkers of diabetes in the Indian subpopulation in newly diagnosed diabetic and long-term diabetic individuals. To utilize the global polar metabolomic and lipidomic profiles to predict the susceptibility of an individual to diabetes using machine learning algorithms.

MATERIALS AND METHODS

87 individuals, including healthy, newly diabetic, and long-term diabetics on medication, were included in the study. Post consent, their serum was used to isolate polar metabolome and lipidome. NMR and LCMS were used to identify the polar metabolites and lipids, respectively. Statistical analysis was done to determine significantly altered molecules. NMR and LCMS comprehensive data were utilized to generate diabetic models using machine learning algorithms. 10 more individuals (pre-diabetic) were recruited, and their polar metabolomic and lipidomic profiles were generated. Pre-diabetic metabolic profiles were then utilized to predict the diabetic status of the metabolome and lipidome beyond glucose levels.

RESULTS

Mannose, Betaine, Xanthine, Triglyceride (38:1), Sphingomyelin (d63:7), and Phosphatidic acid (37:2) are some of the top key biomarkers of diabetes. The predictive model generated showed the receiver operating characteristic area under the curve (ROC-AUC) as 1 on both test and validation data indicating excellent accuracy. This model then predicted the diabetic closeness of the metabolism of pre-diabetic individuals based on probability scores.

CONCLUSION

Polar metabolic and lipid profile of diabetic individuals is very different from that of healthy individuals. Lipid profile alters before the polar metabolic profile in diabetes-susceptible individuals. Without regard to glucose, the diabetic closeness of the metabolism of any individual can be determined.

Identification of Biomarkers Related to Metabolically Unhealthy Obesity in Korean Obese Adolescents: A Cross-Sectional Study

BACKGROUND

The current study aimed to screen for relationships and different potential metabolic biomarkers involved between metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) in adolescents.

METHODS

The study included 148 obese adolescents aged between 14 and 16. The study participants were divided into MUO and MHO groups based on the age-specific adolescent metabolic syndrome (MetS) criteria of the International Diabetes Federation. The current study was conducted to investigate the clinical and metabolic differences between the MHO and MUO groups. Multivariate analyses were conducted to investigate the metabolites as independent predictors for the odds ratio and the presence of the MetS.

RESULTS

There were significant differences in the three acylcarnitines, five amino acids, glutamine/glutamate ratio, three biogenic amines, two glycerophospholipids, and the triglyceride-glucose index between the MUO group and those in the MHO group. Moreover, several metabolites were associated with the prevalence of MUO. Additionally, several metabolites were inversely correlated with MHO in the MUO group.

CONCLUSIONS

In this study, the biomarkers found in this study have the potential to reflect the clinical outcomes of the MUO group. These biomarkers will lead to a better understanding of MetS in obese adolescents.

Identification of stable reference genes in peripheral blood mononuclear cells from type 2 diabetes mellitus patients

Reference genes are obligatory for accurate normalization of mRNA transcript levels across samples and experimental conditions in Real Time-polymerase chain reaction (qRT-PCR) based quantitative gene expression assays. Selection of stably expressed reference genes is therefore crucial for ensuring reproducibility of such assays. However, there is a complete dearth of data on stability of commonly used reference genes in Peripheral Blood Mononuclear Cells (PBMCs) from Type 2 diabetes mellitus (T2DM) patients. We have evaluated the gene expression stability of 4 widely used reference genes (Beta-actin, ACTB; Peptidylprolyl Isomerase B, PPIB; Tyrosine 3 Monooxygenase/Tryptophan 5-Monooxygenase Activation Protein Zeta, YWHAZ; and Glyceraldehyde-3-Phosphate Dehydrogenase, GAPDH); in PBMCs from 39 T2DM patients and 47 normoglycemic (NGT) subjects. ACTB and YWHAZ were found to be the most stable genes in PBMCs from T2DM patients and therefore, can be recommended as suitable reference genes in similar contexts. GAPDH and PPIB expressions were not stable in PBMCs from T2DM patients. On using ACTB and YWHAZ as reference genes for measuring relative expression of GAPDH and PPIB in these subjects, relative GAPDH expression was found to be significantly lower in female T2DM patients, compared to female NGT subjects [GAPDH relative normalization unit (RNU): female T2DM (n = 19), median (Q1, Q3): 9.0 (8.1, 9.9); female NGT (n = 18): median (Q1, Q3): 10.1 (9.1, 11.0); P = 0.034]. Dysregulation of GAPDH in PBMCs from female T2DM patients could be associated with sex-specific differences in pathogenesis and outcomes of T2DM.

Comprehensive Study of Untargeted Metabolomics and 16S rRNA Reveals the Mechanism of Fecal Microbiota Transplantation in Improving a Mouse Model of T2D

Background

Fecal microbiota transplantation (FMT) has emerged as a new therapy targeting gastrointestinal microbiota for the treatment of a growing number of diseases in recent years. Previous studies have suggested that FMT may be a potential therapy for type 2 diabetes (T2D), but the underlying mechanism remains unclear. Therefore, in the present study, we aimed to investigate the role of FMT in T2D and its underlying mechanisms.

Methods

To induce T2D, mice were fed a high-fat diet and injected with low-dose streptozotocin (STZ) for four weeks. The mice were then randomly divided into four groups: control group (n = 7), T2D group (n = 7), metformin (MET)-treated group (n = 7), and FMT group (n = 7). The MET group was orally administered 0.2 g/kg MET, the FMT group was orally administered 0.3 mL of bacterial solution, and the other two groups were orally administered the same volume of saline for four weeks. Serum and fecal samples were collected for non-targeted metabolomics, biochemical indicators, and 16S rRNA sequencing, respectively.

Results

Our results demonstrated that FMT had a curative effect on T2D by ameliorating hyperlipidemia and hyperglycemia. Using 16S rRNA sequencing and serum untargeted metabolomic analysis, we found that FMT could restore the disorders of gastrointestinal microbiota in T2D mice. Moreover, corticosterone, progesterone, L-urobilin, and other molecules were identified as biomarkers after FMT treatment. Our bioinformatics analysis suggested that steroid hormone biosynthesis, arginine, proline metabolism, and unsaturated fatty acid biosynthesis could be potential regulatory mechanisms of FMT.

Conclusion

In summary, our study provides comprehensive evidence for the role of FMT in the treatment of T2D. FMT has the potential to become a promising strategy for the treatment of metabolic disorders, T2D, and diabetes-related complications.

Biogenic Amine Levels Markedly Increase in the Aqueous Humor of Individuals with Controlled Type 2 Diabetes

The composition of the aqueous humor of patients with type 2 diabetes is relevant to understanding the underlying causes of eye-related comorbidities. Information on the composition of aqueous humor in healthy subjects is limited due to the lack of adequate controls. To carry out a metabolomics study, 31 samples of aqueous humor from healthy subjects without ocular pathology, submitted to refractive surgery and seven samples from patients with type 2 diabetes without signs of ocular pathology related to diabetes were used. The level of 25 molecules was significantly (p < 0.001) altered in the aqueous humor of the patient group. The concentration of a single molecule, N-acetylornithine, makes it possible to discriminate between control and diabetes (sensitivity and specificity equal to 1). In addition, receptor operating characteristic curve and principal component analysis for the above-mentioned six molecules yielded significantly (p < 0.001) altered in the aqueous humor of the patient group. In addition, receptor operating characteristic curve and principal component analysis for six compounds yielded cut-off values and remarkable sensitivity, specificity, and segregation ability. The altered level of N-acetylornithine may be due to an increased amount of acetate in diabetes. It is of interest to further investigate whether this alteration is related to the pathogenesis of the disease. The increase in the amino form of pyruvate, alanine, in diabetes is also relevant because it could be a means of reducing the formation of lactate from pyruvate.

Interaction between Plasma Metabolomics and Intestinal Microbiome in db/db Mouse, an Animal Model for Study of Type 2 Diabetes and Diabetic Kidney Disease

Evidence has demonstrated that either metabolites or intestinal microbiota are involved in the pathogenesis of type 2 diabetes (T2D) and diabetic kidney disease (DKD). To explore the interaction between plasma metabolomics and intestinal microbiome in the progress of T2D-DKD, in the current study, we analyzed metabolomics in the plasma of db/db mice with liquid chromatography–mass spectrometry and also examined intestinal prokaryotes and entire gut microbiome dysbiosis at the genus level with both 16S rDNA and metagenomic sequencing techniques. We found that Negativibacillus and Rikenella were upregulated, while Akkermansia, Candidatus, Erysipelatoclostridium and Ileibacterium were downregulated in the colon of db/db mice compared with non-diabetic controls. In parallel, a total of 91 metabolites were upregulated, while 23 were downregulated in the plasma of db/db mice. The top five upregulated metabolites included D-arabinose 5-phosphate, estrone 3-sulfate, L-theanine, 3′-aenylic acid and adenosine 5′-monophosphate, and the five most significantly downregulated metabolites were aurohyocholic acid sodium salt, calcium phosphorylcholine chloride, tauro-alpha-muricholic acid sodium salt, galactinol and phosphocholine. These plasma metabolites were interacted with intestinal microbiomes, which are mainly involved in the pathways related to the biosynthesis of unsaturated fatty acids, fatty acid elongation, steroid biosynthesis, and D-arginine and D-ornithine metabolism. In the differential metabolites, N-acetyl-L-ornithine, ornithine and L-kyn could be metabolized by the correspondingly differential ontology genes in the intestinal metagenome. The current study thereby provides evidence for a gut–metabolism–kidney axis in the metabolism of db/db mice, in which the gut microbiome and circulating metabolomics interact, and suggests that information from this axis may contribute to our understanding of T2D and DKD pathogenesis.

Metabolic and Metabo-Clinical Signatures of Type 2 Diabetes, Obesity, Retinopathy, and Dyslipidemia

Macro- and microvascular complications of type 2 diabetes (T2D), obesity, and dyslipidemia share common metabolic pathways. In this study, using a total of 1,300 metabolites from 996 Qatari adults (57% with T2D) and 1,159 metabolites from an independent cohort of 2,618 individuals from the Qatar BioBank (11% with T2D), we identified 373 metabolites associated with T2D, obesity, retinopathy, dyslipidemia, and lipoprotein levels, 161 of which were novel. Novel metabolites included phospholipids, sphingolipids, lysolipids, fatty acids, dipeptides, and metabolites of the urea cycle and xanthine, steroid, and glutathione metabolism. The identified metabolites enrich pathways of oxidative stress, lipotoxicity, glucotoxicity, and proteolysis. Second, we identified 15 patterns we defined as "metabo-clinical signatures." These are clusters of patients with T2D who group together based on metabolite levels and reveal the same clustering in two or more clinical variables (obesity, LDL, HDL, triglycerides, and retinopathy). These signatures revealed metabolic pathways associated with different clinical patterns and identified patients with extreme (very high/low) clinical variables associated with extreme metabolite levels in specific pathways. Among our novel findings are the role of N-acetylmethionine in retinopathy in conjunction with dyslipidemia and the possible roles of N-acetylvaline and pyroglutamine in association with high cholesterol levels and kidney function.

Variability of human fasted venous plasma metabolomic profiles with tourniquet induced hemostasis

Venous plasma metabolomics is a potent and highly sensitive tool for identifying and measuring metabolites of interest in human health and disease. Accurate and reproducible insights from such metabolomic studies require extreme care in removing preanalytical confounders; one of these is the duration of tourniquet application when drawing the venous blood sample. Using an untargeted plasma metabolomics approach, we evaluated the effect of varying durations of tourniquet application on the variability in plasma metabolite concentrations in five healthy female subjects. Tourniquet application introduced appreciable variation in the metabolite abundances: 73% of the identified metabolites had higher temporal variation compared to interindividual variation [Intra-Class Correlation (ICC) > 0.50]. As such, we recommend tourniquet application for minimal duration and to wait for 5 min with the needle in situ after removing the tourniquet, to reduce hemostasis-induced variability and false flags in interpretation.